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Heart, brain, and breath: studies on the neuromodulation of interoceptive systems

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Heart, brain, and breath: studies on the neuromodulation of interoceptive systems

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Heart, Brain, and Breath: Studies on the Neuromodulation of Interoceptive Systems

By

Natalie ‘Tasha’ Poppa

_________________________________________________________________

A Dissertation Presented to the
FACULTY OF THE USC GRADUATE SCHOOL
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Fulfillment of the Requirements for the Degree
DOCTOR OF PHILOSOPHY
(PSYCHOLOGY)

May 2020

Copyright 2020 Natalie ‘Tasha’ Poppa
ii

Dedication
I dedicate my dissertation to Lars Benschop. Literally and figuratively, I could not have
done this without you.

iii
Acknowledgments
This work would not have been possible without the support, insights, and dedication of
my mentors, research collaborators, friends, and family. First of all, I wish to thank Antoine
Bechara for accepting me as his graduate student. I could not have imagined the directions, and
sometimes very literal journeys my research would take me on. Antoine always showed
complete support of my research interests, trust in my abilities, the freedom to pursue my ideas,
and opportunities to develop them. I would also like to thank John Monterosso for his
mentorship throughout the years, and for bringing me on as a neuroimaging team member
associated with the MMWR clinical trial. His belief in me and my work and has always been a
great source of confidence. I am also grateful to David Black and Hortensia Amaro for creating
the opportunity to have a neuroimaging study in their MMWR clinical trial. I would also like to
thank my other committee members, Mara Mathers and Rael Cahn for their support and interest
in my research. It is an honor to have them on my committee.
I am profoundly grateful to my Flemish mentors, collaborators, and friends in the
Psychiatry and Neurology departments at the Ghent University Hospital, Belgium. First of all, I
thank Chris Baeken for opening up his lab to me for an initial summer research exchange, which
has since turned into years of mentorship and collaboration; I look forward to many more years
of pursuing ‘wild ideas’. I also thank Marie-Anne Vanderhasselt for the kindness she always
extends to me, for arranging our collaboration with Kristl Vonck in the Department of Neurology
at Ghent University Hospital, and for inviting me to collaborate on a new project that will allow
me to realize some of the future directions of this thesis. My gratitude also extends to Sara De
Witte, for training me to use TMS and general assistance with projects. I am also very
iv
appreciative of Paula Horczak for her friendship and exceptional hard work and dedication as my
research assistant.
I would also like to acknowledge the crucial role of the funders. My doctoral work was
largely supported by a fellowship from the National Science Foundation Graduate Research
Fellowship Program (NSF GRFP), a University of Southern California Graduate School
Research Enhancement Fellowship, and a grant from the National Institutes of Health:
R01DA038648 (PIs: Hortensia Amaro and David Black).
I also want to express my gratitude to Nina Christie, Eustace Hsu, James Melrose,
Rodrigo Riveros Miranda, Katie Zyuzin, and Jens Allaert for all the fun, fond memories, and the
occasional commiseration too. I would also like to make a special acknowledgement of Damien
Brevers, who apart from his friendship, has always been a curiously instrumental figure in the
series of events that resulted in big, meaningful changes in the direction of my life, both
professionally and personally.
I also thank my family for the love and support they have given me throughout the
graduate school. Their pride in my pursuit of a PhD has motivated me throughout. Lastly, I am
eternally grateful to have met Lars Benschop, my best friend, adventure-seeker, and partner in
life. Every day you give me the purpose and strength to reach higher.

v
List of Tables

2.1 – LMER estimates of tVNS effects on BRS
PTT
………………………………………………79
2.2 – LMER estimates of tVNS effects on heart rate…………………………………………….80
3.1 – LMER estimates of TBS on state anxiety………………………………………………....119
3.2 – LMER estimates of effects of TBS during Slow Breathing…………………………….….120
3.3 – LMER estimates of TBS during Spontaneous Breathing……………………...…………..122
4.1 – Demographic and clinical characteristics of SUD patients……………………………….138
4.2 – MNI coordinates of peak OFC network differences……………………………………...151

vi
List of Figures

1.1 – The ascending primary interoceptive pathways…………………………………..……….....4
1.2 – Anatomical pathways mediating sensory signals contributing to the HEP……………….....20
1.3 – Anatomy of the auricle…………………………………………………………………..….28
1.4 – The insula………..…………………………………………………...………………….…35
2.1 – Study 1 design…….…………………………………………………………………….….61
2.2 – Cardiovascular timeseries……………………………………………………………….….65
2.3 – tVNS stimulation artifact and power spectrum density plots………………………………68
2.4 – Regions of interest for EEG resting state analyses…………………………………………71
2.5 – Amplitude envelope correlation process diagram………………………………………….73
2.6 – Regions of interest for HEP analyses………………………………………………………75
2.7 – Fitted LMER model predictions for BRS
PTT
……………………………………………….79
2.8 – Resting state beta-band AEC wire diagram……………………………………………..…..82
2.9 – Topographical differences for verum and sham HEPs……………………………………..83
2.10 – HEP differences - temporal and topographic……………………………………………...84
2.11 – Averaged ECG waveforms for sham and verum tVNS……………………………………85
2.12 – HEP theta band AEC differences…………………………………………………………86
2.13 – HEP alpha band AEC differences…………………………………………………………87
2.14 – HEP SMR band AEC differences…………………………………………………………87
2.15 – HEP beta band AEC differences………………………………………………………..…88
2.16 – Scatterplot HEP alpha band AECs versus heart rate……………………………………….90
3.1 – EEG 10-20 electrode schematic for guiding placement of the stimulation coils………….113
3.2 – Study 2 design………..……………………………………………………………………114
3.3 – Boxplots visualizing state anxiety after each TBS condition……………………………..119
4.1 – Task-modulated independent components………………………………………………..148
4.2 – Group differences in functional integration of the OFC network…………………………150
4.3 – Association of sexual violence exposure with OFC network integration strength……….153

vii
List of Abbreviations

AEC Amplitude envelope correlation
ACC Anterior cingulate cortex
AV Atrioventricular node
AVBN Auricular branch of the vagus nerve
BEM Boundary element method
BOLD Blood oxygen level dependent
BNST Bed nucleus of the stria terminalis
BRS Baroreceptor sensitivity
CAN Central autonomic network
CBT Cognitive behavioral therapy
CFA Cardiac field artifact
CNS Central nervous system
CRH Corticotropin releasing hormone
CSF Cerebrospinal fluid
cTBS Continuous theta-burst stimulation
DMN Dorsal motor nucleus of the solitary tract
ECG Electrocardiogram
EEG Electroencephalography
EOG Electrooculogram
fMRI Functional magnetic resonance imaging
GABA Gamma aminobutyric acid
GSA General somatic afferents
GVA General vagal afferents
GVE General vagal efferents
HEP Heart evoked potential
HF-HRV High-frequency heart rate variability
HPA-axis Hypothalamic-pituitary-adrenal axis
HR Heart rate
HRV Heart rate variability
iEEG Intracranial electroencephalography
ICA Independent components analysis
IN-OUT task Interoceptive-exteroceptive attention task
iTBS Intermittent theta burst stimulation
LC Locus coeruleus
LF-HRV Low-frequency heart rate variability
LIFU Low-intensity focused ultrasound
LMER Linear mixed effects regression
MBSR Mindfulness-based stress reduction
MEG Magnetoencephalography
MELODIC Multivariate Exploratory Linear Optimized Decomposition into Independent
Components
MMWR Moment-by-Moment in Women’s Recovery
MSNA Muscle sympathetic nerve activity
NA Nucleus ambiguus of the solitary tract
viii
NIBS Non-invasive brain stimulation
NST Nucleus of the solitary tract
OFC Orbitofrontal cortex
PAG Periaqueductal gray
PEP Pre-ejection period
PET Positron emission tomography
Pf Parafascicular nucleus of the thalamus
PFC Prefrontal cortex
PPG Photoplethysmography
PTSD Post-traumatic stress disorder
PTT Pulse transit time
rCBF Regional cerebral blood flow
RESP Respiration
RMSSD Root mean square of successive differences
ROI Region of interest
RRI Time interval between peaks of successive R-waves
RSA Respiratory sinus arrythmia
RVLM Rostral ventrolateral medulla
SA Sinoatrial node
SCD Source current density
SUD Substance use disorder
SVE Special visceral efferent fibers
TBS Theta-burst stimulation
TFCE Threshold free cluster enhancement
TMS Transcranial magnetic stimulation
TPJ Temporo-parietal junction
VMb Ventromedial nucleus of the thalamus
VMPFC Ventromedial prefrontal cortex
VMpo Posterior ventromedial nucleus of thalamus
tVNS Transcutaneous vagus nerve stimulation
VNS Vagus nerve stimulation

ix

Table of Contents

Dedication..………………………………………………………………………………………..ii

Acknowledgments……………………………………………………………………………..…iii

List of Tables………………………………………………………………………………………v

List of Figures…………………………………………………………………………………….vi

List of Abbreviations…………………………………………………………………………….vii

Abstract…………………………………………………………………………………………..xv

1. General Introduction………………………………………………………………………….1

1.1 Interoception: Definition, history and development of the concept …………………………..1

1.2 Functional significance of interoception………………………………………………………4

1.2.1 Allostasis and homeostasis…………………………………………………………..4

1.2.2 Allostatic dysregulation and psychopathology……………………...…………….…8

1.2.3 Modulating interoceptive-allostatic systems……………………………………….11

1.3 Phenomenology and subjective feelings of the body’s internal state…………………………14

1.3.1 Bodily awareness……………………………………………………………...……14

1.3.2. Attention…………………………………………………………………………...16

1.3.3 Cardiac interoceptive awareness…………………………………………………...17

1.3.4 The heart evoked potential (HEP)…………………………………………………..18

1.3.5 Respiratory interoceptive awareness…………………….………………………....20

1.4 Select physiological concepts related to interoception and cardiovascular autonomic

regulation………………………………………………………………………………………...23

1.4.1 Vagus nerve functional (neuro)anatomy…………………………………………...23

1.4.2 Vagal efferent fibers……………………………………………………………......23
x

1.4.3 Vagal afferent fibers………………………………………………………………..24

1.4.4 Auricular branch of the vagus nerve (AVBN).……………………………………...26

1.4.5 Autonomic influence on heart rate, heart rate variability………………………..…28

1.4.6 Respiratory sinus arrythmia (RSA)..……………………………………………….30

1.4.7 The baroreflex…………………………………………………………………...…31

1.5 The central autonomic network (CAN)……………………………………………………….32

1.5.1 Forebrain involvement in cardiovascular autonomic regulation…………………...33

1.5.2 Insula is central to cardiac interoception and regulation..…………………………..34

1.5.3 Role of medial prefrontal, orbitofrontal, and cingulate cortices……………………38

1.5.4 Functional imaging of vagus nerve stimulation (VNS)..……………………………41

1.5.5 Patient VNS studies………………………………………………………………...41

1.5.6 Neurochemical effects of VNS……………………………………………………..42

1.6 Transcutaneous vagus nerve stimulation (tVNS)……………………………………………..42

1.6.1 Does AVBN stimulation affect vagal efferent outflow?..…………………………..46

1.6.2 The role of sham in studies of tVNS……………………………………………….49

1.7 Summary and objectives………………………………………………………………..…….51

2. Transcutaneous vagus nerve stimulation modulates neural activity and functional

connectivity in interoceptive cortices: An EEG study………………………………………..54

2.1 Introduction…………………………………………………………………………………..54

2.2 Methods………………………………………………………………………………………59

2.2.1 Participants…………………………………………………………………………59

2.2.2 Study protocol……………………………………………………………………...60

2.2.3 Self-report questionnaires…………………………………………………….…….61
xi

2.2.4 Transcutaneous vagus nerve stimulation (tVNS)………………………………..….62

2.2.5 Electroencephalography (EEG) and physiological data acquisition…………….….63

2.2.6 Preprocessing………………………………………………………………………64

2.2.6.1 Physiological data………………………………………………………...64

2.2.6.2 EEG………………………………………………………………………66

2.2.7 Head models and source localization………………………………………………69

2.2.8 Resting-state EEG analyses…………………………………………………….…..70

2.2.8.1 Source-localized spectral power……………………………………….…70

2.2.8.2 Amplitude envelope connectivity (AEC) among regions of interest

(ROIs)……………………………………………………………………………72

2.2.9 HEP Analysis………………………………………………………………………73

2.2.9.1 Temporal characteristics of HEPs………………………………………..73

2.2.9.2 HEP source-localized functional connectivity…………………………...74

2.2.10 Statistical Modeling…………...…………………………………………………………..75

2.3 Results………………………………………………………………………………………..77

2.3.1 Current-intensity, stimulation-elicited pain and anxiety…………………………...77

2.3.2 Cardiovascular responses…………………………………………………………..78

2.3.3 Resting state EEG……………………………………………………………….….81

2.3.4 HEPs…………………………………………………………………………….….82

2.4 Discussion……………………………………………………………………………………90

2.4.1 tVNS effects on cardiovascular indices…………………………………………….91

2.4.2 tVNS effects on resting state source-localized AECs and power…………………..94

2.4.3 tVNS effects on HEPs………………………………………………………….…...96
xii

2.4.4 Limitations and future directions…………………………………………….……100

2.4.3 Conclusion....……………………………………………………………………...102

3. Frontotemporal theta-burst stimulation alters cardiovascular autonomic function: The
role of state anxiety…………………………………………………………………………….104
3.1 Introduction…………………………………………………………………………………104

3.2 Methods…………………………………………………………………………………..…109

3.2.1 Participants………………….……………………….……………….……...……109

3.2.2 Study protocol…….……………….……………….……………………………..110

3.2.3 Motor threshold testing and stimulation site……………….……….……….….…112

3.2.4 Theta-burst stimulation parameters and hardware………………………………...113

3.2.5 Physiological measurement……………….…………………….………………...114

3.2.6 Statistical analysis……………….……………….……………….………………116

3.3 Results………………………………………………….……………….…………………..118

3.3.1 State anxiety…………………………………….……………….………………..118

3.3.2 Slow breathing…………………………………….……………….……………...120

3.3.3 Spontaneous breathing…………………………………….……………….…..…121

3.3.4 Correlational analyses…………………………………….……………….……...123

3.4 Discussion……………………………………………………….………………………….123

3.4.1 Limitations……………………………………………….…………………….…128

3.4.2 Conclusion……………………………………………….…………………….…130

4. Sexual trauma history is associated with reduced interoception-linked orbitofrontal

network integration in women with substance use disorder………………………..………131

4.1 Introduction…………………………………………………………………………………131
xiii

4.1.1 Interoceptive awareness is compromised in substance use disorders, and is a

functional resource for recovery…………………………………………………..……131

4.1.2 Atypical interoception is a feature of post-traumatic stress disorder..…………….133

4.1.3 Trauma and substance abuse comorbidity………………………………….……..133

4.2 Methods……………………………………………………………………………………..136

4.2.1 Participants………………………………………………………………………..136

4.2.2 Measures………………………………………………………………………….138

4.2.2.1 Life-stressors checklist revised (LSC-R)…………………………….…138

4.2.2.2 Addiction severity index (ASI)..………………………………………...139

4.2.2.3 PTSD symptom scale (PSS-I)………………………………………..…139

4.2.2.4 Interoceptive-Exteroceptive Attention Task……………………………139

4.2.3 Functional MRI acquisition and analysis…………………………………………141

4.2.3.1 Imaging set-up…………………………………………………………..141

4.2.3.2 Functional MRI preprocessing……………………………………….…141

4.2.3.3 Group independent components analysis…………………………….…142

4.2.3.4 Dual regression to obtain subject-level independent networks…………143

4.2.3.5 Identification of task-modulated networks…………………………...…144

4.2.3.6 Functional connectivity of task-modulated networks………………...…145

4.3 Results………………………………………………………………………………………145

4.3.1 Demographic and clinical comparisons…………………………………..………145

4.3.2 Lifetime trauma exposure………………………………………………………...146

4.3.3. Group independent components analysis and cross-correlation with a canonical

resting state network template………………………………………………………..…147
xiv

4.3.4 Identification of task-modulated intrinsic networks……………………………....147

4.3.5 Spatial differences in network functional connectivity…………………………....149

4.3.6 Mean orbitofrontal network strength is associated with lifetime

sexual trauma exposure…………………………….…………………………….…..…151

4.4 Discussion…………………………………………………………………………………..153

4.4.1 Stress regulation, interoceptive exposure, and mindfulness

for relapse prevention…………………..…………………………………………....….158

4.4.2 Limitations and future directions………………………………………….………158

4.4.3 Conclusion……………………………………………………………………...…160

5. General Discussion………………………………………………………………………….162

5.1 Study 1: Interoceptive neural pathways are relevant to (t)VNS mechanisms of action……...162
5.2 Study 2: TMS may modulate visceromotor systems, but there are caveats………………….165

5.3 Study 3: Engagement of interoceptive networks depends on traumatic stress exposure….…168

5.4 Concluding Remarks……………………………………………………………………..…170

References………………………………………………………………………………………172

Appendix A – Effect of source current density transformation on HEP time courses……..…….251

Appendix B – General linear model of IN-OUT Task and N-Back task performance………….252

xv
Abstract
Interoception concerns the afferent vagal and spinothalamic lamina I systems, and their
projection to regions of the brain comprising the central autonomic network (CAN). At the level
of the cortex, the CAN includes regions such as the insula, medial prefrontal and cingulate
cortices, which interact with subcortical and brainstem networks to regulate autonomic,
neuroendocrine, immune, and other visceral functions of the body. Interoception is an important
concept linking ‘primitive’ homeostatic functions of the brain to its ‘high-order’ cognitive
functions. This view is supported by an increasing body of experimental evidence indicating the
relevance of interoceptive neural systems to motivational drives, mood, emotion, self-awareness,
body-ownership, somatic disorders and psychopathology. However, constructs, paradigms and
other methodology for investigating neural interoception in humans require additional
development and validation. Additionally, neural interoceptive processing in psychopathology
has not been thoroughly characterized, hence limiting the translational relevance of findings
from this field. Given the emerging role of interoception in many psychological functions, a key
question would be whether we could access and modulate neural interoceptive systems in
humans. Hence, the first aim of this thesis was to investigate whether interoceptive neural
processing can be modulated through non-invasive stimulation of the cortex or through
peripheral nerve stimulation. To accomplish these aims, transcranial magnetic stimulation (TMS)
and transcutaneous vagus nerve stimulation (tVNS) were used to modulate heart-brain
interactions. A second aim of this thesis was to determine whether traumatic stress exposure in
female psychiatric patients alters the degree to which neural interoceptive systems are engaged
when asked to attend to somatic and visceral feelings during mindful breathing.
xvi
Study 1 is a randomized, sham-controlled investigation to determine whether tVNS
affects cardiovagal responses and neurocardiac integration in interoceptive cortices. The ability
of tVNS to evoke cardiovagal responses was mixed. tVNS was found to increase baroreceptor
sensitivity, but not heart rate variability, whereas both sham and tVNS elicited reductions in
heart rate. At the level of the brain, tVNS increased electroencephalographic (EEG) functional
connectivity between regions of the CAN. In particular, stronger functional connectivity was
obtained for the right somatosensory and anterior insula in the beta frequency band. The effect of
tVNS on an evoked potential reflecting neural cardiac interoceptive processing (the heart-evoked
potential or ‘HEP’) was also assessed. At the sensor-levels, tVNS was associated with greater
HEP negativity in left-lateralized frontal, temporal, parietal and central electrodes. Source-
localized functional connectivity between regions where HEPs have been observed intracranially
revealed patterns of greater and lesser connectivity in several frequency bands. Insula-prefrontal
connectivity features correlated with heart rate during tVNS. Altogether, the results indicate that
tVNS modulates neural systems relevant to cardiac interoceptive processing, which may be
relevant to the mechanisms of action by which tVNS improves cardiovascular autonomic
function in somatic and psychiatric conditions.
Study 2 applied transcranial magnetic stimulation to the right frontotemporal cortex to
test whether modulating cortical excitability within regions putatively accessing the CAN alters
cardiovascular autonomic responses. Intermittent theta-burst stimulation increased vagally-
mediated heart rate variability, but this effect appears to have been confounded by stimulation-
induced state anxiety. However, continuous theta-burst stimulation increased pulse-transit time
latency, an effect that was not explained by stimulation-induced anxiety. This study supports the
use of TMS for modulating ‘top-down’ neurocardiac integration, and discusses approaches for
xvii
optimizing TMS for investigating neural interoceptive and visceromotor processing, and its
translational relevance.
Study 3 investigated the functional MRI correlates of respiratory interoception in women
in residential treatment for stimulant dependence (SUD) who have varying histories of physical,
psychological and/or sexual trauma. A subset of patients had a concurrent diagnosis of post-
traumatic stress disorder (PTSD). Reduced functional connectivity of an interoception-linked
network was found in women with SUD-PTSD comorbidity. Specifically, an orbitofrontal
network showed diminished strength of correlation with the insular, somatosensory and cognitive
control regions during a mindfulness-based breathing task. Additionally, orbitofrontal network
strength was negatively associated with sexual violence exposure beyond the contribution of
PTSD diagnosis alone. This study contributes to scientific understanding concerning
interoceptive dysfunction in psychopathology and potential mechanisms through which psycho-
behavioral techniques such as mindfulness may improve mental health.
Overall, these results of this dissertation support the utility of non-invasive cortical or
peripheral nerve stimulation in accessing and modulating neural interoceptive systems related to
cardiovascular autonomic regulation. The results also support the utility of using certain psycho-
behavioral techniques, such as mindfulness, to engage interoceptive brain systems, and they
highlight how different psychopathological conditions may respond differently to treatment
modalities involving interoceptive manipulations. Altogether, this work enhances basic
understanding of brain-body interactions, and advances the translational value that can be
derived from interoceptive theoretical frameworks.

1
1. General introduction

1.1 Interoception: definition, history and development of the concept
Interoception describes the process by which the nervous system transduces, perceives,
integrates, and interprets autonomic, hormonal, visceral, immunological and somatic signals
which constitute the moment-by-moment sense of the physiological condition of the body
(Craig, 2002; Khalsa et al., 2018). Temporally dynamic maps of the body’s physiological milieu
occurs at conscious to non-conscious levels of awareness (Hassanpour et al., 2018), and conveys
information essential to homeostasis and allostatic adaptation (Barrett & Simmons, 2015).
The original concept of interoception is attributed to the physiologist and Nobel Laureate
Charles Sherrington, who, in 1906 published lectures codifying bodily senses into teloreceptive
(hearing and vision), proprioceptive, exteroceptive (touch, including temperature and pain),
chemoreceptive (smell and taste) and interoceptive modalities (Sherrington, 1906). In this
definition, interoceptive receptors were narrowly defined as those pertaining only to the visceral
organs. Nearly a century later, Bud Craig’s lifetime work on the functional neuroanatomy of the
lamina I spinothalamic system has shifted contemporary understanding of interoceptive

sensation, as well as its extensive functional significance (Craig, 2002, 2009, 2015). The lamina I
spinothalamic system is an afferent pathway that conveys signals from small diameter nerves
(thinly myelinated Ad and unmyelinated C-fibers) that innervate all tissues of the body. Whereas
pain and temperature were once considered exteroceptive cutaneous sensations that are relayed
to somatosensory cortex, the lamina I spinothalamic neurons responsible for conveying pain and
temperature actually relay these signals to the insula (Craig, 2002; Ostrowsky et al., 2002). In
fact, intracranial stimulation of the primary somatosensory cortex does not produce sensations of
pain, whereas stimulation of the posterior insula and medial parietal operculum do elicit painful
2
sensations (Mazzola, et al., 2009). Although the spinothalamic lamina I system is often
associated with pain and temperature, this is a descriptive heuristic that conceals the variety of
physiological conditions to which these sensory fibers are sensitive, including pruritic factors
such as histamines (Andrew & Craig, 2001), inflammatory factors such as cytokines (Al-Chaer,
Westlund, & Willis, 1997), as well as metabolic and hormonal activity (Carlton, Du, Zhou, &
Coggeshall, 2001; Ghorbanpoor, et al., 2014; Neugebauer, Chen, & Willis, 2000). Beyond pain
and temperature, lamina I cutaneous C-fibers are also responsive to soft or pleasant touch
(Löken, et al., 2009), pointing to the central role of lamina I spinothalamic fibers in
communicating information relevant to homeostatic regulation as well as its hedonic valence.
The lamina I spinothalamic pathway forms the afferent branch of the sympathetic
autonomic nervous system (Craig, 2002). It first projects to the sympathetic cell columns in the
spinal cord, then to homeostatic integration sites in the brainstem (the rostral ventrolateral
medulla, nucleus of the solitary tract, catecholaminergic cell groups, the parabrachial nucleus,
and the periaqueductal gray [PAG]). Additionally, in primates the lamina I fibers project directly
to the posterior ventromedial nucleus of the thalamus (VMpo), which in turn project to the dorsal
posterior insula (Pritchard, Hamilton, Morse, & Norgren, 1986), as well as to the cingulate
cortex (Dum, Levinthal, & Strick, 2009), which are considered viscerosensory and visceromotor
cortices, respectively, due to strong interconnections with amygdala, hypothalamus, orbitofrontal
cortex (OFC), and brainstem autonomic sites (Caruana et al., 2018; Cechetto, 2014; Floyd, Price,
Ferry, Keay, & Bandler, 2000; Mesulam & Mufson, 1982c; Vogt & Palomero-Gallagher, 2012).
Just as the lamina I spinothalamic tract provides sensory input for the sympathetic
division of the ANS, the vagus nerve and glossopharyngeal nerves provide the sensory input for
the parasympathetic division of the autonomic nervous system (Yuan & Silberstein, 2016).
3
However, the vagal afferent pathway is distinct from the spinothalamic lamina I pathway. First,
vagal afferent input projects from the nucleus of the solitary tract (NST) to the parabrachial
nucleus and other brainstem regions (detailed in section 1.5.4). The thalamocortical projections
include the basal ventromedial nucleus of the thalamus (VMb) to a dorsal insula area anterior to
the region activated by lamina I spinothalamic inputs (Evrard, 2019). In addition to the VMb
focus of vagus nerve afferents, vagal evoked potentials have been recorded from the
parafascicular nucleus (Pf) of the thalamus, whose subcortical terminations include the caudate,
putamen and ventral striatum/nucleus accumbens, which are interconnected with the
hypothalamus, substantia innominata, and anterior cingulate (Ito & Craig, 2005; Vogt, Pandya,
& Rosene, 1987). Follow-up experiments on the projection regions of vagal responsive areas of
the Pf in monkeys confirmed connections to the ventral striatum, caudate head, anterior putamen,
substantia innominata, amygdala, hypothalamus, and weak projections to the prefrontal,
premotor, and cingulate cortices (Ito & Craig, 2008).
4

Figure 1.1. The ascending primary interoceptive pathways for the sympathetic lamina I
spinothalamic system and the parasympathetic nervous system, adapted from Craig (2002).
1.2 Functional significance of interoception
1.2.1 Allostasis and homeostasis

