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Structure and function of the locus coeruleus across the lifespan
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Structure and function of the locus coeruleus across the lifespan
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Content
STRUCTURE AND FUNCTION OF THE LOCUS COERULEUS
ACROSS THE LIFESPAN
by
Shelby Lane Bachman
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
(GERONTOLOGY)
August 2022
Copyright 2022 Shelby Lane Bachman
ii
Acknowledgements
This work would not have been possible without the support and encouragement of my
mentors, collaborators, colleagues, friends, and family.
First and foremost, I thank my advisor, Mara Mather, for guidance and support
throughout graduate school. Your passion and creativity are an inspiration to me, and I am
grateful to have developed as a scientist under your mentorship.
I want to thank my coauthors on the manuscripts included in this dissertation and the
larger team of researchers involved in the HRV biofeedback study described in Chapters 1 and 2.
In particular, I am grateful to Noah Mercer for help implementing ANTs commands, Julian
Thayer for helpful methodological advice on physiological data analysis, and Hyun Joo Yoo and
Sumedha Attanti for validating the analyses in this dissertation. I also want to thank the National
Science Foundation and National Institute on Aging for my doctoral funding.
I am grateful to many members of the Emotion and Cognition Lab who provided
feedback on my ideas and results. I especially want to thank Martin Dahl for helpful
methodological advice and Alex Ycaza Herrera for always cheering me on.
My time in graduate school was particularly special because of the fantastic team of
research assistants I had the chance to mentor and work alongside. I want to thank Sumedha
Attanti, Katherine Chan, Ivy Hsu, Michael Kwan, and Juliana Lee, who were instrumental in
making the projects in this dissertation happen and who brought so much joy to my day-to-day
life during graduate school.
Finally, I am so grateful to my parents, Steve and Marjorie Bachman, and to my partner,
Matthias Elgeti, for unwavering support along my graduate school journey.
iii
Table of Contents
Acknowledgements …………………………..………………………….…………………. ii
List of Tables.…………………………………………………………………………..…… v
List of Figures ……………………………………..…………………………….…………. vii
Abbreviations ………………………...……………………….…….…………….……....... ix
Abstract ……………………………………………………….……………..……………… x
Introduction ………………………………………………………………….….…………. 1
The locus coeruleus: The brain’s arousal hub region ……………………………...... 1
Studying the locus coeruleus in vivo using magnetic resonance imaging ………...... 3
A potentially neuroprotective role of the locus coeruleus in aging …………………. 4
The role of the locus coeruleus in stress responses ………………………………….. 6
Potential influences on LC structure across the lifespan ……………………………. 9
Open questions regarding locus coeruleus structure and function ………………….. 12
Chapter 1: Daily heart rate biofeedback training decreases locus coeruleus MRI
contrast in younger adults ……………………………...………………………………….
14
Introduction ………………………………………………………………………….. 14
Methods .…………………………………………………………..……….………… 18
Results ……………………………………………………………………..………… 27
Discussion …………………………………………………………………………… 34
Chapter 2: Associations between locus coeruleus MRI contrast and physiological
responses to acute stress in younger and older adults ……………………………………
39
Introduction ………………………………………………………………………….. 39
Methods .…………………………………………………………..……….………… 43
Results ……………………………………………………………………..………… 61
Discussion …………………………………………………………………………… 71
Chapter 3: Isometric handgrip exercise speeds working memory responses in younger
and older adults ……………………………………………………………….....................
78
Introduction ………………………………………………………………………….. 78
Methods .…………………………………………………………..……….………… 80
Results ……………………………………………………………………..………… 93
Discussion …………………………………………………………………………… 109
Concluding Remarks ………………………………………..…………….……………….. 114
References ……….……………….…………………………………………………..…...... 117
iv
Appendix A: Chapter 1 Supplementary Material .…..…………………………………... 155
Supplementary Methods …………………………………………………………..... 155
Supplementary Results …………………………………………………………….... 162
Appendix B: Chapter 2 Supplementary Material .…..…………………………………… 167
Supplementary Methods …………………………………………………………….. 167
Supplementary Results …………………………………………………………….... 169
Appendix C: Chapter 3 Supplementary Material .…..………………………………....... 175
Supplementary Methods …………………………………………………………….. 175
Supplementary Results …………………………………………………………….... 179
v
List of Tables
Chapter 1: Daily heart rate biofeedback training decreases locus coeruleus MRI
contrast in younger adults ……………………………...………………………………..
14
Table 1. Sample characteristics …………………………………………………… 19
Table 2. Results of linear mixed effects analyses testing the fixed effects of
timepoint, training condition, and hemisphere on LC MRI contrast in younger and
older participants ……………………………………………………………...
29
Table 3. Results of linear mixed effects analysis testing the fixed effects of
training oscillatory power, hemisphere, and sex on change in LC MRI contrast in
younger participants ……………………………………………………………….
31
Chapter 2: Associations between locus coeruleus MRI contrast and physiological
responses to acute stress in younger and older adults …………………………………
39
Table 1. Sample characteristics …………………………………………………… 45
Table 2. Results of linear mixed effects analyses testing that during the acute
stress induction protocol, arousal measures differed during the baseline and
challenge phases and during the challenge and recovery phases ………………….
63
Table 3. Results of linear mixed effects analyses testing that during the acute
stress induction protocol, measures of HRV differed during the baseline and
challenge phases and during the challenge and recovery phases ………………….
66
Chapter 3: Isometric handgrip exercise speeds working memory responses in
younger and older adults ………………………………………………………………...
78
Table 1. Sample characteristics …………………………………………………… 82
Table 2. Results of mixed-design ANOVAs testing the effects of group, phase,
and age group on pupil diameter, heart rate, and sympathetic tone during the
handgrip protocol ……………………………………………………………….....
95
Table 3. Results of permuted, mixed-design ANOVAs testing the effects of group
and working memory load on n-back accuracy and reaction times ……………….
99
Table 4. Results of permuted, mixed-design ANOVAs testing the effects of
timepoint, group and age group on salivary alpha amylase and cortisol levels……
101
vi
Table 5. Results of mixed-design ANOVAs testing the effects of group and n-
back block number relative to handgrip offset on pupil diameter during n-back
fixation periods, heart rate during n-back blocks, and sympathetic tone during n-
back blocks …………………………………………………………………….......
105
Table 6. Results of mixed-design ANOVAs testing the effects of group and
working memory load on maximum pupil diameter and the onset time of
maximum pupil diameter during n-back trials …………………………………….
108
vii
List of Figures
Introduction ………………………………………………………………….….……….. 1
Figure 1. Hypothesized influences on LC MRI contrast in younger and older
adults ……………………………...………………………………………………
10
Chapter 1: Daily heart rate biofeedback training decreases locus coeruleus MRI
contrast in younger adults ……………………………...………………………………..
14
Figure 1. TSE scans from selected participants and LC delineation procedure ….. 20
Figure 2. Effect of HRV biofeedback training on LC MRI contrast in younger and
older participants …………………………………………………………………..
28
Figure 3. Effect of HRV biofeedback training on LC MRI contrast in younger
participants, stratified by sex ………………………………………………………
32
Figure 4. CREB activity changes that were associated with changes in LC MRI
contrast …………………………………………………………………………….
33
Chapter 2: Associations between locus coeruleus MRI contrast and physiological
responses to acute stress in younger and older adults …………………………………
39
Figure 1. Acute stress induction protocol ……………………………..………….. 46
Figure 2. LC delineation procedure …………………………….……………….... 54
Figure 3. Average measures of physiological arousal during each phase of the
stress induction protocol …………………………………………………………..
62
Figure 4. LC MRI contrast in the sample …………………………………………. 68
Figure 5. Visualizations of correlation matrices reflecting pairwise Pearson
correlations between LC MRI contrast and arousal during the stress induction
protocol ……………….............................................................................................
69
Figure 6. Results of partial least squares correlation analyses examining the
association between caudal LC MRI contrast and physiological arousal during the
stress induction protocol …………………………………………………………..
71
viii
Chapter 3: Isometric handgrip exercise speeds working memory responses in
younger and older adults ………………………………………………………………...
78
Figure 1. Overview of experiment, handgrip protocol, and auditory n-back task ... 83
Figure 2. Pupil diameter, heart rate and sympathetic tone and their associations
with handgrip effort during the handgrip protocol ………………………………...
94
Figure 3. Mean accuracy and reaction times on the n-back task …………………. 98
Figure 4. Salivary alpha amylase and cortisol levels during the experiment ……... 100
Figure 5. Pupil diameter during n-back fixation periods, heart rate during n-back
blocks, and sympathetic tone during n-back blocks ……………………………….
104
Figure 6. Smoothed mean pupil diameter time courses, maximum pupil diameter
during n-back trials, and onset time of maximum pupil diameter during n-back
trials ………………..……………………………………………………………....
107
ix
Abbreviations
ANOVA Analysis of variance
CES-D Center for Epidemiologic Studies Depression Scale
CI Confidence interval
CREB cAMP-responsive element binding protein
ECG Electrocardiogram
EMG Electromyogram
HF High-frequency
HRV Heart rate variability
LC Locus coeruleus
LF Low-frequency
MPRAGE Magnetization prepared rapid acquisition gradient echo
MRI Magnetic resonance imaging
PASAT Paced Auditory Serial Addition Task
PLS Partial least squares
RMSSD Root mean square of the successive differences
TAI Trait Anxiety Inventory
TSE Turbo spin echo
x
Abstract
There is growing evidence that the locus coeruleus (LC), a small nucleus in the brainstem,
is important for cognitive aging. As the brain’s primary source of the neurotransmitter
noradrenaline, the LC serves as an arousal hub region in the brain, integrating signals from many
brain regions and releasing noradrenaline to adaptively regulate behavior and cognition. Recent
advances in magnetic resonance imaging (MRI) have allowed for in vivo assessment of LC
structure, and many studies suggest that having a more structurally intact LC in later life is
associated with better cognition and brain health.
Beyond its protective role in aging, the LC is bidirectionally connected to the autonomic
nervous system and helps orchestrate the central nervous system’s response to stress. During
acute stress, the LC promotes adaptive responses that help complete the stress cycle. Chronic
stress, on the other hand, has maladaptive effects on LC function and structure. Understanding
how the LC’s structure and function contribute to cognition and arousal responses across the
lifespan will be important for understanding why the LC is important for cognitive aging.
The aim of this dissertation was to identify physiological and behavioral correlates of LC
structure and function across the lifespan. In Chapter 1, we tested whether LC MRI contrast, an
in vivo measure of LC structure, can be changed through heart rate variability biofeedback, an
intervention targeting the autonomic nervous system. We found that LC MRI contrast decreased
in younger participants who performed daily biofeedback training and that decreases in left LC
MRI contrast were related to decreases in sympathetic nervous system signaling. These findings
provide novel evidence that LC structure can be changed over time in younger adults.
In Chapter 2, we attempted to fill a gap in the literature regarding potential functional
correlates of LC MRI contrast by testing associations between LC MRI contrast and
xi
physiological arousal during an acute stress induction task. We found that in older adults, having
higher MRI contrast of the caudal LC, which sends relatively more noradrenergic projections to
the spinal cord compared to the rostral LC, was associated with more pronounced physiological
responses to acute stress. These results suggest that having a more intact LC in aging is
associated with better neurovisceral integration.
Finally, in Chapter 3, we built upon animal and pharmacological research linking working
memory to noradrenaline and tested whether changes in LC activity, induced via isometric
handgrip exercise, affected subsequent working memory performance. We found that both
younger and older adults who performed handgrip had faster working memory reaction times,
and we found that handgrip both increased concurrent arousal and decreased subsequent tonic
pupil diameter. Our findings suggest that performing short bouts of isometric handgrip exercise
can temporarily improve working memory, and they implicate the noradrenergic system in
handgrip’s effects on performance. Collectively, these findings link non-invasive measures of
LC structure and function to cognition and physiological arousal across the lifespan.
1
Introduction
A central goal of aging research is to identify factors that protect against age-related
cognitive decline. Although decades of research have focused on the importance of brain regions
such as the prefrontal cortex and hippocampus for later-life cognitive outcomes, the role of
neuromodulatory brain systems in cognitive aging has recently come into focus (Li et al., 2001;
Theofilas et al., 2015). In particular, a growing body of literature has focused on the role of the
brain’s noradrenergic system, the hub of which is a small brainstem nucleus known as the locus
coeruleus (LC; Schwarz & Luo, 2015).
The locus coeruleus: The brain’s arousal hub region
Consisting of only 50,000 neurons in humans and located at the floor of the fourth
ventricle within the pons, the LC serves as the brain’s primary source of the neurotransmitter
noradrenaline (Dahlström & Fuxe, 1964; Schwarz & Luo, 2015). Efferent projections originating
in the LC release noradrenaline throughout the brain and spinal cord, regulating wakefulness,
promoting arousal, and modulating sensory processing, selective attention, cognitive flexibility,
working memory and episodic memory (Aston-Jones & Bloom, 1981; Bari et al., 2020; Berridge
& Waterhouse, 2003; Sara, 2009). Noradrenaline released from the LC reaches distributed
cortical and subcortical regions including the frontoparietal attention network, the hippocampus
and the thalamus (Loizou, 1969; Schwarz & Luo, 2015). The LC also regulates components of
the autonomic nervous system, with noradrenergic projections from the LC providing excitatory
input to preganglionic sympathetic neurons and inhibitory input to parasympathetic nuclei
(Samuels & Szabadi, 2008; Wood et al., 2017).
The LC also receives inputs from brain regions implicated in arousal. These include the
central nucleus of the amygdala and paraventricular nucleus of the hypothalamus, inputs that
2
mediate cardiovascular stress responses (Wood & Valentino, 2017), as well as medullary nuclei
including the nucleus paragigantocellularis, an autonomic control center that regulates breathing
and sympathetic activity (Aston-Jones et al., 1986; Aston-Jones et al., 1991). The vagus nerve
within the parasympathetic nervous system furthermore provides inputs to the LC by way of the
medullary nucleus tractus solitarii (Badran et al., 2018; Fornai et al., 2011). As a result of its
distributed connections, the LC is well-positioned to serve as an arousal hub region of the brain,
integrating signals from multiple brain systems and releasing noradrenaline throughout the
central nervous system to regulate vigilance, arousal, behavior, and cognition (Berridge &
Waterhouse, 2003).
As the primary source of noradrenaline for a distributed set of brain regions, the LC
modulates cognitive processes such as selective and sustained attention, cognitive flexibility,
working memory, and episodic memory (Berridge & Waterhouse, 2003; Sara, 2009). The
widespread modulatory effects of the LC are achieved in part due to noradrenaline’s effects on
target neurons; specifically, noradrenaline promotes neural gain, enhancing activity related to
task-relevant information while reducing spontaneous activity (Waterhouse et al., 1980)
potentially also through interactions with glutamate (Mather et al., 2015). In other words, at
cortical targets, LC release of noradrenaline facilitates processing of information that is salient to
the task at hand while reducing the processing of irrelevant information.