Fundamentally, the function of the brain is to maintain the integrity and vitality of the
body, hence it is the organ that coordinates homeostasis and allostasis. Homeostasis is a
dominant framework for explaining physiological regulation, which is concerned with
autonomic, hormonal, behavioral and other effector systems that maintain efficient regulation
and stability of bodily physiology (Ramsay & Woods, 2014). A foundational principle of
homeostasis is the negative feedback loop, wherein regulatory effectors are engaged as a reactive
5
and compensatory response to the perturbation of a regulated physiological variable from an
optimal (but not necessarily fixed) set-point (Ramsay & Woods, 2014).
Allostasis is an alternative theory of physiological regulation, and describes a process
through which the brain seeks to maintain biological viability by regulating physiological
parameters along a dynamic range. Specifically, organisms survive in probabilistic
environments, and must change the levels of regulated variables dynamically to meet
environmental and internal demands to maintain viability (Sterling & Eyer, 1988). The
foundational principles of allostasis are that 1) physiological regulation is anticipatory rather than
reactive, relying on learning and prediction; 2) regulated variables operate on a dynamic range to
cope with environmental and internal demands; 3) regulation is achieved through the central
nervous system, which directs effector responses to bring about changes in physiological
variables (Sterling & Eyer, 1988). The allostatic effector systems that promote adaptation include
skeletomotor behaviors, autonomic nervous system activity, cortisol, immune responses, and
metabolic hormones. These effectors create a network in which each effector can regulate other
effectors (Karatsoreos & McEwen, 2011). For instance, inflammatory responses are
downregulated by cortisol and vagal cholinergic mechanisms, and vagal afferents can alter
hypothalamic-adrenal pituitary-axis function via central projections (Bonaz & Pellissier, 2016).
Interoceptive neural activity is essential to homeostasis and allostasis by providing
continuous sensory information to efferent systems involved in autonomic, endocrine, immune,
and behavioral functions. Hence, interoception is an integrated part of a distributed viscero-
sensorimotor hierarchy comprising multiple cortical and subcortical brain regions that ‘sit at the
core of the brain’s computational architecture’ (Kleckner et al., 2017, p. 2). An influential model
of interoceptive-allostatic regulation (Barrett & Simmons, 2015) identifies a system of prefrontal
6
agranular visceromotor regions that includes the posterior orbitofrontal cortex, the ventromedial
prefrontal cortex, anterior cingulate cortex, and anterior insula that regulate visceromotor output
via connections to subcortical regions such as the amygdala, ventral striatum, hypothalamus,
PAG, parabrachial nucleus, nucleus of the solitary tract and other brainstem sites to mobilize the
autonomic, immune, metabolic, and hormonal resources necessary to meet allostatic demands. In
particular, Barrett and Simmons’ model specifies that visceromotor regions 1) engage in ‘active
inference’ whereby the predicted sensory consequences of visceromotor actions are
communicated to primary interoceptive cortices, and 2) modulate neural ascending
viscerosensory signals, which is to say, that interoceptive experiences are modified or
constrained by the brain’s predictive functions. Barrett and Simmons’ model emphasizes how
viscerosensory and visceromotor regions interact with higher-order cortical regions to create a
predictive/anticipatory allostatic system, whereas a purely homeostatic perspective may see
interoceptive information as the ascending component of a stimulus-response loop, where it is
fed-forward in a sequential manner to the visceromotor cortices that engage reactive,
compensatory effector responses.
Although some concepts of homeostasis incorporate learning processes (e.g. [Dworkin,
1993]), contemporary authors, particularly in the field of interoception, prefer allostasis as a
more comprehensive explanatory mechanism for physiological regulation. Specifically, an
allostatic perspective provides a theoretical bridge between interoception and its interactions
with complex adaptive and maladaptive behaviors and emotions. Such a concept has been
advanced by Bernston, Cacioppo and Bosch (2017) as ‘allodynamic regulation’ in which levels
of the central autonomic network integrate visceral afferents with emotional and cognitive
information to dynamically regulate autonomic outflows to achieve metabolic states appropriate
7
to a given context. To illustrate the operation of an allostatic neural circuit, activation of the
central nucleus of amygdala, such as during the detection of threats, can inhibit barosensitive
neurons in the medulla, resulting in simultaneous heart rate and blood pressure increases, counter
to the reflex pattern that characterizes the baroreflex negative feedback loop (Saha, Batten, &
Henderson, 2000). Yet, by inhibiting the reflex, the amygdala increases the capacity of the body
to respond to the threat.
Allostatic perspectives also allow for the concept of allostatic overload, which describes
how allostatic adaptations, which are intended to allow an organism to respond to environmental
and internal stressors can be inefficient, inappropriate, or prolonged, which gradually produces
physiological ‘wear and tear’, eventually leading to dysregulation and disease states (Karatsoreos
& McEwen, 2011). For example, individuals with exaggerated and prolonged rises in
autonomically-mediated heart rate and blood pressure in response to stressors have an increased
risk of hypertension, stroke, myocardial infarction, and early death (Ginty, Kraynak, Fisher, &
Gianaros, 2017). The dysfunction could also extend to the viscerosensory pathways. For
example, in the scenario in which an individual experiences an inappropriately exaggerated
increase of blood pressure in response to a perceived stressor, feedback via viscerosensory routes
may initiate an inhibitory effect on the pathways driving the exaggerated blood pressure
response. However, such errors of prediction by visceromotor cortices may remain uncorrected if
the viscerosensory pathways are themselves insensitive (or hypersensitive, in other cases). For
example, aortic stiffness, such as due to calcification, chronic inflammation, chronic high
systolic blood pressure, etc., renders the baroreceptors less sensitive to changes in blood pressure
(Mitchell, 2014). However, interoceptive deficits could arise at any level of the ascending
viscerosensory neural pathways, including in the primary interoceptive cortices (e.g., see Song et
8
al. [2019] and Marins, et al. [2017] for structural abnormalities of the insula in hypertension and
heart failure).
1.2.2 Allostatic dysregulation and psychopathology
Psychopathological conditions are often associated with multiple changes in interoceptive
and visceromotor function, such as chronic inflammation (Michopoulos, Vester, & Neigh, 2016;
Savitz & Harrison, 2018), reduced parasympathetically-mediated HRV (Beauchaine & Thayer,
2015), hypothalamic-pituitary-adrenal (HPA)-axis abnormalities (Doom & Gunnar, 2013),
increased risk of cardiovascular disease (Gianaros and Sheu, 2009; Ginty et al., 2017; Thayer
and Lane, 2007), and atypical functional brain responses within interoceptive cortices (Avery et
al., 2014; Khalsa et al., 2018). Abnormal or atypical interoceptive-allostatic processing has even
been suggested to be a general, transdiagnostic factor underlying psychopathology (Murphy,
Catmur, & Bird, 2018). Supporting this hypothesis, a large-scale meta-analysis of gray matter
volume from 193 studies and 15,892 individuals found that gray matter loss converged across six
diagnostic categories (schizophrenia, bipolar disorder, depression, addiction, obsessive-
compulsive disorder, and anxiety) in three key cortical regions of the CAN, including the dorsal
anterior cingulate cortex, and the mid- to anterior right and left insular cortices (Goodkind et al.,
2015). Major depression was uniquely associated with additional gray matter loss in the anterior
hippocampus and amygdala (Goodkind et al., 2015), subcortical regions which are also directly
relevant to the functioning of the CAN hierarchy (Sklerov, Dayan, & Browner, 2019).
The established link between inflammation and major depression (Amodeo, Trusso, &
Fagiolini, 2017) provides a clear illustration as to how visceral processes interact with neural
systems underlying mood. Acute inflammation, such as due to infectious pathogens, induces
‘sickness behavior’ which is characterized by decreased motivation (e.g. fatigue, lethargy,
9
psychomotor retardation, etc.), altered feeding and thirst, impaired concentration and memory,
fever, cognitive and affective changes (Harrison et al., 2009). Experimental induction of mild
systemic inflammation in healthy human volunteers by typhoid vaccine temporarily increases
systemic pro-inflammatory markers to levels observed in patients with major depression. In this
inflammation model, insula metabolic responses to pro-inflammatory cytokines correlate with
subjective feelings of lethargy (Harrison et al., 2009). Typhoid-induced inflammation also
diminishes behavioral and neural sensitivity to reward, but enhances it for punishments, which
may reflect the neural processes underlying the motivational impairments and anhedonia that is
observed in major depression (Harrison et al., 2016). Indeed, in major depression, peripheral
inflammation is associated with reduced functional connectivity within cortico-striatal reward
circuitry, which has been shown to mediate the relationship between inflammation and degree of
anhedonia and psychomotor slowing (Felger et al., 2016).
Interoceptive signals can also serve as conditioned cues for behaviors and visceromotor
responses (which may be adaptive or maladaptive). Maladaptive interoceptive conditioning is
most readily apparent from studies of drug addiction, which show that the visceral and somatic
effects of drugs become sufficient to trigger dopamine release and produce drug-seeking
behaviors. For instance, the administration of a cocaine-analog that produces the peripheral
effects of cocaine, but does not cross the blood-brain-barrier, triggers the release of dopamine
from the ventral tegmental area and reinstates conditioned place preference in cocaine-trained,
but not cocaine-naïve rats (Wang et al., 2013). Lesion paradigms indicate the relevance of the
insula to interoceptive conditioning as well. In one such paradigm, lesions of the granular and
agranular insula disrupt conditioned place preference for opioids as well as conditioned place
aversion to naloxone-precipitated withdrawal (Li, Zhu, Meng, Li, & Sui, 2013). Hence,
10
interoceptive signaling from the insula appears to guide goal-directed and habitual behaviors.
Interoceptive conditioning is likely relevant to multiple psychopathological conditions apart from
addictions, including panic disorder, for which interoceptive exposure therapies such dyspnea
induction are effective at diminishing panic attacks (Meuret, et al., 2018).
The chronic inflammation, increased cardiovascular risk, neuroendocrine disruptions,
alterations in circadian rhythms, and diminished vagal function observed across mental health
disorders indicate that psychopathology may be associated with allostatic overload, that is, the
cumulative ‘wear and tear’ seen in bodily systems after prolonged or inappropriately regulated
allostatic responses. Psychopathology often emerges in the presence of stressors across the
lifespan (Brilman & Ormel, 2001; Brown et al., 2014), perhaps reflecting a limited capacity for
allostatic systems (including relevant brain structures of the CAN) to develop normally or return
to normal functioning after stress-exposure in individuals with certain vulnerabilities
(Karatsoreos & McEwen, 2011). For instance, adult women with histories of childhood trauma
display sensitization of autonomic and endocrine stress responses (Heim et al., 2000), which may
create a diathesis for the development or maintenance of psychopathology in the face of
cumulative life stressors (Maughan & Collishaw, 2015). Post-traumatic stress disorder is an
exemplar of allostatic dysregulation throughout all levels of the neuroaxis: patients with PTSD
fail to exhibit cardiovascular recovery (sustained heart rate increases and reduced HRV) after
exposure to trauma-related stimuli (Norte et al., 2013). PTSD is also generally associated with
paradoxically blunted HPA-axis cortisol responses and impaired HPA-axis negative feedback
regulation, but also greater glucocorticoid receptor sensitivity, greater levels of corticotropin
releasing hormone (CRH), increased systemic pro-inflammatory cytokines, increased central and
peripheral catecholamine levels (e.g. norepinephrine), and atrophy in brain regions with high
11
levels of glucocorticoid receptors and inhibitory influence over the HPA-axis, such as the
hippocampus and prefrontal cortex (Daskalakis, McGill, Lehrner, & Yehuda, 2014; Liston et al.,
2006; Sherin & Nemeroff, 2011).
1.2.3 Modulating interoceptive-allostatic systems
Stimulation of interoceptive vagal, cardiovascular and cardiorespiratory systems reduces
psychiatric symptomatology (Goessl, Curtiss, & Hofmann, 2017; Lamb, Porges, Lewis, &
Williamson, 2017), perhaps by inducing plasticity in the brain systems that interact with
visceromotor effectors, thereby enhancing resilience to stressors that contribute to allostatic load.
Consistent with this premise, chronic Vagus Nerve Stimulation (VNS) has been shown to
facilitate the development of efficient neuroendocrine feedback mechanisms in response to
stressors (Thrivikraman, et al., 2013). VNS is also an effective treatment for severe depression
and bipolar disorder (Aaronson et al., 2017). Although the mechanisms of action through which
VNS improves mood dysfunction have not been firmly established, it can be speculated that one
mechanism (among multiple simultaneously acting mechanisms [Vonck & Larsen, 2018]),
occurs through vagal afferent and efferent regulation of inflammation (Das, 2007). The efferent
vagus nerve suppresses the synthesis and release of pro-inflammatory cytokines through
cholinergic mechanisms, while the afferent vagus regulates inflammation via central pathways.
Specifically, vagal projections from the NST to noradrenergic cell groups ascend to the
paraventricular nucleus of the hypothalamus where CRH and immunoreactive neurons are
located (Bonaz, et al., 2013). Through this central nervous system pathway, the vagus nerve is
able to activate the HPA-axis response to a peripheral inflammatory signal (Bonaz et al., 2013).
VNS also activates thalamocortical interoceptive pathways, thus may alter allostatic functions
through higher levels of the CAN hierarchy. Accordingly, the strength of functional connectivity
12
between the rostral anterior cingulate cortex and the bilateral medial hypothalamus during
transcutaneous vagus nerve stimulation (tVNS) via the auricular branch of the vagus nerve
(AVBN) predicted improvements in depression symptoms (Tu et al., 2018). tVNS has also been
shown to increase vagally-mediated heart rate variability and reduce autonomic startle responses
in patients with PTSD (Lamb et al., 2017).
Transcranial magnetic stimulation (TMS) may be an alternative means of improving
neural allostasis in the context of psychiatric and medical disorders (Cogiamanian et al., 2010).
The principle underlying TMS is Faraday’s law of induction, which describes how a time-
varying magnetic field in conductive environment produces an electrical current (Vidal-Dourado
et al., 2013). A magnetic field applied to the head will pass the tissues of the skin and skull to
induce electrical currents in populations of neurons (Kobayashi & Pascual-Leone, 2003).
Depending on the parameters of the magnetic pulses, the resulting electrical stimulation can
produce an excitatory or inhibitory effect on neurons (Huang, et al., 2005), alter neurotransmitter
concentrations (Baeken & De Raedt, 2011), and potentially modulate interconnected functional-
anatomical networks (Gratton, Lee, Nomura, & D’Esposito, 2013), as opposed to just localized
changes in the focally stimulated patch of cortex. Prospective studies of patients with depression
indicate that TMS treatments may reduce depressive symptoms through alterations in
neuroendocrine function (Baeken et al., 2009; Mingli, Zhengtian, Xinyi, & Xiaoping, 2009).
Although less attention has been directed at cardiovascular autonomic effects of TMS and their
relation to mood symptoms, at least one prospective trial revealed that TMS was associated with
improvements in mood as well as increases in vagally-mediated heart rate variability (Udupa et
al., 2011). Hence, TMS of cortical regions may be a promising means of inducing plasticity in
interoceptive-allostatic systems that relate to vulnerabilities to mood and emotional disturbances.
13
Behavioral methods aimed at improving the function of interoceptive-allostatic systems
and reducing psychopathological symptoms includes mindful breathing, yoga, and heart-rate
variability (HRV) bio-feedback. HRV biofeedback is a cardiorespiratory exercise that identifies
the rate of breathing that maximizes HRV (which coincides with an individual’s baroreflex
resonance frequency, which averages around 0.1 Hz [Lehrer & Gevirtz, 2014]). It has been found
to be promising for improving cardiovascular autonomic indices and reducing mental health
symptoms in anxiety disorders (Henriques, et al., 2011; Moss, 2016; Reiner, 2008); major
depression (Karavidas et al., 2007; Siepmann, et al., 2008); PTSD (Tan, et al., 2011; Zucker, et
al., 2009); and substance abuse (Eddie, et al., 2014). Meta-analysis reveals that HRV-
biofeedback reduces stress and anxiety with a large effect size (Goessl et al., 2017). Mindfulness
and yogic practices are more diverse, but breathing exercises, as well as attentional focus on
respiratory, interoceptive and other bodily sensations are important to the praxis of these
traditions. Yogic mantras (as well as other forms of prayer) are known to induce baroreflex
cardiorespiratory resonances (Bernardi et al., 2001). Relatedly, mindfulness and yoga
interventions yield improvements in cardiovascular autonomic function (Pascoe, Thompson, &
Ski, 2017). Mindfulness is also shown to reduce symptoms of substance abuse (Li, et al., 2017)
depression and anxiety disorders (Hofmann, Sawyer, Witt, & Oh, 2010). A mindfulness
intervention for general anxiety disorder found that efficacy was associated with altered patterns
of connectivity between the amygdala and VMPFC (Hölzel et al., 2013). Mindfulness training
may also increase the degree of fractional anisotropy (an index of white matter integrity)
between the right-lateralized insula and its anatomical connections which included the anterior
cingulate and medial orbitofrontal cortices (Sharp et al., 2018). Hence, certain mindfulness
practices may also improve efficiency of allostatic-interoceptive systems by inducing resonance
14
or entrainment of rhythms throughout multiple levels of the neuroaxis (as described in section
1.3.5, breathing entrains rhythms of neuronal ensembles in the limbic system, with unique
changes associated with intentional breathing states).
1.3 Phenomenology of interoception and monitoring the body’s internal state
Other conceptualizations of interoception are less constrained by models of functional
neuroanatomy and allostatic regulation, and instead emphasize phenomenological aspects,
particularly subjective states of bodily feeling. In such conceptualizations, interoception refers to
the multimodal integration of sensory information (regardless of whether it arises from
interoceptive neural systems per se) with learned associations, memories, attention, and emotions
that engender a subjective representation of the condition of the body at any given moment in
time (Ceunen, Vlaeyen, & Van Diest, 2016), as well as an associated motivational or affective
state (Craig, 2009).
1.3.1 Bodily awareness
For theories of phenomenal interoception, the insula is a structure central to the
representation of a material self-consciousness or ‘homeostatic sentience’ (Craig, 2015) that is
derived from viscerosensory inputs from the body. The insula is hypothesized to function as a
neural hub that produces a subjective (first-person) frame of reference from which exteroceptive
percepts are experienced and interpreted (Park, et al., 2014; Tallon-Baudry, et al., 2018).
Multiple lines of inquiry support the assertion that the insula is important for bodily awareness,
self-other distinctions, and that interoceptive vagal and lamina I spinothalamic inputs contribute
to that experience. A quantitative spatial analysis of brain lesions producing out-of-body
experiences in patients with epilepsy and stroke identified that damage to the left posterior insula
can produce heautoscopic hallucinations, characterized by the experience of seeing one’s body in
15
extrapersonal space, but difficulty determining whether the center of conscious experience is
located in the physical or autoscopic body. Heautoscopy is further accompanied by strong
emotional experiences, such as of fear, pleasure, or anger. Hence, damage to the left posterior
insula can produce abnormal bodily self-consciousness and the breakdown of self-other
boundaries possibly due to the disintegration of visual and somatosensory information with
interoceptive and affective information (Heydrich & Blanke, 2013). Along similar lines, damage
to the dorsal posterior insula can also result in somatopharaphrenia wherein patients misattribute
their limb contralateral to the lesion side as belonging to another person, or in other cases,
attribute the hands of others to themselves (Blanke, 2012; Karnath & Baier, 2010). Dorsal
dysgranular regions of the insula are interconnected with the somatosensory cortex and motor-
executive regions (Evrard, 2019), consequently somatoparaphrenic delusions could emerge from
failed integration of interoceptive activity with proprioceptive and mechanoreceptive inputs
related to the limb. Finally, electrical stimulation of left and right insular subregions can induce
partial and whole-body parasthesias in human neurological patients (Dionisio et al., 2019).
Although body-awareness involves multisensory integration from diverse neural sources
(including the extrastriate body areas, somatosensory cortex, temporo-parietal junction,
supramarginal gyrus, etc. [Salvato, et al., 2020]), lesion studies clearly demonstrate the
importance of primary interoceptive cortices for the integration of a unified percept of the body,
sensations arising from the body, and sense of body-ownership.
Apart from lesion studies, individual differences in interoceptive sensitivity in healthy
adults has been shown to be relevant to the stability of body-image: individuals who perform
better at a heartbeat tracking task (an established behavioral paradigm of interoceptive
sensitivity) are less susceptible to the manipulation of the sense of body-ownership as induced by
16
the rubber hand illusion. The effect was not attributable to differences in proprioceptive drift or
skin temperature prior to illusion induction. Additionally, synchronous stroking (the necessary
condition for the induction of the illusion) results in skin temperature decreases, indicating
sympathetic withdrawal from the limb (an objective marker of ‘loss of identification’ [Moseley
et al., 2008]) which was of a greater magnitude in those with low interoceptive sensitivity
(Tsakiris, Tajadura-Jiménez, & Costantini, 2011). The neural response to the heartbeat (an
electrophysiological signal of neural interoceptive processing) is also altered by induction of the
full-body illusion, during which the sense of the embodied self is translocated to a virtual avatar
(Park et al., 2016). Such findings also point to the tight integration of interoceptive signals with
bodily awareness and sense of ownership. Interoceptive body integration appears to be relevant
to certain domains of psychiatric symptomatology as well. For instance, dissociative post-
traumatic stress disorder (PTSD) is a DSM-5 subtype of PTSD that is distinguished by symptoms
of derealization and depersonalization, which can include out-of-body type experiences. Relative
to non-dissociative PTSD and healthy controls, the dissociative subtype is associated with
reduced functional connectivity between vestibular brainstem nuclei and a parieto-posterior
insula network (Harricharan et al., 2016).
1.3.2 Attention
Neuroscientific studies of phenomenal interoceptive awareness have derived paradigms
based on observations that focusing attention on a particular sensory modality enhances brain
metabolism within the sensory region corresponding to that modality. For instance, such
‘attentional spotlight’ phenomena have been observed for the primary visual cortex for visual
stimuli (Brefczynski & Deyoe, 1999) and for the primary and secondary somatosensory cortices,
in which the hemodynamic response to attended versus attended touch was greater in these
17
regions (Johansen-Berg, et al., 2000). The attentional spotlight effect (and conversely, the
sensory suppression effect) has been applied to fMRI and intracranial EEG studies of discrete
interoceptive domains, including respiratory (Dickenson, et al., 2013; Farb, Segal, & Anderson,
2013a, 2013b; Hasenkamp, et al.,, 2012; Herrero, et al.,, 2018); cardiac (Avery et al., 2014; Kerr
et al., 2016; Kuehn, Mueller, Lohmann, & Schuetz-Bosbach, 2016; Pollatos, Schandry, Auer, &
Kaufmann, 2007; Simmons et al., 2013; Tan et al., 2018; Wu, Shi, Wei, & Qiu, 2019), urogenital
(Nejad et al., 2015; Kuhtz-Buschbeck et al., 2009), gastrointestinal sensations (Kerr et al., 2016;
Simmons et al., 2016) and pain (Freund et al., 2009). Details of studies examining interoceptive
awareness or attention in the cardiac and respiratory domains are detailed below.
1.3.3 Cardiac interoceptive awareness
Functional MRI studies involving attention to, or detection of, the heartbeat define brain
metabolic responses to interoceptive attention relative to a matched exteroceptive condition (for
instance, auditory tones or visual cues). Activation of the insula during interoceptive attention is
invariably found, but other regions consistent with a distributed viscero-sensorimotor system are
also observed, which includes the somatosensory and motor cortices, the inferior frontal cortex,
posterior to subgenual cingulate, thalamus, middle and superior frontal cortices, orbitofrontal
cortex, amygdala and caudate (Avery et al., 2014; Critchley, et al., 2004; Kuehn et al., 2016;
Pollatos et al., 2007; Simmons et al., 2013; Tan et al., 2018). Functional connectivity studies
highlight the involvement of a cingulo-opercular network involving the bilateral insula, mid-
cingulate cortex, middle frontal gyrus, superior frontal gyrus, and claustrum in cardiac attention
(Wu et al., 2019). A meta-analysis of cardiac interoceptive versus exteroceptive paradigms
identified right-hemisphere dominance of the mid-insula regions, along with the right inferior
frontal gyrus, medial frontal gyrus, and claustrum (Schulz, 2016). Attention to cardiac sensations
18
relative to exteroceptive stimuli enhances metabolism within expected neural interoceptive
systems. Given the association of cardiovascular sensation with anxiety symptoms, such as due
to anxiety sensitivity or fear of bodily sensations (Marker, Carmin, & Ownby, 2008) as well as
overlap between neural regions processing cardiac sensation and anxiety symptoms (Tan et al.,
2018; Wu et al., 2019), cardiac sensation or attention may be useful for examining interoceptive
function in psychiatric populations, particularly those characterized by anxiety symptoms.
1.3.4 The Heart-Evoked Potential (HEP)
The heart-evoked potential (HEP) is an electroencephalographic (EEG) evoked potential
that is time-locked to the onset of the ECG R-wave (Schandry, Sparrer, & Weitkunat, 1986).
HEPs are believed to represent central processing of beat-to-beat cardiovascular information.
The HEP signal may be a useful biomarker to investigate interoceptive neural processes in
various clinical populations. With regards to interoceptive attention, directing attention to the
heartbeat, as well as accuracy of heartbeat detection, is associated with greater amplitude HEPs
(Pollatos, Kirsch, & Schandry, 2005; Pollatos & Schandry, 2004; Schandry, Bestler, & Montoya,
1993; Schulz et al., 2015; Terhaar, et al., 2012). HEPs are also associated with the presumed
integrity of interoceptive autonomic neural systems: individuals with diabetic neuropathy show a
reduction in HEP amplitude over right frontal, temporal, and central electrodes, which correlates
with individual differences in subjectively experienced severity of autonomic neuropathy
(Leopold & Schandry, 2001). Additionally, HEP amplitude in the temporal and lateral prefrontal
regions correlates with ventricular repolarization inhomogeneity and cardiac output during
mental stressor tasks in patients with ventricular dysfunction (Gray et al., 2007).
Heart beat detection accuracy is also diminished in psychiatric disorders including
depression (Terhaar et al., 2012) and depersonalization/derealization disorders (Schulz et al.,
19
2015), which is also reflected as attenuation of HEP amplitudes in frontal and central electrodes.
Source localization studies of HEP waveforms using EEG and magnetoencephalography (MEG)
have identified the anterior and posterior insula (Babo-Rebelo et al., 2016; Couto et al., 2015;
Pollatos et al., 2005), cingulate, ventromedial/orbitofrontal prefrontal cortices (Babo-Rebelo et
al., 2016; Canales-Johnson et al., 2015; Park, et al., 2014; Pollatos et al., 2005), and
somatosensory cortex (Pollatos et al., 2005) as the origin of these signals. Inhibitory TMS of the
right frontotemporal cortex and somatosensory cortex decreased cardiac and respiratory
interoceptive accuracy and reduced HEP amplitudes over frontocentral locations (Pollatos,
Herbert, Mai, & Kammer, 2016). Intracranial EEG recordings confirm that HEPs originate in the
insula, operculum, cingulate cortex, amygdala, somatosensory cortex, orbitofrontal, and inferior
frontal gyrus (Canales-Johnson et al., 2015; Kern, et al., 2013; Park & Blanke, 2019; Park et al.,
2017). However, concrete evidence as to the underlying neural and physiological generative
mechanisms of HEPs are currently lacking (Park and Blanke, et al., 2019), although they are
believed to reflect afferent processing from the baroreceptors (vagal afferent pathway), intrinsic
cardiac neurons (vagal and spinothalamic lamina I pathways), cutaneous mechanoreceptors
(medial lemniscal pathway), and intra-cerebral neurovascular coupling (Park & Blanke, 2019).
See Figure 1.2.
20

Figure 1.2. Anatomical pathways mediating sensory signals contributing to the HEP. Images
created from Biodigital and Brainstorm software, adapted from Park and Blanke (2019).
1.3.5 Respiratory interoceptive awareness
Some researchers have recently theorized that respiratory rhythms are a basic organizing
principle of neural dynamics, modulating the temporal organization of large-scale oscillations in
the brain (Heck et al., 2017; Herrero et al., 2018; Tort, Brankačk, & Draguhn, 2018). By means
of rhythmic entrainment of neural ensembles, respiration is directly linked to high-order cortical
21
processes involving emotion, cognition, sensory perception and motor control (Heck et al., 2017;
Zelano et al., 2016). For example, slow diaphragmatic breathing reduces negative affect and
induces analgesia (Strigo & Craig, 2016; Zautra, et al., 2010), and alters intra-and-inter
hemispheric EEG coherence (Chan, et al., 2011). Perhaps for this reason, it is not surprising that
breathing practices are central to yogic and meditative traditions. Accordingly, focused attention
to the breathing cycle is a fundamental meditative technique that characterizes meditative
traditions. Consequently, the majority of functional neuroimaging studies concerning attention to
breathing have their theoretical focus of mechanisms of mindfulness.
The current body of functional MRI studies that contrast attention to the breath versus an
exteroceptive target highlight increased metabolism in a network of regions that across all
studies includes the mid-posterior insula, anterior insula, central operculum, parahippocampal
gyrus, hippocampus and amygdala, somatosensory cortex, temporo-parietal junction, angular
gyrus, precuneus, posterior cingulate, dorsal anterior cingulate, superior and middle frontal gyri,
frontal pole, dorsomedial PFC, brainstem, cerebellar vermis, and caudate (Dickenson et al.,
2013; Farb et al., 2013a; Hasenkamp et al., 2012). Farb et al., (2013a) further conducted a
psychophysiological interaction analysis to determine which areas of the brain exhibited greater
functional connectivity with the posterior insula during respiratory interoceptive attention
relative to exteroceptive attention, finding interactions with the ventromedial thalamus,
parahippocampal gyrus, vermis, pulvinar, middle temporal gyrus, and another region of the
posterior insula. In a subsequent paper, Farb et al. (2013b) report that Mindfulness-based stress
reduction (MBSR) training increases posterior insula and somatosensory responses during
focused breathing as a function of hours practiced, indicating that interoceptive representations
in the brain are plastic (perhaps reflecting meditation-induced changes in neural
22
cardiorespiratory integration, since meditation and slow breathing practice is known to increase
heart rate variability and other cardiovascular indices with moderate effect sizes [Pascoe et al.,
2017]).
As compared to cardiac interoceptive attention, respiratory attention appears to engage a
more diverse network of regions. Moreover, the frequent observation of medial temporal
responses in these fMRI studies is consistent with intracranial EEG (iEEG) in humans, which
demonstrates that spontaneous nasal breathing synchronizes electrical activity in the piriform
(olfactory) cortex, amygdala and hippocampus (Zelano et al., 2016), and increases respiratory-
iEEG coherence in the amygdala, insula, lateral and medial OFC, pars opercularis,
somatosensory cortex, and the superior temporal lobe (Herrero et al., 2018). Attention to
breathing, alternatively, enhances respiratory-iEEG coherence in the hippocampus, anterior
cingulate, premotor and insular cortices (Herrero, et al., 2018).
Given the strong connection between respiration and emotion (Bordoni, Marelli, &
Bordoni, 2016; Homma & Masaoka, 2008; Masaoka, Izumizaki, & Homma, 2014; Mather &
Thayer, 2018; Sakaki et al., 2016), and the role of slow, diaphragmatic breathing in regulating
stress and negative affect (Arch & Craske, 2006; Doll et al., 2016; Harris, Katkin, Lick, &
Habberfield, 1976; Oneda, Ortega, Gusmão, Araújo, & Mion, 2010), improving cardiovascular
function in hypertension (Joseph, et al., 2005; Zou et al., 2017), and reducing sympathoexitation
in chronic obstructive pulmonary disease (Raupach et al., 2008), respiratory interoception has
direct translational relevance to mood and stress-linked mental and somatic disorders.

23

1.4 Select physiological concepts related to interoception and cardiovascular autonomic

regulation
In this section, I discuss the physiological background relevant to understanding
cardiovascular and cardiorespiratory integration in the brain-body axis, as well as the
physiological basis supporting the use of select methodologies employed in the subsequent
studies.
1.4.1 Vagus nerve functional (neuro)anatomy
The vagus nerve is a complex autonomic, endocrine, gastrointestinal, and immune
interface through which the body communicates with the brain (the afferent division), and the
brain with the body (the efferent division). The vagus nerve is composed of several types of
fibers, each of which correspond to their physiological roles and origin or termination within
four specific medullary nuclei (dorsal motor nucleus, nucleus ambiguus, nucleus of the solitary
tract, and the spinal trigeminal nucleus). The sensory nerve types include the general somatic
afferent, general visceral afferent, and the special visceral afferent. The efferent nerve types
include the general visceral efferent and special visceral efferent.
1.4.2 Vagal efferent fibers
General vagal efferent (GVE) fibers. GVEs are composed of parasympathetic
preganglionic fibers that originate in the dorsal motor nucleus and the nucleus ambiguus to
innervate thoracic and abdominal organs primarily through cholinergic mechanisms. The GVE
fibers synapse on ganglia close to or within the walls of the target organ, allowing for discrete,
localized parasympathetic effects within the cardiovascular, respiratory and gastrointestinal
systems (Yuan & Silberstein, 2015). The rostral ventrolateral section of the nucleus ambiguus
24
contains parasympathetic motor neurons, which have selective effects on cardiac function
relative to the dorsal motor nucleus. Specifically, GVEs from the nucleus ambiguus and the
dorsal motor nucleus have different morphology, conduction velocity, firing patterns, and
ultimately, effects on the heart: fibers originating from the nucleus ambiguus are primarily thinly
myelinated B-fibers, with phasic firing patterns that are cardiac pulse synchronous (which
appears to be in response to inputs from the nucleus of the solitary tract or other projections such
as from the hypothalamus or pons, as nucleus ambiguus neurons have strong effects on heart rate
via the sinoatrial node, but do not appear to have intrinsic pace-making properties (Dergacheva,
et al., 2010; Farmer, et al., 2016). Separate groups of nucleus ambiguus neurons exert control
over heart rate, atrioventricular conduction time, and ventricular contractility (Silvani, et al.,
2016). In contrast, DMN neurons are primarily unmyelinated C-fibers, tonic in firing pattern, and
have a small to null effect on heart rate, but instead influence atrioventricular node conduction
and contractility (Coote, 2013). Dorsal motor nucleus instead specializes in parasympathetic
innervation of the gastrointestinal system (Yuan and Silberstein, 2015).
Special visceral efferent (SVE) fibers. SVEs are also associated with the nucleus
ambiguus, known as the branchiomotor division, which supplies the muscles of the soft palate,
pharynx, larynx, and upper esophagus. The spinal trigeminal nucleus and the nucleus of the
solitary tract provide sensory inputs to the branchiomotor division to generate reflexes such as
gagging, coughing, and vomiting (McKinley, Clarke, & Oldfield, 2012; Ruffoli et al., 2011).
1.4.3 Vagal afferent fibers
The afferent vagus nerves have cell bodies originating bilaterally in the jugular (superior)
and nodose (inferior) ganglia, immediately external to the jugular foramen where the vagus nerve
bundle exits the cranial cavity (Ruffoli et al., 2011).
25
General visceral afferent (GVA) fibers. GVAs provide a multitude of mechanical,
thermal, chemical, metabolic, immune and hormonal information from thoracic and abdominal
viscera. For instance, chemoreceptors in the carotid body, heart, bronchi, and abdomen
innervated by GVAs provide information concerning carbon dioxide concentrations (Câmara &
Griessenauer, 2015). Other forms of interoceptive information conveyed by GVAs include blood
pressure via baroreceptors located in the aortic arch, as well as lung inflation from stretch
sensitive receptors in the lungs (Dergacheva et al., 2010). The presence of local and systemic
immune factors such as cytokines also active vagal receptors (Steinberg et al., 2016), as do
dozens of gastrointestinal hormones that contribute to digestion, absorption and satiety
(Browning, Verheijden, & Boeckxstaens, 2017). The cell bodies of the GVAs originate in the
nodose ganglion and terminate in the caudal nucleus of the solitary tract (NST). The NST is a
critical medullary hub for the regulation of visceral, cardiovascular and respiratory homeostasis,
and interacts with forebrain regions that includes the insula, hypothalamus, amygdala, among
other regions of the central autonomic network (Benarroch, 1993). Hence, the vagal GVA
projections form the parasympathetic complement of the lamina I thalamocortical pathway.
Special visceral afferents. These vagal fibers supply the taste buds of the epiglottis, and
project to the rostral sectors of the NST, which projects in monkeys to the gustatory region in a
rostral granular area of the insula and to a region in the frontal operculum (Pritchard et al., 1986).
General somatic afferent (GSA) fibers. GSAs originate in the jugular ganglion and carries
touch, pain and temperature sensation from the pharynx, larynx, trachea, bronchi, esophagus,
concha and cymba conchae of the external ear, the external auditory meatus, tragus, and the
tympanic membrane (Kiernan, 2009; Peuker & Filler, 2002) to the spinal trigeminal nucleus.
Although conventional knowledge assigns general somatic afferents of the vagus nerve as
26
projecting from the spinal trigeminal nucleus to the contralateral primary somatosensory cortex
via the ventral posteromedial nucleus of the thalamus, several lines of evidence described below
highlight likely cross-communication of the vagal general somatic afferents with the visceral
afferent and efferent vagus nerve fibers.
1.4.4 The auricular branch of the vagus nerve (AVBN)
The auricular branch of the vagus nerve (ABVN), provides sensory information from the
external meatus, tragus, concha, and cymba conchae of the ear. Of special importance to this
dissertation, the ABVN is the most common target of transcutaneous vagus nerve stimulation
(tVNS). The ABVN is usually described as projecting to the spinal trigeminal nucleus, rather
than the NST (Yuan & Silberstein, 2015), however there is direct evidence from rodent studies
showing that cavum conchae stimulation produces c-Fos staining in the bilateral NST and the
bilateral locus coeruleus (LC) similar to that resulting from invasive cervical vagus nerve
stimulation, whereas sham (earlobe) and trigeminal nerve stimulation do not (Ay, Napadow, &
Ay, 2015), and that projections of the ABVN to the NST produces cardio-inhibitory effects (Gao
et al., 2010). Specifically, in rats the ABVN terminates on the ipsilateral caudal part of the lateral
NTS, as well as the dorsomedial area of the spinal trigeminal nucleus, the rostrolateral cuneatus
and spinal dorsal horn in cervical segments 2 – 3 (He et al., 2013), illustrating the multiple
afferent pathways of the ABVN and its intersection with the NST. Direct projection of ABVN to
the NST has also been demonstrated in cats (Nomura & Mizuno, 1984). While there is currently
no direct anatomical evidence in humans, functional neuroimaging studies confirm that the
ipsilateral NST is also co-activated with the ipsilateral spinal trigeminal nucleus during
stimulation of the ABVN via the cymba conchae compared to sham stimulation of the earlobe
(Frangos, Ellrich, & Komisaruk, 2015; Sclocco et al., 2019). These results lend confidence to the
27
assumption that ABVN fibers also project to the caudal NST in humans. Activations in fMRI
studies of ABVN stimulation are also consistent with known vagal projections to the cerebrum
(see section 1.6). Clinical anecdotes support the possibility for either direct or indirect
connections between the NST and the ABVN in humans, such as referential pain in the ear as a
symptom of myocardial infarction (Amirhaeri & Spencer, 2010) and bradycardia and syncope in
response to stimulation of ear territories innervated by the ABVN (Thakar, et al., 2008), among
other curious clinical anecdotes (Murray, et al., 2016). More direct experimental evidence in
humans and animals suggests that tVNS can induce changes in cardiovascular autonomic
parameters (as discussed in section 1.6.1), further indicating the utility of tVNS as an
experimental means of accessing interoceptive and allostatic regulatory systems in humans.
The human ear is supplied by the greater auricular nerve (as part of the superior cervical
plexus originating in spinal segments C
2
– C
3
(Cesmebasi, 2015), the auriculotemporal nerve, the
lesser occipital nerve, and the auricular branch of the vagus nerve. Focusing on innervation
territories relevant to tVNS, distributions of nerves overlap in some parts of the auricle (the
external ear), but are distinct in others. According to Peuker and Filler’s (2002) dissection of 16
cadavers, the greater auricular nerve exclusively supplied the lobule, anti-tragus, scapha, and tail
of the helix. The AVBN exclusively supplied the cymba conchae in 100% of the examined
cadavers. There was overlap between the greater auricular nerve and the ABVN in the tragus,
however, and AVBN innervation of the tragus was not present in all specimens, although there
are some concerns as to the reliability of these reports (Burger & Verkuil, 2018) given
unresolved numerical inconsistencies in Peuker and Filler’s publication. However, it is clear that
there is individual variability in nerve supply to particular territories of the ear, except, perhaps
for the cymba conchae which was invariability innervated by the AVBN (although He et al.
28
[2012] suggest the cymba conchae could also be dually innervated by the auriculotemporal
nerve, although it is not clear from what empirical source they drew this claim). See Figure 1.3
for anatomical labels for regions of the auricle relevant to this discussion.

Figure 1.3. Anatomical regions of the ear relevant to the discussion of transcutaneous vagus
nerve stimulation.
1.4.5 Autonomic Influences on Heart Rate and Heart Rate Variability
While the heart has autorhythmic fibers that generate pace-making potentials that initiate
contractions at a rate of approximately 100 beats per minute, rate and rhythm are primarily under
the control of the autonomic nervous system, with additional influences from circulating
hormones and ions (HRV Task Force, 1996). The heart is dually innervated by both the
sympathetic and parasympathetic nerves. The vagus nerve primarily innervates the
atrioventricular (AV) and sinoatrial (SA) nodes. Sympathetic nerves are distributed throughout
29
the atria, ventricles, SA and AV nodes. Sympathetic stimulation is mediated by the release of
epinephrine and norepinephrine, activating beta-adrenergic receptors on the heart muscle
(Shaffer & Venner, 2013). Sympathetic stimulation of the heart increases heart rate, contractility,
and conduction velocity, whereas parasympathetic stimulation of the heart exerts an opposing
effect (Thayer, et al., 2012). In a healthy heart, there is a dynamic balance between sympathetic
and parasympathetic effects, with the parasympathetic system dominating under resting
conditions evidenced from the fact that a healthy resting heart rate is lower than its intrinsic heart
rate (Thayer & Lane, 2007). However, heart rate cannot be assumed to be a linear sum of
opposing sympathetic and parasympathetic effects, at least in part because these systems act over
different time scales. Sympathetic effects have an approximately 1 – 2 s latency with a steady
state at 30 – 60 s (Berger, Saul, & Cohen, 1989; Levy, 1990, 1997). Sympathetic activity is
required in order to increase heart rate above the intrinsic level generated by the autorhythmic
pacemaker cells. In contrast, the heart responds to parasympathetic input with an approximately
150 – 400 ms latency with a steady state at 1 – 2 s (Berger et al., 1989; Levy, 1997). The effect
of a single vagal impulse is fleeting because the sinus node is rich in the enzyme that metabolizes
acetylcholine, thus it is rapidly broken down (HRV Task Force, 1996). These autonomic
dynamics generate a wide range of heart rates, as well as a range of latencies between successive
heart beats, referred to as heart rate variability (HRV). Parasympathetic influences are present
over the entire frequency range of the heart rate power spectrum, whereas relatively slower
sympathetic and renal influences are not capable of influencing heart rate at a frequency above
0.15 Hz (Saul, 1990). Hence, HRV spectral power below 0.15 Hz reflects a mix of
parasympathetic and sympathetic influences from multiple oscillating cardiovascular processes
such as baroreceptor and renal hormone cycles. Consequently, parasympathetic inputs from the
30
vagus nerve is the only source capable of producing rapid changes in the beat-to-beat timing of
the heart period above 0.15 Hz. Apart from spectral indices of HRV, the temporal domain index
root-mean square of successive differences (RMSSD) is an alternative measure of
parasympathetically-mediated HRV that is believed to be relatively insensitive to the effects of
respiratory rate (Laborde, Mosley, & Thayer, 2017).
1.4.6 Respiratory sinus arrhythmia (RSA)
Cardiac vagal neurons are profoundly gated by respiratory rhythms. The most prominent
component of HRV is respiratory sinus arrhythmia (RSA). RSA is HRV that is modulated
(more-or-less) in-phase with the respiratory cycle, in which heart rate falls during expiration and
rises during inspiration. Although the central drivers of RSA are not entirely known, there are
two primary mechanisms that have been identified as likely generating RSA. One source is
afferent feedback to the NST from stretch sensitive mechanoreceptors and chemoreceptors
present in the atria, aortic and carotid arteries which respond to variations in blood pressure,
intrathoracic pressure, venous return, and blood oxygenation, as well as stretch sensitive
mechanoreceptors in the lungs which respond to lung inflation (Dergacheva, et al., 2010).
However, this effect (particularly that which is related to lung mechanoreceptors) may be small
compared to that of efferent central mechanisms (Chapleau & Sabharwal, 2011; Farmer et al.,
2016). Specifically, an important contribution to RSA comes from direct modulation of nucleus
ambiguus neurons by central respiratory drivers (e.g. the Kölliker-Fuse nucleus), which inhibit
vagal motor neurons during inspiration (Palma & Benarroch, 2014; Song, et al., 2012).
Ultimately, however, RSA is expressed through vagal motor inputs to the heart, as it is abolished
following cervical vagotomy, cholinergic blockade (but not adrenergic blockade), and heart
transplant (Dergacheva et al., 2010; Yasuma & Hayano, 2004). The physiological significance of
31
RSA is also not clear, although hypotheses include optimal balance of blood CO
2
, minimizing
cardiac energy expenditure, and optimizing pulmonary ventilation-perfusion matching (Ben-Tal,
Shamailov, & Paton, 2012; Farmer et al., 2016; Sin et al., 2010; Yasuma & Hayano, 2004).
While RSA is the primary contributor to the inter-beat interval time series that characterizes
HRV measurement, it is a reasonable proxy of cardiac vagal efference as evidenced by the high
correlation (r = .88) between high-frequency spectral HRV power (HF-HRV) and direct
recordings of vagal nerve activity in the rat (Kuo, Lai, Huang, & Yang, 2005).
1.4.7 The baroreflex
The baroreflex constrains short-term oscillations in beat-to-beat blood pressure around a
dynamic set-point by adjusting heart rate, cardiac contractility, venous return, and vascular
resistance through negative feedback changes in sympathetic and parasympathetic activity
(Eckberg, 2004; Eckberg & Slight, 1992). The baroreceptors are stretch-sensitive
mechanoreceptors located in the aortic and carotid arteries which fire in response to increases in
arterial blood pressure, and reduce their firing rate in the presence of low blood pressure (relative
to an adaptive set-point) (Lanfranchi & Somers, 2002). The baroreceptors located in the aortic
arch send excitatory signals to the NST via the afferent vagus while baroreceptors located in the
carotid arteries send afferents via the glossopharyngeal nerve (Yuan and Silberstein, 2015).
Baroreceptor excitation of the NST excites the caudal ventrolateral medulla, which in turn
inhibits the rostral ventrolateral medulla (RVLM). The RVLM, in turn, provides excitatory input
to sympathetic motor neurons located in the intermediolateral nucleus of the spinal cord
(Dampney et al., 2002). Baroreceptor excitation of the NST also stimulates the vagal motor
nuclei (NA and DMN) (Dampney et al., 2002). Thus, the net effect of baroreceptor activation is
sympathetic inhibition and parasympathetic activation, resulting in lower heart rate, increased
32
vasodilation, and reduced cardiac contractility. Similarly, low blood pressure reduces
baroreceptor firing, leading to a reduction in vagal outputs and an increase in sympathetic nerve
activation via disinhibition of the RVLM, and therefore an increase in heart rate, cardiac
contractility and vasoconstriction (as postganglionic sympathetic nerves innervate the arteries,
arterioles, and veins, release of norepinephrine by the sympathetic motor nerves causes smooth
muscle contraction through activation of alpha-1 and alpha-2 adrenoreceptors [Rowell, 1993;
Thomas & Segal, 2004], thereby increasing resistance and blood pressure).
Baroreceptor negative feedback operates on a delay, due in part to the relatively slow
time course of sympathetic inhibition/disinhibition on cardiovascular processes (Rienzo, Conci,
& Castiglioni, 2009; Vaschillo, Vaschillo, & Lehrer, 2006). These delayed effects create cyclical
waves in arterial blood pressure occurring at around 0.1 Hz in humans, sometimes referred to as
‘Mayer waves’ (Swenne, 2013). Baroreceptor oscillations around 0.1 Hz contribute to the low-
frequency component of HRV (Swenne, 2013). Damage to the NST and denervation of vagal
fibers carrying baroreceptor information eliminates baroreflex oscillations and results in an
increase in average arterial blood pressure and large fluctuations in blood pressure variability
(Lanfranchi & Somers, 2002; Pilowsky & Goodchild, 2002). Furthermore, beat-by-beat
fluctuations in blood pressure (diastolic versus systolic) cause opposing changes in muscle
sympathetic nerve activity (MSNA), highlighting the role that the baroreceptor circuit has on
both the cardiac pulse synchronous and slower-scale responses (Eckberg, 2004; Macefield, et al.,
2013). Baroreceptor sensitivity, that is, how efficiently the heart rate adjusts to changes in blood
pressure, is also believed to be a useful index of cardiovagal function (Thayer and Lane, 2007).
1.5 The central autonomic network (CAN)
As previously discussed in the context of Barrett and Simmons’ (2015) model of
33
interoception and allostasis, stimulation and neuroanatomical tracing studies have established a
role for the forebrain in regulating the integrated neuroendocrine, metabolic, autonomic,
respiratory, and behavioral responses that support allostatic regulation, originally referred to as
the central autonomic network or CAN (Bennaroch, 1993).
1.5.1 Forebrain involvement in cardiovascular autonomic regulation
Neural regulation of the heart is integrated through all levels of the CAN. Clearly
illustrating this point, pseudorabies virus injection into the rat ventricular myocardium labeled
regions that included the cardiac vagal motor neurons, along with multiple brainstem, midbrain
and forebrain areas: the NST, caudal spinal trigeminal nucleus, ventrolateral reticular formation,
raphe nucleus, locus coeruleus, Kölliker-Fuse nucleus, parabrachial nucleus; PAG, hypothalamus
(paraventricular, lateral, dorsomedial nuclei), bed nucleus of the stria terminalis (BNST); the
anterior cingulate, pre-and infralimbic cortices (corresponding to ventral regions of the medial
prefrontal cortex in humans), insula, and the central and medial amygdala (Ter Horst & Postema,
1997).
Human neuroimaging studies identify regions of the CAN that are consistent with animal
and neuroanatomical studies. A meta-analysis of eight studies examining correlations between
HRV and BOLD or PET identified consistent peak activation foci in the left ventral striatum,
amygdala (basolateral and central nuclei), sub-and-perigenual cingulate (Thayer et al., 2012).
Another meta-analysis of fifteen studies examining heart rate and HRV identified consistent foci
in the brainstem, hypothalamus, thalamus, amygdala, hippocampus, parahippocampal gyrus,
dorsal and ventral anterior cingulate, ventromedial prefrontal cortex (VMPFC), rostral medial
prefrontal cortex, mid/anterior insula, caudate and putamen, inferior parietal lobe, precentral
gyrus, middle and superior frontal gyrus (Vargas, et al., 2016).
34