How LC activity affects behavior and cognition furthermore depends on its firing
patterns, which can be described as tonic or phasic (Usher, 1999). Tonic, or basal, firing can be
distinguished from phasic responses that occur in response to salient or task-relevant stimuli
(Aston-Jones & Cohen, 2005). The relationship between levels of tonic LC activity and
performance on tasks requiring focused attention demonstrates an inverted-U shape: very low
3
and high tonic activity levels are linked to drowsiness and hypervigilance, respectively, leading
to impaired performance, whereas moderate levels of tonic activity permit enhanced phasic, task-
related responses and more optimal task performance (Arnsten, 1998; Aston-Jones et al., 1994;
Aston-Jones & Cohen, 2005; Bouret & Sara, 2005). Supporting this idea, phasic LC activity is
thought to serve as a network reset signal that guides attentional processing in frontoparietal
brain regions towards what is most relevant to the task at hand (Corbetta et al., 2008; Dahl et al.,
2022a; Lee et al., 2018; Sara & Bouret, 2012). By shifting its firing patterns, the LC can
therefore adaptively regulate behavioral and cognitive responses (Aston-Jones & Cohen, 2005;
Uematsu et al., 2017).
Studying the locus coeruleus in vivo using magnetic resonance imaging
Due to the LC’s small size and location within the pons, it has historically been
challenging to study. Recently, assessing the LC in vivo in humans has been possible through the
development of specialized magnetic resonance imaging (MRI) sequences that exhibit elevated
signal intensity in the LC (Betts et al., 2019b; Sasaki et al., 2006). These include turbo spin echo
(TSE) sequences which are subject to incidental magnetization transfer effects and dedicated
magnetization transfer preparations (Betts et al., 2019b; Trujillo et al., 2019; Trujillo et al.,
2017). On resulting MRI scans, the LC appears as a hyperintense region relative to surrounding
pontine tissue (Keren et al., 2009; Shibata et al., 2006). Furthermore, the LC can be reliably
distinguished from surrounding regions using both manual segmentation and semi-automatic
delineation approaches (Betts et al., 2017; Dahl et al., 2019; Dünnwald et al., 2021; Liu et al.,
2017; Ye et al., 2021).
Although the precise mechanism giving rise to signal contrast in these MRI protocols
remains to be determined (Betts et al., 2019b; Watanabe et al., 2019), signal contrast is thought
4
to reflect neuromelanin, a pigment synthesized upon catecholamine oxidation that accumulates
within neurons across the lifespan (Mann & Yates, 1974), at especially high concentrations
within neurons of the catecholaminergic LC and substantia nigra (Fedorow et al., 2005; Zecca et
al., 2002). Histologic approaches have provided additional evidence that elevated signal intensity
in these so-called “neuromelanin-sensitive” MRI sequences reflects neuromelanin. For one,
locations of elevated signal intensity correspond to the locations of neuromelanin-containing
cells (Keren et al., 2009). Further, signal intensity in these locations relative to surrounding tissue
is correlated with neuromelanin cell content (Cassidy et al., 2019; Keren et al., 2015).
Accordingly, peak contrast of LC signal intensity relative to intensity in a neighboring pontine
reference region is calculated from these sequences as a ratio (Liu et al., 2017). This ratio will
henceforth be referred to as “LC MRI contrast” and is thought to reflect LC structural integrity
(Betts et al., 2019b).
A potentially neuroprotective role of the locus coeruleus in aging
A growing body of literature suggests that the LC may be important for later-life
cognitive outcomes (Beardmore et al., 2021; Matchett et al., 2021; Mather & Harley, 2016). Post
mortem investigations have demonstrated that the LC is one of the first brain regions where
neurofibrillary tangles composed of hyperphosphorylated tau accumulate in the progression of
Alzheimer’s disease (Braak et al., 2011; Theofilas et al., 2017). In fact, tau deposition in the LC
can occur years or even decades prior to the accumulation of amyloid or onset of cognitive
symptoms (Braak et al., 2011; Ehrenberg et al., 2017). Neuronal loss in the LC, which tends to
occur years after tau deposition (Chalermpalanupap et al., 2017; Theofilas et al., 2017) is
especially pronounced relative to that in other subcortical nuclei in Alzheimer’s disease (Zarow
et al., 2003), having effects on regulation of noradrenergic signaling throughout the nervous
5
system (Ciampa et al., 2022; Malatt & Tagliati, 2022; Weinshenker, 2018). In mice, lesions to
the LC worsen pathological episodic memory deficits in models of Alzheimer’s disease
(Chalermpalanupap et al., 2018) and lead to elevated levels of inflammation and amyloid
accumulation in brain regions innervated by the LC (Heneka et al., 2006). In humans, the
importance of having a relatively intact LC has also been demonstrated: A longitudinal study of
older adults reported that individuals with relatively higher post mortem cell density in the LC
had slower rates of cognitive decline before death relative to older adults with lower LC cell
density (Wilson et al., 2013).
Recent studies using neuromelanin-sensitive MRI to study LC structure also support the
notion of a neuroprotective role of having a structurally intact LC in aging (Beardmore et al.,
2021; Betts et al., 2019b; Kelberman et al., 2020). Relative to those with lower LC MRI contrast,
older adults with higher LC contrast have better episodic memory performance (Dahl et al.,
2019; Hämmerer et al., 2018), global cognitive performance (Elman et al., 2021a; Liu et al.,
2020), higher cognitive reserve (Clewett et al., 2016), greater cerebral cortical thickness
(Bachman et al., 2021), and more intact cortical gray matter microstructure (Elman et al., 2021b).
Lower LC contrast has furthermore been linked to elevated risk for mild cognitive impairment
(Elman et al., 2021a), and individuals with Alzheimer’s disease and mild cognitive impairment
have lower LC contrast relative to healthy controls (Dahl et al., 2022b; Hou et al., 2021;
Takahashi et al., 2015). In line with these findings, having lower LC contrast is correlated with
more advanced Braak stage (Cassidy et al., 2022), higher levels of amyloid-beta in cerebrospinal
fluid (Betts et al., 2019a) and tau accumulation in medial and lateral temporal brain regions
(Jacobs et al., 2021), and in tau-positive individuals, LC contrast is correlated with severity of
neuropsychiatric symptoms (Cassidy et al., 2022). Together, this body of evidence suggests that
6
LC contrast in aging may index neurodegenerative processes and that having a more structurally
intact LC in older adulthood may be neuroprotective.
Why might having a more structurally intact LC in aging be neuroprotective? One
possibility is that having relatively higher cell density in the LC leads to relatively higher levels
of noradrenergic signaling in the brain. Noradrenaline has a host of protective effects against
common sources of age-related neurodegeneration: It facilitates clearance of amyloid-beta
(Heneka et al., 2010; Kong et al., 2010), promotes anti-inflammatory responses (Braun et al.,
2014; Feinstein et al., 2016), and facilitates hippocampal neurogenesis (Jhaveri et al., 2010;
Kulkarni et al., 2002). Therefore, having more intact LC structure in older adulthood could lead
to relative preservation of cortical gray matter and intact noradrenergic neuromodulation in
cortical regions such as the frontoparietal attention network, manifesting as better selective
attention and working memory performance. On the other hand, declines in LC structure in aging
could lead to relatively less preservation of cortical gray matter and poorer cognitive
performance. Consistent with a potentially important link between the noradrenergic system and
frontoparietal cortex in aging, one theory is that a right-lateralized frontoparietal network
facilitates the protective effects of cognitive reserve (Robertson, 2014). Therefore, LC
noradrenergic modulation of the right frontoparietal network may be important for age-related
outcomes (Robertson, 2013).
The role of the locus coeruleus in stress responses
Besides having a potentially neuroprotective role in aging, the LC plays a major role in
the central nervous system’s response to acute stress. The LC is robustly activated in response to
stressful, threatening, and fearful stimuli (Morilak, 2007; Valentino & Van Bockstaele, 2008). In
these contexts, corticotropin-releasing factor from the hypothalamus, bed nucleus of the stria
7
terminalis, and central nucleus of the amygdala acts on the LC to increase its tonic firing and
release of noradrenaline (Curtis et al., 1997; Valentino & Foote, 1988), promoting heightened
vigilance, attentional reorienting, and the fight-or-flight response (Bouret & Sara, 2005;
Valentino & Van Bockstaele, 2005; Wood & Valentino, 2017). The LC also plays a role in
coordinating cardiovascular responses to acute stress due to its projections to autonomic nuclei
(Lamotte et al., 2021; Wood & Valentino, 2017). These include increased heart rate and blood
pressure, reflecting increased sympathetic activation, as well as decreased heart rate variability
(HRV), reflecting parasympathetic withdrawal (Rab & Admon, 2021). More broadly, the LC’s
role in the acute stress response highlights its position within the central autonomic network, the
set of brain regions responsible for regulating physiological responses to situations that disrupt
homeostasis (Benarroch, 1993; Lamotte et al., 2021).
In the short term, the responses of the LC to stress are adaptive, facilitating the detection
and avoidance or elimination of the stressor at hand (Valentino & Van Bockstaele, 2008).
However, prolonged exposure to stress can have maladaptive consequences for the LC and
central noradrenergic signaling (Morris et al., 2020a; Yamamoto et al., 2014). Specifically,
chronic stress exposure alters LC morphology, with corticotropin-releasing factor exposure
promoting growth of neuronal processes (Swinny & Valentino, 2006). In rodents, repeated stress
exposure leads to terminal axonal sprouting, more complex dendritic arborization and a greater
number of primary processes on LC neurons (Borodovitsyna et al., 2018; Nakamura et al., 1989).
Chronic stress also affects the functioning of LC neurons: In rodents, repeated stress exposure
increases LC activity and LC sensitivity, manifesting as anxiety-like behaviors (Jedema & Grace,
2003; Mana & Grace, 1997; McCall et al., 2015; Pavcovich et al., 1990). The effects of stress
exposure may also interact with ongoing neurodegenerative processes in the LC, with a stress-
8
related tonic pattern of LC firing reported to worsen LC neuronal health and accentuate the
deleterious effects of early tau accumulation (Omoluabi et al., 2021).
Based on evidence of LC structure and function being affected by stress over time, one
hypothesis is that the LC plays a role in the development of pathological anxiety and
susceptibility to stress (Morris et al., 2020a). In humans, noradrenergic hyperactivity and
concomitant hyperarousal symptoms are common in stress- and anxiety-related disorders (Brehl
et al., 2020; Hendrickson & Raskind, 2016; Ressler & Nemeroff, 2000). For instance, individuals
with generalized anxiety disorder exhibit elevated levels of noradrenaline and the noradrenaline
metabolite 3-methoxy-4-hydroxyphenethylene glycol relative to controls (Sevy et al., 1989).
Furthermore, individuals with post-traumatic stress disorder exhibit increased LC responses to
loud sounds, elevated baseline levels of noradrenaline, and exaggerated levels of sympathetic
arousal at rest and during stress (Blechert et al., 2007; Geracioti et al., 2001; Naegeli et al., 2018;
O’Donnell et al., 2004). Supporting the potential relevance of the LC for stress vulnerability, a
recent study reported that in healthy younger adults, greater responsivity of the LC during an
affective conflict task predicted later increases in stress and depression symptoms, whereas lower
LC responsivity predicted stress resilience (Grueschow et al., 2021).
Yet despite evidence that the LC is engaged during acute stress and affected by chronic
stress, there have been no studies examining whether LC structure is associated with
physiological responses to stressful situations, which are affected in stress- and anxiety-related
disorders (Blechert et al., 2007; Naegeli et al., 2018; Tolin et al., 2021) and associated with many
adverse health outcomes (Chida & Steptoe, 2010; Panaite et al., 2015; Treiber et al., 2003;
Turner et al., 2020). A recent study reported that MRI-assessed LC volume was positively
correlated with anxious arousal in younger adults and higher in those with stress and anxiety
9
disorders relative to healthy controls (Morris et al., 2020b). We found that younger adults with
higher MRI signal contrast in the LC’s caudal aspect had lower cortical thickness (Bachman et
al., 2021), with lower cortical thickness being linked to pathological anxiety across studies
(Molent et al., 2018). In addition, during a fear conditioning task, individuals with higher LC
volume had lower HRV (Mather et al., 2017), a measure of parasympathetic control over heart
rate which is lower in individuals with anxiety disorders (Chalmers et al., 2014) and considered a
marker of psychopathology (Beauchaine & Thayer, 2015). These findings provide initial support
for the notion that in younger adults, a more structurally intact LC may be related to poorer
stress- and anxiety-related outcomes.
Potential influences on LC structure across the lifespan
Taken together, the bodies of evidence outlined in the sections above suggest that LC
structure is associated with poorer stress and anxiety outcomes in younger adults but better
cognitive and neural outcomes in older adults. Why might relationships with LC structure differ
for younger and older adults? One possibility is that multiple factors affect LC structure, each
manifesting differently across the lifespan. One factor which likely impacts LC structure is
neurodegeneration, which increases across the adult lifespan and begins especially early in the
LC (Braak et al., 2011; Theofilas et al., 2017). Older adults with relatively less
neurodegeneration are expected to have relatively more intact LC structure and LC MRI contrast
and fare better in terms of cognitive outcomes than those with higher levels of
neurodegeneration. In contrast, older adults with relatively more neurodegeneration should
exhibit relatively less intact LC structure, lower LC contrast and poorer cognitive outcomes.
A second factor which may influence LC structure - and one whose effects may manifest
earlier in adulthood - is stress. Chronic stress affects not only LC morphology, increasing
10
neuronal outgrowth and primary processes on LC neurons (Borodovitsyna et al., 2018;
Nakamura et al., 1989), but also LC excitability and sensitivity (Jedema & Grace, 2003; Mana &
Grace, 1997). These effects should in turn be associated with anxiety-like responses and
behaviors. Consequently, younger adults with higher LC contrast are expected to exhibit greater
physiological arousal responses to stress and report higher levels of stress and anxiety than those
with lower LC contrast. Considering how neurodegeneration and stress may affect LC structure
across the lifespan, our hypothesis is that earlier in adulthood, when relatively less
neurodegeneration has occurred, stress is the primary factor contributing to LC contrast, whereas
later in adulthood, when relatively more neurodegeneration has taken place, LC contrast begins
to reflect neurodegeneration processes moreso than stress (Figure 1).
Figure 1
Hypothesized influences on LC MRI contrast in younger and older adults
11
Yet another possibility is that associations with LC structure in younger versus older
adults depend on LC topography. Growing evidence suggests that the LC is modular, with
groups of neurons projecting to distinct brain regions and controlling different aspects of
behavior (Chandler et al., 2014; Totah et al., 2019; Uematsu et al., 2017). In particular, recent
work suggests a distinction between the LC’s rostral and caudal segments; the LC’s more caudal
aspect sends relatively more projections to the spinal cord than its rostral aspect, which contains
neurons that project throughout the brain and spinal cord (Hirschberg et al., 2017; Loughlin et
al., 1986). Thus a functional distinction between the rostral and caudal LC has been proposed,
with the rostral LC regulating wakefulness, attention and behavior and the caudal LC regulation
cardiovascular and respiratory activities (Beardmore et al., 2021). There is also evidence that the
LC’s rostral segment may be the segment more affected in aging and therefore more associated
with age-related outcomes. For instance, the rostral LC demonstrates greater age-related decline
in structure compared to the caudal LC (Dahl et al., 2019; Liu et al., 2019), as well as greater
Alzheimer’s-related neuronal loss than the caudal LC (Zarow et al., 2003). In addition, rostral
LC structure has been associated with episodic memory performance (Dahl et al., 2019) and
patterns of cortical microstructure that may reflect early neurodegenerative processes (Elman et
al., 2021b). Furthermore, we found that contrast of the LC’s rostral aspect was positively
associated with cortical thickness in older adults, whereas contrast of the LC’s caudal aspect was
negatively associated with thickness in younger adults (Bachman et al., 2021). Since projections
from the LC’s caudal segment to the spinal cord propagate peripheral arousal responses, one
untested possibility is that structure of the caudal LC may be more associated with stress- and
arousal-related outcomes.