1.5.2 The insula is central to cardiovascular regulation
The insula is an essential region of the CAN. It is found buried within the Sylvian fissure,
covered by the frontal, parietal, and temporal sections of the operculum (Augustine, 1996). From
an architectonic perspective, the insula is subdivided into a posterior granular and intermediate
dysgranular region, characterized by laminar differentiation of cell layers and thalamic
projections, and an anterior agranular zone, characterized by undifferentiated cell layers, the
presence of large bipolar Von Economo neurons (Evrard, 2019) that provide strong reciprocal
connectivity with the anterior cingulate cortex (Craig, 2003), particularly on the right side, which
may be related to asymmetries of the autonomic nervous system (Allman, et al., 2011). From a
functional MRI perspective, the insula has been parcellated in a manner that broadly
approximates cytoarchitectonic boundaries. One parcellation scheme suggests a tripartite
division of the insula consisting of a ventral-anterior region that is functionally connected to
limbic regions including the amygdala, ventral tegmental area, posterolateral OFC, medial
prefrontal cortex, and temporal poles; a dorsal-anterior region functionally connected to the
anterior cingulate, dorsolateral prefrontal cortex, dorsal striatum; and a mid-posterior division
that is functionally connected to the primary sensorimotor cortices, supplementary motor area,
posterior temporal lobe, hippocampus and rostral anterior cingulate (Chang, Yarkoni, Khaw, &
Sanfey, 2013). Kurth, et al’s. (2010) convergent meta-analytic parcellation of insular functions
identified a ventral-anterior ‘social-emotional’ region, mid-posterior ‘sensorimotor region’,
central ‘olfactory-chemosensory-gustatory’ region, and a dorsal-anterior ‘cognitive’ region.
These analyses highlight the multi-modal function of the insula. Distinct anatomical connections
of the insula subregions to diverse sectors of the brain are confirmed in humans (Dionisio et al.,
35
2019), which underlies the diverse and multimodal functional roles of various insular gyri
(Mesulam & Mufson, 1982b, 1982a; 1982c). See Figure 1.4 for an anatomical illustration of the
insula.

Figure 1.4. Morphology of the human insula (bottom) (left hemisphere). The human insula is
composed of five main gyri. The posterior and anterior long gyrus (l1 – l2) in the posterior sector
of the insula, and the short gyri (s1 – s3) in the mid to anterior sectors of the insula. The anterior
insula also contains the accessory gyrus (ac), which forms a part of the fronto-insular cortex,
which contains many bipolar von Economo neurons, which facilitate rapid reciprocal
communication between the anterior insula and the anterior cingulate cortex (Allman, et al.,
36
2010) Image modified from Evrard (2019). Imaged licensed under the Creative Commons
Attribution 4.0 International license.
With regards to autonomic nervous system interactions, the insula projects to various
subcortical and brainstem nuclei involved in cardiovascular and cardiorespiratory function, such
as the NST, hypothalamus, parabrachial nucleus, and central nucleus of the amygdala (Cechetto
& Saper, 1990). A substantial neurological literature links the insula to cardiovascular autonomic
function. Stimulation of insula produces cardiovascular responses in humans, as first
demonstrated by Oppenheimer, et al. (1992) who observed bradycardic and depressor responses
associated with left-sided stimulation, and tachycardic and pressor responses with right-sided
stimulation. A more recent investigation (Chouchou et al., 2019) with a large number of patients
indicated that left/right stimulations were as likely to generate bradycardic and tachycardic
responses (with corresponding changes in LF/HF HRV ratios and HF-HRV power indicating that
tachycardiac and bradycardic effects were associated with changes in autonomic state).
However, while no clear pattern of hemispheric asymmetry emerged, posterior stimulation of the
insula was more likely to generate tachycardia, whereas a more anterior site near the insular
central sulcus was more likely to produce bradycardia. This effect echoes earlier observations in
the rat that dorsal posterior insula stimulation produces tachycardia (an effect mediated through
the lateral hypothalamus [Oppenheimer, Saleh, & Cechetto, 1992]) whereas stimulation more
caudally generates bradycardia (Oppenheimer & Cechetto, 1990). These observations may also
be consistent with the posterior insula projection regions of the vagal afferents, which may be
anterior to that of lamina I spinothalamic projection areas (Evrard, 2019; Small, 2010).
Patients with stroke affecting the insular cortex are also more likely to experience the
development of cardiac arrythmias and other disturbances of cardiovascular regulation. Seifert,
37
et al. (2015) investigated new onset cardiac arrhythmias in 150 patients with acute ischemic
stroke (56 right, 94 left hemisphere) during the first 72 hours of hospital admission. Severe
arrhythmias (including ventricular arrhythmias, ventricular fibrillation, ventricular flutter,
sustained and non-sustained ventricular tachycardia or new-onset ventricular ectopy,
supraventricular tachycardia, sinus tachycardia, bradycardias, sinus arrest due to sinoatrial block,
asystole, second- and third-degree atrioventricular block, or atrial fibrillation) developed in
32.7% of the sample. Arrhythmias were as equally likely to occur in patients with lesions
localized in either hemisphere. The association of arrhythmia to lesion was analyzed using voxel-
based lesion-symptom mapping. Curiously, significant associations of cardiac symptoms with
brain regions were only found for the right-hemisphere. Specifically, the strongest association
was found in the right ventral anterior insula, posterior insula and within parietal areas (including
the primary somatosensory cortex), although several other areas were also identified, including
within the thalamus, basal ganglia, medial temporal lobes, and lateral prefrontal areas. Multiple
other reports link insular stroke to cardiovascular autonomic abnormalities as well, with possible,
but still equivocal, lateralization effects with respect to sympathetic versus parasympathetic
dysregulation (e.g. Colivicchi, et al., 2004; Lale et al., 1999; Meyer, et al., 2004; Nayani, et al.,
2016; Sykora, et al., 2009). Beyond stroke, neurovascular remodeling of the insular cortex is
associated with essential hypertension, which was found to contribute to sympathetic
hyperactivity and hypertensive symptoms in rodents (Marins et al., 2017), and in heart failure,
structural and functional alterations of the insula were associated with autonomic dysfunction as
tested through the Valsalva maneuver (Song, et al., 2019).
Insular dysplasias in epilepsy are also linked to autonomic dysregulation and
cardiovascular events (Jeppesen, et al., 2014; Lacuey et al., 2016; Surges, Scott, & Walker,
38
2009). One particularly striking case study observed the occurrence of cardiac asystole during
intracranial recording from depth electrodes implanted in the left temporal, frontal, and insular
regions covering all five insular gyri, OFC, and anterior cingulate, hippocampus, temporal pole,
and left and right amygdala. The seizures originated in the temporal pole, which rapidly
propagated to all gyri of the insula apart from the anterior long gyrus, with the strongest, high-
frequency discharges in the posterior-long gyrus occurring two seconds before asystole
(Catenoix, et al., 2013).
Aside from efferent cardiovascular autonomic effects of insula damage, one lesion case
study (Khalsa, et al., 2009) identified multiple discrete sensory pathways through which
cardiovascular sensations arise to the cortex. The patient had virtually complete bilateral insula
and ACC damage, but intact primary somatosensory cortex bilaterally. After injection with
isoproterenol, a peripheral beta-adrenergic agonist, the patient could detect the time course of
heart beat sensation intensity in a manner similar to healthy controls. However, after a topical
anesthetic was applied to the patient’s skin over the areas where subjects reported maximal heart
beat sensation, the patient was no longer able to track his heart beat as before. In contrast,
healthy individuals were unaffected by the application of topical anesthetic. Thus, cardioception
is supported by at least two pathways, one involving visceral afferents that project to the insula
and another involving sensory afferents projecting to the somatosensory cortex through the
medial-lemniscal pathway. All together, these studies present a clear case for the centrality of the
insula in cardiovascular function.
1.5.3 Medial prefrontal, orbitofrontal, and anterior cingulate cortices
Cardiovascular autonomic function after medial PFC damage has not been as thoroughly
characterized as insula damage in humans, and many of the relevant studies primarily concern
39
the contextual interaction of emotional or reward stimuli with cardiovascular responses rather
than ‘resting-state’ regulation and autonomic challenge tasks. Hilz, et al. (2006) did not observe
differences between healthy controls and thirteen patients with ventromedial prefrontal cortex
(VMPFC) damage in baseline HRV, blood pressure, respiration, end-tidal carbon dioxide levels,
and oxygen saturation during rest, nor during autonomic challenge by metronomic breathing,
Valsalva maneuver, and orthostatic challenge. Instead, patients with right hemisphere VMPFC
lesions demonstrated significant increases in heart rate and systolic blood pressure to pleasant
and unpleasant emotional visual stimuli, whereas left hemisphere VMPFC patients as well as
healthy controls exhibited a pattern of heart rate decreases to pleasant stimuli. In contrast, in a
combined lesion-fMRI study, Motzkin, et al. (2014) found suppressed baseline HF-HRV in four
patients with bilateral VMPFC lesions, as well as abnormal bilateral insula responses to valenced
anticipatory cues that correlated with individual differences in HRV. These same patients
demonstrated exaggerated amygdala responses, but reduced cardiac deceleration when viewing
aversive images (Motzkin, et al., 2015). In frontotemporal dementia, atrophy of the bilateral
medial OFC, right rostromedial PFC, left frontopolar cortex and superior frontal gyrus were
associated with reduced HRV, while atrophy within the left frontopolar cortex, left anterior
insula, and rostromedial PFC were associated with systolic, diastolic, and mean arterial pressure
during emotional film viewing (Sturm et al., 2015). In the context of a Pavlovian conditioning
task, OFC lesions in the marmoset impair the animal’s ability to suppress cardiovascular arousal
after termination of a conditioned stimulus and to uncouple behavioral and cardiovascular
responses after reversal of reward contingencies (Reekie, et al., 2008). Other human lesion
studies also emphasizes the interaction of autonomic function with a learning and motivational
context in the ventromedial prefrontal cortex (including the work of Bechara and colleagues
40
[2005] on affective decision making). These examples highlight the relevance of the prefrontal
cortex in integrating behavioral with visceromotor responses, suggesting that medial prefrontal
influence on autonomic control is highly adaptive to context, prominently involving multi-modal
associative processing (such as from the lateral OFC [Ongur & Price, 2000; Rolls, 2004]) that
supports allostatic adjustments of visceromotor effectors and behavioral responses (Barrett &
Simmons, 2015; Ongur & Price, 2000). However, autonomic responses and interoceptive
sensations can be elicited by electrical stimulation of the peri-genual sectors of the anterior
cingulate in human neurological patients (Caruana et al., 2018).
Experimental animal studies also support a tonic role of the medial PFC and subgenual
cingulate in cardiovascular autonomic control, with other subgenual regions involved in other
visceromotor functions, including gastric motility, gastric secretions, and cortisol responses (Fisk
& Wyss, 1997). For example, bilateral blockade of glutamatergic transmission in the
ventromedial PFC of the awake rat shifts the threshold of reflex bradycardia toward higher
pressures after intravenous infusion of phenylephrine, suggesting that glutamatergic transmission
in the VMPFC tonically supports parasympathetic control of the baroreflex (Resstel & Corrêa,
2006). Depressor responses elicited by electrical stimulation of the VMPFC of the rat are
mediated by the nucleus of the solitary tract, as GABAergic inhibition of the NST abolishes
medial prefrontal depressor effects on blood pressure and baroreflex responses (Owens, Sartor,
& Verberne, 1999). In rats and primates, influence of the medial prefrontal, orbitofrontal, and
anterior cingulate regions on cardiovascular autonomic functions are mediated by descending
projections to the hypothalamus and amygdala, as well as through projections to autonomic
nuclei in the brainstem and spinal cord, including the PAG, parabrachial nucleus, NST, dorsal
motor nucleus, nucleus ambiguus, ventrolateral medulla, lamina I, and intermediolateral column
41
(Hurley, Herbert, Moga, & Saper, 1991; Ongur & Price, 2000; Owens et al., 1999).
1.5.4 Functional neuroimaging correlates of vagus nerve stimulation (VNS)
The primary termination of general visceral afferents of the vagus is the caudal NST. The
solitary tract also receives inputs from the area postrema, periaqueductal gray, parabrachial
nucleus, Kölliker-Fuse nucleus, hypothalamus, amygdala, cerebellum, and central nucleus of the
amygdala (Nieuwenhuys, Voogd, & van Huijzen, 2008; Nieuwenhuys, 2011). Projections of the
NST include the spinal trigeminal nucleus, PAG, nucleus accumbens, amygdala, cerebellum,
BNST, hypothalamus, parabrachial nucleus, dorsal raphe nuclei, and the locus coeruleus (Butt,
Albusoda, Farmer, & Aziz, 2019; Nieuwenhuys et al., 2008; Ruggiero, et al., 2000). The
thalamic projections of the vagus nerve include at least two nuclei: the VMb and the
parafascicular nucleus, which project to the striatum and insula, among other regions of the
cerebrum (Craig, 2002; Ito & Craig, 2005). However, vagal projections beyond these sites, as
well as the functional effects of vagal inputs to potentially widely distributed regions of the CAN
are not clearly known, particularly in humans.
1.5.5 Invasive VNS in patients
Patients implanted with vagus nerve stimulators due to intractable epilepsy and major
depression provide a unique opportunity to understand the distribution and functional effects of
cortical and subcortical projections of the vagus nerve. In patients with major depression or
epilepsy, acute stimulation of the vagus nerve was associated with increased PET or SPECT
regional cerebral blood flow (rCBF) or fMRI BOLD responses in the insula, anterior cingulate,
medial OFC, hypothalamus, medulla, thalamus, postcentral gyrus, cerebellum, inferior and
superior frontal gyri, frontal pole and putamen, whereas decreases are observed prominently in
the medial temporal lobes (amygdala, hippocampus, parahippocampal gyrus), thalamus, lateral
42
OFC, parietal operculum, pre- and post-central gyri (Conway et al., 2006; Liu, et al., 2003;
Lomarev et al., 2002; Narayanan et al., 2002; Ring et al., 2000;Vonck et al., 2008).
1.5.6 Neurochemical effects of VNS
NST fiber trajectories have been identified in humans, which includes three major
bundles to the intermediate reticular zone, dorsal medullary raphe, and the nucleus
gigantocellularis (Ruggiero, et al., 2000), indicating that the vagus nerve projects sensory
information via the NST to the locus coeruleus and the raphe nuclei, which are the primary
sources of noradrenaline and serotonin in the brain (Ruffoli et al., 2011; Vonck & Larsen, 2018).
VNS treatments increase the firing rate of serotonin and norepinephrine neurons in rats (Dorr &
Debonnel, 2006). The neurochemical effects of VNS in humans also indicates alterations in
amino acid precursors and neurotransmitter metabolites as measured from the cerebral spinal
fluid (CSF) of patients with epilepsy, including GABA, 5-hydroxyindoleacetic acid (the main
serotonin metabolite), tryptophan (a serotonin precursor), phenyalanine (a dopamine,
norepinephrine, and epinephrine precursor) (Ben-Menachem et al., 1995; Hammond et al., 1992)
and anthrallic acid (a tryptophan metabolite) (Klinkenberg et al., 2014). VNS was also associated
with increased in homovanillic acid, a dopamine metabolite in depression (Carpenter et al.,
2004), as well as in epilepsy (Hammond, et al., 1992). Acute VNS also increases norepinephrine
levels in the CSF of dogs (Martlé et al., 2011), and in rats it has been shown to modulate cortical
synchrony and excitability associated with the cholinergic system (Nichols et al., 2011).
1.6 Transcutaneous vagus nerve stimulation (tVNS)
To what degree does transcutaneous stimulation of the auricular branch of the vagus
nerve produce changes in brain metabolism in regions identified as vagal projection regions from
neuroanatomical and patient studies? Frangos, et al. (2015) stimulated the left cymba conchae in
43
12 healthy human adults and compared BOLD activation patterns to sham stimulation of the
earlobe. Within the brainstem, ipsilateral rostrocaudal NST, spinal trigeminal nucleus,
hypoglossal nucleus, locus coeruleus, parabrachial area activations were found; in the midbrain
activations included the PAG, dorsal raphe, substantia nigra and red nuclei; while in the
forebrain activations were found in the bilateral insula, nucleus accumbens, stria terminalis and
BNST, fornix, septal area, bilateral anterior thalamic nuclei, amygdala, and primarily
contralateral primary somatosensory cortex. The hypothalamus, hippocampus and
parahippocampal gyrus were sites of deactivations. Relative to cymba conchae stimulation, sham
earlobe stimulation produced activations in the spinal trigeminal nucleus, nucleus cuneatus, and
ventrocaudal medulla. Sham relative to baseline (no stimulation) additionally engaged primarily
contralateral primary somatosensory cortex and posterior insula, although it was not greater than
that which was produced by cymba conchae stimulation, and thus disappeared in the statistical
contrast of sham>cymba conchae. The cervical vagus nerve can also be accessed through non-
invasive stimulation of the anterolateral surface of the neck, which produced similar activation
patterns including the NST, parabrachial nucleus, PAG, ventral tegmental area, raphe nuclei,
anterior insula, caudate, ACC and deactivations within the hippocampus and parahippocampal
gyrus (Frangos & Komisaruk, 2017).
Sclocco et al. (2019) published a 7T fMRI study of brainstem and cardiovagal responses
to stimulation of the left cymba conchae. A distinctive feature of their stimulation protocol is
delivery of pulses during the inspiratory versus expiratory phase of respiration. The rationale for
accounting for respiratory phase in tVNS responses is that the caudal NST is inhibited by
respiratory neurons during inspiration, released from inhibition during expiration (Farmer, et al.,
2016). In Sclocco, et al.’s study, expiratory-gated stimulation of the left cymba conchae evoked
44
greater responses in the ipsilateral pontomedullary junction in a region consistent with the NST,
whereas no responses were evoked from inspiratory-gated cymba conchae stimulation.
Brainstem responses to control stimulation of the earlobe (inspiratory- and expiratory-gated)
evoked a partially overlapping, but more ventrolateral cluster that was consistent with the spinal
trigeminal nucleus. Region-of-interest analysis of the noradrenergic (locus coeruleus) and
serotonergic regions (dorsal and median raphe nuclei) found that expiratory cymba conchae
stimulation significantly enhanced BOLD in these regions relative to inspiratory-gated
stimulation and control stimulation (which effectively showed no response). Moreover, BOLD
responses from the median raphe nucleus were positively associated with the instantaneous,
peristimulus estimates of HF-HRV (r = 0.51).
Another study of respiratory-gated left cymba conchae stimulation in migraine sufferers
also found that BOLD NST responses were greater for the expiratory versus inspiratory phase of
respiration, during which NTS activation had significant functional connectivity with the left
dorsal and ventral anterior insula, as well as the left anterior mid-cingulate/pre-supplementary
motor cortex (Garcia, et al., 2017). Together results indicate ipsilateral projections of the ABVN-
NST-thalamic pathway, but also highlight that the brain and cardiovascular effects of vagus
stimulation may be strongly modulated by respiratory phase due to NST inhibition from ventral
respiratory group nuclei in the medulla during expiration.
The observation of locus coeruleus, raphe nuclei and NST responses to tVNS relative to
sham give credence to the idea that stimulation of the ABVN does in fact access visceral vagal
afferent systems, given that the NST has close projections to these nuclei (Ruffoli, et al., 2011).
Additional fMRI studies also observe consistent effects of LC involvement: Dietrich et al. (2008)
(although failing to use sham stimulation for comparison), found that left tragus stimulation
45
enhanced BOLD in the locus coeruleus, left thalamus, left insula, left prefrontal cortex, and
bilateral primary somatosensory cortex. Decreases were observed in the left nucleus accumbens.
1 Hz stimulation of the left cymba conchae in migraineurs produced BOLD signal decreases in
the ipsilateral LC relative to sham (Zhang et al., 2019). Using the LC as a seed region, cymba
conchae stimulation increased functional connectivity with the right temporoparietal junction,
right parahippocampal gyrus, left parietal operculum, and left amygdala as compared to sham
(Zhang, et al., 2019). Other studies observing increases or decreases within the NST, LC, and
raphe nuclei include for ABVN stimulation relative to sham include Yakunina, et al. (2017), who
observed greater LC and NST BOLD particularly for cymba conchae stimulation as compared to
tragus and inner ear canal stimulation; and Kraus, et al. (2013), who reported stronger BOLD
decreases in the LC and NST for stimulation of the anterior part of the auditory canal relative to
sham (as compared to posterior auditory canal relative to sham, although direct contrast of
anterior vs. posterior was not significant). Increases or decreases may be due to differences in
stimulation site, subject-level variability in ear innervation, baseline conditions, or stimulation
parameters.
These, and additional studies also highlight ABVN stimulation effects within the
cerebrum. Studies using tragus stimulation report the following outcomes using stimulation
parameters their group previously showed as most effective at reducing heart rate (see Badran,
Mithoefer, et al., [2018]) resulted in significantly increased BOLD in the contralateral
somatosensory cortices, bilateral insula, right operculum, cerebellum, anterior and mid-cingulate
cortex, right caudate, left middle and superior frontal gyri (Badran, Dowdle, et al., 2018). Kraus
et al. (2007) report BOLD decreases in the amygdala, hippocampus, and parahippocampal gyrus
and increased activation in the insula, somatosensory cortex, and thalamus. Yakunina et al.
46
(2016) highlight differences between tragus and cymba conchae stimulation relative to sham,
finding similar patterns of activation, which include LC, NST, caudate, cerebellum for both, and
additional hypothalamus, putamen, and thalamus activations for tragus stimulation. Other studies
compare stimulation within regions of the ear canal, reporting anterior stimulation relative to
sham as associated with greater medial prefrontal and insula response in the left hemisphere, but
decreased left parahippocampal gyrus, LC, and NST responses. Posterior ear canal relative to
sham produced reduced BOLD in the bilateral superior gyrus, medial frontal gyrus, and
subgenual cingulate (Kraus et al., 2013).
Although not an invariant result, a potentially interesting trend that emerged from these
tVNS-fMRI experiments is the observation of ‘limbic deactivations’ related to the amygdala,
hippocampus, and parahippocampal gyrus (Frangos, et al. (2015; 2017); Kraus, et al. (2007;
2013); Yakunina, et al., 2016). Reduced metabolism of medial temporal structures is a common
finding in VNS patient studies (e.g. Vonck, et al. [2008]; Narayanan, et al. [2002]; Lomarev, et
al., [2002]), indicating that medial temporal lobe responses may reflect specificity of vagal
afferent cascades within the cerebrum.
1.6.1 Does AVBN stimulation affect vagal efferent outflows?
Although the AVBN is an afferent nerve, affecting brain metabolism within regions
putatively comprising vagal projections of the central autonomic network, a prevailing question
is whether AVBN stimulation also modulates efferent cardiovascular responses? So far the
indications are somewhat equivocal. Clancy et al. (2014) report that stimulation of the tragus
reduces the LF/HF HRV ratio relative to sham (an effect also found by Antonino, et al. [2017]),
as well as the frequency and incidence of muscle sympathetic nerve activity based on
microneurographic recording. However, Clancy et al. (2014) also report that both sham and
47
verum stimulation generate a reduction in heart rate. One other study reporting increased HRV
includes Sclocco, et al. (2019), who find peristimulus increases in instantaneous HRV for
expiratory tVNS as well as for expiratory sham stimulation, although, they note that there was a
more sustained increase in HRV for the expiratory tVNS relative to sham. Inspiratory stimulation
did not elicit HRV increases. Expiratory-gated and non-respiration-gated stimulation of the left
tragus produces reductions in heart rate (~2% reduction), but no effect on HRV or baroreceptor
sensitivity (Paleczny, Seredyński, & Ponikowska, 2019). Again, inspiratory stimulation did not
elicit any cardiovagal responses (Paleczny, et al., 2019). This result is in partial contrast to
Antonino, et al. (2017) who find decreased heart rate, but also increased baroreceptor sensitivity
relative to sham for tragus stimulation. However, it should be noted that the Paleczny et al.
(2019) study did not include a sham condition. Borges, Laborde, & Raab (2019) did find
increased RMSSD during cymba conchae stimulation under various stimulation intensities,
however, RMSSD also increased during sham stimulation. Similarly, De Couck et al., (2017) did
not find compelling evidence that stimulation of the cymba conchae increased HRV, although
there was a trending effect of increased HRV when the stimulation was applied to the right, as
opposed to the left cymba conchae, which is potentially relevant given the asymmetry of vagal
cardiac innervation. Badran, et al. (2018) compared different tragus stimulation parameters to
identify those that maximally reduce heart rate, finding that pulse width of 500 µS with 10Hz or
25Hz frequency induced the greatest heart rate decreases relative to sham (~2-3% reduction from
baseline). Again, this study also demonstrates that sham stimulation of the earlobe reduces HR
during the period of stimulation, albeit of a smaller magnitude (~1-2% reduction relative to
baseline). A non-exhaustive list of studies with null findings for tVNS effects on heart rate, heart
rate variability, or blood pressure indices includes those that were not examining these variables
48
as primary outcomes in the context of neuroimaging or behavioral psychological experiments is
presented here: (Burger et al., 2019; Frangos et al., 2015; Kraus et al., 2007; Laqua, Leutzow,
Wendt, & Usichenko, 2014; Ventura-Bort et al., 2018; Villani, Tsakiris, & Azevedo, 2019).
However, these mixed findings, as well as demonstrations of an active effect of sham on
cardiovascular responses in healthy participants should be balanced against outcomes obtained
from clinical and older populations, for whom the autonomic nervous system, as well as the
CAN, may be functioning sub-optimally. Sham-controlled tragus stimulation improved
baroreflex sensitivity, increased RMSSD and HF-HRV power in older adults (Bretherton et al.,
2019) while another sham-controlled intervention using expiration-gated tVNS of the left cymba
conchae reported improved HF-HRV in hypertensive patients (Garcia et al., 2017). Another
clinical investigation reported reduced vascular and cardiac sympathetic activation (increased
PPG amplitude, increased pre-ejection period and decreased skin conductance levels) for left-
sided transcutaneous cervical vagus nerve stimulation in individuals with a history of trauma
after stress induction with personalized scripts (Gurel et al., 2020). Acute stimulation of the
tragus increased HF-HRV in patients with PTSD and mild traumatic brain injury (Lamb et al.,
2017). 12-week treatment with tVNS improved glucose tolerance and decreased systolic blood
pressure in pre-diabetic individuals (Huang et al., 2014). Moreover, stimulation of the ABVN in
patients with coronary heart disease decreased the need for vasodilatory medications and
improved exercise tolerance (Zamotrinsky, et al., 1997; Zamotrinsky, et al., 2001), and reduced
fibrillation and the pro-inflammatory cytokine TNF-a in patients with paroxysmal atrial
fibrillation (Stavrakis et al., 2015). From these plethora of clinical studies it is evident that tVNS
generates not only cardiac, but also neuroendocrine and cholinergic anti-inflammatory effects,
consistent with parasympathetic influence on the body. Even in healthy adults, cervical
49
transcutaneous vagus nerve stimulation reduces systemic pro-inflammatory cytokines (Lerman et
al., 2016), although it remains an open question as to whether ABVN stimulation specifically can
produce similar anti-inflammatory effects in healthy young adults. Hence, the evidence favors
vagal efferent effects of AVBN stimulation, although it may be difficult to consistently observe
in healthy adults, for whom effect sizes may be small, particularly relative to sham stimulation of
the earlobe.
1.6.2 The role of sham stimulation in studies of tVNS
Stimulation of the earlobe is typically used as a control region in the majority of
published studies. The rationale is that because the earlobe is not innervated by the AVBN, but
rather by the superficial cervical plexus (specifically, by the greater auricular nerve), stimulation
of this region can control for non-specific sensory aspects of stimulation, such as touch, pressure,
vibration, and pain, which should be equally matched between experimental conditions.
However, it has been argued that earlobe stimulation is not necessarily ideal as it is not
physiologically inert (Rangon, 2018). The earlobe is a site of stimulation in cranial
electrotherapy, which is an FDA-approved treatment for insomnia, depression, and anxiety.
Craniotherapeutic stimulation of the earlobe generates BOLD deactivations in the pre-and-
postcentral gyri, supplementary motor regions, supramarginal gyrus, superior frontal gyrus, and
posterior cingulate and precuneus (Feusner et al., 2012). Additionally, acupuncture stimulation
of another region supplied by the superior cervical plexus (the anti-tragus) also shows patterns of
cortical and subcortical responses, including increased activity in the left posterior insula, pre-
and-post central gyri, supramarginal gyrus, thalamus, amygdala, and cerebellum, with
deactivations in the medial frontal gyrus, subgenual cingulate, superior frontal gyrus, and
orbitofrontal gyrus (Romoli et al., 2014). The superior cervical ganglion is the source of
50
sympathetic innervation to the ear (Takeuchi et al., 1993). Additionally, the vagus nerve
communicates with C
1
– C
2
sympathetic nerve fibers from the superior cervical ganglion
(Cesmebasi, 2015) which includes the greater auricular nerve that originates in C
2
– C
3
(He et al.,
2013). Indeed, an analysis of human cadavers shows that the communication branches of the
superior cervical ganglion are primarily connected to the vagal and glossopharyngeal nerves
(Mitsuoka, Kikutani, & Sato, 2016). Accordingly, tVNS studies reporting effects of sham
earlobe stimulation also highlight regions of potential interest with regards to the vagal central
autonomic network. This includes BOLD fMRI deactivations in the posterior and middle
cingulate gyrus, superior frontal gyrus, hippocampus, pre-and-post central gyri, and precuneus
(Yakunina, et al., 2016) as well as increases in the right central operculum, right post-central
gyrus, and insula (Badran, et al., 2018).
Rangon (2018) recommends using the low frequency stimulation (e.g. 1 Hz) of the
cymba conchae as a control for studies of tVNS. However this presents its own uncertainty since
different stimulation parameters can generate unique physiological effects; some studies use 1
Hz stimulation as an experimental parameter of interest (e.g. Zhang, et al. [2019]). Additionally,
the recommendation is not entirely practical to implement given that ‘validated’ commercial
tVNS devices (such as from Cerbomed) have fixed parameter settings. It is also important to
acknowledge observations that AVBN stimulation (especially when stimulated via the cymba
conchae) engages the NST, LC, and raphe nuclei, whereas earlobe stimulation does not (in
particular, see Sclocco, et al. (2019), Frangos, et al. (2015), and Ay, et al., 2015). Nevertheless,
the physiological features of the superior cervical ganglion (including its innervation of the
tragus in a large proportion of individuals, and possible absence of AVBN in the tragus in
another proportion of individuals) provides a caveat with regards to inferring the physiological
51
mediators underlying neural and autonomic effects of tVNS in contrast to sham stimulation of
the earlobe, which is currently the standard practice. Cymba conchae stimulation is more likely
to consistently provide AVBN-specific effects.
1.7 Summary and objectives
Interoception is a broad, encompassing construct concerning how visceral and bodily
sensations arising from vagal and lamina I spinothalamic pathways contribute to mood, emotion,
behavior, as well as autonomic function. Additionally, interoception provides an important
context in which the concepts of the central autonomic network and allostasis can be neatly
integrated. However, the multidimensionality of interoception requires multisystem, multilevel
analyses to arrive at a coherent perspective as to how interoceptive-allostatic systems interact to
produce regulated and dysregulated brain-body interactions underlying health and disease. I
propose that non-invasive stimulation of the vagus nerve and cortical regions participating in the
central autonomic network are important, but highly underutilized techniques to ‘perturb’
interoceptive-allostatic systems from the ‘bottom-up’ and the ‘top-down’. An advantage of these
non-invasive techniques is that they can be safely applied in healthy populations. In combination
with psychophysiological, self-report, behavioral and functional neuroimaging data, non-
invasive brain stimulation methods permit a multilevel perspective on the dynamics of systems
involved autonomic regulation. Additionally, modulating interoceptive systems, whether through
non-invasive neural stimulation or through psycho-behavioral methods have demonstrable
therapeutic value. Hence, using these methodologies provides insight on the systems-level
interactions that can reduce allostatic dysregulation in clinical populations.
This thesis has two lines of research. The first line of inquiry focuses on the modulation
of heart-brain interactions using non-invasive brain stimulation techniques, specifically:
52
transcutaneous vagus nerve stimulation and transcranial magnetic stimulation.
Study 1 integrates EEG source-localization techniques with tVNS and cardiovascular
measurement to evaluate whether tVNS alters neural cardiac interoceptive processing. The study
will test the hypotheses that tVNS (1) increases baroreceptor sensitivity, heart rate variability,
and reduces heart rate; (2) alters functional connectivity between cortical regions of the CAN; (3)
modulates HEP amplitudes at the scalp, and functional connectivity between brain regions
identified from intracranial studies as HEP foci. Lastly, it is also expected that tVNS will alter
covariation between EEG features and concomitant cardiovascular responses.
Interoceptive cortices are involved in the regulation of nociception, inflammation, cardiac,
gastrointestinal, and HPA-axis function, all of which are systems targeted by VNS and tVNS.
The significance of this study is that interoceptive thalamocortical pathways have not been
proposed to be an important systems-level mechanism through which VNS and tVNS improves
symptom burdens for a range of somatic and psychiatric disorders. This appears to be the first
study that demonstrates altered representations of cardiovascular information in interoceptive
cortices in response to tVNS.
Study 2 applies intermittent and continuous theta-burst TMS protocols to the right
frontotemporal cortex to test whether stimulation of this target produces changes in heart rate
variability and pulse transit time under spontaneous and 0.1 Hz paced-breathing conditions. The
frontotemporal target is a candidate region for accessing the central autonomic network, based on
prior studies which have shown that continuous theta-burst stimulation to this region modulates
HEP amplitudes. Hence, stimulation of the right frontotemporal cortex may also access
visceromotor pathways related to vagal and sympathetic cardiovascular function. However, due
to possible confounding influences of the sensory aspects of TMS stimulation, this study also
53
tests whether TMS-induced changes in cardiovascular response are spurious, driven by non-
specific anxious responses to sensory aspects of the stimulation, rather than by ‘top-down’
changes in visceromotor networks.
The study provides novel insights on the relevance of confounding covariates when using
TMS to investigate heart-brain interactions, provides recommendations for the development of
effective TMS protocols for investigating neurocardiac integration, and discusses the potential
translational relevance of this work to individuals with stress-linked disorders and maladaptive
cardiovascular reactivity patterns.
The second line of research concerns the influence of traumatic stress on interoceptive
attention in a cohort of women with stimulant use disorders (SUD) and varying histories of
trauma exposure, a subset of whom have concurrent PTSD diagnoses.
Study 3 used functional MRI and independent components analysis (ICA) to investigate
whether PTSD comorbidity was associated with altered brain metabolism during a mindful-
breathing task, during which patients alternately focused on the bodily sensations associated with
breathing versus a visual (exteroceptive) target. It was expected that PTSD comorbidity would
result in decreased integration of interoception-linked intrinsic brain networks. Additionally, it
was also tested whether the strength of interoceptive network integration was related to lifetime
sexual trauma exposure given that interpersonal trauma concerning the violation of bodily
boundaries is highly relevant to female psychopathology.
The implications of Study 3 are that altered interoceptive processing associated with
traumatic-stress history is relevant to understanding the brain mechanisms associated with, and
potential efficacy of mindfulness interventions for women with SUD.
54
2. Transcutaneous vagus nerve stimulation modulates neural activity and functional
connectivity in interoceptive cortices: An EEG study
2.1 Introduction
Brain regions distributed throughout all levels of the neuroaxis are involved in the
regulation of cardiovascular homeostasis. This includes brainstem nuclei such as the solitary
tract, parabrachial nucleus, periaqueductal gray and ventrolateral medulla, but also the
hypothalamus, medial temporal lobes, the anterior cingulate, ventromedial prefrontal cortex, and
insula (Silvani et al., 2016; Sklerov et al., 2019; Vargas et al., 2016). Collectively, the distributed
brainstem, midbrain and forebrain regions involved in interoceptive and visceromotor processing
are referred to as the central autonomic network (CAN) (Benarroch, 1993). The vagus nerve
contributes critical interoceptive information to cortical regions of the CAN via thalamic
pathways that project to the insula and anterior cingulate cortices (Craig, 2002; Ito, 1992; Ito &
Craig, 2008; Strigo & Craig, 2016). A question that remains unanswered is how does selective
stimulation of the vagus nerve in humans influence neurocardiac processing in the central
autonomic network?
Vagus Nerve Stimulation (VNS) provides insight into the spatial distribution, metabolic
and functional effects of vagal afferent projections in the cerebrum- changes within the medial
temporal cortices, thalamus, insula, cingulate cortex, medial, lateral and orbitofrontal regions are
commonly observed (Lomarev, et al., 2002; Conway, et al., 2005; Vonck, et al. 2008; Ring, et
al., 2000; Narayanan, et al., 2002; and Liu, et al., 2003). However, interoceptive and
neurocardiac interactions in patients has not been a focus of VNS-neuroimaging studies, as
clinical outcomes (e.g. seizure severity, depression symptoms) are the usual focus of such
investigations (e.g. Marrosu et al. [2003]; Nahas et al. [2007]). However, there is interest in
55
modulating interoceptive-vagal neural systems as a means of investigating heart-brain
interactions, as well as to understand neural mechanisms through which modulation of
interoceptive processing may alter clinical outcomes in multiple somatic and psychiatric health
conditions (Khalsa et al., 2018; Sclocco et al., 2019; Strigo & Craig, 2016; Villani et al., 2019).
Currently, stimulation of the auricular branch of the vagus nerve (AVBN) is proposed as
a non-invasive means of selectively accessing central vagal afferent pathways. AVBN
stimulation is more generally referred to as transcutaneous vagus nerve stimulation (tVNS),
which can be safely applied to healthy individuals (Redgrave et al., 2018). Recent human
evidence from BOLD fMRI investigations indicates that tVNS excites the caudal nucleus of the
solitary tract (NST) (Frangos, et al., 2015; Sclocco, et al., 2019), which is the primary
termination site of vagal sensory inputs to the brain. The NST sends afferents directly to thalamic
nuclei, or indirectly via the parabrachial nucleus, which in turn, project to the insular cortex and
other forebrain regions that participate in the tonic and phasic regulation of cardiovascular
autonomic rhythms (Craig, 2002; Oppenheimer & Cechetto, 2016; Palma & Benarroch, 2014).
Additionally, tVNS fMRI studies identify patterns of BOLD responses consistent with expected
vagal projections to the forebrain, which include the insula, anterior cingulate, hypothalamus,
thalamus, and medial temporal lobes (e.g. Frangos, et al., 2015; Badran et al., 2018). Additional
evidence highlights that tVNS increases neural activity and BOLD responses within the locus
coeruleus (LC) (Ay, et al., 2015; Frangos, et al., 2015; Sclocco, et al., 2019; Yakunina, et al.,
2016; Dietrich, et al., 2008; Zhang, et al., 2019) which is a major vagal-NST innervation target
(Ruffoli, et al., 2011). Moreover, tVNS has the potential to increase parasympathetically-
mediated heart rate variability (HRV) and baroreceptor sensitivity in healthy (Antonino, et al.,
2015; Badran, et al., 2018) and clinical populations (Garcia, et al., 2017; Lamb, et al., 2017),
56
although additional studies are required to firmly establish such cardiovagal effects.
Nevertheless, the growing body of evidence increases confidence in the hypothesis that in
humans, general somatic vagal afferents (externally accessible through the AVBN) join the
thalamocortical and extrathalamic pathways of the general visceral afferent vagus nerve at the
level of the caudal NST.
The purpose of this study is to provide an account of neurocardiac integration in response
to tVNS through a series of analyses using electroencephalographic (EEG) and cardiovascular
data. The first experiment tests whether tVNS increases vagal influences on cardiovascular
parameters. It is expected that tVNS will decrease heart rate (HR), increase heart rate variability
and baroreceptor sensitivity (BRS) relative to ‘sham’ stimulation of the ear lobe, which is instead
innervated by the greater auricular nerve which arises from the ventral rami of C
2
– C
3
spinal
nerves (Cesmebasi, 2015).
Second, it is not known whether cardiovagal effects of tVNS relate to changes in cortical
function. To date, direct comparisons between tVNS-induced cardiovagal and BOLD responses
have been limited to a single investigation of the brainstem (Sclocco, et al., 2019). Consequently,
it remains a novel question as to whether cardiovascular autonomic responses to tVNS are
associated with the concomitant responses in cortical levels of the CAN. Determining whether
responses in cortical CAN regions are more strongly coupled with heart rate, heart rate
variability or baroreceptor sensitivity during tVNS may elucidate whether cardiovagal effects of
tVNS occur only at the level of brainstem reflex circuits (e.g. via projections of the NST to the
dorsal motor nucleus or ventrolateral medullary nuclei), or whether these changes reflect the
propagation of vagal afferents throughout the CAN hierarchy. The answer to this question is
relevant to understanding the mechanisms through which tVNS may improve autonomic
57
function and allostatic resilience in aging (Bretherton, et al., 2019), somatic diseases (Garcia, et
al., 2017; Huang, et al., 2014; Stavrakis, et al., 2015) and psychiatric disorders (Lamb, et al.,
2017; Gurel, et al., 2020). To investigate these issues, we examine tVNS-induced changes in
source-localized EEG power spectral density and functional connectivity associated with cortical
regions of the CAN. The insula is expected to be a particularly important region of power and
connectivity changes. Assuming significant effects of tVNS on cardiovascular variables and
EEG features, post-hoc tests will be performed to determine whether cardiovascular and EEG
responses are correlated, and whether the strength or direction of association depends on sham or
verum stimulation.
For the third experiment, the effects of tVNS on interoceptive event-related potentials.
Electrophysiological signatures associated with neural interoceptive processing are limited,
however the heart-beat evoked potential (HEP) is proposed as an objective biomarker of cardiac
interoception (Park et al., 2017; Pollatos et al., 2005). The HEP is an endogenous evoked-
potential that is time-locked to the peak of the R-wave (Schandry et al., 1986). However, it is not
precisely known which sensory sources contribute to the HEP waveform. As outlined in Park
and Blanke (2019), it was first theorized that HEPs reflect the neural impulses from vagal and
glossopharyngeal baroreceptors that detect phasic (i.e., intra-beat) and tonic (i.e., inter-beat)
arterial pressure changes, such as due to increased stroke volume or other cardiodynamic
parameters (Gray et al., 2007; Rainer Schandry & Montoya, 1996). Baroreceptor signals reach
regions of the CAN, including the insula, cingulate, and medial temporal lobes through vagal
thalamocortical projections (Henderson, James, & Macefield, 2012; Kimmerly, 2017; Macefield
& Henderson, 2016). However, it is likely that multiple sensory receptors contribute to HEPs,
including cardiac afferent neurons within the cardiac muscle itself that encode mechanical and
58
chemical properties of the atria and ventricles (Gray, et al., 2007). Specifically, cardiac
mechanoreceptors fire in response to polarization and repolarization of the cardiac tissue (such as
during the R- and T-waves) (Armour & Ardell, 2004; Armour, 2004); these signals may be
transmitted to the CAN through either vagal or lamina I spinothalamic pathways. A third route
may be through the primary somatosensory pathways. Cardiac sensation appears to be mediated
not only by interoceptive pathways to the insula and cingulate, but also via cutaneous
mechanoreceptors associated with the primary somatosensory pathways (Khalsa, et al., 2009).
HEPs have also been recorded intracranially from the somatosensory cortex (Kern, et al., 2013),
providing more direct evidence for the relevance of this afferent pathway.
Hence, the final experiment will evaluate tVNS modulation of the HEP signal. Based on
the assumption that (1) tVNS alters neural activity in thalamocortical projection regions of the
vagus nerve and (2) that HEPs are processed through similar neural routes, it is expected that
tVNS will modulate HEP activity in frontal, temporal, central, and parietal electrode sites,
following prior observations of spontaneous HEP sensor-level distributions (e.g. Gray et al.,
2007; Pollatos & Schandry, 2004; Shao, et al., 2011). Finally, there is only one prior study that
has investigated the functional connectivity properties of source-localized HEPs (Jiang et al.,
2019). Thus, functional connectivity between ROIs identified from studies that localized HEPs
from intracranial EEG or source-modeling (Canales-Johnson et al., 2015; García-Cordero et al.,
2017; Kern et al., 2013; Park et al., 2017; Pollatos et al., 2005) will be used to determine whether
tVNS alters HEP connectivity properties among these regions, and whether these changes
correlate with cardiovascular responses.
As there are few electrophysiological studies of the effects of tVNS, and none that have
investigated its relationship to the CAN or to neural interoceptive processing, this study seeks to
59
investigate potential mechanisms through which tVNS improves cardiovascular autonomic
function and allostatic resilience in health and disease.