12
Open questions regarding locus coeruleus structure and function
Although large bodies of work have demonstrated the LC’s involvement in central
nervous system stress responses and the LC’s potential relevance for age-related outcomes, there
remain many outstanding questions regarding structure and function of the LC. For instance,
there have been no studies assessing whether LC MRI contrast can be changed over time. As
current work suggests that LC contrast is associated with cognitive outcomes in aging and may
offer an index of neurodegenerative processes (Beardmore et al., 2021; Jacobs et al., 2021; Liu et
al., 2020), understanding factors that affect LC contrast over time could help identify strategies
to slow cognitive aging. In particular, interventions targeting the autonomic nervous system offer
potential to change LC contrast due to the LC’s bidirectional connections with the autonomic
nervous system (Aston-Jones et al., 1991; Wood et al., 2017). One such intervention is HRV
biofeedback, which elicits high-amplitude oscillations in heart rate and has been demonstrated to
reduce self-reported stress and anxiety (Goessl et al., 2017). Thus, in Chapter 1 (Bachman et al.,
2022b), we will report results of a study testing the effects of a 5-week HRV biofeedback
intervention on LC contrast in younger and older adults.
In addition, although many studies have linked LC contrast to cognitive and brain
outcomes in aging (Dahl et al., 2019; Elman et al., 2021a; Elman et al., 2021b; Liu et al., 2020)
and current evidence suggests that LC contrast reflects LC structure (Betts et al., 2019b), it is
unclear whether the LC contrast measure has a relationship with functioning of the LC.
Furthermore, despite the LC’s demonstrated innervation by and projections to the autonomic
nervous system, there has been little empirical work testing whether LC structure is related to
sympathetic and parasympathetic activity. Testing these possibilities could identify whether LC
structure provides an index of stress vulnerability and advance current understanding of why
13
having a structurally intact LC is important for age-related outcomes. Therefore, in Chapter 2
(Bachman et al., 2022c), we will examine associations between LC contrast and measures of
physiological arousal during rest, acute stress reactivity and acute stress recovery in younger and
older adults.
Finally, turning to aspects of LC function, a prominent theory of LC function suggests
that tuning patterns of LC firing from a tonic to a phasic mode can affect cognitive performance
(Aston-Jones & Cohen, 2005). Shifting noradrenergic activity patterns thus represents a potential
strategy to temporarily improve cognitive performance in aging, yet studies testing this
possibility in humans remain scarce. In particular, modulating noradrenergic activity may offer
potential for temporarily improving performance on domains such as selective attention and
working memory that engage the brain’s frontoparietal attention network, a major target of
noradrenergic modulation by the LC (Pickel et al., 1974; Schwarz & Luo, 2015). Performing
isometric exercise can shift patterns of LC activity indicated non-invasively via pupil diameter
(Joshi et al., 2016; Murphy et al., 2014), and boost cognitive performance (Mather et al., 2020;
Nielson et al., 1996; Washio et al., 2021). Therefore, in Chapter 3 (Bachman et al., 2022a), we
will present the results of a study testing how isometric exercise affects patterns of noradrenergic
activity and subsequent working memory performance in younger and older adults.
14
Chapter 1: Daily heart rate variability biofeedback training decreases locus coeruleus MRI
contrast in younger adults
1. Introduction
The LC, a small nucleus in the brainstem, helps coordinate the brain’s arousal system.
Situated at the lateral floor of the fourth ventricle, the LC serves as the brain’s primary source of
the neurotransmitter noradrenaline (Schwarz & Luo, 2015). Noradrenaline released from the LC
to the brain and spinal cord regulates wakefulness, coordinates adaptive behavior, and modulates
processes of learning and memory (Berridge & Waterhouse, 2003; Sara, 2009). As an arousal
center, the LC is involved in the central stress response (Koob, 1999): During acute stress,
corticotropin-releasing factor released onto the LC promotes elevated levels of tonic LC neuronal
activity, thereby facilitating cortical noradrenaline release, the peripheral sympathetic fight-or-
flight response and anxiety-like behaviors (Curtis et al., 1997; McCall et al., 2015; Valentino &
Van Bockstaele, 2008). Experienced over the longer term, stress can have maladaptive effects on
LC function and structure (Morris et al., 2020a), with chronic corticotropin-releasing factor
exposure being linked to increased LC neuronal sensitivity, firing rates, and dendritic
arborization in rodents (Borodovitsyna et al., 2018). Yet the LC has not only been implicated in
arousal and stress: It is also the first brain location where tau pathology accumulates in the
progression of Alzheimer’s disease (Braak et al., 2011). LC neurodegeneration is characteristic
of Alzheimer’s disease (Chalermpalanupap et al., 2017), and older adults with relatively lower
cell density within the LC exhibit faster rates of cognitive decline prior to death (Wilson et al.,
2013).
In recent years, specialized MRI sequences have accelerated study of the human LC via
their ability to quantify LC structure in vivo (Sasaki et al., 2006). In these sequences, the LC
15
exhibits elevated MRI signal contrast relative to surrounding tissue, and LC MRI contrast is
thought to reflect LC structural integrity (Betts et al., 2019b). Across studies, having higher LC
contrast has been linked to better cognitive outcomes in older adults (Dahl et al., 2019;
Hämmerer et al., 2018; Liu et al., 2020), as well as reduced risk of developing mild cognitive
impairment (Elman et al., 2021a) and fewer preclinical Alzheimer’s disease processes (Jacobs et
al., 2021). We recently reported that contrast of the rostral LC was positively associated with
cortical thickness in various brain regions in older adults, whereas caudal LC contrast showed
some negative associations with cortical thickness in younger adults (Bachman et al., 2021).
Thus, higher LC contrast may not always be a positive indicator. For instance, one study found
that LC volume - also quantified using an MRI sequence that yields elevated signal intensity in
the LC - was positively correlated with anxious arousal and self-reported general distress in
younger adults (Morris et al., 2020b). Likewise, another study found that participants with
relatively higher LC contrast had lower heart rate variability (Mather et al., 2017). These
findings suggest that tonic high noradrenergic activity associated with stress, or a relative
dominance of sympathetic over parasympathetic activity, are also associated with high LC
contrast in younger adults. Our hypothesis for these seemingly discrepant findings in younger
and older adults is that different factors contribute to LC contrast across the lifespan: Earlier in
life, stress is the primary factor influencing individual differences in LC contrast, whereas later
in life, neurodegeneration is the dominant factor influencing individual differences in LC
contrast.