2.2 Methods
2.2.1 Participants
Participants were recruited from the greater community in Flanders, Belgium from flyers
posted in Antwerp, the Free University of Brussels, and Ghent University, personal contacts, and
through a social media platform dedicated to advertising neuroscience and psychology
experiments at the Ghent University. Participants were eligible if they were between the ages of
18 – 45, right-handed, free of medical and psychiatric conditions, non-smokers/vapers, free of
extensive piercings on the left ear (e.g. gauges, etc.), and not taking medication that could affect
the autonomic nervous system. Potential participants were first screened through a telephone
interview concerning their medical and psychiatric history. Psychiatric history was obtained
through the MINI Psychiatric Examination (Sheehan et al., 1998). 48 participants were recruited,
but three participants were later dropped due to discovery of left-handedness, psychiatric illness,
and presence of ECG abnormalities. Hence, 45 participants (27 female) were retained for
analysis of physiological data (Age: M = 23.1, SD = 5.01). 43 were included in EEG analysis,
and for paired t-tests of self-report data, as two participants completed only one of the two
experimental sessions. Participants received 55 euros compensation plus train or parking
reimbursement if they traveled from outside Ghent. The research was approved by the University
of Southern California Institutional Review Board and the Ghent University Hospital Medical
Ethics Committee.

60
2.2.2 Study protocol
Procedures took place in quiet experimental testing rooms at the Ghent University
Hospital (UZ Gent), department of Neurology. To complete the study protocol, participants
completed two experimental sessions (sham stimulation of the earlobe and verum stimulation of
the AVBN via the cymba conchae) consisting of physiological (electrocardiogram, respiration,
finger pulse plethysmography), neural (EEG) recording and self-report questionnaires. The order
of sham and verum stimulation was randomized across participants. The experimental sessions
were spaced at least 24 hours but no more than 7 days apart. On the first day, the participants
were familiarized with the tasks, tVNS device, and EEG system, although they were blind to
purpose of stimulation and the anatomical correlates of each site. For recording periods on both
days, participants sat upright with their legs elevated slightly below the level of the hips, with
hands placed either on the laps or their sides. Pillows were provided for support and comfort,
participants were instructed to keep their eyes ‘half’ open during EEG recording, such that they
find a spot on the wall on which to fix their gaze at a level low enough so as to reduce tension in
the facial muscles and prevent eye-strain. Data recordings were obtained for a baseline period
(10 minutes), after which participants completed self-report questionnaires administered via
Qualtrics software. Then, participants were fit with the tVNS device and the individual current
intensity was established (see 2.2.4). Data recordings were obtained concurrent to stimulation
(15 minutes), after which participants provided self-report data a second time. The device was
turned off and data was recorded for the recovery period (10 minutes) after which participants
again provided self-report data. See Figure 2.1 for study design. After the second session,
participants were compensated and debriefed.
61

Figure 2.1. Study design. Within-subject, placebo-controlled design. Participants returned to the
lab to complete protocol on two separate occasions, between one day and one week apart.
2.2.3 Self-report questionnaires
STAI-Y State. The State-Trait Anxiety Inventory Y (STAI-Y) (Spielberger, 1983) was
used to assess potential increases in state anxiety due to stimulation, and to verify that any
potential autonomic responses were not explained by non-specific anxious responses to the
procedure per se. STAI-Y State consists of 20 questions that evaluate the respondent's current
state of anxiety by asking “how do you feel right now” using items that measure subjective
experiences of nervousness, worry, apprehension, autonomic arousal, fear, and tension on a 4-
point Likert-type scale. Scores range from 20 – 60; higher scores indicate greater state anxiety.
Sensory qualities of tVNS. After stimulation for both sham and verum sessions,
participants were asked to report the intensity of any stimulation-related pain or discomfort, as
62
well as the quality of that pain in terms of dull, pressing, and prickling sensations on a 0 – 10
visual analogue scale. By asking participants to discriminate the quality of the sensations, it is
possible to determine whether the pain or discomfort is mediated by Ad (prickling) or C-fibers
(dull, pressing) as these descriptors have been shown to provide greater than 95% sensitivity and
specificity in distinguishing pain elicited through these nerve fiber types (Beissner et al., 2010).
2.2.4 Transcutaneous vagus nerve stimulation (tVNS)
Participants were fit with the NEMOS
â
device (Cerbomed, Erlangen, Germany)
designed for electrical stimulation of the cymba conchae of the external ear via an adjustable
earpiece containing anodal and cathodal titanium ball-point electrodes connected to a stimulator.
The device was used to provide transcutaneous electrical stimulation of the left cymba conchae
(verum tVNS) and control stimulation of the left earlobe (sham tVNS). Stimulation of the
earlobe was achieved by positioning the earpiece upside down and securing with medical tape, as
necessary. Cymba conchae stimulation was conducted by positioning the earpiece upright (see
study design Figure 2.1). Cerbomed provided the Neurology department at UZ Gent with a
custom-programmed device that delivers rapid-cycle stimulation consisting of 0.25 ms-duration
monophasic square wave pulses at 25 Hz with a duty cycle of 7 seconds on and 18 seconds off.
Studies using invasive VNS has been shown that ‘rapid-cycle’ stimulation parameters provide
greater anticonvulsant efficacy and effects on hippocampal electrophysiology and
norepinephrine concentrations (Larsen, Wadman, Marinazzo, et al., 2016; Raedt et al., 2011), as
compared to the standard duty cycle (30 sec. on and 30 sec. off). Presently, there appear to be no
published tVNS studies that have used rapid-cycle stimulation parameters.
The stimulation site was first exfoliated with abrasive gel and cleaned with alcohol to
minimize impedance. Impedance is measured automatically by the device and insufficient
63
electrode contact with the skin evokes a beep. To set individual current intensities, a method of
limits was used to determine the level that evoked a clear tingling sensation without pain or
significant unpleasantness. Tingling sensations are expected to stimulate primarily thickly-
myelinated Aβ fiber afferents (Ellrich, 2011). Specifically, the intensity was increased from 0.1
mA in increments of 0.1 mA until the participant reported feeling a ‘tingling’ sensation, recorded
as the perceptual threshold. The intensity was increased in 0.1mA increments until the
participant reported the sensation to be unpleasant or prickling (presumably exciting Ad fibers
[Beissner, et al., 2010]). This procedure was repeated three times. The average of the detection
and pain threshold was computed and used as the stimulation intensity. Current intensity was
adjusted if participants reported sensitization to painful sensations over the course of the
experiment.
2.2.5 Electroencephalography (EEG) and physiology data acquisition
A Micromed System Plus (Micromed, Mogliano, Italy) in combination with Ag/AgCl
electrodes was used to record EEG at 60 standard locations according to the extended
international 10-20 system using a WaveGuard cap (ANT Neuro, Netherlands), including the
mastoids. Cz was used for referencing during online acquisition, and AFz was used as the
ground. Vertical and horizontal electrooculogram (EOG), respiration (RESP) using a strain-
gauge transducer placed around the abdomen, electrocardiogram (ECG) and pulse
photoplethysmogram (PPG) were recorded using bipolar channels (O1, O2, PO7, and PO8 were
omitted from the EEG montage to make recording channels available for physiological data). For
ECG, one electrode was placed below the right clavicle and the other was placed on the left
ribcage, producing a high-amplitude R-wave to facilitate automatic peak detection. EEG, EOG,
RESP, ECG, and PPG were digitized online using a sampling rate of 1024 Hz, anti-aliasing filter
64
of 250 Hz, 16 bit resolution, online high-pass filter of 0.008 Hz. Electrode impedances for the
EEG channels were kept below 5 kW before recordings commenced.
2.2.6 Preprocessing
2.2.6.1 Physiological data
Respiratory. RESP channel data were extracted and processed in MATLAB using the
BreathMetrics, a respiratory signal processing toolbox (Noto, Zhou, Schuele, Templer, &
Zelano, 2018). Respiratory rate was extracted for each experimental condition (baseline,
stimulation, recovery) for the sham and verum sessions.
ECG. To identify R-waves in the ECG record for HRV and HEP analyses, custom
MATLAB scripts were used to detrend the ECG signal with a no phase-distortion filter and
identify the peaks of the R-waves. Recordings were manually inspected for errors in R-peak
identification and ectopic beats. The inter-beat intervals (RRIs) were computed and HRV was
determined in accordance with the HRV Task Force guidelines (HRV Task Force, 1996) and the
Kubios software user's guide (Tarvainen, et al., 2014). Analysis of power spectral density was
performed using the Fast-Fourier Transform with default settings in Kubios: high-frequency
band 0.15–0.4 Hz; low-frequency band 0.04–0.15 Hz; and very low-frequency band 0.0–
0.04 Hz. The estimates of spectral density for each frequency band (in milliseconds squared per
Hz) were transformed logarithmically. Additionally, we extracted the temporal domain indices
Root Mean Square of Successive Differences (RMSSD) and heart rate (HR). The RRI timeseries
were used to generate event-marker files that were subsequently uploaded to the raw EEG
timeseries using EEGLAB. The accuracy of the event-markers was manually inspected and
confirmed.
65
PPG. The ECG R-wave was used as the starting point for estimation of pulse transit time
(PTT). Peaks of the first derivative of the zero-phase shift Butterworth low-pass filtered PPG
series were used to identify the point of arrival of the arterial pulse-wave. Then the time
difference between the peak of the R-wave and the peak of the corresponding differentiated PPG
wave for all successive beats was computed. See Figure 2.2 for visualization of PTT
computations. Kubios software was again used to perform an FTT on the PTT timeseries in order
to obtain an estimate of its low-frequency power (0.04–0.15 Hz) using the default settings,
matching that which was used for HRV analysis.

Figure 2.2. Illustration of the physiological timeseries and relationships among the variables
used for obtaining indices of cardiovascular autonomic function. The timeseries data in orange
reflects the differentiated PPG signal overlaid on the ECG and original PPG trace. Pulse transit
time (PTT); R-wave peak-to-peak intervals (RRI).
Baroreceptor sensitivity (BRS). Baroreflex sensitivity is typically estimated using
fluctuations in beat-to-beat systolic arterial blood pressure and RR intervals either via spectral or
sequencing methods (Hughson, et al., 1993). Due to lack of access to a non-invasive beat-to-beat
66
blood pressure monitor with which to obtain time-resolved blood pressure fluctuations,
baroreflex sensitivity was determined using PTT and the RRI timeseries (BRS
PTT
). As the PTT
signal has a strong inverse correlation with systolic blood pressure (Payne, 2006), BRS
PTT

provides a surrogate measure of baroreflex sensitivity based on spontaneous pulse transit time
fluctuations. BRS
PTT
was defined following Wang, et al. (2012):

$%%
= (
*+
/
*+

As both low-frequency RRI and low-frequency PTT have the same unit (ms
2
), BRS
PTT
is
unitless.
2.2.6.2 EEG
EEG data were processed using custom scripts, EEGLAB (Delorme & Makeig, 2004)
and Brainstorm (Tadel, Baillet, Mosher, Pantazis, & Leahy, 2011) running in the MATLAB
(Mathworks, Natick, MA) environment. Preprocessing steps for the data used in the resting state
and HEP analyses were identical to facilitate comparability of the results. The continuous raw
EEG data were filtered between 1-35 Hz using a Hanning-windowed sinc FIR filter, then
downsampled to 256 Hz. Bad segments of data were automatically removed using the
pop_rejcont function. Cleanline was then applied to remove any potential remaining influences
of line noise and clean_rawdata function was used with default parameters to perform artifact
subspace reconstruction (ASR) and to remove additional bad data segments. Bad data channels
were automatically detected using kurtosis and spectral methods with default parameters using
the pop_rejchan function. Channels identified as poor in one experimental session were also
removed in the other session to ensure that results were not influenced by differences in
interpolated channels. Omitted channels were interpolated using spherical spline. Data were re-
referenced to the average (not including the mastoid, EOG, and physiological data channels).
67
Then, EEG channels including the mastoids and EOG were submitted to the pop_runica ICA
algorithm to isolate noise components. Artifactual components were identified using the Multiple
Artifact Rejection Algorithm (MARA; Dowding et al., 2014), which is a supervised machine
learning algorithm that extracts six features from spatial, spectral, and temporal domains.
Posterior artifact probability of > 0.6 was used to mark a given component as artifactual. As a
pre-trained algorithm, MARA did not adequately identify tVNS stimulation artifacts, hence the
ICA output for each EEG record was manually inspected. Components clearly reflecting artifacts
not identified by MARA were manually flagged and removed. The 25 Hz stimulation artifact
was best removed using ICA, as notch filtering at 25 Hz prior to ICA decomposition produced
distorted independent components. However, after ICA cleaning, examination of power
spectrum density (PSD) plots indicated residual 25 Hz contamination, which was suppressed
using a notch filter (see Figure 2.3 for example of a typical stimulation artifact and average PSD
for sham and verum after notch-filtering). Notch filtering was further applied to the baseline data
despite lack of 25 Hz contamination, as it was used for constructing the noise covariance matrix
for source localization (see section 2.2.6).

68

Figure 2.3. A representative independent component stimulation artifact from a single subject’s
epoched data (left panel). Power spectrum density plots for sham (upper) and verum (lower)
sessions after ICA denoising and 25 Hz notch-filtering (right panel). Data reflected here was
recorded concurrent to stimulation.
For resting state data analysis, the preprocessed continuous data were uploaded to
Brainstorm. For HEP analysis, epochs were defined -200 ms to 800 ms relative to the peak of the
R-wave, and baseline corrected from -200 ms to -50 ms to avoid smearing peri-R-wave activity
into the baseline. Epoched data were uploaded to Brainstorm. As a last pre-processing step on
the epoched data, source current density (SCD) transformation using Brainstorm’s Fieldtrip plug-
in (Oostenveld, et al., 2011) was used with the following parameter settings: spherical spline
with lambda = 0.000010, spline order = 4, and Legrendre polynomial degree = 14. The purpose
of SCD transformation was to minimize the influence of the cardiac field artifact (CFA) which
occurs due to volume conduction. That is, the heart generates strong electrical activity that is
69
reflected in EEG recordings and is confounded with the HEP signal. SCD and related
transformations have previously been used to attenuate the CFA (e.g., Pollatos, et al., 2005;
Shao, et al., 2011). An alternative approach to mitigate the effects of the CFA would have been
to remove independent components whose waveforms have an appearance of an ECG trace,
following prior HEP studies (e.g. Al et al., 2019; Gentsch, Sel, Marshall, & Schütz-Bosbach,
2019; Terhaar et al., 2012). Yet, the ICA method for CFA correction may not be optimal since it
cannot remove all artifacts from the HEP. Moreover, such a correction method risks removing
genuine neural contributions to the HEP (Park, et al., 2019). The majority of HEP studies
provide no correction whatsoever for the CFA, however. See Appendix A for differences in
SCD-transformed versus untransformed HEPs. However, for analyses at the level of source
space, SCD transformation was not conducted, as source models are themselves a form of spatial
filter. To verify that potential HEP differences are not driven by differences in electrical
properties of the ECG for sham and verum stimulation sessions, the ECGs from the respective
sessions were epoched and averaged analogous to an ERP, then submitted to a non-parametric
permutation test.
2.2.7 Head models and source localization
To identify the source of EEG signals originating in the cortex, estimation of the forward
problem (head model) was computed using Brainstorm’s OpenMEEG BEM (Gramfort,
Papadopoulo, Olivi, & Clerc, 2010) software interface. The symmetrical Boundary Element
Method (BEM) uses three realistic layers (scalp, inner skull, outer skull), plus the source space
(brain) to estimate a head model. First, EEG sensors were converted from Cartesian to MNI
coordinates using a landmark method to co-register sensors to the scalp layer. For the brain
model, the USCBrain atlas from BrainSuite (Shattuck & Leahy, 2002) was used. 30,000 vertices
70
were used for estimation of the BEM model with adaptive integration. The resulting head model
was applied to each subject. The second step produces the inverse solution, which estimates
where an EEG feature originates, given the head model. First, to estimate the inverse solution,
the full noise-covariance matrix was computed in order to model neural signals of no-interest.
For the resting state data, the ten-minute baseline period prior to stimulation was used for the
noise-covariance matrix. Hence, the inverse solution inherently adjusts for baseline neural
activity. For HEP analyses, the noise-covariance matrix was estimated from the baseline period -
200 to -50 ms prior to the peak of the R-wave. The inverse solution was estimated using the
unconstrained weighted minimum norm, which finds a cortical current source density image that
is compatible with the forward model and constrains solutions to those that are of minimum
energy (Baillet, et al., 2001). ‘Unconstrained’ refers to the orientation of the dipoles to be
estimated at each vertex, defined with three dipoles in orthogonal directions, each of which are
estimated independently. This option is preferable to a constrained (single dipole) solution due to
the lack of individual MRI scans. Regions of interest (ROIs) were defined from the USCBrain
atlas (Joshi et al., 2017). The USCBrain atlas is a functional-anatomical hybrid probabilistic atlas
based on 40 individual (f)MRI scans from the Human Connectome Database. The USCBrain
Atlas provides 130 cortical and 29 non-cortical parcellations.
2.2.8 Resting State EEG analysis
2.2.8.1 Resting-state source-localized spectral power
A priori ROIs comprising canonical cortical regions of the central autonomic network
were selected for determining power differences between sham and verum stimulation, which
included the anterior and posterior insula, inferior and superior postcentral gyrus, anterior
cingulate cortex, subcallosal gyrus, gyrus rectus, and the parahippocampal gyrus. ROIs were
71
selected for both the right and left hemispheres for a total of 16 ROIs. See Figure 2.4 for
visualization of the ROIs. Welch power spectral density (log power) was computed for the
following frequency bands based on a window length of 4 sec. and window overlap of 50%:
delta: 1 – 3 Hz; theta: 3 – 8 Hz; alpha: 8 – 12 Hz; sensorimotor rhythm (SMR): 12 – 15 Hz; beta:
15 – 30 Hz; and gamma: 30 – 40 Hz.

Figure 2.4. Regions of interest for power and amplitude envelope correlations in the source
space, defined from the USCBrain atlas (Joshi, et al., 2017). A: medial view; B: anterior view on
inflated brain; C: lateral view on inflated brain. ACC: anterior cingulate cortex; aINS: anterior
insula; mOFC: medial orbitofrontal cortex PHG: parahippocampal gyrus; S1 Inf.: inferior
postcentral (primary somatosensory) gyrus; S1 Sup.: superior postcentral (primary
somatosensory) gyrus; sgACC: subgenual ACC.

72
2.2.7.2 Amplitude envelope correlations (AECs) among regions of interest (ROIs)
Amplitude envelope correlations (AEC) were computed on the source-localized ROIs.
The amplitude envelope reflects energy fluctuations in cortical oscillations over time, and the
AECs measure the degree to which two envelope fluctuations within a bandpass filtered signal
are temporally correlated. Envelopes are computed using the Hilbert transform. AEC has
excellent properties with which to measure EEG functional connectivity as it is a lagged measure
that orthogonalizes the signals prior to computing the amplitude envelope time series for each set
of ROIs. This procedure has the effect of reducing spatial leakage artifacts and ‘self-interaction’,
which are volume conduction-linked problems pervasive to MEG/EEG measures of functional
connectivity (Colclough et al., 2016; Nolte et al., 2004). Additionally, AEC is shown to be the
most reliable form of stationary functional connectivity (group-level test-retest reliability, and
within- and between-subject consistency), as compared to phase- or coherence-based metrics
such as the phase-lag index and imaginary coherence (Colclough, et al., 2016). Moreover, MEG
studies have consistently found that amplitude modulation of neural oscillations in the 8 – 30 Hz
range correspond to resting-state functional connectivity measured with BOLD fMRI (Cabral et
al., 2014). Frequency bands for the AEC analysis matched those for the power spectral density
analysis. See Figure 2.5 for diagram of AEC analysis.

73

Figure 2.5. Amplitude Envelope Correlation (AEC) process diagram. After solving the inverse
solution, the bandpass filtered time series from each region of interest (ROI) is extracted and
orthogonalized. The orthogonalized signals are submitted to Hilbert transform to obtain the
amplitude envelope timeseries. Pearson’s correlations of the amplitude envelopes for each ROI
at each frequency band are computed for each subject. Within each frequency band, the
correlation matrices are averaged across subjects, which yields a set of correlation matrices (one
per frequency band), that is subjected to permutation testing to threshold for statistical
significance.
2.2.9 HEP Analysis
2.2.9.1 Temporal characteristics of the HEP waveform
SCD-transformed epochs were averaged for sham and verum stimulation for each subject
and channel. Previous research (Park et al., 2017) demonstrated significant intracranial HEP
74
activity localized to the insula, operculum, medial temporal poles, and inferior frontal cortex
between approximately 175 – 400 ms. Therefore, this interval was used to evaluate HEP activity.
2.2.9.2 HEP source-localized functional connectivity
AECs were also computed for HEPs from 175 – 400 ms. However, the delta frequency
band was not included in the analysis, due to the length of the epoch segment. ROIs included
regions relevant to HEP source-localized activity, which included the 16 used in the resting state
analysis, plus an additional 12 ROIs comprising subdivisions of the operculum and inferior
frontal gyrus for the left and the right (specifically: pars opercularis [inferior and superior], pars
orbitalis, and pars triangularis [anterior, middle, posterior]) (Canales-Johnson, et al., 2015;
Garcia-Cordero, 2017; Park, et al., 2017; Kern, et al., 2013; Pollatos, et al., 2005). See Figure
2.6.

75

Figure 2.6. Additional regions of interest comprising the operculum and inferior frontal gyrus to
investigate HEP functional connectivity. Visualized on an inflated cortical surface.
2.2.10 Statistical Modeling
To model the effects of verum and sham tVNS effects on cardiovascular physiology,
linear mixed effects regression (LMER) was used. LMER models were estimated via the
‘lmerTest’ package (Kuznetsova, Brockhoff, & Christensen, 2017) (Kuznetsova, et al., 2017). To
model random effects, an intercept was included for each subject. Session (sham, verum) and
time (baseline, stimulation, recovery), and their interaction were entered as fixed effects. Sham
was set as the reference level for session, and baseline was set as the reference level for
condition. Physiological response variables included BRS
PTT
, RMSSD, LF- and HF-HRV,
Respiratory Rate, PTT, and HR. State anxiety was also entered as a response variable in these
LMERs, to determine whether state anxiety changes as a consequence of stimulation, and
76
whether it depended on the type of stimulation. lmerTest provides p-values for fixed effects
using the Satterthwaite approximation to the degrees of freedom. Two-tailed paired t-tests were
used to compare session-level differences in pain, mA, prickling, pressing, and dull sensations.
With Brainstorm utilities, statistical inference for resting state EEG data used non-parametric
permutation two-tailed t-tests (n = 10,000 permutations), FDR-corrected at q < 0.05 across all
relevant dimensions, or uncorrected at p < 0.005. HEPs were evaluated using Fieldtrip’s cluster-
based permutation statistics (2-tailed t-test) with significance threshold of p < 0.05 and alpha
clustering 0.05. Post-hoc Pearson’s correlations were used to assess the association between
tVNS-modulated cardiovascular variables and AECs involving insular regions, with Bonferroni-
correction for multiple testing.
Missing Data. A few data points from self-report questionnaires were missing. These data
were missing at random due to technical issues with Qualtrics, or due to participant omissions
(10/264 observations for STAI state, 1/86 observations for Prickling, 13/86 observations for
Pressing and Dull, 11/86 observations for Pain). Missing data points for were imputed using
median values with the ‘hmisc’ R-package for the paired t-tests and bivariate correlations (t-tests
conclusions for imputed and non-imputed data were largely identical, but for a single case in
which both imputed and non-imputed results are reported). It also did not make an effective
difference whether the mean or median was used for imputation. The PPG records in some cases
were very poor due to movement artifacts or technical problems with the PPG transducer (26 of
264 total observations). In these cases, data points from derivative variables (i.e. PTT, BRS
PTT
)
were not imputed for LMER models, as these LMERs can accommodate missing data points.