Yet despite what has been learned about the LC from recent MRI studies, no published
studies have assessed whether LC MRI contrast can be changed. What might give rise to changes
in LC contrast over time? Because the LC is a key player in the stress response and has
16
bidirectional connections with the parasympathetic and sympathetic branches of the autonomic
nervous system (Wood et al., 2017), we reasoned that an intervention targeting the autonomic
nervous system could influence both LC structure and function. One such intervention is HRV
biofeedback (Lehrer & Gevirtz, 2014). Individuals with higher HRV, an index of
parasympathetic control over heart rate and autonomic regulation (Mulcahy et al., 2019; Thayer
& Lane, 2000), are better able to regulate their emotions and exhibit reduced physiological
responses to stressors (Thayer & Lane, 2009; Weber et al., 2010), relative to those with lower
HRV. HRV can be systematically manipulated through biofeedback that involves slow, paced
breathing and simultaneous feedback on the coupling between heart rate oscillations and
breathing (Lehrer & Gevirtz, 2014). Slow breathing, particularly at a pace around 10 seconds per
breath, elicits maximally high-amplitude oscillations in heart rate (Lehrer et al., 2003). Slow
breathing also stimulates the vagus nerve (Brown & Gerbarg, 2005), which sends projections to
the LC by way of the nucleus tractus solitarii (Badran et al., 2018; Fornai et al., 2011).
Performing HRV biofeedback over a period of weeks has been shown to reduce levels of stress
and anxiety (Goessl et al., 2017) in younger as well as older adults (Jester et al., 2019), but it is
unknown whether HRV-biofeedback affects the LC’s structure and function.
Here, we examined whether HRV biofeedback affected LC MRI contrast and sympathetic
activity. Younger and older participants completed 5 weeks of HRV biofeedback training as part
of a clinical trial testing the effects of HRV biofeedback training on brain regions involved in
emotion regulation (Clinicaltrials.gov NCT03458910 “Heart Rate Variability and Emotion
Regulation,” Nashiro et al., 2021). Participants in an experimental condition completed daily
biofeedback involving slow, paced breathing to increase heart rate oscillations and HRV,
whereas participants in an active control condition completed daily biofeedback training
17
designed to decrease heart rate oscillations and HRV. Both before and after the 5-week training
period, we assessed LC contrast in all participants using TSE MRI scans that exhibit elevated
signal intensity in the LC. Based on prior work demonstrating beneficial effects of HRV
biofeedback on emotional well-being, and in line with our hypotheses regarding LC contrast
reflecting stress in younger adults, we expected that performing 5 weeks of HRV biofeedback
training would decrease LC contrast in younger participants. Conversely, as we hypothesize that
neurodegeneration, more so than stress, shapes LC contrast in older adulthood, we predicted that
HRV biofeedback would either not change or would increase LC contrast in older participants.
Furthermore, in a subset of younger participants, we collected blood samples before and after the
training period to assess changes in a health-relevant index of sympathetic nervous system
activity – blood cell expression of genes regulated by the cAMP-responsive element binding
protein (CREB) family of transcription factors, which mediates beta-adrenergic signaling from
the sympathetic nervous system (Cole et al., 2010; Mayr & Montminy, 2001). In this subset, we
predicted that training-related decreases in LC contrast would be coupled with decreases in
sympathetic nervous system signaling and thereby reduce expression of CREB-regulated gene
transcripts. Previous research has validated blood cell CREB-associated RNA expression levels
as a measure of beta-adrenergic signaling (Brown et al., 2010; Cole et al., 2005; Powell et al.,
2013).
18
2. Methods
2.1. Participants
Data were collected as part of an intervention study testing the effects of 5 weeks of HRV
biofeedback training in younger and older adults (for a full description of the study, see Nashiro
et al., 2021). Participants in the study were assigned to one of two conditions. Those in an
increase-oscillations (Osc+) condition completed 20-40 minutes of daily biofeedback training
involving slow, paced breathing which was designed to increase heart rate oscillations and HRV.
Participants in a decrease-oscillations (Osc-) condition completed 20-40 minutes of biofeedback
training per day designed to decrease heart rate oscillations and HRV. Eligible participants were
healthy, MRI-eligible younger and older adults recruited from the University of Southern
California and Los Angeles communities. Individuals who regularly practiced biofeedback
training or breathing techniques were excluded from participation. Older adults were screened
for cognitive dysfunction by telephone using the TELE interview (Gatz et al., 1995); individuals
scoring below 16 were excluded from participation.
As part of the intervention study which lasted 7 weeks, MRI assessments were conducted
at a pre-training timepoint (second study week), before participants learned about or practiced
the intervention, and following 5 weeks of biofeedback training (seventh study week). A total of
175 participants (115 younger, 60 older) completed pre- and/or post-training MRI assessments,
yielding a total of 325 TSE scans (detailed breakdown in Appendix A, Section 1.1). Following
exclusions for artifact or motion on native TSE scans (Section 2.3.1), 287 scans were used for
LC delineation. Additional exclusions were applied due to artifact after warping TSE scans to
MNI152 space (Section 2.3.1). In addition, blood samples were collected from a subset of 54
younger participants at pre- and post-training timepoints (first and sixth study weeks,
19
respectively) to assess change in expression of genes regulated by CREB. A total of 129
participants (93 younger, 36 older) with LC contrast values and/or blood-based measures
available at both timepoints were included for analysis. Characteristics of this sample are
presented in Table 1. The University of Southern California Institutional Review Board approved
the study. All participants provided written, informed consent prior to participation and received
monetary compensation for their participation.
Table 1
Sample characteristics
Age
group
Condition N
N (%)
Female
Age, mean
(SD)
Age,
range
Edu, mean
(SD)
Edu,
range
Younger Osc+ 47
26
(55.3%)
22.64
(2.57)
18-28
15.99
(1.87)
12-20
Younger Osc- 46
24
(52.2%)
22.57
(3.24)
18-31
15.74
(2.64)
12-24
Older Osc+ 17
12
(70.6%)
65 (6.86) 55-80
17.12
(2.71)
14-25
Older Osc- 19
15
(78.9%)
65.21
(5.61)
57-77
16.53
(2.39)
14-22
Note. Age and education are expressed in years. Osc+ = increase-oscillations condition; Osc- =
decrease-oscillations condition.
2.2. MRI data collection
MRI data were collected at the University of Southern California David and Dana
Dornsife Neuroimaging Center, on a Siemens Magnetom Trio 3T MRI scanner with a 32-
channel head coil. Sequences relevant to the present analyses are described below.
20
A high-resolution, T1-weighted magnetization prepared rapid acquisition gradient echo
(MPRAGE) scan was acquired (TR = 2300ms, TE = 2.26 ms, flip angle = 9°, bandwidth = 200
Hz/Px, isometric voxel size = 1.0mm
3
, no gap between slices, 175 volumes). Based on the
MPRAGE scan, a two-dimensional, multi-slice TSE scan was collected by aligning the field of
view perpendicular to the respective participant’s brainstem. Parameters of this TSE sequence
were as follows: TR = 750ms, TE = 12ms, flip angle = 120°, bandwidth = 287 Hz/Px, voxel size
= 0.43 x 0.43 x 2.5mm, gap between slices = 1mm. The TSE sequence included 11 axial slices
and covered the entire pons. TSE scans from randomly selected participants are shown in Figure
1A.
Figure 1
TSE scans from selected participants and LC delineation procedure
Figure 1. (A) Turbo spin echo (TSE) scans from randomly selected younger (top) and older
(bottom) participants. (B) Sagittal view of TSE template (green) overlaid onto whole-brain
template, both warped to MNI152 0.5mm (linear) space. (C) Detailed axial view of TSE
template, warped to MNI152 space. (D) TSE template, warped to MNI152 space, overlaid with
locus coeruleus meta-mask and pontine reference region from Dahl et al. (2022b), which were
used for calculation of LC contrast ratios.