77
2.3 Results
2.3.1 Current intensity, stimulation-elicited pain and anxiety
mA. The objective current intensity was similar for sham (M = 0.87, SD = 0.55) and
verum (M = .78, SD = .49) stimulation as the difference was not significant: t(42) = 0.98, p =
0.33.
Pain. Reported levels of pain elicited by stimulation was low for sham (M = 1.31, SD =
1.28) as well as for verum (M = 1.62, SD = 1.18). The difference in pain was not significant
t(42) = -1.44, p = 0.16. In terms of qualitative descriptors of stimulation sensation, there was no
significant difference between sham and verum for dull (Sham: M = 1.42, SD = 1.38; verum: M
= 1.72, SD = 1.53) and pressing (sham: M = 1.77, SD = 2.06; verum: M = 1.65, SD = 1.67)
sensations (dull: t(42) = -1.1, p = 0.28; pressing: t(42) = 0.34, p = 0.73), although there was a
significant difference in the perception of prickling (sham: M = 4; SD = 2.59; verum: M = 4.63;
SD = 2.76; t(42) = -2.06, p = 0.046) indicating that stronger prickling sensations were associated
with cymba conchae stimulation (however, the importance of this difference should be balanced
against the non-imputed data, which showed only a trending difference: t(41) = -1.82, p = 0.077).
In all other cases results from imputed and non-imputed data were effectively identical. It is not
clear whether participants distinguished prickling from tingling (mediated by Ab fibers), as the
average prickling intensity was rated as very high compared to the average pain/discomfort
intensity. However, the subjective significance of prickling appeared to depend on the site of
stimulation as correlative tests revealed that prickling sensations were positively associated with
pain during earlobe stimulation (r = 0.32, t(41) = 2.16, p = 0.037, two-tailed), but not during
cymba conchae stimulation (r = 0.136, t(41) = 0.88, p = 0.38, two-tailed), despite the average
78
perception of pain being similar for the two conditions. Hence, earlobe stimulation may have
recruited Ad fibers to a greater degree than cymba conchae stimulation.
Anxiety. There was a consistent increase in state anxiety during stimulation (b = 2.77, SE
= 0.74, t = 3.8, p = 0.00016) and recovery (b = 1.45, SE = 0.73, t = 1.97, p = 0.049) relative to
baseline. This increase occurred irrespective of whether or not the stimulation was of the earlobe
or cymba conchae (session*time interaction for stimulation: (b = 0.08, SE = 1.03, t = 0.081, p =
0.94) and session*time for recovery: (b = -0.06, SE = 1.1, t = -0.05, p = 0.96). Intercept: (b =
29.3, SE = 1.0, t = 29, p < 0.00001). Hence, state anxiety was introduced as a covariate in models
of physiological response to rule of the possibility that non-specific anxious responses to
stimulation drive any observed physiological effects of tVNS.
2.3.2 Cardiovascular responses
Baroreceptor sensitivity. LMERs testing the critical time (baseline, stimulation, recovery)
x session (sham, verum) interaction yielded significantly increased BRS
PTT
for verum as
compared to sham during the period of stimulation (p = 0.004) relative to baseline, although the
increases were transient, as BRS
PTT
during the verum recovery period was not significantly
enhanced (p = 0.19). There were additional significant main effects of stimulation (p = 0.0017)
and recovery (p = 0.0028). There was no significant main effect of verum stimulation (relative to
sham) (p = 0.138). See Table 2.1 for fixed effects estimates and standard errors; see Figure 2.7
for a visualization of model predictions. To account for potential confounding effects on BRS
PTT
,

state anxiety, pain, sex, age, prickling, dull, and pressing sensations were entered into separate
models as covariates. None of these covariates impacted the observed interaction effects of tVNS
on BRS
PTT
responses, however prickling sensations were independently associated with a
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trending negative effect on BRS
PTT
(p = 0.053), and females were found to have lower BRS
PTT
values compared to males (p = 0.0014).

Table 2.1. LMER estimates of tVNS effects on BRSptt.

BRS PTT
FE SE p-Val
Intercept 9.30 0.53 <0.0000001
Session - Verum -0.57 0.38 0.137
Time - Stimulation -1.20 0.38 0.0017
Time - Recovery -1.13 0.37 0.0028
Session*Time - Stimulation 1.53 0.53 0.0044
Session*Time - Recovery 0.71 0.54 0.187

Figure 2.7. Model predicted means of BRS
PTT
for Sham and Verum stimulation sessions over
time. Error bars indicate standard error of the mean.
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Additional physiological variables. There were no significant effects of tVNS for any
other physiological variable tested, which includes LF- and HF-HRV, RMSSD, PTT, RESP, and
HR. In exploratory covariate models, the interaction effects of interest were not significantly
influenced (i.e., unmasked) by including the variables sex, age, pain, mA, prickling, dull,
pressing, or state anxiety. However, upon examining sham and verum separately for HR (i.e.,
examining only the main effects of condition for each verum and sham sessions), there were
significant main effects of stimulation (p = 0.003) and recovery (p = 0.053) during the verum
stimulation session, and of stimulation (p = 0.035) during sham, indicating that both types of
stimulation reduced heart rate. See Table 2.2 for details.

Table 2.2. LMER estimates of tVNS effects on HR for sham and verum separately.

Heart Rate - Verum
FE SE p-val
Intercept 67.82 1.21 <0.0000001
Time - Stimulation -1.163 0.38 0.0031
Time- Recovery -0.75 0.38 0.0531

Heart Rate - Sham
FE SE p-val
Intercept 68.35 1.28 <0.0000001
Time - Stimulation -0.79 0.37 0.035
Time - Recovery -0.63 0.37 0.091

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2.3.3 Resting-state EEG
Source-localized resting-state power. Increased power spectral density for verum relative
to sham was observed for the left and right anterior insula, left posterior insula, and left
parahippocampal gyrus from beta to gamma frequencies, FDR-corrected q < 0.05 over the
frequency dimension only, which corresponded to an average corrected p-value of 0.0167 (no
ROIs were significant after correcting for frequency*signal dimensions, however). No power
decreases were observed for verum relative to sham.
Source-localized amplitude-envelope correlations. Significant differences between sham
and verum AECs were observed only for the beta frequency band. Specifically, the right superior
postcentral gyrus and the right anterior insula displayed greater functional connectivity for verum
relative to sham (p < 0.005 uncorrected). See Figure 2.8.

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Figure 2.8. Significantly increased resting-state amplitude envelope connectivity for verum
relative to sham in the beta frequency band between the right anterior insula and the right
superior postcentral gyrus. The colored bar reflects t-statistics.
Association of resting-state AECs with autonomic variables. Individual beta frequency
AEC values for the right superior postcentral gyrus and the right anterior insula were extracted
for sham and verum and correlated with the corresponding HR and BRS
PTT
value. No significant
associations were obtained (Sham HR – AEC: r = 0.07, t(41) = 0.46, p = 0.65; Sham BRS
PTT
–
AEC: r = -0.03, t(38) = -0.22 , p = 0.82; Verum HR –AEC: r = -0.21, t(41) = -1.36 , p = 0.18)
although Verum BRS
PTT
– AEC may be considered as trending ( r = -0.27, t(37) = -1.7, p = 0.1).
2.3.4 HEPs
Temporal characteristics. SCD-transformed HEP amplitudes were found to have a
significantly greater magnitude for verum relative to sham in the left frontotemporal,
frontocentral, centroparietal, central and temporal electrode sites, specifically in channels FT7,
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T7, C5, CP5, TP7, and FC5 approximately between 200 – 280 ms. See Figure 2.9 and Figure
2.10.
To determine whether HEP amplitude differences may be driven by session-level
differences in the ECG, the ECGs were averaged analogous to an ERP. No differences in ECG
waveforms were observed for the sham and verum experimental sessions, hence it is unlikely
that the observed HEP effect is attributable to differences in the conductive properties of the
heart. See Figure 2.11 for averaged ECGs from the sham and verum sessions.

Figure 2.9. Qualitative illustration of topographical differences for verum and sham HEPs
between 100 – 500 ms. Voltages are on equivalent scales.
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Figure 2.10. Fieldtrip cluster-based permutation testing (2-tailed t-test) was used to determine
significance of HEP differences in the range of 175 – 400 ms. Left panel illustrates SCD-
transformed HEPs for verum and sham experimental sessions from all channels. Right panel
(upper) displays HEP averages over significant channels with verum (green) and sham (red).
Verum was associated with significantly reduced HEP amplitude in left frontotemporal,
frontocentral, centroparietal, central and temporal electrode sites from approximately 200 – 280
ms. Error bands reflect standard error of the mean. Right panel (lower) indicates topographical
regions of significant difference between verum and sham.
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Figure 2.11. Averaged ECG waveforms for verum (yellow) and sham (blue) stimulation
sessions, with the time dimension corresponding to the entire HEP epoch. Differences between
sham and verum ECGs were not significant.
Source localization of HEP functional connectivity. AECs were computed for 175 – 400
ms relative to the R-wave. Significant AEC effects emerged for multiple nodes in theta, alpha,
SMR, and beta frequency bands at p < 0.005 uncorrected. Positive values indicate greater
functional connectivity between ROIs for verum versus sham, whereas negative values indicate
less functional connectivity for verum relative to sham, although it does not indicate whether the
amplitude envelopes for two given regions were anti-correlated or positively correlated on
average. In the theta band, increased AEC between the left parahippocampal gyrus and the right
posterior pars triangularis was observed for verum relative to sham. For alpha frequencies,
reduced HEP connectivity was found between the right pars orbitalis and right superior pars
opercularis; and between the right posterior insula with the left and right subcallosal gyri. There
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was also reduced AEC strength in the SMR frequency band between the left and right anterior
insulae, as well as between the left inferior pars opercularis and the left posterior pars
triangularis. Finally, for beta frequencies, there was a mix of positive and negative AECs.
Greater HEP functional connectivity was observed for the left anterior cingulate with left middle
and anterior pars triangularis; and for right anterior cingulate with the left anterior pars
triangularis. Reduced connectivity was observed between the left and right gyrus rectus with the
right parahippocampal gyrus; left subcallosal gyrus with the right superior pars opercularis; right
anterior insula with right posterior pars triangularis; posterior insula with the right middle pars
triangularis; right pars orbitalis with the right anterior pars triangularis; and right inferior pars
opercularis with the right inferior post-central gyrus. See Figures 2.12 – 2.15.

Figure 2.12. Increased connectivity in theta between 175 – 400 ms relative to the R-wave
between the left parahippocampal gyrus and the right posterior pars triangularis.
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Figure 2.13. Decreased alpha HEP functional connectivity for verum (175 – 400 ms relative to
R-wave) between the right pars orbitalis and right superior pars opercularis; and between the
right posterior insula with the left and right subcallosal gyri.

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Figure 2.14. Reduced HEP functional connectivity (175 – 400 ms relative to R-wave) in SMR
frequencies between the left and right anterior insulae, as well as between the left inferior pars
opercularis and left posterior pars triangularis.

Figure 2.15. Increased HEP beta functional connectivity (175 – 400 ms) between the right and
left anterior cingulate with the left-sided regions of the pars triangularis. Decreased beta
functional connectivity between the left and right gyrus rectus with the right parahippocampal
gyrus; left subcallosal gyrus with the right superior pars opercularis; right anterior and posterior
insula with right-lateralized regions of the pars triangularis; right pars orbitalis with the right
anterior pars triangularis; and right inferior pars opercularis with the right inferior post-central
gyrus.
Association of HEP functional connectivity with autonomic variables. Significant AECs
involving the insula were selected to test for bivariate associations with HR and BRS
PTT
. This
included (1) the right posterior insula AECs with the left and right subcallosal gyri in the alpha
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frequency band; (2) the AEC between the left and right anterior insula for the SMR frequency
band; (3) and the AEC between the right anterior insula with the right posterior pars triangularis,
and the AEC between the right posterior insula with the right middle pars triangularis for the beta
frequency band.
For the alpha band, right posterior insula AECs with both the left and right subcallosal
gyri revealed significant inverse associations with HR for verum stimulation (right posterior
insula<->left subcallosal: r = -0.46, t(41) = -3.33, p = 0.0018; right posterior insula<->right
subcallosal: r = -0.45, t(41) = -3.25, p = 0.0023). However, sham revealed no such patterns of
association (right posterior insula<->left subcallosal: r = -0.05, t(41) = -0.36, p = 0.72; right
posterior insula<->right subcallosal: r = -0.13, t(41) = -0.89, p = 0.38). Left and right anterior
insula connectivity in the SMR band was not significantly associated with HR for neither sham
nor verum (p-values > 0.40). For beta band connectivity, HR was positively associated with the
right posterior insula<->right middle pars triangularis connectivity for verum (r = 0.33, t(41) =
2.23, p = 0.03), but not for sham (r = -0.1, t(41) = -0.67, p = 0.51). No AECs were significantly
associated with BRS
PTT
for any frequency band, or stimulation condition (all p-values > 0.088).
Given that many correlative tests were performed to determine associations of AECs with
cardiovascular parameters that were affected by either sham or verum tVNS, p-values should be
adjusted by the number of tests performed. In this case, 20 individual post-hoc tests were
performed, therefore Bonferroni-corrected p-values should be less than 0.0025 to be considered
significant. Hence, the inverse correlations between HR with alpha band AECs for right posterior
insula with the bilateral subcallosal gyri were robust to multiple comparisons. Additionally, the
Fisher z-score transformation was used to determine whether the correlation coefficients were
stronger for verum than for sham. Fischer z-score transformation did support inference that the
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correlation coefficients were significantly stronger for verum stimulation (p = 0.023 for right
posterior insula<->right subcallosal and p = 0.057 right posterior insula<->left subcallosal). See
Figure 2.16 for a scatterplot of these associations for verum.

Figure 2.16. Negative correlations between HR and alpha-band AEC between the right posterior
insula and the subcallosal gyri.
2.4 Discussion
This study examined the effects of tVNS on interoceptive systems involved in cardiac
visceral sensation and visceromotor regulation. Broadly, we observed tVNS-induced changes in
baroreceptor sensitivity and heart-rate, enhanced beta-band connectivity between viscerosensory
and somatosensory cortices, modulated HEP amplitudes, and altered patterns of HEP functional
connectivity in multiple frequency bands that were correlated with heart rate responses.
Altogether, these results support the inference that tVNS modulates interoceptive neural systems,
their interactions with visceromotor regions, and that these cortical changes contribute to
mechanism by which tVNS alters cardiovascular autonomic function.

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2.4.1 tVNS effects on cardiovascular autonomic indices
Stimulation of the left cymba conchae (verum tVNS) resulted in a transient increase in
baroreceptor sensitivity relative to sham and baseline stimulation. This effect occurred only
during the period of stimulation and was not sustained into recovery. Sham (left earlobe
stimulation) was not expected to generate changes in cardiovascular parameters, although the
acute response to sham was a reduction in baroreceptor sensitivity which remained suppressed
throughout the recovery period. The main effect of recovery was associated with a significant
reduction in BRS
PTT
across stimulation conditions, which is a somewhat unexpected result,
although other investigations have demonstrated ‘rebound effects’ of cardiovascular parameters
for both verum and sham stimulation (e.g. post-stimulation HR significantly exceeding baseline
HR) during the off periods of the duty cycle (Badran, et al., 2018; Sclocco, et al., 2019). The
effects of tVNS on BRS
PTT
were not found to be attributable to any potentially confounding
covariates, including subjective pain perception, state anxiety, or degree of prickling sensations.
Counter to the hypotheses, verum tVNS did not elicit significant increases in HRV nor
decreases in heart rate relative to sham and baseline. There was, however, indication that both
sham and verum tVNS were effective at reducing HR. Specifically, verum stimulation produced
an approximately 1.7% reduction in heart rate during stimulation, and a 1.1% reduction during
recovery, whereas sham was associated with a 1.2% HR reduction during stimulation only. HR
decreases and HRV increases after earlobe stimulation have previously been noted (Badran, et
al., 2018; De Couck, et al., 2017; Sclocco et al., 2019; Borges, et al., 2019) although the
magnitude of the HR decrease observed here for verum appears to be somewhat lower than that
seen in the studies mentioned. Secondly, due to asymmetrical vagal innervation of the heart
(right vagal fibers are the dominant source of efferent fibers to the sinoatrial node), it is possible
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that stimulation of the left auricle is suboptimal. However, there is little experimental research
concerning right-lateralized stimulation of the AVBN (for an exception: De Couck, et al., 2017).
The efficacy of AVBN stimulation for increasing cardiovagal function in healthy
populations has not been firmly demonstrated in the literature as ambiguous and conflicting
reports are common. There are several reasons as to why this may be. First, healthy young adults
may have robust physiological systems that may readily adjust to autonomic perturbations. Such
changes may be especially difficult to observe during resting conditions, during which allostatic
or homeostatic dynamics would not be substantially challenged. A more sensitive test of
cardiovascular effects may be obtained from autonomic challenge or stressor tasks, such as
Valsalva, tilt-table, or cold-pressor. Another important consideration is that the earlobe is not
physiologically inert (Rangon, 2018). There is cross-talk between the AVBN and the superior
cervical plexus that innervates the earlobe (He et al., 2013; Mitsuoka et al., 2016), hence effects
on the sympathetic nervous system at the level of the upper cervical segments can occur
concurrently to the stimulation of vagal afferents. Additionally, innervation by auricular nerves
to specific territories of the ear are variable across individuals, and can include dual innervation
within the same territory (Peuker and Filler, 2002), resulting in the excitation of multiple sensory
nerves during stimulation of a particular territory of the auricle, with potential off-target effects
in sympathetic nuclei. In fact, it has been reported that tragus afferents in the rat project to the
ipsilateral dorsal horn of the upper cervical spinal cord, the ipsilateral paratrigeminal, spinal
trigeminal nuclei and the nucleus cuneatus, with only sparse projections to the nucleus of the
solitary tract (Mahadi, et al., 2019). Direct recording of sympathetic nerves during stimulation of
the tragus produced sympatho-inhibitory effects up to 36% (Mahadi, et al., 2019). Although
these results are specific to the tragus as opposed to the cymba conchae (with the latter
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innervated by the superior cervical plexus and/or the AVBN in humans, and the former
exclusively by the AVBN [Peuker & Filler, 2002]), it seems probable that stimulation of the
AVBN via the cymba conchae can produce mixed effects on the vagal and sympathetic systems.
Moreover, it is clear that stimulation of the superior cervical plexus (such as via the earlobe) can
influence sympathetic state via its afferent targets. Hence, autonomic outcomes of AVBN versus
earlobe stimulation cannot be simply interpreted in terms of parasympathetic effects.
With regards to the results of the present experiment, the observed changes in BRS may
be attributable to alterations in vagal and/or sympathetic nerve systems. At the level of the
medulla, increased efficiency of baroreceptor responses after cymba conchae stimulation could
involve the excitation of the caudal NST and subsequently the caudal ventrolateral medulla,
thereby inhibiting the rostral ventrolateral medulla and reducing sympathetic nerve outflow.
Alternatively, BSR could have been increased through NST activation of the dorsal motor
nucleus, which may be possible given the slight decrease in HR that accompanies the BRS
increase. Suppression of sympathetic outputs may be associated with earlobe (i.e. superior
cervical plexus) stimulation (Mahadi, et al., 2019), which could render the baroreceptor system
less sensitive to reductions in blood pressure. However, as continuous blood pressure and
sympathetic microneurographic measurements were not obtained, the underlying dynamics
producing the observed BRS
PTT
effects remains highly speculative.
An alternative possibility is that BRS
PTT
and HR effects were not mediated by simple
medullary reflexes, but may involve altered neural processing in higher-order regions of the
CAN, which can produce cardiovascular autonomic patterns not predicted by traditional
medullary negative feedback mechanisms for cardiovascular homeostasis (Saha, et al., 2000).
Although it has been previously speculated that autonomic effects of AVBN stimulation could be
94
an indirect result of afferent inputs to interoceptive cortices (Badran, et al., 2018), the question
has not been empirically investigated. In pursuit of an answer to this question, the present study
also evaluated tVNS-evoked changes in resting state and event-related, source-localized EEG
power and functional connectivity in viscerosensory and visceromotor cortices, as well as the
association of these EEG features with concurrent autonomic responses to tVNS.
2.4.2 tVNS effects on resting state source-localized power and functional connectivity
The primary finding from the resting state analysis is that verum tVNS-modulated
amplitude envelope connectivity in cortical regions of the CAN was associated primarily with
increased connectivity between the right anterior insula and the right superior somatosensory
cortex in the beta frequency range. At less stringent levels of significance thresholding there
were additional increases in power spectral density for verum relative to sham within the left and
right anterior insula, left posterior insula, and left parahippocampal gyrus for beta to gamma
frequencies.
Multiple lines of evidence indicate that synchronized beta oscillations bind multiple
sensorimotor areas into a large-scale network (Brookes et al., 2011; Brovelli et al., 2004;
Hillebrand, et al., 2012; Hipp, Hawellek, Corbetta, Siegel, & Engel, 2012; Simoes-Franklin,
Jensen, Parkkonen, & Hari, 2003). For instance, Hillebrand, et al. (2012) find that MEG resting-
state beta-band amplitude envelope connectivity corresponds to a sensorimotor network. More
generally, band-limited amplitude envelope correlations appear to correspond to large-scale
intrinsic networks analogous to those observed from resting state fMRI. With regards to the
present results, it may suggest that AVBN stimulation via the cymba conchae drives
synchronization of interoceptive and somatosensory signals more strongly than does earlobe
stimulation, specifically in the ‘sensorimotor’ beta band. Although the anterior insula is not
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commonly considered viscerosensory, it is considered to be visceromotor in function (Barret &
Simmons, 2015), and has anatomical connections to the primary somatosensory cortices in
humans (Dionsio, et al., 2019). Additionally, beta amplitude oscillations appear to relate to the
spiking output of spinal motor neurons to their motor effectors (Bayraktaroglu, et al., 2013).
Analogously, visceromotor processing in the anterior insula could be regulated by similar beta-
rhythmic dynamics. However, functional interpretation of this connectivity feature remains
elusive as the individual-level beta-band AECs were not significantly associated with tVNS-
modulated baroreceptor sensitivity or heart rate in either stimulation session. Additionally, given
that the insula was not parcellated into granular, dysgranular, agranular regions in the brain
template, it is possible that anterior insula activity refers to viscerosensory, rather than
visceromotor functions of the insula.
It is also possible that that only the beta-band AEC between the right anterior insula and
somatosensory cortex emerged (given the chosen significance threshold) because it was a
relatively stable connectivity feature across the 15 minute stimulation period. However, the
assumption that connectivity would be stationary across the entire period is tenuous. It is known
that EEG connectivity measures are sensitive to epoch length (Fraschini et al., 2016), and that
brain activity, even during rest, is composed from the formation and dissolution of transient
network states (O’Neill et al., 2018). Studies of dynamic functional connectivity clearly
demonstrate fluctuations in connectivity strength within, for instance, sensorimotor networks
during rest (Brookes, et al., 2011). Furthermore, insula-linked network properties have been
shown to depend on whether VNS was turned on/off in patients with epilepsy (Bartolomei et al.,
2016; Wostyn et al., 2016). In the present data, the 7 sec. on/18 sec. off stimulation cycle creates
an externally-imposed structure on the EEG resting-state data, which was not accounted for by
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an aggregating over the entire resting-state recording. Hence, modeling the dynamics of the duty
cycle may better identify changes in brain network configurations associated with tVNS.
Nevertheless, the resulting connectivity profile is consistent with the BOLD fMRI effects
of tVNS relative to earlobe stimulation. Specifically, the enhanced beta AEC between the right
somatosensory cortex and anterior insula was contralateral to the side of stimulation, an
observation that parallels fMRI tVNS studies which find greater BOLD responses in the
somatosensory cortex contralateral to the side of stimulation as well as stronger insula BOLD
bilaterally relative to earlobe stimulation (Frangos, et al., 2015; Badran, et al., 2018). The
increased insula/postcentral gyrus beta-band AEC may also be consistent with the only presently
existing report of resting state EEG responses to cervical tVNS, which finds increased beta
power in channel Cz relative to sham (Lewine, Paulson, Bangera, & Simon, 2018). Hence,
verum tVNS appears to broadly modulate connectivity between primary somato-and-
viscerosensory cortices within the central autonomic network.
2.4.3 tVNS effects on HEPs
Verum tVNS significantly modulated HEPs in a time window that was previously found
to be significant in intracranial recordings of regions that included the insula, operculum, and
medial temporal lobes (Park, et al., 2017). Specifically, verum tVNS resulted in greater HEP
amplitude negativity approximately between 200 – 300 ms relative to sham. The effect was
distributed topographically in left frontotemporal regions. It is not likely that the HEP differences
were due to cardiac or stimulation artifacts. First, the spread of the CFA was attenuated by SCD-
transformation, and second, the ECGs themselves were effectively identical between the two
sessions. Third, artifacts relating to stimulation were removed by ICA and further suppressed
using a notch filter. As HEPs are believed to reflect cortical processing of cardiac interoceptive
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and somatosensory afferents, modulation of HEPs by tVNS supports the hypothesis that tVNS
accesses thalamocortical pathways relevant to cardiac interoceptive signaling at the level of the
cortex. Recently, it was shown that tVNS increases accuracy on a heart-beat discrimination task,
during which a subject determines whether an auditory tone is synchronous or asynchronous to
the heartbeat (Villani, et al., 2019). It has furthermore been shown that HEP amplitudes are
correlated with individual differences in the ability to discriminate heartbeats (Pollatos, et al.,
2005). These findings, along with the present novel results, suggest that tVNS may increase the
signal-to-noise ratio of interoceptive afferents (Khalsa, et al., 2018; Barrett & Simmons, 2015) as
they ascend through the heart-brain neuroaxis.
Based on intracranial EEG and source-estimation studies, HEPs have been localized to
distributed regions corresponding to the CAN, including the insula, anterior cingulate,
somatosensory, orbitofrontal, fronto-opercular, and medial temporal regions (Pollatos, et al.,
2005; Park, et al., 2017; Kern, et al., 2013; Canales-Johnson, et al., 2015). An investigation of
source-localized HEP connectivity based on the imaginary coherence metric found reduced
gamma band connectivity between regions that included the opercular, triangular, and orbital
sectors of the left inferior frontal gyrus, right dorsolateral PFC and left insula during mediation
as compared to rest in Tibetan Buddhist monks (Jiang, et al., 2019). These findings positively
indicate the relevance of fronto-opercular and insula as components of a functional network
processing beat-to-beat cardiac information. We hypothesized that tVNS would modulate
connectivity among HEP-relevant regions of the cortex. It was also hypothesized that HEP insula
connectivity with other ROIs would be differentially correlated with heart rate and/or
baroreceptor sensitivity depending on whether stimulation was sham or verum, based on the
assumption that HEPs are relevant to cardiovascular autonomic function (Gray, et al., 2007) and
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given extensive evidence that the insula is intricately involved in regulation of cardiovascular
autonomic function (Oppenheimer and Cechetto, 2016). Therefore, we pursued a novel
investigation into the band-limited HEP amplitude-envelope connectivity between brain regions
that have been identified as cortical sources of HEPs. Amplitude-envelope connectivity was
tested on a window of time that intracranial HEP studies have previously identified as significant
(175 – 400 ms) (Park, et al., 2017).
Effects of verum tVNS relative to sham on HEP AECs were observed in several
frequency bands; the discussion will highlight the broad patterns that emerged. First, HEP AECs
involving the insula were right-lateralized (save for the inter-hemispheric connectivity between
the right and left anterior insula in the SMR band). The right-lateralization is highly consistent
with functional MRI meta-analyses which identify relatively stronger associations of cardiac
interoception with the right insula (Schulz, 2016), and right-dominant effects of insula damage
on the development of cardiac arrhythmias in acute stroke patients (Siefert, et al., 2015).
This analysis also observed increased left parahippocampal gyrus connectivity with the
right posterior pars triangularis in the theta band for verum tVNS. Medial temporal theta rhythms
are well-described and are associated with various functions in humans including spatial
navigation and sensorimotor integration (Caplan et al., 2003), memory, and other cognitive
processes (Guitart-Masip et al., 2013; Mormann et al., 2008), as well as respiration (Zelano, et
al., 2013). Reduced BOLD in the hippocampus, parahippocampus and amygdala are effects that
are commonly observed in fMRI studies of tVNS (Frangos et al., 2015; Yakunina et al., 2017).
Additionally, EEG medial temporal activity is associated with response to VNS in patients with
epilepsy (Wostyn, et al., 2017), and VNS appears to modulate hippocampal theta rhythms
(Larsen, Wadman, Marinazzo, et al., 2016; Larsen, Wadman, van Mierlo, et al., 2016). Hence,
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medial temporal responses appear to be a consistent feature of vagal afferent effects within the
forebrain.
The greatest number of significant AECs were associated with the beta-band.
Specifically, verum tVNS generally resulted in reduced HEP beta-band AECs in primarily right-
lateralized regions involving somatosensory and insula connectivity with regions of the inferior
frontal gyrus/operculum, parahippocampal gyrus connectivity with the gyrus rectus bilaterally,
and intra-opercular/inferior frontal connectivity. There was also greater beta AEC for the left and
right anterior cingulate cortex with the left-lateralized regions of the inferior frontal gyrus.
However, correlations of the insula AECs with heart rate and baroreceptor sensitivity were not
significant for either stimulation session after corrections for multiple comparisons. Although it
may be worth emphasizing that the beta-band AEC between the right posterior insula with the
right middle pars triangularis was positively associated with heart rate during verum (uncorrected
for multiple comparisons), but not during sham stimulation. The beta band has been previously
identified as the EEG frequency for bidirectional information transfer relevant to cardiac-vagal-
brain interactions in a study utilizing dynamical systems modeling (Faes, Nollo, Jurysta, &
Marinazzo, 2014). The observation of parahippocampal beta-band connectivity with the gyrus
rectus is also interesting in this context, as beta frequencies have been recorded from the human
medial temporal lobes (Uchida, et al., 2003). Medial temporal beta rhythms are suggested to
establish transient connections among neurons in the medial temporal cortex with related
structures (Leung, 1992). Additionally, spontaneous firing rates of anterior parahippocampal and
anterior cingulate neurons in humans relate to visceromotor control of the cardiac cycle (Kim et
al., 2019); that is, the lagged connection between neuronal firing rate and duration of the cardiac
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cycle implied that the firing rate at a given moment predicted future changes in heart rate
variability for one to two cycles (Kim, et al., 2019).
Alpha-band HEP amplitude envelopes involving the right posterior insula connectivity
with the left and right subcallosal gyri (henceforth referred to as ‘insula<->subcallosal
connectivity’) were also found to be relatively reduced during verum tVNS relative to sham.
Moreover, insula<->subcallosal connectivity was found to be differentially related to heart rate
under sham and verum stimulation. Specifically, during verum tVNS, heart rate had an inverse
association with insula<->subcallosal connectivity, such that greater connectivity was associated
with lower heart rate, whereas this connectivity feature was found to be unrelated to heart rate
under sham stimulation. The posterior insula is the primary cortical termination site for vagal and
spinothalamic lamina I (sympathetic) afferents, which includes beat-to-beat cardiac information
arising from baroreceptors and intracardiac mechanoreceptors. The posterior insula also displays
effective connectivity with the anterior cingulate (which includes the subcallosal gyrus)
(Dionisio, et al., 2019), which is involved in the generation of visceromotor responses (Carauna,
et al., 2018). Altogether, the results of the HEP analyses imply that verum tVNS alters beat-to-
beat cardiovascular information transfer between viscerosensory and visceromotor regions.
These results further support the general hypothesis that autonomic cardiovascular responses to
tVNS involves changes occurring at cortical levels of the central autonomic network.
2.4.4 Limitations and future directions
Several methodological limitations should be acknowledged. With regards to
cardiovascular responses to tVNS, we used BRS
PTT
, which is a surrogate measure of baroreceptor
sensitivity. As a surrogate measure, it is potentially a less reliable indicator of baroreceptor
sensitivity, whereas the use of continuous non-invasive blood pressure monitors would provide
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gold-standard measurement. We did not find verum tVNS effects on heart-rate variability, and
unclear effects on heart rate. Although this may not be unusual given the brief treatment in a
healthy population, a contributing factor could be that the rapid-cycle stimulation parameters are
not be optimal for driving cardiovagal responses. Alternative stimulation parameters should be
considered for future studies, including stimulation of the right cymba conchae, given the
possible (but unknown) relevance of AVBN stimulation to asymmetrical innervation of the vagi
to the heart (Chen et al., 2015). Autonomic challenge and stressor tasks may also be more
sensitive for revealing changes in autonomic dynamics in response to tVNS. Additionally, the
use of impedance cardiography would yield rich information concerning tVNS effects on
cardiovascular dynamics, which has so far appears to not have been used in the tVNS literature.
Impedance cardiography may also help to determine what cardiac information is encoded by
HEPs, since they are not typically found to relate to heart rate or heart rate variability (Park and
Blanke, 2019), although they may be more closely correlated with parameters such as stroke
volume, which concerns ‘the transfer of energy between the ejected blood mass and
pressosensitive vascular tissue’ (Schandry and Montoya, 1996, p. 80) or repolarization of cardiac
muscle as encoded by intrinsic cardiac neurons (Armour, 2004) as demonstrated by Gray, et al.
(2007).
It is well known that EEG is not an ideal method for inferring the spatial origin of brain
signals. We applied advanced source-localization methods to estimate the inverse solution,
however, the head and brain models were not optimized to the individual. An optimal source
reconstruction scheme could be achieved by obtaining the MRI of each participant’s head and
brain, in combination with 3D scanners to precisely co-register EEG sensors to locations on the
scalp (e.g., see Koessler et al. [2011]; Taberna, Marino, Ganzetti, & Mantini [2019]). Hence, it is
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not clear how the use of standardized templates for co-registration affected our source estimates.
Another caveat is that the AECs were reported at an uncorrected threshold of p< 0.005, and did
not survive FDR-correction, although this threshold is consistent with other similar analyses (e.g.
Jiang, et al., 2019). Nevertheless, the AEC results should be interpreted with caution, and inform
future sample size planning.
We also infer that the patterns of correlations obtained in this study support the
hypothesis that tVNS induces changes in cardiovascular function at least in part through high-
order cortical mechanisms. A stronger claim could be made by using methods to estimate the
directionality of information transfer between cardiac and EEG dynamics. Such methods have
been successfully used to demonstrate that phase-amplitude coupling between infra-slow gastric
rhythms with the amplitude of MEG alpha rhythms in the brain is explained by gut-to-brain
information transfer to the insula and posterior cingulate cortex (Richter, et al., 2016). Other
studies using directed information metrics describe bidirectional information transfer between
the heart and brain in the EEG beta frequency band (Faes, et al., 2014).
2.4.5 Conclusion
The mechanisms by which tVNS improves cardiovascular autonomic function in various
populations is unclear. However, a major mechanism underlying these effects may involve vagal
thalamocortical projections to the insula and extended interoceptive systems. Our findings
provide evidence that tVNS alters neural processing of cardiac information, as well as
connectivity between the insula and other regions comprising a network involved in cardiac
interoception and visceromotor control. These findings represent the first direct demonstration of
tVNS-induced modulation of cortical heart-brain interactions, suggesting that cardiovascular
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autonomic effects of tVNS may be an indirect consequence of altered neural function in the
cortical systems underlying interoceptive-allostatic integration and regulation.