21
2.3. MRI data analysis
2.3.1. LC delineation. We used a semi-automated method to delineate the LC on all
available pre- and post-training TSE scans based on approaches described by Dahl et al. (2019)
and Ye et al. (2021). LC delineation steps were performed using Advanced Normalization Tools
(ANTs; Version 2.3.4; Avants et al., 2011; http://stnava.github.io/ANTs/). Visualization steps
were performed using ITK-SNAP (Version 3.6.0; Yushkevich et al., 2006;
http://www.itksnap.org). Parameters for each step are described in Appendix A (Section 1.2).
All TSE scans were first visually inspected; scans with excessive motion or susceptibility
artifact overlapping the LC or pons (n = 34), incorrect positioning (n = 3), or different resolution
(n = 1) were excluded from LC delineation (Appendix A, Section 1.1). The remaining TSE and
corresponding MPRAGE scans were upsampled to twice their native resolution using the
ResampleImage ANTs routine. Upsampled MPRAGE scans were used to generate a whole-brain
template with the antsMultivariateTemplateConstruction.sh routine (Figure 1B; see Appendix A,
Section 1.2 for a description of template-building procedures). Each TSE scan was then
coregistered to its corresponding whole-brain template-coregistered MPRAGE scan, using the
antsRegistrationSyNQuick.sh routine. All coregistered TSE scans were used to build a TSE
template (Figures 1B and 1C). Using the antsRegistrationSyN.sh routine, the resulting TSE
template was coregistered to the whole-brain template to ensure spatial alignment. The whole-
brain template was then coregistered to MNI152 0.5mm (linear) standard space, in order to
facilitate comparison with previously-published LC maps. Transforms from all template-building
and coregistration steps described above were applied in a single step to warp upsampled TSE
scans to MNI152 space, using the antsApplyTransforms.sh routine. In addition, transforms from
the final coregistration steps were applied to warp the TSE template to MNI152 space (Figure
22
1C). As a validation step, we examined whether locations of hyperintensity on the TSE template
in MNI152 space aligned with the location of the Dahl et al. (2022b) meta-map, which was
generated by aggregating across published LC maps and thus reflected a plausible LC volume of
interest with high agreement across studies. We found high correspondence between
hyperintensities on the TSE template and the LC meta-map (Appendix A, Section 1.2).
At this stage, a total of 6 warped TSE scans were excluded from LC delineation after
visually confirming that, once warped to MNI152 space, they contained artifacts overlapping the
LC or central pons. This left data from 78 younger (39 Osc+, 39 Osc-) and 36 older (17 Osc+, 19
Osc-) participants included for LC delineation and analyses of change in LC contrast. We
proceeded to delineate the LC for individual participants and timepoints by applying the Dahl et
al. (2022b) LC meta-map as a mask on all warped TSE scans (Figure 1D). Within the masked
region of each scan, we extracted the intensity and location of the peak-intensity LC voxel in
each z-slice and hemisphere. As another validation step, we compared the resulting intensity
values to intensity values determined through manual delineation of the LC on native-resolution
TSE scans (Appendix A, Section 1.3). Two-way intra-class correlation analyses based on
consistency indicated high correspondence between peak LC intensity values from the semi-
automated and manual methods for the left LC (ICC(3, 1) = 0.939, 95% CI = 0.921 - 0.953, p <
.001) and right LC (ICC(3, 1) = 0.924, 95% CI = 0.902 - 0.941, p < .001). To compute LC
contrast ratios reflecting peak LC intensity relative to that of surrounding tissue, we also
extracted intensity values from a central pontine region (Figure 1D). Specifically, we applied the
central pontine reference map from Dahl et al. (2022b) as a mask on individual TSE scans that
had been warped to MNI space and extracted the peak intensity value within the masked region.
23
2.3.2. Calculation of LC MRI contrast. LC MRI contrast is typically calculated as a
ratio reflecting peak signal intensity in the LC relative to peak intensity within a pontine
reference region (Liu et al., 2017):
𝐿𝐶 𝑐𝑜𝑛𝑡𝑟𝑎𝑠𝑡 =
,𝑖𝑛𝑡𝑒𝑛𝑠𝑖𝑡𝑦
!"#$ &'
−𝑖𝑛𝑡𝑒𝑛𝑠𝑖𝑡𝑦
!"#$ (")"("*+"
1
𝑖𝑛𝑡𝑒𝑛𝑠𝑖𝑡𝑦
!"#$ (")"("*+"
No published studies have examined the stability of peak LC signal intensity locations
across time, or factors which influence locations of peak LC intensity, thus we performed an
exploratory step to guide our calculation of LC contrast. Specifically, we assessed whether, for
each participant, locations of peak intensity in each left and right LC shifted from pre- to post-
training. To do so, we calculated for each participant the 3-dimensional distance between peak
LC intensity locations at the pre- and post-training timepoints, for left and right LC separately
(visualizations in Appendix A, Section 2.1). A linear mixed effects analysis indicated that these
distances differed from 0 across training conditions, age groups and hemispheres (p < .001;
Appendix A, Section 2.1), suggesting that locations of peak LC intensity were not consistent
within individuals across time. We therefore aimed to calculate LC contrast in a way that was not
biased by peak LC signal intensity location at either the pre- or post-training timepoint.
Specifically, for each participant, we calculated LC contrast at each timepoint as an average of
LC contrast at the locations of pre- and post-training peak LC signal intensity.
2.4. Blood sampling and RNA sequencing analysis
For a subset of participants (N = 54 younger adults), peripheral blood samples were
collected under resting conditions at the pre- and post-training timepoints by antecubital
venipuncture into PAXgene RNA tubes. Following collection, samples were gently inverted ten
times and kept at room temperature for between 2.00 and 70.20 hours (mean = 6.95 hours).
24
Samples were then stored frozen at -80°C at the USC School of Gerontology before they were
transferred and assayed in a single batch at the UCLA Social Genomics Core Laboratory, as
previously described (Cole et al., 2020). Briefly, total RNA was extracted from 2.5 ml blood
samples using an automated nucleic acid processing system (QIAcube; Qiagen), checked for
suitable RNA integrity and mass (>50 ng by NanoDrop One spectrophotometry; achieved mean
= 4497 ng) and assayed by RNA sequencing in the UCLA Neuroscience Genomics Core
Laboratory using Lexogen QuantSeq 3’ FWD cDNA library synthesis and multiplex DNA
sequencing on an Illumina HiSeq 4000 instrument with single-strand 65-nt sequence reads (all
following the manufacturer’s standard protocol). Analyses targeted >10 million sequence reads
per sample (achieved mean 15.1 million), each of which was mapped to the RefSeq human
transcriptome sequence using the STAR aligner (achieved average 94% mapping rate) to
generate transcript counts per million total transcripts (TPM). TPM values were floored at 1
TPM to reduce spurious variability, log2-transformed to reduce heteroscedasticity, and analyzed
by linear statistical models with promoter sequence-based bioinformatics analyses of CREB
activity as described below.
2.5. Statistical analysis
We fit a linear mixed effects model to assess the fixed effects of timepoint, training
condition, age group and hemisphere on LC contrast. Mixed models were also fit for each age
group separately to examine the fixed effects of timepoint, training condition, hemisphere and
their interactions on LC contrast. Significant timepoint x condition interactions were
supplemented with post hoc comparisons of pre- versus post-training LC contrast for each
training condition and hemisphere.
25
Next, we tested whether changes in LC contrast were related to the extent to which
participants increased their heart rate oscillations during biofeedback training. For each
participant, values of change in left and right LC contrast were calculated as the difference
between pre- and post-training LC contrast values. As a measure of how much participants
increased heart rate oscillations during practice, we calculated a value of training oscillatory
power using each participant’s pulse data collected during training sessions (Appendix A,
Section 1.4). We then fit another mixed model testing the fixed effects of training oscillatory
power, hemisphere, age group, and their interactions on LC contrast. For each age group and
hemisphere separately, we also performed planned Pearson correlation analyses to test
associations between change in LC contrast and training oscillatory power.