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3. Frontotemporal theta-burst stimulation alters cardiovascular autonomic function: The
role of state anxiety
1
3.1 Introduction
Effective regulation of blood volume, arterial pressure, and heart rhythm is critical for
maintaining cardiovascular homeostasis. Impaired cardiovascular regulation may lead to
conditions such as hypertension, which is the leading risk factor for global disease burdens
(Bromfield & Muntner, 2013). Many lines of evidence point to neural mechanisms in the
pathogenesis of hypertension, arrhythmias and heart failure (Mancia & Grassi, 2013; Shen &
Zipes, 2014). For instance, blood pressure and cardiac rhythm are regulated via sympathetic and
parasympathetic pathways, which are under the control of brainstem and midbrain reflexes
involving the hypothalamus, PAG, NST, vagal and sympathetic motor nuclei, among other
regions (Silvani et al., 2016). However, the cortex also exerts an influence on tonic and phasic
autonomic outflows. In particular, activity of the medial and orbital prefrontal cortices, the
ventrolateral prefrontal, insular and opercular cortices have been consistently associated with
heart rate and heart rate variability (Vargas et al., 2016; Thayer et al., 2012), baroreflex loading
and unloading (Goswami, et al., 2012; Kimmerly, et al., 2006), and muscle sympathetic nerve
firing (Macefield & Henderson, 2016). Lesion studies further highlight the role of the insula in
cardiovascular control- acute ischemic stroke affecting the insula is associated with severe

1
A form of this manuscript has been published: Poppa, T., De Witte, S., Vanderhasselt, M-A.,
Bechara, A., Baeken, C. (2020). Theta-burst stimulation and frontotemporal regulation of
cardiovascular autonomic outputs: The role of state anxiety. International Journal of
Psychophysiology, 149, 25 – 34. DOI: 10.1016/j.ijpsycho.2019.12.011
105
cardiac arrhythmias (Seifert et al., 2015), depressed heart rate variability (Colivicchi et al., 2004)
and baroreflex impairment unexplained by atherosclerosis (Sykora et al., 2009). Thus, the insula
and prefrontal cortices are key cortical regions of a cardiovascular viscerosensory and
visceromotor network.
Noninvasive brain stimulation (NIBS) may be useful to enhance understanding of cortical
involvement in cardiovascular autonomic regulation in relation to illness and health. Previous
physiological and psychophysiological studies have indicated that transcranial magnetic
stimulation (TMS) of cortical regions may modulate cardiovascular autonomic responses. For
instance, Macefield and colleagues (1998) reported inhibition of muscle sympathetic nerve
activity after cardiac synchronous single-pulse TMS applied to the vertex or motor cortex, while
Berger et al. (2017) reported enhanced cardiac deceleration in response to affective pictures after
repetitive TMS (rTMS) to the right dorsolateral prefrontal cortex. The potential for TMS to
modulate the cortical-autonomic network may also be relevant to the treatment of hypertension
(Cogiamanian et al., 2010) and stress-related psychiatric disorders which are associated with
depressed cardiac vagal function and increased risk of cardiovascular disease (Gianaros & Sheu,
2009; Ginty et al., 2017; Thayer & Lane, 2007). However, there is currently only very limited
prospective evidence that rTMS treatments may improve cardiac vagal function in clinical
populations (Udupa et al., 2007, 2011).
Although several studies indicate positive effects of (r)TMS on cardiovascular autonomic
responses in both clinical and healthy populations, the findings remain equivocal: the recent
publication of two meta-analyses (Makovac, Thayer, & Ottaviani, 2017; Schestatsky, Simis,
Freeman, Pascual-Leone, & Fregni, 2013) which amalgamated a highly heterogeneous body of
literature, arrived at somewhat divergent conclusions regarding the effects of NIBS on
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autonomic cardiovascular control. Schestatsky et al. (2013) could not identify consistent effects
of TMS and related parameters on autonomic responses in general, although there was some
evidence that HRV is most sensitive to TMS effects on the autonomic nervous system relative to
other autonomic response systems, although the magnitude of this effect was not clear. Makovac
et al. (2017) instead identified a moderate effect size for the influence of TMS on heart rate
reductions and increases in high frequency heart rate variability (HF-HRV), and a small effect
size for blood pressure reductions. In addition, Makovac and colleagues identified the prefrontal
cortex as the relevant area for stimulation compared to other sites, (such as the motor cortex),
although studies targeting different brain regions are lacking. There are differences between
these analyses that may have accounted for the divergent conclusions. The Schestatsky et al.'s
study was semi-qualitative (i.e. in terms of the “frameworks” for partitioning groups of studies
for meta-analytic assessment), and focused on which brain stimulation parameters may best
induce autonomic responses, whereas the Makovac and colleagues used a more sensitive and
rigorous quantitative approach that exclusively focused on cardiovascular autonomic response
effect sizes over methodological variability across studies. Furthermore, these meta-analyses are
distinct in that Makovac et al. included only NIBS studies that measured heart rate, heart rate
variability, and blood pressure, whereas Schestatsky et al. included studies measuring any
autonomic response system, including skin conductance, cortisol, body temperature, respiration,
etc., resulting in greater heterogeneity of studies included in their analysis.
Despite the divergence, both meta-analyses identified several pervasive methodological
limitations within the research they reviewed, including: an insufficient number of placebo-
controlled studies, under-utilization of autonomic perturbation tasks (e.g. tilt-table test, Valsalva
maneuver, etc.), lack of diversity in sites of stimulation, limited time-scales on which responses
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were measured (typically concomitant to the stimulation), and importantly, failure to assess
whether acute stress or arousal induced by the neurostimulation procedures were sufficient to
drive any observed effects. This last point warrants careful attention since the sensations of
verum stimulation can be arousing or stress-provoking, which may alter autonomic responses in
parallel during experimental measurements, or worse, entirely account for observed effects. The
potential impact of such covariates raises the possibility that the effects identified in Makovac
and colleagues' meta-analysis could be influenced by such confounds. Ideally, sham stimulation
controls for the sensory experience associated with (r)TMS procedures (which have the potential
to induce anxiety or arousal), however the sham coils and other placebos currently used are often
not adequately matched in this respect (Duecker & Sack, 2015). Although a single-session of
rTMS is usually not found to acutely affect mood in healthy volunteers (Remue, Baeken, & De
Raedt, 2016; Remue, Vanderhasselt, Baeken, & Rossi, 2016), it has been reported that state
anxiety prior to stimulation (perhaps related to expectations concerning the TMS procedures)
affects both cognitive-affective and cortisol responses to rTMS (Baeken, Vanderhasselt, & De
Raedt, 2011; Vanderhasselt, Baeken, Hendricks, & De Raedt, 2011). These findings emphasize
the need to examine the influence of state anxiety as a covariate in non-invasive brain
stimulation studies of cardiovascular autonomic regulation.
The present study examined possible differential effects between continuous and
intermittent theta burst stimulation (cTBS and iTBS) to a right frontotemporal target on HRV
and pulse transit time (PTT) in a sham-controlled, repeated measures design. Similar to rTMS,
theta-burst stimulation (TBS) protocols may induce long-term potentiation or long-term
depression-like effects, but may also produce more enduring effects on cortical excitability
(Lizbeth, et al., 2010). Consequently, theta-burst protocols may be more effective than rTMS at
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achieving tonic changes in autonomic balance under resting conditions. iTBS protocols have, to
our knowledge, not yet been used to examine its effect on neural autonomic cardiovascular
control. However, cTBS has been used to examine neural cardiac interoception. Pollatos et al.,
(2016) reported reductions in heart-beat detection accuracy and altered amplitude of the heart-
evoked potential (HEP) in frontocentral electrode sites after cTBS of a right frontotemporal
target. The HEP is an endogenous evoked-potential that reflects neural processing of cardiac
afferents (Gray et al., 2007), which intracranial EEG studies have localized to the insula,
operculum, temporal pole, and inferior frontal gyrus (Park et al., 2017). Therefore, cTBS of this
right-lateralized frontotemporal target appears capable of modulating neural cardiovascular
processing. However, Pollatos and colleagues did not report whether cTBS affected autonomic
outflows, nor could they determine whether iTBS may exert distinct, or even similar effects
compared to cTBS.
As there is no a priori basis for assigning directional effects on cardiovascular responses
to the right frontotemporal cortex depending on TBS frequency, our hypotheses for this
preliminary study were non-directional. We expected to find an enhancement of HRV for at least
one of the TBS stimulation protocols after stimulation to the right frontotemporal cortex. In
addition to HRV, we also examined PTT, a surrogate beat-to-beat measure of systolic blood
pressure (albeit a noisy measure that is also influenced by the cardiac pre-ejection period (PEP)
[Payne, 2006]). We expected an increase in PTT latency in response to TBS for at least one of
the TBS stimulation protocols, which may reflect a reduction in systolic blood pressure or
greater PEP duration. HRV and PTT were further assessed under two breathing conditions
consisting of slow-paced breathing and spontaneous breathing. Slow paced-breathing at 0.1 Hz is
an autonomic challenge which is believed to generate large-amplitude resonance power in the
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baroreflex feedback loops (Lehrer, Vaschillo, & Vaschillo, 2000). It is reflected as a large
increase in respiratory sinus arrhythmia (RSA) spectral power centered at 0.1 Hz. In healthy
individuals, RSA power during 0.1 Hz breathing has a correlation of 0.77 with baroreceptor
sensitivity (BRS) measured using the Finapres method (Davies, et al., 2002). Thus, RSA power
during 0.1 Hz breathing can also be taken as a proxy measure of baroreceptor gain on heart rate,
which is expected to increase with stimulation. Finally, we evaluated the influence of
stimulation-provoked state anxiety. We expected that state anxiety will increase in response to
verum stimulation relative to sham, and that state anxiety will at least partially account for
effects of TBS on cardiovascular state for any given cardiovascular index and breathing
condition.
3.2 Methods
3.2.1 Participants
Twenty-eight participants were recruited through a Ghent University social media
platform dedicated to advertisement of psychology and neuroscience studies. Four were excluded
from the study due to the following reasons: failure to meet inclusion criteria during the Mini
International Neuropsychiatric Interview (MINI) (n = 1) (Sheehan et al., 1998), frequent ectopic
beats observed during visual inspection of the electrocardiogram (ECG) trace (n = 1), or lack of
tolerance to the physical sensations of TBS protocols (n = 1) or motor threshold (MT)
stimulation (n = 1). The remaining participants included 24 individuals (14 female, 10 male;
ages: M = 25.39, SD = 6.15). All retained participants were right-handed, physically healthy and
non-smoking. A few participants reported having previously participated in TMS experiments,
however the large majority were naïve to TMS. Participants were free of contraindications for
TMS (including personal or family history of epilepsy, migraine, implanted medical devices,
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pregnancy), and free of other medical conditions including cardiovascular, pulmonary,
metabolic, neurological and psychiatric disorders, and not experiencing any current serious
psychosocial distress, such as recent death of a family member, as determined by a pre-screening
health questionnaire and MINI examination. No subject was using prescribed or over-the-counter
medications, apart from hormonal birth control pills in women. Written informed consent was
obtained and study procedures were approved by the University of Ghent Ethics Committee.
Participants were financially compensated.
3.2.2 Study protocol
Potential participants were first screened via email for TMS contraindication and other
exclusion criteria. If eligible, they were scheduled for three visits that were spaced at least four
days, but less than ten days apart. All testing sessions took place in the afternoon to minimize
potential circadian influences. Participants were asked to refrain from alcohol and strenuous
exercise for at least 24 h prior, to avoid caffeine at least 4 h prior, and to wear comfortable
clothing. After consent, the MINI Interview was given to further rule out the presence of any
mental health history. Participants were then familiarized with the six-minute Slow Breathing
task, in which the rate of inspiration and expiration was guided by an animated geometric object
presented on a computer screen. The object expanded during the period of inspiration (5.0 s) and
shrunk during the period of expiration (5.0 s), resulting in a complete cycle (and breathing rate)
of 0.1 Hz. We verified that each participant could engage the diaphragm during slow breathing,
match the oscillations of the object at the appropriate phase, and breathe comfortably and
naturally, without hyperventilation or light-headedness. The other condition consisted of 6 min
of Spontaneous Breathing, during which the participant was instructed to direct their gaze to a
static black fixation cross on the center of a gray screen and not attempt to actively manipulate
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their breathing pattern. To facilitate spontaneity, subjects were advised that they could let their
mind wander during this period.
Electrocardiograms (ECG), pneumogram (RESP), finger pulse photoplethysmogram
(PPG) were acquired with a 1000 Hz sampling rate with the Biopac MP150 system and
Acqknowledge software. The animation and fixation cross for the Slow and Spontaneous
Breathing conditions, respectively, were presented via computer screen using Psychtoolbox 3.0
and MATLAB (Mathworks, Nantucket, MA). Three electrodes were attached for measurement
of Lead II ECG. RESP was measured using a strain-gauge transducer placed around the
abdomen. PPG was measured by attaching the transducer on the middle finger of the left hand.
Subjects were seated in a reclining chair with legs and feet raised to approximately the
same level as the hips, with their hands resting either at their side or on their lap. Participants
practiced slow breathing prior to stimulation. The goal of the practice was to provide enough
time for each subject to achieve hemodynamic stability and to enter into a proper mental state for
the task. This was intended to reduce sources of variability of subjects' physiological and mental
states upon arrival to the lab. After this period, we prepared the subject for stimulation, which is
described in detail in below. Immediately after theta-burst or sham theta-burst stimulation,
subjects reported their state anxiety (STAI_TBS) using the State subscale of the commonly used
State-Trait Anxiety Inventory Y (STAI-Y) (Spielberger, 1983). STAI-Y State consists of 20
questions that evaluate the respondent's current state of anxiety by asking “how do you feel right
now” using items that measure subjective experiences of nervousness, worry, apprehension,
autonomic arousal, fear, and tension on a 4-point Likert-type scale. Higher scores indicate
greater state anxiety. STAI-Y State was chosen as the means of estimating state anxiety based on
previous literature from our group indicating that baseline state anxiety using this measure
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affects hypothalamic-pituitary-adrenal axis and attentional bias responses to rTMS applied to
prefrontal targets (Baeken et al., 2011; Vanderhasselt et al., 2011). After reporting state anxiety,
participants then performed the breathing task: first Slow, then Spontaneous Breathing in a fixed
order. Physiological recordings were taken during this period. After completing the breathing
tasks, subjects again reported their state anxiety (STAI_POST), at which point the session was
concluded. At the end of the third testing day, participants were debriefed.
3.2.3 Motor threshold testing and stimulation site
Motor threshold testing occurred only on the first testing day. To establish individual
motor thresholds we used the visual method of limits to identify the minimum intensity required
to produce a visible twitch in the abductor pollicis brevis in 5/10 consecutive trials (Pridmore,
Filho, Nahas, Liberatos, & George, 1998; Varnava, Stokes, & Chambers, 2011).
The site of stimulation was determined using the international 10–20 EEG system
heuristic introduced by Pollatos et al. (2016) for targeting the right anterior insula (aINS) for a
study of cardiac interoception (which we describe in the present report as a frontotemporal
region). Specifically, the figure-of-eight coil was positioned over the right frontotemporal
locations that built a triangle corresponding to the 10–20 positions F8, FC6, with the center-top
of the coil pointing to F6 (with the handle of the coil pointing towards FT10). See Figure 3.1 for
an illustration of the electrode sites, and see Pollatos et al. (2016) for additional details. After
fitting the EEG cap, we drew points on the subject's scalp with a marker under the electrode
positions indicating the points along which to orient the coil. The cap was removed before
stimulation.
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Figure 3.1. EEG 10–20 electrode schematic for guiding placement of the stimulation coils. The
wings of the coils overlapped FC6 and F8, while the center-top of the coil pointed to F6. The
figure was rendered using BrainNetViewer using a template brain (Xia, Wang, & He, 2013).
3.2.4 Theta-burst stimulation parameters and hardware
Continuous, intermittent, or sham TBS was delivered in randomized order for each
subject at 100% MT. The stimulation was applied using a Magstim Rapid
2
Plus
1
magnetic
stimulator (Magstim Company Limited, Wales, UK) with an active and a sham 70 mm Double
Air Film figure-of eight-shaped cooled coil. The Magstim 70 mm Double Air Film sham coil is
identical to its active variant in all but stimulation output. Each session delivered 600 pulses
consisting of a burst of three stimuli at 50 Hz, repeated every 200 ms. Continuous theta burst
stimulation (cTBS) consisted of 600 consecutive pulses applied in 40 s, while for intermittent
theta burst stimulation (iTBS), the 600 pulses were delivered in separate trains with a duration of
2 s and an inter-train interval of 6 s for a total of 160 s in accordance with Huang et al. (2005).
The stimulation parameters for sham were randomly assigned to follow either the continuous or
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intermittent theta burst pattern for each individual (12 subjects received sham-iTBS, 12 subjects
received sham-cTBS). See Figure 3.2 for an overview of the study design.

Figure 3.2 Study design schematic. Participants came to the lab on three occasions during which
they received Sham, cTBS and iTBS stimulation. The order in which stimulation protocols were
applied was randomized for each participant. After stimulation, participants reported state
anxiety, performed the breathing tasks, then reported state anxiety again.
3.2.5 Physiological measurement
Heart rate variability. The data processing was performed offline using in-house custom
scripts with MATLAB. The peaks of the ECG R-wave were identified separately for
Spontaneous and Slow Breathing conditions using in-house developed MATLAB scripts
centered around the built-in functions filtfilt to detrend the ECG signal with a no phase distortion
Butterworth filter and findpeaks to identify the peaks of the R-waves. Recordings were manually
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inspected for errors in R-peak identification and ectopic beats. No errors or ectopic beats were
found. The inter-beat intervals were computed and HRV was determined in accordance with the
HRV Task Force guidelines (HRV Task Force, 1996) and the Kubios software user's guide
(Tarvainen et al., 2014). Analysis of power spectral density was then carried out using the Fast-
Fourier Transform with default settings in Kubios: high-frequency band 0.15–0.4 Hz; low-
frequency band 0.04–0.15 Hz; and very low-frequency band 0.0–0.04 Hz. The estimates of
spectral density for each frequency band (in milliseconds squared per Hz) were transformed
logarithmically. Additionally, we extracted the temporal domain index Root Mean Square of
Successive Differences (RMSSD). In the spontaneous breathing condition, RSA power in the
high frequency band reflects vagal influences on heart rate, assuming breathing rate is above
0.15 Hz (HRV Task Force, 1996). During slow breathing, RSA shifts to the low-frequency band
and no longer purely reflects vagal influences on heart rate, but instead reflects a mixture of
vagal efference and resonance power in the baroreflex feedback loops (Davies et al., 2002;
Vaschillo et al., 2006). The temporal domain index RMSSD acts as a high-pass filter on the
interbeat interval time series and can be assumed to reflect parasympathetic effects on heart rate.
RMSSD is less sensitive to respiratory rate compared to spectral-domain indices (Penttilä et al.,
2001; Schipke, Arnold, & Pelzer, 1999). Thus, low-frequency HRV power (LF-HRV) was
extracted for the Slow Breathing condition, whereas RMSSD was extracted for the Spontaneous
Breathing condition.
Pulse transit time. Pulse transit time (PTT) refers to the time it takes for blood to travel
between two arterial sites. To measure PTT, the R-wave of the ECG is used as a starting point,
and the upslope of the PPG wave is used as the end point. However, there is a short delay
between the occurrence of the R-wave and the opening of the aortic valve (PEP). Hence, PTT
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measured using the R-wave includes the PEP time interval which may account for 12%–35% of
the PTT measurement (Payne, 2006). PEP is influenced by beta-adrenergic stimulation, which
shortens PEP (although it is also influenced by blood pressure and ventricular stroke volume)
(Smith, et al., 1999). Nevertheless, PTT provides a useful dynamic, beat-to-beat estimate of
cardiovascular processes that has a strong inverse correlation with systolic blood pressure, but a
less reliable association with diastolic or mean arterial pressure (Gao, Olivier, & Mukkamala,
2016; Payne, 2006; Wibmer et al., 2014). For our analyses, the ECG R-wave was used as the
starting point for estimation of PTT. We used the peaks of the first derivative of the zero-phase
shift Butterworth low-pass filtered PPG series to identify the point of arrival of the arterial pulse-
wave, and then computed the time difference between the peak of the differentiated PPG wave
and the corresponding R-wave following for all successive beats. The mean of the PTT series in
milliseconds was used for subsequent statistical analyses.
3.2.6 Statistical analysis
Linear mixed effects regression (LMER) was used. All analyses were carried out using R
Statistical Software v3.3.2 (R Development Core Team, 2016). LMER models were computed
using the package ‘lmerTest’ (Kuznetsova et al., 2017). For random effects, we included
intercepts for each subject, and stimulation condition was entered as the fixed effect. Sham was
set as the reference level for Stimulation (sham, iTBS, cTBS). Thus, cTBS and iTBS should be
interpreted with respect to sham. Response variables were the autonomic responses after
stimulation (LF-HRV during Slow Breathing, RMSSD during Spontaneous Breathing, or PTT
for both conditions). To determine whether anxiety induced by stimulation accounts for the
effects of stimulation on HRV and/or PTT, state anxiety immediately after stimulation
(STAI_TBS) was entered as a fixed effect covariate in a subsequent set of regressions. To assess
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the significance of theta-burst stimulation and STAI_TBS, lmerTest provides p-values for the
fixed effects using the Satterthwaite approximations to degrees of freedom. Confidence intervals
for fixed effects were estimated with bootstrapping using the confint function. The contrasts for
cTBS versus iTBS (i.e. the difference cTBS – iTBS) were obtained through the least squares
means of the fitted models, also computed from the lmerTest library. Lastly, LMER was also
used to assess whether any increase in state anxiety due to stimulation was transient and/or
particular to the type of stimulation. To test this, we modeled the main fixed effect of Stimulation
(cTBS, sham, iTBS) with sham as the reference level and Time (TBS, Post) with TBS as the
reference level (where TBS refers to the period immediately after stimulation, and Post refers to
the period of time after completing the breathing tasks), as well as a fixed-effect interaction
between Stimulation and Time to test whether the change in state anxiety immediately following
stimulation to the end of the breathing tasks was different for iTBS and cTBS relative to sham.
Additional analyses were run to examine Pearson's correlations (2-tailed tests) among
physiological responses and STAI_TBS scores. Slow and Spontaneous Breathing conditions
were modeled separately.
Effect size estimation for mixed effects models. R
2
was computed for each model using
Nakagawa and Schielzeth's (2013) method. The approach yields both the marginal and
conditional effect sizes (that is, for the fixed effects and for the fixed plus random effects,
respectively), and overcomes the problems associated with most definitions of marginal R
2
for
mixed effects models, such as decreasing or negative values. The values were obtained using the
R function r.squared.GLMM from the package MuMln which implements the method.
Missing data. There were three participants for whom two of the three experimental
sessions were available. STAI_TBS was missing three data points. Instances where the PPG
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signal was of poor quality due to technical issues or corrupted with many movement artifacts
were removed since it would not be possible to reliably compute PTT on those segments (n = 2
for Slow Breathing, and n = 1 for Spontaneous Breathing).
3.3 Results
3.3.1 State anxiety
There was a significant main effect of Stimulation on state anxiety. Specifically, both
cTBS and iTBS increased state anxiety relative to sham (cTBS: p = .026; iTBS: p = .003). There
was an additional main effect of Time, in which anxiety at the end of the breathing tasks was
significantly lower than state anxiety immediately after stimulation (p = .003), suggesting that
the anxiety promoting effect of stimulation was transient. As the interaction terms were not
significant, there were no differential changes in anxiety from stimulation to the end of the
breathing task that depended on whether the stimulation was continuous or intermittent
(cTBS*Post: p = .199; iTBS*Post: p = .117). The fixed (marginal) effects of Stimulation, Time,
and their interaction explained 20.6% of the variance in state anxiety, while overall (conditional)
model explained 60.9% of the variance in state anxiety. See Table 3.1 for an overview of the
LMER fixed effects. Average state anxiety immediately after each stimulation condition was
Sham: (M = 31.57; SD = 7.85), cTBS: (M = 35.62; SD = 8.66), iTBS: (M = 36.29; SD = 10.26),
with a range of scores from 20 to 59. Figure 3.3 illustrates the differential levels of state anxiety
after each stimulation condition.

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Table 3.1. LMER fixed effects examining changes in state anxiety from immediately after
Stimulation to completion of the breathing tasks (Post) for each experimental day.
State anxiety FE L-95% CI U-95% CI SE p-Value
Intercept 31.78 28.7 34.8 1.51 <.00001
cTBS 3.53 0.6 6.73 1.56 0.026
iTBS 4.51 1.52 7.9 1.5 0.003
Post −4.56 −7.96 −1.65 1.52 0.003
cTBS ∗ Post −2.79 −7.03 1.29 2.15 0.199
iTBS ∗ Post −3.31 −7.48 0.69 2.09 0.117

Figure 3.3. State anxiety immediately after stimulation for each experimental condition (sham,
continuous theta-burst (cTBS), and intermittent theta-burst (iTBS)). Empty diamonds reflect the
condition mean. Data points are jittered to enhance visualization of individual scores.

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3.3.2 Slow breathing
LF-HRV. For the LMER model including only TBS, there was a significant main effect
of iTBS (p = .03) such that iTBS increased HRV power in the LF range during slow breathing.
However, cTBS did not significantly change LF-HRV (p = .52). STAI_TBS was not significant
(p = .54), nevertheless, adding it as a covariate suppressed the significance of iTBS on LF-HRV
(p = .06). The fixed effect of TBS explained only 2.4% of the variance in LF-HRV (conditional
explained variance was 71.4%). Adding STAI_TBS to the model did not appreciably increase
the explained variance in LF-HRV (marginal: 2.8% and conditional: 71.5%). The least squares
mean differences between cTBS and iTBS were not significant in either model. See Table 3.2 for
LMER estimates.

Table 3.2. LMERs testing fixed effects (FE) of TBS and TBS including state anxiety after
stimulation (STAI_TBS) for the Slow Breathing condition. The rows labeled cTBS – iTBS
represents the least squares means estimate of the difference between these conditions.
Slow Breathing
TBS TBS + STAI_TBS
FE SE L-95 U-95 p-Val FE SE L-95 U-95 p-Val
LF-
HRV
(log)
Intercept 8.43 0.16 8.1 8.7 <.0001 8.22 0.37 7.5 8.9 <.0001
iTBS 0.28 0.126 0.04 0.53 0.03 0.26 0.13 −0.008 0.53 0.06
cTBS 0.09 0.132 −0.16 0.34 0.52 0.07 0.14 −0.20 0.33 0.63
cTBS – iTBS −0.2 0.13 −0.45 0.06 0.12 −0.19 0.13 −0.45 0.07 0.14
STAI_TBS 0.006 0.011 −0.01 0.02 0.54
PTT
Intercept 357.6 7.48 343.6 371.6 <.0001 383.3 17.5 347 416.1 <.0001
iTBS 0.56 6.4 −10.4 13 0.93 4.1 6.9 −8.8 17.9 0.56
cTBS 13.7 6.8 0.98 26.4 0.052 16.5 7.3 0.7 33 0.029
cTBS − iTBS 13.2 6.4 0.19 26.1 0.046 12.4 6.7 −1.1 26 0.07
STAI_TBS −0.81 0.5 −1.78 0.19 0.11
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PTT. For the model including only TBS, cTBS was a significant predictor of PTT
(p = .05) indicating an average increase in PTT latency by 13.7 milliseconds. iTBS did not exert
a significant effect on PTT (p = .93). With STAI_TBS added, cTBS remained significant and the
effect was enhanced (p = .029), with the average latency increased to 16.5 ms, even though
STAI_TBS was not itself significant. The least squares mean difference between cTBS and iTBS
was significant in the model without STAI_TBS. For the model including state anxiety, that
difference was marginalized. TBS alone explained 3.1% of the variance in PTT (conditional
variance: 65.8%) whereas the addition of STAI increased the explained variance to 7.7%
(conditional variance: 60.18). See Table 3.2 for detailed LMER results.
3.3.3 Spontaneous breathing
RMSSD. The effect of iTBS on RMSSD during spontaneous breathing was significant
(p = .009), however the effect of cTBS was not (p = .11). TBS alone explained 4.2% of the
variance in RMSSD (conditional explained variance: 64.5%). Once included as a covariate,
STAI_TBS predicted RMSSD (p = .02) and suppressed the significance of iTBS (p = .056). The
suppression of the effect of iTBS appears to be meaningful, as adding STAI increased the
marginal explained variance in RMSSD to 10.9% (conditional explained variance 74%). The
difference between cTBS and iTBS was not reliable in either model. See Table 3.3 for LMER
results.
PTTm. There was a significant effect of cTBS on mean pulse transit time (p = .01) and no
significant effect of iTBS (p = .79), with a marginal effect size of 5.0% and conditional effect
size of 67.5%. With the inclusion of STAI_TBS, the effect of cTBS increased (p = .007),
whereas iTBS remained non-significant, as did STAI_TBS, although by including STAI_TBS
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the explained marginal variance increased to 7.95% (conditional explained variance: 63%). The
difference between cTBS and iTBS was significant in both models, with higher PTT values for
cTBS. In the model with STAI as a covariate, with the average PTT latency in response to cTBS
increased from 16.7 ms to 18.7 ms. See Table 3.3 for detailed LMER results.

Table 3.3. LMERs testing the fixed effects (FE) of TBS and TBS plus the covariate STAI_TBS
for the Spontaneous Breathing condition. The rows cTBS − iTBS represents the least squares
means estimate of the difference between cTBS and iTBS.
Spontaneous
Breathing
TBS TBS + STAI_TBS
FE SE L-95 U-95 p-Val FE SE L-95 U-95 p-Val
RMSSD
Intercept 38.7 4.7 29.2 48.2 <.0001 15.5 11.2 −6.4 40.5 0.17
iTBS 11.2 4.1 3.6 19.1 0.009 7.9 4 −0.02 16.3 0.056
cTBS 7.1 4.3 −1.7 16.1 0.11 4.6 4.1 −3.98 13.5 0.27
cTBS − iTBS −4.1 4.1 −12.4 4.2 0.32 −3.3 3.8 −10.9 4.4 0.39
STAI_TBS 0.73 0.31 0.08 1.36 0.023
PTT
Intercept 357.6 7 343.9 370.3 <.0001 377.2 16.3 342.6 406.8 <.0001
iTBS 1.6 5.9 −13.0 12.6 0.79 4.2 6.4 −8.5 16.4 0.51
cTBS 16.7 6.2 0.21 27.9 0.01 18.7 6.5 5.85 32.9 0.007
cTBS − iTBS 15.1 5.9 3.4 26.8 0.013 14.5 6 2.3 26.7 0.02
STAI_TBS −0.61 0.46 −1.43 0.24 0.19
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3.3.4 Correlational analyses
State Anxiety and PTT. Follow-up Pearson's correlations (two-tailed) assessed the
relationship between PTT and state anxiety in the period immediately after stimulation
(STAI_TBS) during Slow and Spontaneous Breathing for each stimulation condition. For
Spontaneous Breathing. No significant association between PTT and STAI_TBS emerged in
response to sham (r = −0.26 df = 18, p = .27). However, there was a significant inverse
association between these variables for both cTBS (r = −0.49 df = 19, p = .026) and iTBS
(r = −0.41 df = 22, p = .046). For Slow Breathing, PTT and STAI_TBS were not significantly
correlated for sham (r = −0.2, df = 18, p = .39), whereas there were significant inverse
associations between PTT and STAI for cTBS (r = −0.52, df = 18, p = .02) and iTBS
(r = −0.494, df = 22, p = .015).
State Anxiety and HRV. There were no significant simple correlations between state
anxiety scores and RMSSD and LF-HRV.
3.4 Discussion
This randomized, sham-controlled repeated-measures study compared the effects of a
single application of 600 pulses of intermittent, continuous, and sham theta-burst stimulation
over the right frontotemporal cortex on resting state cardiovascular responses in healthy adults.
We examined these effects under conditions of Spontaneous and Slow (0.1 Hz) Breathing.
Furthermore, due to the potential for brain stimulation procedures to transiently increase anxiety
in participants, resulting in an altered cardiovascular state, we determined whether stimulation-
induced anxiety confounds cardiovascular responses to non-invasive brain stimulation.
Consistent with our expectations that verum stimulation increases anxiety in participants, we
found that verum iTBS and cTBS significantly increased state anxiety relative to sham TBS. The
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importance of controlling for stimulation-induced anxiety in studies of neurocardiac interactions
was supported, and the influence of state anxiety as a covariate is discussed below in the context
of each model.
For the stimulation effects, we observed significantly increased RMSSD after iTBS
relative to sham during Spontaneous Breathing. Additionally, we observed significantly
increased LF-HRV power during Slow Breathing after iTBS relative to sham. State anxiety was
subsequently added as a covariate to determine whether the increase in anxiety during verum
stimulation accounts for HRV responses to iTBS. Once added as a covariate, state anxiety
attenuated the significance of iTBS on both RMSSD during Spontaneous Breathing and LF-HRV
during Slow Breathing. For LF-HRV, the degree of attenuation was not meaningful, as state
anxiety only explained an additional half percent of variance in LF-HRV during Slow Breathing
exercise. Yet for RMSSD, the inclusion of state anxiety was significant, positive in sign
(indicating higher RMSSD with greater anxiety) and resulted in iTBS becoming a non-
significant predictor of RMSSD. The change appears to be meaningful, since state anxiety
increased the marginal explained variance in RMSSD from 4.2% to 10.9%. The differential
relevance of state anxiety for LF-HRV versus RMSSD is likely because RMSSD is considered a
vagally-mediated measure of HRV, whereas LF-HRV during Slow Breathing largely reflects
resonance processes between heart rate and blood pressure.
In contrast, cTBS resulted in a distinct pattern of effects on cardiovascular responses.
Specifically, it elicited increased PTT latency relative to both iTBS and sham. When state
anxiety was included as a covariate, it enhanced the strength of the effect of cTBS on PTT even
though it was not statistically significant in either model (which may point to an issue of low
power). Despite its non-significant p-value, including state anxiety as a covariate explained
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approximately twice or more variance in PTT than the models without across both breathing
conditions. In the follow-up analysis of the correlation between PTT and state anxiety, we found
that verum stimulation drove strong and significant inverse associations between PTT and state
anxiety, whereas the association between PTT and anxiety was not significant after sham
stimulation. These patterns of association suggest that PTT is sensitive to acute inductions of
psychological stress and anxiety, given that verum stimulation was significantly more anxiety-
provoking compared to sham. In other words, greater anxiety decreases PTT latency, whereas
cTBS increases PTT latency. Adding state anxiety appears to have a potential to “unmask” the
effect of cTBS on PTT.
These results contribute to evidence that TMS is effective at altering cardiovascular
autonomic outflows in a ‘top-down’ manner. However, estimates of these effects are impacted
by stimulation-induced state anxiety. The potential influence of such confounds was raised in the
meta-analyses by Makovac et al. (2017) and Schestatsky et al. (2013), and here we provide novel
evidence supporting the relevance of this issue. For example, Makovac and colleagues reported
only a small effect size for blood pressure reductions. In light of the present evidence that cTBS
increases PTT latency, it is reasonable to speculate that controlling for confounds inversely
associated with cardiovascular responses could increase observed effects. On the other hand, the
reported effect sizes for vagally-mediated HRV may be lower if stimulation induced anxiety was
accounted for. Schestatsky and colleagues' conclusions may have also been constrained by their
inability to assess the influence of important covariates. Measuring stimulation-induced state
anxiety or similar covariates during experiments would increase the precision of these estimates.
It must be mentioned that these concerns are specific to experimental situations in which
cardiovascular responses are measured concurrent or proximal to the stimulation. If studies were
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carried out such that cardiovascular changes were measured at a different time compared to
stimulation, then the confound created by stimulation procedures should not be a concern.
However, this approach is may be more easily applied in studies of clinical populations who are
undergoing repeated brain stimulation treatments with an aim of reducing symptoms.
We obtained a positively signed effect of state anxiety on RMSSD. The positive sign is
notable since an inverse relationship between vagally-mediated HRV and negative affective
states may be expected (Sloan et al., 2017). However, in healthy individuals, cardiac reactivity
and subsequent cardiac vagal recovery are processes that may reflect adaptive responses to acute
stressors (Balzarotti, et al., 2017). Such a dynamic could produce positive associations between
acute stress induction and HRV in experimental settings. The relationship between acute stress
and cardiac vagal response is likely to have significant heterogeneity across individuals or
populations, however. For instance, individuals with major depression show reduced cardiac
reactivity and recovery in response to physiological and psychological stressors (Salomon, et al.,
2013).
It is also notable that iTBS and cTBS did not induce opposing effects on cardiovascular
responses given that these protocols are expected to have excitatory and inhibitory effects on
cortical excitability, respectively. However, single session iTBS or cTBS may not exert opposing
effects on prefrontal systems. Transcranial magnetic stimulation protocols (including i- and
cTBS) facilitate GABAergic (ϒ-aminobutyric acid) and glutamatergic transmission, with
complex effects on intra-cortical and cortico-limbic interactions (Baeken, Lefaucheur, &
Schuerbeek, 2017). This is a complexity that is reflected in functional connectivity studies of
cTBS and iTBS on prefrontal targets (Gratton et al., 2013; Iwabuchi et al., 2017).
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With regards to possible neurofunctional pathways that may have mediated the observed
effects of iTBS and cTBS on HRV and PTT, Pollatos et al. (2016) describe their stimulation
heuristic as targeting the anterior insula. However, it is doubtful that the figure-of-eight coils can
directly stimulate this region (Coll, et al., 2017). Yet, the anterior insula may be stimulated
transynaptically via the frontal operculum, which is plausible based on simulation studies of their
heuristic (Coll et al., 2017; Pollatos & Kammer, 2017). Direct electrical stimulation of the insula
and operculum in humans also reveals strong reciprocal connectivity between these regions
(Almashaikhi et al., 2014; Dionisio et al., 2019), an anatomical feature that is echoed in
functional neuroimaging studies (Gratton et al., 2013). Additionally, cTBS of a left frontal
operculum target caused the dorsolateral prefrontal cortex to become more tightly coupled with
nodes in the default mode network, including the anterior cingulate (Gratton et al., 2013), which
is a region that participates in visceromotor control through direct and indirect connections with
the amygdala, hypothalamus, PAG, NST, and medullary autonomic nuclei (Silvani et al., 2016).
Consequently, it may be sufficient to access visceromotor networks by stimulating
frontotemporal targets, although identifying the cortical and subcortical changes that may
mediate neurocardiac effects of TMS will require functional neuroimaging studies with
concurrent cardiovascular measurement. It is also worth considering an alternative, but not
necessarily mutually exclusive mechanism through which TMS could exert autonomic influences
is through the cranial nerves which are stimulated during the delivery of magnetic pulses. In this
case, afferent projections of the trigeminal nerves to the brainstem could also indirectly engage
autonomic pathways (Colzato & Vonck, 2017; De Cicco et al., 2018).
If TMS can indirectly benefit cardiovascular function through brain plasticity, then there
are direct clinical implications. Major depression, anxiety disorders, and chronic stress are
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independent risk factors for the development of cardiovascular disease (Song et al., 2019;
Steptoe & Kivimäki, 2013). The prefrontal cortex and insula are essential to the regulation of the
stress response and are implicated in depression and anxiety disorders (Baeken, Duprat, Wu,
Raedt, & Heeringen, 2017). Consequently, dysregulation of prefrontal and insula systems may -
in the long-term - result in autonomic and HPA-axis dysfunction that contributes to the
development of cardiovascular disease (Cogiamanian et al., 2010). However, TMS may be
relevant to more than just psychiatric conditions if the aim of treatment is improved
physiological stress regulation. For instance, multivariate cluster analysis of cardiovascular
reactivity patterns to laboratory psychological stressors identifies older individuals at risk for the
development of hypertension at a 5-year follow-up (Brindle et al., 2016). In such a context, TMS
or TBS could be used as a repeated intervention with the aim of reducing maladaptive
cardiovascular reactivity patterns by inducing plasticity in cortical circuits involved in stress
regulation.
3.4.1 Limitations
Although we employed a previously published frontotemporal heuristic which is
described as targeting the anterior insula that has been shown to modulate neural cardiovascular
processing, we did not use structural MRI guided neuro-navigation, which would have helped to
more precisely define the target of stimulation. Additionally, since the sample size was modest,
non-significant findings may have been a consequence of Type-II error, and the potential for
Type-I errors are also enhanced when samples are small. However, it should be considered that
our sample size was over one-third larger than the average sample size in the 18 TMS studies
included in the Makovac et al. meta-analysis. Only three of these 18 studies reported a greater
sample size than that of the present study. Clearly, this area of research is hampered by the small
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sample sizes that currently characterize the literature. The field would greatly benefit from larger
studies that can yield more robust estimate of TMS effects on cardiovascular function. Towards
this end, an advantage of the present study is that we provide a basis for estimating power in
future studies that employ mixed-effects models. Estimating power for mixed effects regression
can be challenging as it requires the specification of multiple parameter values that can be
difficult to determine without pilot data. Future studies with larger sample sizes will permit an
assessment of the heterogeneity of responses to TBS (which may be best modeled using mixed-
effects methods) since individuals may have a large degree of response variability to
neurostimulation with multifactorial determinants (Ridding & Ziemann, 2010), including
baseline cortical excitability or metabolism within a region or network (Salomons et al., 2014).
Other limitations include the site and laterality of stimulation: since we only stimulated a single
area on the right hemisphere, we cannot evaluate the effects of left-sided stimulation, or whether
the medial prefrontal cortex, another area implicated in cardiovascular control, could produce
similar outcomes to those observed here. As there may be several prefrontal targets that could
exert top-down effects on the relevant subcortical networks, future studies could be optimized by
incorporating information from combined neurostimulation-fMRI studies. Another limitation is
that our analyses used a specific measure of psychological anxiety, although other measures of
stimulation-induced arousal, anxiety or fear should also be investigated in covariate analyses to
improve estimates of cardiovascular responses to TMS. Lastly, although we used proxy measures
for baroreceptor sensitivity (LF-HRV during Slow Breathing) and beat-to-beat systolic blood
pressure (PTT), estimation of cardiovascular variables and their dynamics would be improved by
gold-standard non-invasive measurement, such as with continuous blood pressure monitors, and
impedance cardiography.
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3.4.2 Conclusion
This study provides preliminary evidence that cTBS to the right frontotemporal cortex
increases pulse transit time latency, which may suggest a reduction of systolic blood pressure or
cardiac pre-ejection period via inhibition of beta-adrenergic outputs. We also provide evidence
that iTBS to the same region enhances HRV (both RMSSD during spontaneous breathing and
LF-HRV during slow breathing). However, controlling for anxiety induced by stimulation
attenuates the effect of iTBS on vagally mediated HRV (RMSSD). These findings emphasize
that stress or arousal in response to the sensory components of the stimulation (e.g. noise,
peripheral nerve stimulation, etc.) influence cardiovascular responses to TBS, but that the
direction of these effects may depend on stimulation parameters, stimulation site, participant
characteristics, and the physiological response system measured. TMS remains a promising
approach for the study of cortical regulation of cardiovascular autonomic function. We discuss
ideas for optimizing studies with the aim of characterizing TMS effects on cardiovascular
function with greater precision. Such knowledge may contribute to the development of non-
invasive brain stimulation protocols for the treatment of stress-linked maladaptive cardiovascular
function.

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4. Sexual trauma history is associated with reduced interoception-linked orbitofrontal
network integration in women with substance use disorder
2
4.1 Introduction
4.1.1 Interoceptive awareness is compromised in SUD, and is a functional resource for
recovery
Interoception describes the process by which the nervous system transduces, integrates, and
interprets visceral and somatic sensory signals (Khalsa et al., 2018). These signals provide
temporally dynamic maps of the body’s homeostatic and physiological milieu at both conscious
and unconscious levels of awareness (Hassanpour et al., 2016) which convey information critical
for adaptive behaviors (Poppa & Bechara, 2018). The various sources of interoceptive
information (e.g., cardiovascular, respiratory, gastrointestinal, immunological, etc.) are conveyed
to the Central Nervous System (CNS), where they become integrated in somatosensory and
viscerosensory representation. The primary neurofunctional pathways associated with
interoception include the spinothalamic lamina I afferents, vagal afferent fibers, and specific
brainstem and thalamic nuclei, which project to the insula (Craig, 2002). The influence of ‘top-
down’ control in interoception is evident from the up-regulation of activity linked to
interoceptive signal processing in CNS when attention is explicitly directed at bodily sensations

2
A version of this manuscript was published: Poppa, T., Droutman, V., Amaro, H., Black, D.,
Arnaudova, I., Monterosso, J. (2019). Sexual trauma history is associated with reduced
orbitofrontal network strength in substance-dependent women. NeuroImage: Clinical, 24,
101973. DOI: 10.1016/j.nicl.2019.101973

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(Avery et al., 2014; Farb et al., 2013a; Schulz, 2016) and down-regulation when attention is
directed away from bodily sensations (Brooks, et al., 2002).
There is presently growing interest in the role of interoception in psychopathology (for a
summary see the Biological Psychiatry consensus statement from the 2016 Interoception Summit
[Khalsa, et al. 2018]). Interoception may be an important factor in substance use disorder (SUD),
and in recovery from addiction (Verdejo-Garcia, Clark, & Dunn, 2012). For instance,
psychoactive drugs stimulate bodily sensations (e.g., via autonomic system stimulation) and
these sensations may become part of the feelings sought by frequent users. Similarly, withdrawal
is marked by aversive interoceptive signals (e.g., aching and nausea) which can drive motivation
to use. Interoceptive signals contribute to drug craving (Avery et al., 2016) and to mood states
(Harrison et al., 2009, 2015) which can also be triggers for drug use (Cheetham, Allen, Yücel, &
Lubman, 2010; Shiffman, 2005). A chronic use history with a particular drug may alter the
response to interoceptive signals, particularly when those signals are directly linked to the
rewarding drug (Naqvi, Gaznick, Tranel, & Bechara, 2014; Wang et al., 2013). In addition,
altered interoceptive processing could diminish the capacity for insight regarding problem drug
use (Goldstein et al., 2009). Interestingly, alexithymia, the inability to recognize one’s own
emotional states, is associated with interoceptive deficits (Brewer, et al., 2016; Hogeveen, et al.,
2016) and is frequently comorbid with SUD (Dorard et al., 2008). Alexithymia also predicts
poorer long-term outcomes in treatment for SUD (Loas, et al., 1997), suggesting the possibility
that the interoceptive signals informing emotional awareness might be important resources for
addiction recovery. Indeed several models of addiction treatment, including Cognitive
Behavioral Therapy (CBT) and mindfulness-based interventions (Amaro, et al., 2014) explicitly
work with clients to help them better recognize their own bodily sensations. For example, in
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CBT, clients are taught to work to monitor their body for sensations of craving and to, “pay
attention to all the somatic and affective signals and try to put them into words. What is the
feeling like? Where is it?” (Carroll, 1998, pg. 51).
4.1.2 Atypical interoception is a feature of post-traumatic stress disorder
If interoception is an important factor in SUD and recovery from SUD, then it is important to
consider the implication for the significant subset of SUD individuals with co-morbid Post-
Traumatic Stress Disorder (PTSD). Bodily attention appears to be relevant to the pathology of
trauma-linked disorders, which includes intrusive memories and cognitions that may involve, or
be triggered by interoceptive sensations and bodily awareness (Borgmann, et al., 2014; Jung &
Steil, 2013; Price, 2007; Smith-Marek, et al, 2018). In line with these behavioral observations,
PTSD and sexual trauma have previously been associated with altered brain metabolism in
regions associated with interoceptive and somatosensory processing. Specifically, women with
PTSD due to intimate partner violence have blunted subjective pain and pain-linked anterior
insula responses that are inversely correlated with avoidant symptoms (Strigo et al., 2010), and
(primarily) female patients with dissociative PTSD show reduced functional connectivity of
vestibular brainstem nuclei with a parieto-posterior insula network and the dorsolateral prefrontal
cortex (Harricharan, et al., 2017).
4.1.3 Trauma and substance abuse comorbidity
Exposure to traumatic events are as high as 93% in some SUD samples (Reynolds,
Hinchliffe, Asamoah, & Kouimtsidis, 2011). Among substance dependent groups, the lifetime
prevalence of PTSD has been estimated to be between 26% and 52%, with current prevalence
between 15% and 42% (Vujanovic, et al., 2016). Women with SUDs experience markedly higher
rates of traumatic stress and victimization compared to men with SUDs, particularly for traumas
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relating to childhood and adulthood sexual and physical abuse (Daigre et al., 2015; Fernandez-
Montalvo, et al., 2015; Schafer, et al., 2014). Women with SUDs may also experience higher
rates of current and lifetime PTSD compared to men with SUDs (Hyman et al., 2007; Reynolds
et al., 2011). PTSD symptoms exacerbate (Hien et al., 2009; Ouimette, et al., 2007) and are
exacerbated by the presence of an SUD (Jacobsen et al., 2001). Individuals with co-occurring
SUD and PTSD may face worse clinical outcomes, including greater psychosocial problems,
need for services, more severe substance use, and higher rates of relapse than for individuals
with SUD-only (Ouimette, et al., 1998; Rosen et al., 2002).
Despite the established clinical relationship, trauma comorbidity in SUD is understudied
in the neuroimaging literature. The few existing studies (notably: Regier et al. [2016] and
Gawrysiak et al. [2017]) highlight heightened limbic drug-cue reactivity and greater amygdala-
striatal resting-state functional connectivity, respectively, in cocaine-dependent men with
histories of trauma. These findings suggest that there may be distinctive correlates of traumatic
stress and brain function among individuals with SUD. However, neither study included
participants with PTSD diagnoses, a clinically relevant distinction, since exposure to trauma-
categoric events in absence of post-traumatic stress symptoms may not have the same effect on
brain function, nor the course and outcome of a SUD. It remains the case that PTSD has, to our
knowledge, not yet been evaluated in a neuroimaging study of a substance-dependent population.
Given that women with SUDs experience markedly higher rates of trauma and PTSD relative to
men with SUDs, especially due to interpersonal and domestic violence, there is a clear need to
investigate neurobiological correlates in trauma-exposed, substance-dependent women with-and-
without current PTSD diagnoses, as it may reveal functional differences with clinical
implications.
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The primary goal of the present report is to characterize deficits in interoceptive processing
associated with PTSD comorbidity in a sample of women with SUD. To do this, we capitalize on
the robust ‘attentional spotlight’ effect, by which brain network activity supporting interoception
is enhanced when attention is explicitly directed at bodily sensations (Brefczynski & Deyoe,
1999; Johansen-Berg et al., 2000). This allows us to evaluate whether comorbid PTSD is
associated with anomalous BOLD response during an interoceptive challenge (attention to bodily
sensations of breathing) and to characterize the nature of any observed anomaly at the level of
brain functional networks. We study this issue in low socioeconomic-status women who recently
completed detoxification and enrolled in a residential treatment program for SUDs. fMRI data
were acquired during the baseline period (pre-randomization) from a subset of patients
participating in a clinical trial of Moment-by-Moment in Women’s Recovery (MMWR).
MMWR is a trauma-informed, mindfulness-based adjunct intervention for low-income,
ethnically and racially diverse women in residential treatment for SUDs (Amaro & Black, 2017).
If interoception is indeed a critical factor in SUD and SUD treatment response, and if
interoception is compromised among those with comorbid PTSD, then characterizing that
compromise is an important step towards developing tailored treatment approaches for this
subgroup.
We took a data-driven analytic approach, utilizing group independent components
analysis and dual regression to identify task-modulated intrinsic functional networks that may
distinguish female SUD patients with and without PTSD co-morbidity. We hypothesized that
PTSD comorbidity will be associated with reduced integration of the insula within networks
associated with interoceptive attention. Additionally, post-hoc correlational analyses of
independent component networks that demonstrate significant spatial differences between the
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two groups were carried out to assess their association with lifetime exposure to sexual trauma
across all participants.
4.2 Methods
4.2.1 Participants
The full sample of participants were 48 ethnically-diverse, socio-economically
disadvantaged female patients initiating women-only residential treatment for polysubstance use
disorders (primarily moderate-to-severe methamphetamine and/or cocaine use disorders).
Inclusion criteria for the study were as follows: female, between 18-50 years old, diagnosed with
SUD, fluent in English, right-handed, and a current patient in the residential treatment program
partnered with the parent study. Exclusion criteria included contraindications for fMRI: currently
or possibly pregnant, using medical devices (cardiac pacemaker, implanted cardiac defibrillator,
etc.), metal fragments including shrapnel or other nonremovable metal devices including dental
braces or retainers, intrauterine device, history of head trauma resulting in loss of consciousness
for more than 5 minutes, documented or subjectively reported claustrophobia, hair extensions or
a wig connected by wire, permanent eyeliner, and BMI greater than 36. Additionally,
participants were excluded from the parent study if they had an untreated severe chronic mental
health condition or untreated psychotic disorder based on clinical intake DSM-IV-TR or DSM-V
or diagnostic assessment, or reported suicidality during the prior 30 days based on clinical intake
assessment.
Psychiatric diagnoses were based on DSM-IV-TR or DSM-V which were conducted by
staff of the residential treatment program (the residential treatment program transitioned from the
DSM-IV-TR to the DSM-V during the course of the clinical trial). Diagnoses were carried out by
treatment center staff and confirmed by consensus meeting with the lead psychiatrist. Although
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comorbid psychiatric diagnoses were not exclusionary for the parent study, for the present
analysis we excluded individuals with the following diagnoses: schizophrenia, anxiety disorder
other than PTSD, no history of stimulant use (the majority of patients had primarily diagnoses of
stimulant use disorders or had polysubstance use histories that included stimulants). Four
participants were omitted based on these criteria. A large proportion of participants for whom
information was available were taking prescribed psychoactive medications (39.5%) and/or had
mood disorders (23.3%), therefore we did not exclude participants on the basis of medication use
or mood-disorder status. One participant was removed from the study for the presence of non-
removable dental work that the participant did not report during screening which caused signal
dropout. The 43 remaining participants were included in the study, 14 of whom had received a
PTSD diagnosis. Each of the 43 participants contributed two runs of fMRI data, except for four
subjects (two from the PTSD, two from the noPTSD group) who contributed one run due to
excessive motion (> 3mm). See Table 4.1 for demographics and clinical characteristics. All study
procedures were approved by the University of Southern California Institutional Review Board.
Participants provided written informed consent and were compensated for their time.

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Table 4.1. Demographic and Clinical Characteristics of the Study Sample
All Participants SUD SUD+PTSD Test Statistic p-value
N 43 29 14

Age 30.37 (7.7) 29.2 (8.4) 32.9 (5.6) t(41) = 1.5 0.14
Ethnicity c
2
(2) = .14 0.93
Hispanic/Latina 65.1% 65.5%

64.3%

Non-Hispanic Black 16.3% 17.24% 14.3%

Non-Hispanic White 18.6% 17.24% 21.4%

Other

0%

Education c
2
(2) = 3.6 0.16
Less than HS Degree 53.5% 51.7% 57.1%

HS Degree 32.6% 27.6% 42.9%

Some College 13.9% 20.7% 0%

ASI Drug Use 0.18 (0.15) 0.17 (0.14) 0.21 (0.18) t(41) = .74 0.47
ASI Alcohol Use 0.11 (0.17) 0.095 (0.15) 0.13 (0.2) t(41) = .62 0.54
Borderline Diagnosis 16.3% 17.2% 14.3% c
2
(1) = 0.06 0.81
Mood Disorder
Diagnosis
23.3% 24.1% 21.4% c
2
(1) = 0.04 0.84
Psychoactive
Medication

39.5% 41.4% 35.7% c
2
(1) = 0.13 0.72

ASI, Addiction Severity Index Drug and Alcohol use; HS, High School; values in first three
columns refer to means with standard deviations in parentheses, otherwise percentages.
4.2.2 Measures
4.2.2.1 Life stressors checklist – revised (LSR-R)
The Life Stressor Checklist-Revised (LSC-R; Wolfe & Kimerling, 1997) is a measure of
traumatic events and stressors that are particularly relevant to women’s life experiences. Its use
has been validated in women with co-morbid substance abuse and mental disorders, with
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histories of interpersonal violence victimization (McHugo et al., 2005). LSC-R follows a yes/no
response format with follow-up questions that characterize the life stage(s) at which events
occurred and the degree to which the respondent is currently affected by the experience. Events
can be brief, single incidents or repeated traumas that may have occurred at any point in the
lifespan (prior to age eighteen, adulthood, and within the last 8 months of the interview). For the
present study, we use dichotomously scored responses (range: 0 – 30) reflecting events at any
point during the lifespan.
4.2.2.2 Addiction severity index (ASI)
Alcohol and substance use severity for the 30 days prior to treatment entry was assessed
using the Addiction Severity Index (ASI: McLellan et al., 1992) and the Timeline Followback
Interview (TLFB; Robinson, Sobell, Sobell, & Leo, 2012).
4.2.2.3 PTSD symptom scale (PSS-I)
The PTSD Symptom Scale (PSS-I; Foa et al., 2005; Foa, Riggs, Dancu, & Rothbaum,
2018) is a semi-structured interview, which was used to obtain an overall severity score of PTSD
symptoms according to DSM-IV, as well as separate severity scores for symptom subdomains:
re-experiencing, avoidance, arousal. Scores range from 0 to 34, with higher scores reflecting
more severe symptomatology.
4.2.2.4 The interoceptive-exteroceptive attention task
The Interoceptive-Exteroceptive Attention task (The IN-OUT task) was adapted from (N.
A. S. Farb et al., 2013a), which investigated BOLD changes in interoceptive attention in
response to Mindfulness-Based Stress Reduction (MBSR) training. The approach contrasts brain
activity during attention to the sensations of breathing versus attention to an external target. By
contrasting internally and externally directed focus, this approach capitalizes on the “attentional
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spotlight” effect, whereby focus on a sensory quality amplifies the signal within brain regions
associated with processing that sensory modality (Brefczynski & Deyoe, 1999; Johansen-Berg,
et al., 2000). Variations on this task have previously been used to isolate BOLD responses in
regions associated with interoception in expert meditators (Hölzel et al., 2007), healthy
individuals (Kuehn et al., 2016), as well as in clinical populations with depression (Avery et al.,
2014) and anorexia nervosa (Kerr et al., 2015; Kerr, et al., 2017). The magnitude of insula
response during focused breathing tasks appears to track the quality of internal focus, which
increases as a function of hours practiced in novices who participated in a MBSR program (Farb
et al., 2013b). During the IN-OUT task participants performed two experimental conditions
involving sustained attentional targets: the interoceptive (IN) condition and the exteroceptive
(OUT) condition. Participants completed two runs, each containing nine blocks. The blocks were
presented in pseudorandomized order- half of the runs contained five blocks of IN, and four
blocks of OUT, while other blocks contained four blocks of IN and five blocks of OUT. For the
IN condition, subjects were instructed to attend to bodily sensations associated with their
breathing cycle with the following instructions: ‘Please pay attention to the physical sensation of
the breath wherever you feel it most strongly in the body. Follow the natural and spontaneous
movement of the breath, not trying to change it in any way. Just pay attention to it. If you find
that your attention has wandered to something else, gently but firmly bring it back to the
physical sensations of the breath in the body’. During the IN condition, an ‘O’ appeared on the
center of the screen for 36 seconds, on which subjects were instructed to fix their gaze while
simultaneously attending to the sensations of their breathing cycle. The OUT condition consisted
of a ‘1-back’ task, which we considered to be an attention control condition. During the OUT
blocks, a letter from the set (A, B, C, D) was presented for 500 ms in a pseudorandom sequence.
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A fixation cross was presented in between each letter for 900 ms. When a letter repeated, the
participant was instructed to press a key on the button box. The OUT blocks lasted 38.7 seconds.
The sequence of letters presented, key presses, and response times were recorded for each OUT
block. Prior to each block, the subject was presented with an instruction screen for 10 seconds
that cued them to the upcoming block.
4.2.3 fMRI acquisition and analysis
4.2.3.1 Imaging set-up
Images were acquired with a 3T Siemens MAGNETON Prisma System, with a 20-
channel head coil. Functional images were obtained using a gradient echo, echo-planar, T2*-
weighted pulse sequence (TR = 2000 ms, one shot per repetition, TE = 25 ms, flip angle = 90°,
64 x 64 in-plane resolution). Forty-one slices covering the entire brain were acquired with a
voxel resolution of 3 cubic mm. Structural T1-weighted magnetization-prepared rapid gradient
echo (MPRAGE) images were acquired with the following parameters: TR = 1950 ms, TE =
2.26 ms, TI = 900 ms, Flip Angle = 7°, matrix= 256 x 224, 1 mm isotropic resolution, 176
sagittal slices, acquisition time = 241 s.
Respiration and pulse oximetry were measured during scanning using Biopac MP150
hardware and MR-compatible respiratory stretch transducer and pulse oximeter (Nonin Medical,
8600FO). Physiological data were sampled with a 1000 Hz sampling rate. The acquisition was
synchronized to the scanner via a TTL pulse, and recorded in Biopac Acqknowledge software.
The IN-OUT Attention task was scripted using MATLAB and Psychtoolbox 3.
4.2.3.2 fMRI Preprocessing
RETROICOR (Glover, Li, & Ress, 2000) and respiratory and cardiac response functions
(RVHRCOR; Birn, et al., 2008; Chang, Cunningham, & Glover, 2009) were applied to the
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functional volumes to reduce non-neuronal contributions of physiological noise. RETROICOR
models periodic pulsatile noise in the BOLD time series associated with respiratory and cardiac
cycles as a low-order Fourier phase expansion. RVHRCOR removes low-frequency cardiac and
respiratory effects by convolving the respiratory and cardiac data with their respective response
functions. The convolved cardiac and respiratory waveforms are then used as regressors for each
voxel’s time series using least squares. RETROICOR (order = 4) and RVHRCOR were
performed in MATLAB using code obtained from C. Chang. Subsequent pre-processing steps
were carried out in FSL (FMRIB’s Software Library; http://fsl.fmrib.ox.ac.uk/fsl/fslwiki/) and
included skull stripping with BET (Smith, 2002), motion correction (MCFLIRT; Jenkinson,
Bannister, Brady, & Smith, 2002), slice-timing correction, high-pass temporal filtering (90
seconds), and spatial smoothing using a 5mm Gaussian FWHM filter. Additional sources of
movement and scanner noise were removed using FSL’s MELODIC ICA for each individual
run. Subject-level ICA-based denoising substantially improves the reproducibility of group-ICA
decompositions relative to both motion scrubbing and nuisance regression (Pruim, Mennes,
Buitelaar, & Beckmann, 2015). Each component for each subject and run was manually
inspected and labeled by an experimenter blind to participant diagnoses. Components flagged as
artifact were regressed from the functional volume. The functional volumes were realigned to
each participant’s respective T1-weighted anatomical image, then normalized into standard space
using 12 degrees-of-freedom affine transformation and 2mm resolution.
4.2.3.3 Group independent components analysis
To identify group-level intrinsic functional networks, we utilized the Multivariate
Exploratory Linear Optimized Decomposition into Independent Components (MELODIC;
Beckmann & Smith, 2004) algorithm in FSL. Independent components analysis separates
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underlying sources from a linearly mixed multivariate signal. Preprocessed fMRI data from each
subject and run were temporally concatenated into a single 4D file (each subject provided two
runs of data, less the four subjects who provided only one run, for a total of 82 runs) and
submitted to MELODIC analysis, with variance normalization. Thus, the group ICA solution
reflects the combined contributions of both the PTSD and noPTSD groups. The dimensionality
of the solution was constrained to 20 networks, a level of granularity commonly used to identify
large-scale networks (Smith et al., 2009). Thereafter, the 20 group-level normalized networks
were spatially cross-correlated against Smith et al.’s (2009) 20 network solution to determine
their correspondence to a canonical set of intrinsic networks and artifacts.
4.2.3.4 Dual regression to obtain subject-level networks
In the first stage of the dual regression (Beckmann, et al., 2009; Nickerson, Smith, Öngür,
& Beckmann, 2017), the unthresholded group-level networks are first regressed onto each
participant’s fMRI series to extract beta-weights that form a subject-specific time series for each
component and functional run (the spatial regression). Next, the variance-normalized time series
for each component and run obtained from the first stage are used as predictor vectors for each
participant’s fMRI series to obtain a subject-specific component map (the temporal regression).
These whole-brain spatial maps reflect each voxel’s correlation with the group-level network;
consequently, functional connectivity of a given network can manifest in any region of the brain,
regardless of whether that region is typically associated with the canonical network. This
approach allows identification of group differences in spatial connectivity of functional
networks. We carried out dual regression on each group-level network individually, excluding:
1) the six networks deemed artifactual based on visual inspection and comparison with canonical
networks and 2) the six networks that were not found to be significantly task-related (see section
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3.6 below). Thus, we carried out eight separate dual regressions on the networks identified as
significantly associated with IN and OUT conditions of the task. Each dual regression produced
two spatial maps per subject (corresponding to the two runs, less the four exceptions who
contributed one run of data). Each subject’s Z-transformed maps were then averaged using the
fslmaths to form a single 3D file for each component. These averaged subject-specific spatial
maps for a given component were then concatenated to form a 4D file, which served as the input
for cross-subject statistics.
4.2.3.5 Identification of task-modulated networks
To identify task-modulated networks in ICA-analysis of task fMRI data, we regressed the
run-specific activity time courses for each network against the run’s respective task-design
matrix using the fsl_glm utility. This approach essentially answers the question of whether a
network is more active in one task condition, by allowing us to determine the fit between the
network time-course and task design for each run (Clewett et al., 2014; Wang et al., 2018). After
combining the beta estimates and variance from each regression using a fixed effects approach to
obtain a single parameter estimate per subject and component, we could determine which
components were relatively more active during IN blocks compared to OUT blocks and visa-
versa using one-sample t-tests. Since contrast estimates for the relative comparison of the two
conditions (i.e. OUT > IN, IN > OUT) differ only in sign, t-tests were set-up such that positive
values indicated the degree to which the network was relatively more active during the IN
condition, while negative values indicated the degree to which the network was relatively more
active during the OUT condition. The p-values were further Bonferroni corrected, which limited
the number of networks only to those that most strongly differentiated the two task conditions.
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Components which were significantly associated with the task were used to compare spatial
differences in network functional connectivity across the PTSD and noPTSD groups.
4.2.4.6 Functional connectivity of task-modulated networks
Group differences between noPTSD and PTSD were assessed for the networks that were
determined to be significantly task-modulated, constrained to voxels within a binarized mask
containing positive values. We used non-parametric Monte-Carlo based permutation testing with
10,000 permutations and alpha = 0.05 (Winkler, Ridgway, Webster, Smith, & Nichols, 2014).
Clusters of activation were estimated using threshold-free cluster enhancement (TFCE) with a
variance smoothing factor of 5mm (Smith & Nichols, 2009). This procedure corrects for the
family-wise error rate. Mean-centered age and ASI drug use severity scores were included as
covariates of no-interest. Statistical maps were rendered onto a standard MNI brain using
MRIcroGL (http://www.mccauslandcenter.sc.edu/mricrogl/). Probabilistic anatomical labels for
cluster maxima were obtained from the Harvard-Oxford Cortical and Subcortical atlases,
reported in standard MNI space.
4.3 Results
4.3.1 Demographic and clinical comparisons
Compared to those without a PTSD diagnosis, those with a PTSD diagnosis scored
significantly higher on total PSS-I symptom severity (t(41) = 2.01, p = .025), and the subscales
re-experiencing (t(41) = 2.0, p = .025) and avoidance severity (t(41) = 1.9, p = 0.032). However,
compared to those with no PTSD diagnosis, those with a PTSD diagnosis did not exhibit higher
scores on arousal symptoms (t(41) = 1.26, p = .108).
Apart from PTSD symptoms, the two groups did not differ on other clinical
characteristics including current psychiatric medication use, other current co-occurring
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diagnoses, and ASI drug and alcohol use severity over the 30 days prior to intake. The PTSD
group was slightly older on average (3.7 years), but the difference was not significant. Groups
also did not differ on educational achievement. Statistics for these comparisons are presented in
Table 4.1.
4.3.2 Lifetime trauma exposure
Participants in the study experienced varying exposure to traumatic events during
childhood and/or adulthood such as death or incarceration of close relatives, criminal justice
and/or child welfare involvement, homelessness, and victimization through domestic violence,
sexual/physical assaults, emotional abuse/neglect, and human trafficking. Across both groups,
participants endorsed an average of 13.2 (SD: 5.5, range: 3 – 22) out of 30 items from the LSC-
R. The difference in total LSC-R between the groups was not significant (t(41)=1.3, p = 0.19),
indicating that within the sample, the number of stressors experienced was not strongly
predictive of meeting PTSD criteria. There was a positive correlation between age and the
number of life stressors experienced (r(41) = 0.33, p = 0.03). The number of sexual trauma
categories endorsed over the lifetime (yes/no: verbal sexual harassment, molestation, rape, and
sex in exchange for goods or money qualified by the statement “when you did not want to”)
accounted for 65.6% of the variance in total LSC-R item endorsement (r(41) = .81). The
association between LSC-R total and Sexual Violence is comparable for both groups (PTSD:
r(12) = .87; noPTSD: r(27) = .78). Given that sexual abuse is particularly relevant to the
development of psychiatric problems in women, including addiction (Burnette et al., 2008;
Koenen & Widom, 2009; Kendler et al., 2000), and has been linked to somatosensory gray
matter and functional interoceptive deficits (Heim et al., 2013; Strigo et al., 2010), lifetime
exposure to Sexual Violence was carried forward as the variable of interest for post-hoc tests.
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The two groups did not significantly differ in the number of endorsed Sexual Violence categories
(t(41) = 1.5, p = .14, two-tailed).
4.3.3 Group independent components analysis and cross-correlation with a canonical
resting state network template
The 20 ICs were spatially cross-correlated with Smith’s 20 network solution (2009) to
determine their correspondence with a set of canonical ICA templates and establish labels for the
networks. Our ICA components demonstrated high correspondence to the templates: the primary
(medial occipital; r = .80), secondary (occipital pole; r = .68) and tertiary (lateral occipital; r =
.66) visual networks; the default mode (DMN; r = .50); a secondary DMN (DMN 2; r = .48);
sensorimotor network (r = .66); bilateral auditory/insula network (r = .78); right-lateralized
fronto-parietal network (r = .62); left-lateralized fronto-parietal network (r = .67); an
executive/salience network (r = .48); an orbitofrontal network (OFC; r = .39); and a bilateral
frontoparietal network (r = .59). There was an additional visual network in our solution that
correlated with the secondary visual network (r = .46). The orbitofrontal network was also
correlated with the default mode network (r = .29). We further identified a bilateral medial
temporal network that did not have a clear correspondence to any brain networks in the Smith
templates, with peak values located along the hippocampus and amygdala. The remaining six
networks were determined to be artifactual, containing noise relating white matter, ventricles,
and head motion.
4.3.4 Identification of task-modulated intrinsic networks
Of the 14 intrinsic brain networks labeled via template matching, eight satisfied the
criterion for task relevance. Specifically, these networks were significantly differentiated for IN
relative to OUT blocks. Networks with significantly greater activation during IN relative to OUT
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blocks included: medial temporal (t(42) = 7.44, p < .00001), DMN (t(42) = 8.5, p < .00001),
DMN 2 (t(42) = 4.1, p = .0002), OFC (t(42) = 3.75, p = .00054), and medial occipital (t(42) =
5.4, p < .00001). Networks with significantly greater activation during the OUT relative to IN
blocks included: executive (t(42) = -13.5 , p < .00001), occipital pole (t(42) = -5.7, p < .00001),
and lateral occipital (t(42) = -6.0, p < .00001. Accordingly, spatial differences in network
functional connectivity for PTSD and noPTSD groups were tested on these eight networks (see
Figure 4.1).

Figure 4.1. Group-level ICA networks that were significantly task-modulated. Bar plots describe
the beta weights for the fit between the subject level design matrix and their respective network
time course. Negative values indicate the degree to which the network is more active during
exteroceptive attention relative to interoceptive attention (OUT – IN), while positive values
indicate the degree to which the network is more active during the interoceptive (IN) condition
relative to the OUT condition (IN – OUT). DMN 1 = Default Mode network, DMN 2 =
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secondary Default mode network, ECN = Executive network, MTN = Medial temporal network,
OFC = Orbitofrontal network, V1 = Medial occipital network, V3 = Lateral occipital network,
V2 = Occipital pole network.
4.3.5 Spatial differences in network functional connectivity
To infer group differences in functional connectivity for the eight task-modulated
networks, we conducted non-parametric voxel-wise regressions using randomise in FSL. These
tests included the one-tailed contrasts noPTSD > PTSD and PTSD > noPTSD for each network,
controlling for recent drug use and age (demeaned across both groups) as covariates of no-
interest. Given that there were two contrasts performed on each network, a total of 16 tests were
carried out. Accordingly, the TFCE-corrected p-values for cluster significance were further
Bonferroni-corrected, with the alpha criterion for significance adjusted to 0.05/16 = .003125.
Of the eight networks, only the orbitofrontal (OFC) network contained clusters that
exceeded TFCE and Bonferroni-corrected thresholding for the test comparing spatial differences
in functional connectivity for noPTSD > PTSD. Differences in OFC functional connectivity
were located in the bilateral insula, postcentral gyrus, precuneus, lateral OFC/frontal operculum,
and left frontoparietal areas. The OFC network result is displayed at the Bonferroni-corrected
threshold in Figure 4.2. MNI-coordinates for the peaks of clusters obtained from this analysis are
reported in Table 4.2. The PTSD group did not display greater functional connectivity in any
brain region for any of the eight networks.

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Figure 4.2. (A) Group-level OFC network from the MELODIC analysis. (B) Group differences
in functional integration of the OFC network. The group with PTSD exhibited reduced OFC
network functional connectivity in multiple brain regions, including the bilateral mid-posterior
insula, somatosensory cortex, precuneus, left middle and inferior frontal gyrus, lateral occipital
cortex/angular gyrus. Results displayed are TFCE and Bonferroni-corrected for multiple
comparisons.

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Table 4.2. MNI coordinates of cluster peaks
Peak Clusters noPTSD > PTSD Hemisphere Cluster Size
(Voxels)
x y z
Lateral occipital cortex, Angular gyrus, Lateral
orbitofrontal, Inferior frontal gyrus, Middle frontal
gyrus, Pre-central gyrus, Post-central gyrus
L
8891 -54 -68 20
Central operculum, Post-central gyrus, Mid-
posterior insula, Superior temporal gyrus,
Posterior cingulate
R 3857 62 -12 -8
Precuneus, Lingual gyrus
L/R 2773 -14 -52 -2
Angular gyrus, Lateral occipital cortex, Middle
temporal gyrus
R 882 60 -58 20
Mid-posterior insula
L 297 -36 -4 0
Supplementary motor cortex, Cingulate gyrus
L/R 124 10 -6 46
Lateral orbitofrontal cortex
R 57 46 22 -14
Frontal pole
L 11 -44 52 0
Anterior cingulate
L 6 -8 -2 42
All coordinates reported in MNI space. Peak cluster reported for a main cluster, but several
maxima may be observed within a given cluster as large clusters were bridged by several voxels.

4.3.6 Mean orbitofrontal network strength associated with lifetime exposure to sexual
trauma
Given that the majority of participants (62.8%) had experienced at least one LSC-R
sexual trauma category at some point in their life regardless of PTSD diagnosis, and that sexual
trauma accounted for a large portion of variance in total LSC-R, independent post-hoc tests were
carried out to identify whether the average strength of each participant’s OFC network was
associated with lifetime exposure to Sexual Violence above and beyond the effects of PTSD
status. A multiple linear regression was calculated to predict the whole-brain average of the
subject-level variance-normalized networks (masked for positive voxel-values), based on PTSD
status (PTSD = 1, noPTSD = 0), Sexual Violence total, age, ASI drug and alcohol use. For the
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OFC Network, a significant regression was found (F(5, 37) = 5.34, p = .00086, with adjusted-R
2

= 0.34. Age, alcohol and drug use were not significant predictors (b = -.02, SE = .019, p = .26; b
= .28, SE = 0.91, p = .76; b = .78, SE = .99, p = 0.43), whereas both PTSD and Sexual Violence
independently explained significant variance: PTSD was associated with lower average OFC
functional coherence (b = -.92, SE = .30, p = .0039), consistent with the analysis of group
differences in OFC network functional connectivity, while Sexual Violence accounted for
additional variance (b = -.24, SE = .095, p = .015), suggesting a cumulative effect of sexual
traumas on the integrity of the OFC network across substance-dependent women both with and
without PTSD (see Figure 4.3). As a check on whether Sexual Violence accounts for similar
variance as compared to total LSC-R score given the very high correlation between the variables,
the same regression was run using total LSC-R score instead of Sexual Violence. In this model,
LSC-R total was only a marginally significant predictor of OFC strength (b = -.055, SE = 0.027,
p = .052) whereas the estimates of the other predictors (PTSD, age, ASI drug and alcohol use)
remained very similar.
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Figure 4.3. Sexual Violence exposure and PTSD status are significantly negatively associated
with mean OFC Network strength. Scatterplot reflects the simple correlation between Sexual
Violence and OFC Network for each group.
4.4 Discussion
To our knowledge, this is the first study to investigate neural correlates of PTSD and
traumatic stress in women diagnosed with SUD. To address the question of differences between
trauma-exposed, substance-dependent women with and without current PTSD, we employed a
data-driven approach. First, using independent components analysis and dual regression, we
identified eight intrinsic functional networks that were significantly modulated by the
interoceptive and exteroceptive task conditions. These eight task-modulated networks were used
to test network-level differences in functional connectivity for participants with comorbid PTSD
and SUD compared to those with SUD-only. Only the OFC network significantly differentiated
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the PTSD and noPTSD groups. Notably, we did not identify brain or performance differences
(see Appendix B) between PTSD and noPTSD for networks associated with exteroceptive task
demands (the executive, lateral occipital, and occipital pole networks). Rather, group differences
were found for the OFC network, whose time course was more active when participants were
cued to attend to the sensations associated with their breathing cycle. Hence, group differences
were specific to a brain network involved in attentional modulation of viscerosensory processing.
The OFC is a functionally heterogeneous multimodal sensory-motor association region.
The lateral sector of the OFC is a convergence zone for sensory inputs from multiple modalities,
including somatosensory and visceral afferents (Rolls, 2004) while the medial sector of the OFC
provides outputs to brainstem visceromotor and hypothalamic structures (Ongür & Price, 2000).
In general, the OFC is functionally and anatomically coupled with the insula, striatum, lateral
prefrontal cortices, and limbic structures (Barrett & Simmons, 2015; Zald et al., 2014). In
relation to complex behaviors, the OFC is integral to functional states related to homeostasis and
allostasis, such as mood and emotion (Bechara, Damasio, & Damasio, 2000; Zhang, Harris,
Split, Troiani, & Olson, 2016), hypothalamic-pituitary-adrenal-axis (HPA) activity (Sinclair,
Webster, Fullerton, & Weickert, 2012), reward (Howard, Gottfried, Tobler, & Kahnt, 2015), and
decision-making (Bechara & Damasio, 2005). Our observation of diminished OFC strength and
functional connectivity in the PTSD group is highly consistent with many prior studies of post-
traumatic stress in non-substance dependent individuals, who show reduced orbitofrontal or
ventromedial prefrontal cortex (VMPFC) metabolism across a variety of task-demands,
including trauma-related (Daigre et al., 2015; Moser et al., 2015) and trauma-unrelated task
contexts (Felmingham et al., 2009; Herz et al., 2016; Rougemont-Bücking et al., 2011; Sripada et
al., 2012; van Rooij et al., 2016). Confirming the association of PTSD with OFC/VMPFC
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function, a meta-analysis of 79 neuroimaging studies of PTSD also concluded that PTSD is
associated with hypoactivity of ventromedial prefrontal regions, as well as of the inferior frontal
gyrus (Hayes, VanElzakker, & Shin, 2012).
The OFC is also a stress-sensitive cortical structure. Pre-clinical research has established
that chronic stress exposure leads to dendritic atrophy of the medial and orbital OFC (Liston et
al., 2006), and altered activity of forebrain glucocorticoid receptors, which are involved in
feedback regulation of the HPA-axis (Arnsten, 2009; Boyle, Kolber, Vogt, Wozniak, & Muglia,
2006; Herman, et al., 2012). It is repeatedly observed that adults and youth who experience
childhood adversity have structural abnormalities of the OFC, expressed as reduced gray matter
volumes (Hanson et al., 2010; Hart & Rubia, 2012). It is possible that depressed metabolism and
volume of the OFC is a consequence of early experiences, which generate a vulnerability for the
development of PTSD in response to traumas. Alternatively, OFC atrophy and hypometabolism
could also reflect the neurobiological consequences of traumatic stress and PTSD itself.
Suggestively, Morey and colleagues (2015) report that maltreated youth with PTSD have
decreased VMPFC gray matter volume relative both to maltreated youth without PTSD and non-
maltreated controls. More directly, twin studies indicate that diminished medial prefrontal
function is an acquired (rather than pre-existing) feature of PTSD (Dahlgren et al., 2018). Our
data also suggest that diminished OFC network strength is predictive of current PTSD. We
further observed that the strength of the OFC network for each subject was negatively associated
with the severity of sexual trauma history across all participants, over-and-above the contribution
of PTSD status alone. These results suggest that functional integrity of the OFC network is also
sensitive to the cumulative effects of exposure to sexual trauma in women with SUD.
The spatial differences in orbitofrontal network connectivity manifested in a set of brain
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regions associated with interoception, somatosensation, bodily self-consciousness, and cognitive
control. Concerning visceral and somatic sensation, the PTSD group demonstrated reduced
bilateral mid-posterior insula and somatosensory cortex functional connectivity with the OFC
network, which may suggest weaker interoceptive and somatic representations of the body and
viscera during breath awareness. The mid-posterior insula is the terminus of major vagal and
spinothalamic lamina I pathways that convey homeostatic and viscerosensory information to the
cortex (Craig, 2002). Activity in this region is responsive to homeostatic states and interoceptive
manipulations. According to one model, interoceptive information is propagated along a
posterior-anterior axis in the insula, eventually reaching the orbitofrontal cortices, whereby
visceral status can affect mood as well as goal-directed behaviors, including drug seeking (Naqvi
& Bechara, 2010). Mindful or attentive breathing exercises can be seen as an interoceptive
manipulation. Brief interventions that employ attention to breathing are known to transiently
decrease subjective states of distress in both clinical and healthy populations (Brown, Gerbarg, &
Muench, 2013; Johnson, et al., 2015; Ng, et al., 2016). In an fMRI paradigm, attention to
breathing also downregulates amygdala responses and increases prefrontal activity during
emotional picture viewing (Doll, et al., 2016), which suggests a potential brain mechanism
through which mindfulness practices may support emotion regulation. Activation of lateral
prefrontal regions are also observed in fMRI studies of focused-meditation (Brefczynski-Lewis,
Lutz, Schaefer, Levinson, & Davidson, 2007; Tomasino & Fabbro, 2016). In particular,
Hasenkamp, et al. (2012) reported that greater dorsolateral prefrontal activity was associated
with periods of greater self-reported focus during such exercises, whereas it diminished during
periods of mind-wandering and ‘awareness of’ mind-wandering. These reports suggest that
cognitive control is required to sustain attention on body sensations and inhibit mind-wandering.
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In our study we observed reduced OFC functional connectivity with the dorsal and ventrolateral
prefrontal regions in the PTSD group relative to the noPTSD group. Furthermore, it is reported
that in women with PTSD, low executive functioning is related to greater intrusive thought
persistence and cognitive avoidance strategies (Bomyea & Lang, 2016). Initially, women with
SUD-PTSD comorbidity may have more difficulties engaging the interoceptive and attentional
resources that may support emotional regulation in the context of mindfulness practices, which
could be relevant to understanding the efficacy of mindfulness-based interventions for this sub-
population.
In addition to the insula, in the PTSD group we also observed reduced OFC functional
connectivity with the angular gyrus/temporal-parietal junction (TPJ) and lateral occipital cortex
(in particular, an area consistent with the extrastriate body area). These areas are involved in
sensorimotor integration of vestibular, interoceptive, proprioceptive, motor, and visual inputs
that support the sense of body ownership, agency, first-person perspective and localization in
space (Blanke, 2012; Leménager et al., 2014; Suchan et al., 2013). Associative pairing of self to
a visual symbol also activates these regions relative to associative pairing of visual symbols to
non-self-objects (Sui & Gu, 2017), and lesions of the extrastriate and temporo-parietal cortices
can produce disorders of bodily self-consciousness (Anzellotti et al., 2011; Heydrich & Blanke,
2013), and connectivity among these regions appears to be relevant to dissociative PTSD
(Harricharan, et al., 2017). TPJ activations have also been observed during breath awareness
paradigms (Dickenson et al., 2013). Observed differences in functional connectivity of these
regions with the OFC network during interoceptive attention is particularly interesting given that
reduced medial OFC metabolism has previously been associated with dissociative symptoms in
PTSD (Tursich et al., 2015).
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4.4.1 Stress regulation, interoceptive exposure and mindfulness for relapse prevention
Stress increases the likelihood of relapse in individuals with histories of addiction (Blaine
& Sinha, 2017; Sinha, 2008), an effect which may be more pronounced in females with SUDs
(Moran-Santa Maria, et al., 2014). In the context of traumatic stress, drug use may serve as an
avoidance or numbing strategy in response to aversive interoceptive and emotional states. Use-
withdrawal-relapse cycles contribute to dysregulation of HPA, sympathetic-adrenal medullary
and immune response systems (Kubera, Basta-Kaim, & Wydra, 2008; Michopoulos et al., 2016)
which may worsen traumatic stress symptoms (Jacobsen et al., 2001). Mindfulness treatments
tailored to women with comorbid substance abuse and traumatic stress may be especially suited
for addressing dysregulated stress responses that can precipitate relapse. Specifically, certain
mindfulness exercises such as focused breathing and the body scan may act as a form of
interoceptive exposure therapy, as well as serve to re-integrate an embodied sense of self, and
down-regulate stress-reactivity. In healthy individuals, mindfulness training has been found to
strengthen white matter connectivity between insula and the prefrontal cortex, including the OFC
(Sharp et al., 2018). Moreover, part of mindfulness training consists of developing a
metacognitive stance towards aversive physiological states, thoughts, and emotions, which may
reduce dissociation and conditioned behaviors, including drug seeking, in response to
interoceptive or psychological stressors (Amaro et al., 2014; Bowen, Boer, & Bergman, 2018;
Boyd, Lanius, & Mckinnon, 2018).
4.4.2 Limitations and future directions
First, our sample size is modest for group comparisons. However, we employed
nonparametric permutation methods for inference of group differences, a statistically strict
method which has been shown to generate minimal levels of false positives relative to parametric
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methods of inference (Eklund, Nichols, & Knutsson, 2016), and we applied Bonferroni
correction on the tests performed, further reducing the potential for false positives. Moreover, the
observation of depressed OFC network function and connectivity is highly consistent with prior
studies. Nevertheless, the risk of Type 1 error related to “file-drawer problems” is heightened
when sample sizes are small, as is the case in the present study, and so the results presented here
should not be taken as definitive. Second, this study was cross-sectional, therefore the design
does not allow us to distinguish whether group differences in brain metabolism are antecedent
(and perhaps causal contributors) to the development of PTSD or whether these differences are
related to the sequelae of PTSD (or the sequelae of PTSD in the context of addiction). Although
psychoactive medication use was similar across the two groups, the potential influence of these
medications could not be ruled out. We were also not able to exclude subjects on the basis of
psychiatric comorbidities apart from PTSD. However, comorbidity in SUD is common, and in
the present study the rates of mood and borderline disorders were similar between PTSD and
noPTSD groups, thus these data may be more representative of female SUD populations, thereby
supporting the generalizability of the findings. Nevertheless, it is not entirely clear how
substance use may have interacted with the present findings (i.e. limiting its generalizability to
PTSD specifically). OFC gray matter and functional impairments have also been noted in
substance abuse disorders (Goldstein & Volkow, 2011; Tanabe et al., 2009). Similarly,
interoceptive processing deficits have also been observed in substance dependent individuals
without PTSD (May, Stewart, Migliorini, Tapert, & Paulus, 2013; Stewart et al., 2014).
Consequently, it is not clear whether PTSD results in primary deficits in processing somato-
visceral information relative to SUD, or whether this relative difference might be a consequence
of a supraordinate factor such as attentional difficulties (although there were no performance
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differences in the exteroceptive task [see Appendix B]). However, since we did not have a
measure of performance difficulty for the two task conditions, we also cannot be certain whether
perceived difficulty played a role. Behavioral measures of interoception should be incorporated
in future studies, or, alternatively, could utilize assessments of interoceptive function that do not
necessarily impose attentional or cognitive demands (e.g. pharmacological challenges). To
address these interpretive ambiguities, future studies could incorporate demographically
matched, non-substance abusing comparison groups with-and-without trauma/PTSD to identify
associations of the OFC and interoceptive networks that are uniquely related to substance abuse
versus those related to PTSD, and potential interactions between the disorders. Lastly, in the
current study we only assessed participants at a single time-point, soon after admission to SUD
treatment, thus we do not know how interoceptive, somatosensory and orbitofrontal function
may change with treatment. However, we have identified that insula and orbitofrontal
metabolism, as well as functional connectivity between these regions may be useful
neurobiological marker of clinical response for women with PTSD and substance abuse
disorders.
4.4.3 Conclusion
Interoception is increasingly recognized as a construct relevant to the symptomatology of
mental health conditions, including substance abuse and anxiety disorders. As the first
neuroimaging study to examine SUD and PTSD comorbidity in women with varying degrees of
exposure to traumatic stressors, we provide a novel brain network-level account of interoceptive
differences within this sub-population. Specifically, in the PTSD subgroup we observed reduced
functional connectivity of an orbitofrontal network with the insula, somatosensory and cognitive
control regions during an interoceptive task during which participants attended to sensations of
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breathing. Group differences in network functional connectivity were specific to task-modulated
networks associated with interoception, and not those associated with exteroception. Post-hoc
correlational analyses further identified a cumulative association between sexual trauma
exposure and OFC network strength during interoception, independently from PTSD status.
Traumatic stress-dependent differences in the orbitofrontal cortices may be relevant to the
clinical responses to interventions for women with substance use disorders.

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5. General Discussion
Interoception is an emergent construct that provides a framework for understanding the
neurobiological basis of psychological phenomena such as mood (Avery, et al., 2014), emotion
(Seth, 2013), motivational drive and goal-directed behavior (Li et al., 2013; Wang et al., 2013),
body awareness and ownership (Park et al., 2016), psychopathology (Khalsa, et al., 2018), as
well as certain somatic disorders (Labus et al., 2016). But as an emergent and multidimensional
construct, the paradigms and methodology for investigating interoceptive processing require
additional development and validation (Garfinkel, et al., 2015; Khalsa, et al., 2018; Ring &
Brener, 2018). One primary question is how we can access and alter the function of
interoceptive-allostatic neural systems non-invasively. Given the translational potential of
interoceptive neuromodulation, fundamental research questions addressed in this thesis are
oriented towards ameliorating vulnerability to allostatic load, which appears to be a common
factor associated with multiple psychiatric and somatic disease states (Goodkind et al., 2015).
Hence, this thesis was focused on the broad goal of investigating neural processing of respiratory
and cardiac interoception in healthy and psychiatric populations. The methodologies and
hypotheses pursued within each study were distinct, but converged on the themes of ‘top-down’
and ‘bottom-up’ interoceptive neuromodulation. Studies 1 and 2 focused on TMS and tVNS as
potential means of accessing and modulating function the heart-brain axis. Study 3 alternatively,
examined how mindfulness-based psycho-behavioral techniques differentially engage
interoceptive brain systems in women with SUD and comorbid PTSD.
5.1 Study 1: Interoceptive neural pathways are relevant to (t)VNS mechanisms of action
Study 1 investigated whether stimulation of the vagus nerve non-invasively via the
AVBN is associated with changes in neural processing of cardiovascular information in
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interoceptive and visceromotor cortices. The ABVN has been directly and indirectly shown to
project to the caudal nucleus tractus solitarius (NTS) in mammals and humans (Ay et al., 2015;
Frangos et al., 2015; Nomura & Mizuno, 1984; Sclocco et al., 2019). Projections of the ABVN to
the NTS suggests tVNS may be an appropriate means by which to access thalamocortical
interoceptive pathways relevant to cardiovascular autonomic regulation. Hence, the general
hypothesis was that cardiac autonomic effects of tVNS would be related to changes in high-order
regions of the central-autonomic network (as opposed to merely engaging low-level medullary
reflexes). To investigate this hypothesis, the study integrated EEG with time-locked
cardiovascular and respiratory measurement with tVNS in a sham-controlled, within-participant
design. The study demonstrated that tVNS (1) increases source-localized amplitude-envelope
connectivity between the insula and somatosensory cortex in the beta frequency band; (2) alters
neural processing of HEPs at the level of the scalp in left frontotemporal regions between 200 –
300 ms, and (3) changes patterns of HEP source-localized amplitude-envelope connectivity
among cortical regions where HEPs have been identified intracranially, which included the
insula and ventromedial prefrontal/cingulate regions. Moreover, insula-linked connectivity
features were found to be inversely correlated with heart rate during tVNS, but not sham
stimulation, indicating that tVNS alters the covariation between cardiovascular and neural states.
VNS is primarily used for the treatment of refractory epilepsy and major depression, and
tVNS is a non-invasive investigational treatment modality for epilepsy (Bauer et al., 2016),
inflammatory conditions (Hong et al., 2019; Lerman et al., 2016), depression (Fang et al., 2017;
Kong, et al., 2018), PTSD (Lamb, et al., 2017), stroke rehabilitation (Capone et al., 2017),
cardiovascular (Garcia et al., 2017; Stavrakis et al., 2015) and pain (Laqua, Lotze, Leutzow, &
Usichenko, 2016; Laqua, Leutzow, Wendt, & Usichenko, 2014; Usichenko, Laqua, Leutzow, &
164
Lotze, 2017), among other conditions. The mechanisms of action underlying VNS and tVNS
have largely focused on direct and indirect projections of the NST (the primary recipient of vagal
afferent fibers) to endogenous neuromodulatory nuclei. In particular, stimulation of the
noradrenergic locus coeruleus (LC), serotonergic raphe nuclei, dopaminergic ventral tegmental
area, and the cholinergic basal nucleus of Meynert are believed to be the primary mechanisms
through which VNS exerts anticonvulsant and anti-depressant effects (Ruffoli, et al., 2011;
Nichols, et al., 2011). VNS and tVNS are also applied investigationally for the treatment of
cardiovascular (Premchand et al., 2014) and autoimmune conditions, such as Crohn’s disease
(Clarençon et al., 2014). For applications to somatic disorders, the VNS mechanisms of action
focuses on stimulation of parasympathetic fibers to the heart and the efferent anti-inflammatory
cholinergic pathway. However, the anticonvulsant, antidepressant, analgesic, cognitive,
cardioprotective, neuroprotective, and immunoregulatory effects of VNS and tVNS likely result
from multiple mechanisms involving simultaneous activation of afferent and efferent branches of
the vagus nerve (Vonck & Larsen, 2018).
Thalamocortical projections of vagal afferent fibers to interoceptive cortices constitute a
major component of vagal influence on the central nervous system (Craig, 2002). Yet, this
pathway has not been explored as a potential systems-level mechanism by which VNS and tVNS
exert clinical and cardiovascular effects, although interest in the use of tVNS for modulating
interoceptive systems is nascent (Khalsa, et al., 2018; Villani & Tsakiris, 2019). Interoceptive
neural systems are involved in the neural integration or regulation of nociception (Segerdahl,
Mezue, Okell, Farrar, & Tracey, 2015), inflammation (Harrison, et al., 2015), cardiac (Schulz, et
al., 2016), gastrointestinal (Richter et al., 2016) and HPA-axis (Bonaz, Sinniger, & Pellissier,
2016) functions, all of which can be affected by VNS or tVNS. Study 1 represents a novel,
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original effort to determine whether tVNS modulates cortical interoceptive processing of cardiac
representations. As the overall results were positive, thalamocortical interoceptive pathways may
be an important systems-level mechanism through which tVNS (and VNS) improve somatic
disorders and psychiatric symptoms. The results from this study are, to my knowledge, the first
to demonstrate that tVNS alters neural representations of cardiac information in the cortex.
To advance this work, additional neuroimaging studies using not only EEG, but also
MEG and fMRI should be used to map tVNS-modulation of interoceptive pathways. The
additional use of autonomic challenge, stressor, and emotion tasks in healthy and clinical
populations may better elucidate the neural allostatic dynamics that may underlie tVNS effects.
Given the efficacy of both tVNS and HRV-biofeedback training for improving cardiovascular
autonomic function and emotional cardiovascular reactivity in somatic and psychiatric disorders,
a natural extension of this work could be to determine whether combining HRV-biofeedback
during tVNS would provide synergistic benefits to individuals with cardiovascular or mood and
anxiety disorders. Similar paradigms pairing transcranial direct current stimulation with
mindfulness practice have been proposed, which have demonstrated tentative short-term
improvements in mood relative to mindfulness-only (Badran et al., 2017). Such a clinical
intervention could be paired with neuroimaging to evaluate possible changes in interoceptive-
allostatic brain systems that are associated with clinical improvements in physiological and/or
psychiatric function.
5.2 Study 2: TMS may alter visceromotor responses, but there are caveats
In Study 2, inhibitory and excitatory theta-burst TMS protocols were applied to a right
frontotemporal target that had previously been shown to modulate neural processing of HEPs.
The hypothesis was that this right frontotemporal target would access visceromotor systems
166
relevant to the ‘top-down’ regulation of cardiovascular autonomic function. In this study
RMSSD, baroreceptor sensitivity (as expressed by low-frequency HRV under conditions of 0.1
Hz breathing), and pulse transit time were measured as the cardiovascular outcome variables.
Additionally, state anxiety was measured as a covariate to determine whether potential
cardiovascular responses were confounded by stimulation-induced anxiety. It was found that
intermittent (excitatory) theta-burst stimulation increased vagally-mediated HRV (RMSSD), but
that this effect was explained by state-anxiety. However, continuous (inhibitory) theta-burst
stimulation was associated with increased pulse transit time latency and was not affected by
state-anxiety, which suggests possible inhibition of sympathetic nerve fibers.
In 2010, Cogiamanian et al. had proposed the use of TMS for the management of
hypertension, based on the hypothesis that ‘top-down’ cortical mechanisms influence the activity
of subcortical structures and autonomic effectors. However, there still remains a relative paucity
of research investigating the utility of TMS for modulating interoceptive-allostatic cortices with
the aim of regulating cardiovascular function. Study 2 was an effort to systematically evaluate
the efficacy of a right frontotemporal target for altering cardiovascular autonomic state. First,
this study used a stimulation target that was previously found to alter neural processing of HEPs,
indicating its relevance to cardiac interoception. Second, there is a possibility that cardiovascular
responses to TMS are, in some cases, spurious artifacts related to the potentially intense sensory
effects of TMS stimulation on the scalp, which are not adequately matched by placebo coils.
Study 2 confirmed this issue as relevant to discovering the potential of TMS for modulating
brain networks relevant to visceromotor regulation, which had not been previously addressed in
related literature. Study 2 also discussed approaches for advancing and optimizing TMS for
modulating heart-brain interactions, such as by incorporating functional neuroimaging with
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fMRI, and the potential translational relevance to individuals who exhibit maladaptive stress-
linked cardiovascular reactivity patterns.
However, it is important to consider that TMS may not be an optimal method for
modulating heart-brain interactions experimentally. Although in principle TMS should be able to
access subcortical systems transynaptically, stimulation of a given patch of cortex could affect
multiple networks simultaneously. Relatedly, given the limited focality of TMS due to spread of
the magnetic field in cortical tissues (on the order of 3 – 5 cm [Rezayat & Toostani, 2016]), off-
target effects are likely. Together, these limitations could diffuse the ability to uncover reliable
effects in some experimental settings. On the other hand, TMS is an established treatment for
certain psychiatric conditions. Protocols for major depression generally target the left
dorsolateral PFC. Clinical neuroimaging studies consistently show that stimulation of the left
dorsolateral PFC in depression alters metabolism and functional connectivity in the subgenual
cingulate (i.e. subcallosal) cortex (Fox, et al., 2012; Hadas et al., 2019; Weigand et al., 2018),
which is a region that is strongly implicated in the pathophysiology of depression and other
mood disorders (Drevets, Savitz, & Trimble, 2008), but also in visceromotor control (Caruana et
al., 2018; Barrett & Simmons, 2015; Ongür & Price, 2000). Consequently, TMS relevance to
visceromotor or allostatic function may be better observed in clinical contexts.
Emergent technologies such as transcranial low-intensity focused ultrasound (LIFU) will
likely prove more fruitful in future investigations of interoceptive neural systems. LIFU has
spatial resolution on the order of 1 – 5 mm and can target sources in the brain up to a depth of 15
cm or more (Rezayat, et al., 2016), hence regions such as the insula could be stimulated with
sub-gyrus-level resolution. Indeed, LIFU stimulation of the secondary somatosensory areas (i.e.
in the parietal operculum) representing the hand, wrist, forearm, elbow, arm and leg produce
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paraesthetic sensations highly reminiscent of those obtained from electrical intracranial
stimulation of insular cortices, such as warmth, coolness, pressure/heaviness, and brushing
sensations (along with more typical vibrotactile somatosensations) (Lee, Chung, Jung, Song, &
Yoo, 2016; Stephani, et al., 2011). Stimulation of the cervical vagus nerve is also an application
of LIFU (Juan, González, Albors, Ward, & Irazoqui, 2014; Wasilczuk et al., 2019). Hence,
reliable methods of non-invasively modulating interoceptive vagal pathways from the periphery
to deep cortical and subcortical sources are on the horizon.
5.3 Study 3: Engagement of interoceptive networks during focused attention on breathing
depend on traumatic stress symptoms in women with substance use disorders
Study 3 focused on neuroimaging phenomenological aspects of interoception in a female
substance dependent population with variable histories of traumatic stress exposure. Specifically,
patients engaged in a mindful-breathing task while undergoing fMRI scanning, during which
attention is directed at somatosensory and visceral sensations associated with the breathing cycle,
and extraneous thoughts or sensations are inhibited. As a contrasting condition, the patients also
attended to a visual (exteroceptive) target. The task capitalizes on the attentional spotlight effect,
wherein attention directed towards a particular sensory modality enhances metabolism within
regions associated with that modality (Brefczynski & Deyoe, 1999). It was previously shown
that mindful breathing increases brain metabolism in interoceptive and somatosensory regions as
compared to attending to an exteroceptive stimulus (Farb et al., 2013a, 2013b), and that PTSD
and sexual trauma in females may be associated with atypical neural interoception and
somatosensation. Hence, it was hypothesized that post-traumatic stress comorbidity in female
patients with SUD would be associated with altered integration of interoceptive networks. The
task fMRI data were decomposed using ICA to isolate intrinsic functional brain networks.
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Although several networks were identified as relevant to either the interoceptive or exteroceptive
task conditions, the interoceptive orbitofrontal network specifically distinguished patients with
PTSD comorbidity. PTSD-comorbidity was associated with reduced orbitofrontal network
connectivity in insular, somatosensory and cognitive control regions of the brain. Across all
patients, even after accounting for PTSD status, sexual trauma exposure was inversely associated
with strength of interoception-modulated orbitofrontal network integration, indicating that sexual
trauma history in general may also produce atypical engagement of interoceptive brain systems
during mindfulness-based activities.
Study 3 was motivated in part by the reality that traumatic stress exposure is highly
comorbid with substance abuse (Daigre et al., 2015), yet covariation in brain function related to
trauma history is almost never studied in clinical neuroimaging studies of substance abuse, even
though it is highly relevant to recovery outcomes. Moreover, PTSD-SUD comorbidity
specifically had not been investigated in any previous neuroimaging studies. Study 3 therefore
represents the first neuroimaging study to investigate PTSD comorbidity in a SUD population.
The study participants were a subset recruited from a large randomized clinical trial (Moment-
by-Moment in Women’s Recovery: MMWR [Amaro & Black, 2017; Black & Amaro, 2019])
that investigated the efficacy of a trauma-informed mindfulness intervention for relapse
prevention in women with SUDs. The choice to focus on mindful breathing as an interoceptive
manipulation was derived from the goals of the parent clinical trial, as well as literature
demonstrating the relevance of mindfulness to interoception (Farb et al., 2015; Farb et al.,
2013b). The primary implication that can be drawn from this study is that altered interoceptive
processing associated with traumatic-stress history is relevant to understanding the brain
mechanisms associated with, and potential efficacy of mindfulness interventions for women with
170
SUD. A possible follow-up study, given the data collected by the MMWR clinical trial, could
determine whether the degree of interoceptive network integration has predictive value with
regards to treatment outcomes at an 8-month follow-up. Unpublished observations from our
MMWR neuroimaging study also showed that patients exhibited increases in vagally-mediated
HRV from baseline to completion of the treatment intervention. Unfortunately, the post-
treatment sample size was too small to reliably attribute the HRV changes to any particular
group or treatment characteristics, however another follow-up study could investigate fMRI
correlates of changes in HRV, and how these changes may relate to patient symptomatology. It
has been previously observed that patients with PTSD have reduced HRV, and also that they do
not exhibit covariation between HRV and the central autonomic network as measured from
fMRI, whereas strong covariation is present in healthy controls (Thome et al., 2016). Hence,
treatment, particularly when paired with mindfulness-based or HRV-biofeedback methods, may
restore the synchronization of efferent vagal expression and brain function in the central
autonomic network in such patients. A final comment on Study 3 relates to dissociative
symptoms. The DSM-5 now recognizes a dissociative subtype of PTSD (Regier, et al, 2013).
Study 3 did not have a measure of patient dissociative tendencies; however, dissociation may be
an important dimension of atypical neural interoception in substance dependent women with
traumatic stress histories (Scheffers, et al., 2017; Wegen, et al., 2017; Price and Herting, 2013).
Dissociative may be relevant to patient performance of interoceptive tasks as well as to their
engagement with mindfulness interventions.
5.4 Concluding Remarks
In summary, this dissertation provides evidence for the utility of non-invasive brain and
vagus nerve stimulation for accessing and modulating function in interoceptive brain systems
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relevant to cardiovascular autonomic function. In particular, modulation of cardiac information
in interoceptive cortices by tVNS suggests that the vagal thalamocortical projection to the insula
and the extended central autonomic network is an important systems-level mechanism by which
invasive and non-invasive vagus nerve stimulation exerts clinical effects. This dissertation also
identifies that the clinical presentation in psychiatric patients is associated with differential
engagement of interoceptive brain systems while performing activities that require mindful
attention to interoceptive sensations. These findings raise the question as to whether baseline
differences in brain responses in interoceptive networks are relevant to patient outcomes after
mindfulness-based interventions, including vagal efferent function. While this dissertation did
not ultimately pair psycho-behavioral interventions such as mindfulness or HRV-biofeedback
with brain stimulation or tVNS in a clinical population, I believe that this would be a logical
extension of this work as it would determine whether pairing brain stimulation with psycho-
behavioral modalities produces synergistic effects on psychological and cardiovascular
autonomic function. Additionally, incorporating a neuroimaging dimension would provide a
context in which to understand how stimulation of interoceptive, cardiovagal systems may
ameliorate allostatic dysfunction. In conclusion, this work enhances basic understanding of
brain-body interactions, and advances the translational value that can be derived from
interoceptive perspectives.

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Appendix A

Figure A1.1. Effect of source current density transformation on HEP scalp topography. Raw
(upper panels) and source-current density transformed (lower panels).

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Appendix B
Descriptive General Linear Model of the IN-OUT Attention Task
To illustrate the brain regions associated with the IN-OUT Attention task performance
across all 43 subjects, we analyzed the fMRI data using a GLM with FSL’s FEAT module. The
inputs to the model were the pre-processed functional runs in native space. First-level modeling
included the regressors for each of the experimental conditions: IN, OUT and Rest, with the
contrasts of interest IN>OUT and OUT>IN. Functional images were transformed into standard
2mm MNI-152 space using FLIRT with 12 DOF affine transformation. The design matrix was
convolved with a double-gamma hemodynamic response function and pre-whitened (Woolrich,
Ripley, Brady, & Smith, 2001). Each subject’s runs were combined using fixed effects. The
group-level effects of the task were also modeled using fixed effects for descriptive purposes,
where one-sample t-tests were utilized to assess brain activations associated with the contrasts
IN>OUT and OUT>IN across all participants (Woolrich, Behrens, Beckmann, Jenkinson, &
Smith, 2004). Statistical significance was thresholded using cluster-based detection statistics,
with a height threshold of Z > 2.3 and cluster probability p < 0.05, corrected for multiple
comparisons based on Gaussian random field theory (Worsley, 2001).
General Linear Model of IN-OUT Attention Task
For qualitative illustration of the effects of the task, a fixed-effects GLM analysis of the
IN-OUT Attention Task was performed, averaging across all 43 participants. Figure 1 displays
the contrasts IN>OUT and OUT>IN. IN>OUT predominantly shows activations within the
precuneus, medial prefrontal cortex, bilateral amygdala and hippocampus, and posterior insula
253
and parietal operculum. OUT>IN involves the dorsal anterior cingulate, anterior insula, bilateral
frontoparietal areas, basal ganglia and brainstem.

Figure A2.1. Fixed effects general linear model of task performance across all participants (n =
43) to describe patterns of activation during the IN-OUT Attention Task. Warm colors (left)
reflect the brain activations during interoceptive attention relative to exteroceptive attention.
Cool colors (right) reflect the brain activity during exteroceptive attention relative to
interoceptive attention.
OUT 1-Back Behavioral Performance
Due to technical problems, button presses were unavailable for ten participants (one
participant whose responses were unavailable was from the PTSD group). For the remaining
participants, analog triggers from the scanner interfered with the recording of button presses for a
portion of trials (i.e. triggers could be recorded as responses if a TTL pulse was sent during the
254
response period of the trial, and therefore could not reliably contribute to the behavioral data.
Thus, a subset of trials were used to compute behavioral indices where responses were
unambiguously unaffected by TTL pulses. Useable trials averaged 63.7 (SD: 7.1) per participant,
with a range of 40 -77. From the responses, we computed the Hits (a response when the letter
matched the one presented on the previous trial), and False Alarms (FA: the number of responses
for trials in which the letter did not match the previous one) as a percentage. The noPTSD group
had an average 91.5% Hit rate (SD: 16%) and an average FA rate of 4.4% (SD: 11.1%). The
PTSD group had an average Hit rate of 96.3% (SD: 6.2) and FA rate of 1.2% (SD: 3.7%). The
Hit and FA rates did not significantly differ between PTSD and noPTSD (Hits: t(31) = 1.04, p =
.31; FAs: t(31) = -.99, p = .33).

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Creator Poppa, Natalie (Tasha) (author)

Core Title Heart, brain, and breath: studies on the neuromodulation of interoceptive systems

School College of Letters, Arts and Sciences

Degree Doctor of Philosophy

Degree Program Psychology

Publication Date 05/05/2020

Defense Date 03/24/2020

Publisher University of Southern California (original), University of Southern California. Libraries (digital)

Tag cardiovascular,EEG,fMRI,heart evoked potential,heart rate variability,interoception,OAI-PMH Harvest,TMS,transcutaneous vagus nerve stimulation,vagus nerve

Language English

Contributor Electronically uploaded by the author (provenance)

Advisor Bechara, Antoine (committee chair), Cahn, Rael (committee member), Mather, Mara (committee member), Monterosso, John (committee member)

Creator Email npoppa@usc.edu,tashappa@gmail.com

Permanent Link (DOI) https://doi.org/10.25549/usctheses-c89-297651

Unique identifier UC11663307

Identifier etd-PoppaNatal-8310.pdf (filename),usctheses-c89-297651 (legacy record id)

Legacy Identifier etd-PoppaNatal-8310.pdf

Dmrecord 297651

Document Type Dissertation

Rights Poppa, Natalie (Tasha)

Type texts

Source University of Southern California (contributing entity), University of Southern California Dissertations and Theses (collection)

Access Conditions The author retains rights to his/her dissertation, thesis or other graduate work according to U.S. copyright law. Electronic access is being provided by the USC Libraries in agreement with the a...

Repository Name University of Southern California Digital Library

Repository Location USC Digital Library, University of Southern California, University Park Campus MC 2810, 3434 South Grand Avenue, 2nd Floor, Los Angeles, California 90089-2810, USA