Based on previous findings of sex differences in LC contrast (Bachman et al., 2021) as
well as sex differences in the responsiveness of the LC to stress (Bangasser et al., 2016), we
tested for sex differences in LC contrast change and its relationship with training oscillatory
power by fitting the previously described mixed models including sex and its interactions as
fixed effects (Appendix A, Section 1.5). These analyses were performed only for younger
participants because we were underpowered to detect sex differences among older participants.
These analyses were performed in R (Version 4.0.4; R Core Team, 2021). Linear mixed
effects models were fit using the R package `lme4` (Version 1.1-27.1; Bates et al., 2015), and
significance of fixed effects was assessed with Satterthwaite’s method as implemented in the R
package `lmerTest` (Version 3.1-3; Kuznetsova et al., 2017). All models included random
intercepts for participants. A sum coding contrast scheme was applied to factor variables
(condition: Osc+ = 0.5, Osc- = 0.5; timepoint: post-training = 0.5, pre-training = 0.5; age group:
older = 0.5, younger = -0.5; hemisphere: left = 0.5, right = -0.5; sex: female = 0.5; younger =
26
0.5). Post hoc comparisons of model-estimated marginal means were performed with the
`emmeans` R package (Version 1.6.2-1; Lenth, 2021). Effect sizes were calculated using the R
package `effectsize` (Version 0.5; Ben-Shachar et al., 2020) and reported as d.
We also tested whether training condition (Osc+ or Osc-) or change in LC contrast was
associated with change in CREB activity from pre- to post-training using an established
bioinformatic measure of CREB gene regulation employed in previous research (Cole et al.,
2020). Data from 54 younger participants (30 Osc+, 24 Osc-) with available blood-based
measures at both timepoints were included for analysis of CREB activity change by training
condition, and data from 39 younger participants (22 Osc+, 17 Osc-) with available blood-based
measures and LC contrast values at both timepoints were included for analysis of associations
between CREB activity change and LC contrast change. In these analyses, whole transcriptome
profiling data were screened to identify genes that showed > 1.5-fold differential change over
time between conditions or > 1.5-fold differential change in expression per standard deviation
(SD) of pre- to post-training LC contrast change, and the core promoter DNA sequences of those
genes were scanned for the prevalence of CREB-binding motifs using the TELiS database (Cole
et al., 2020; Cole et al., 2005). Analyses were conducted as previously described (Cole et al.,
2020), with CREB activity quantified by the ratio of CREB-binding site prevalence (defined by
TRANSFAC position-specific weight matrix V$CREB_Q4) in genes up-regulated in association
with condition differences in change or LC contrast change (i.e., >1.5-fold upregulation from
pre- to post-training timepoint per SD of LC contrast change) vs. down-regulated (>1.5-fold
down-regulated), and log2-ratios averaged over 9 parametric combinations of promoter sequence
length (-300, -600, and -1000 to +200 bp relative to the RefSeq transcription start site) and
detection stringency (TRANSFAC mat_sim = .80, .90, and .95). Statistical significance was
27
assessed using standard errors derived from bootstrap resampling of linear model residual
vectors in underlying gene expression data, which controls for correlation across genes. For
additional details on analytic methods, see Cole et al. (2005) and Cole et al. (2020).
3. Results
3.1. LC contrast decreased in younger participants in the Osc+ condition
LC contrast at the pre- and post-training timepoints is shown in Figure 2A. Using a linear
mixed effects analysis testing the fixed effects of condition, timepoint and hemisphere on LC
contrast in younger participants (Table 2A), we found a significant training condition x timepoint
interaction on LC contrast, p = .034, d = -0.283. Post hoc comparisons of estimated marginal
means indicated that at the post- relative to the pre-training timepoint, LC contrast was
numerically lower among younger participants in the Osc+ condition (left: t(228) = -2.193, p =
.029, d = -0.497; right: t(228) = -0.059, p = .953, d = -0.013) and numerically higher among
younger participants in the Osc- condition (left: t(228) = 0.599, p = .550, d = 0.136; right: t(228)
= 1.423, p = .156, d = 0.322). For younger participants, we also found a significant fixed effect
of hemisphere on LC contrast, p < .001, d = 1.462, with left LC contrast being higher than right
LC contrast, but no other fixed effects were significant. For older participants (Table 2B), we did
not find a significant training condition x timepoint interaction, p = .713, d = 0.073, but we did
observe a significant fixed effect of timepoint on LC contrast p = .046, d = 0.400, with LC
contrast being higher at the post- compared to the pre-training timepoint. As in the younger
sample, we observed a significant effect of hemisphere on LC contrast in older participants, p <
.001, d = 1.261) driven by higher contrast for the left relative to the right LC. Notably, in a model
including data from both age groups, we did not observe significant timepoint x condition, age x
28
timepoint x condition, or age x timepoint interactions on LC contrast (p’s > .05; Appendix A,
Section 2.2).
Figure 2
Effect of HRV biofeedback training on LC MRI contrast in younger and older participants
Note. (A) LC contrast at the pre- and post-training timepoints is shown for the Osc+ and Osc-
conditions, for younger (top) and older (bottom) participants. (B) Scatterplots depict associations
between pre- to post-training change in LC contrast and training oscillatory power, a measure of
how much participants increased their heart rate oscillations across practice sessions, for younger
(top) and older (bottom) participants. Linear regression lines with 95% confidence bands are
shown in gray.
29
Table 2
Results of linear mixed effects analyses testing the fixed effects of timepoint, training condition,
and hemisphere on LC MRI contrast, in younger (A) and older (B) participants
Predictor Estimate SE 95% CI t p
A. Younger participants
Intercept 0.035 0.005 0.026, 0.044 7.657 <.001
Timepoint -0.001 0.005 -0.010, 0.009 -0.115 .909
Condition 0.001 0.009 -0.017, 0.019 0.085 .932
Hemisphere 0.054 0.005 0.044, 0.063 11.036 <.001
Timepoint x Condition -0.021 0.010 -0.04, -0.002 -2.137 .034
Timepoint x Hemisphere -0.014 0.010 -0.033, 0.005 -1.480 .140
Condition x Hemisphere 0.011 0.010 -0.008, 0.030 1.106 .270
Timepoint x Condition x Hemisphere -0.013 0.019 -0.051, 0.025 -0.655 .513
B. Older participants
Intercept 0.066 0.008 0.051, 0.081 8.630 <.001
Timepoint 0.014 0.007 0.000, 0.0290 2.021 .046
Condition 0.008 0.015 -0.022, 0.038 0.515 .610
Hemisphere 0.046 0.007 0.032, 0.060 6.366 <.001
Timepoint x Condition 0.005 0.014 -0.023, 0.033 0.368 .713
Timepoint x Hemisphere 0.002 0.014 -0.026, 0.031 0.170 .865
Condition x Hemisphere 0.012 0.014 -0.016, 0.041 0.867 .388
Timepoint x Condition x Hemisphere 0.004 0.029 -0.053, 0.06 0.122 .903
Note. Models included random intercepts for participants. Factors were coded as: timepoint
(post-training = 0.5, pre-training = -0.5), condition (Osc+ = 0.5, Osc- = -0.5), hemisphere (left =
0.5, right = -0.5). CI = confidence interval.
30
3.2. Training oscillatory power was associated with decreases in left LC contrast
Associations between training oscillatory power and change in LC contrast are depicted in
Figure 2B. In younger participants, we found a significant negative correlation between training
oscillatory power and change in left LC contrast, r(74) = -0.249, 95% CI = -0.449 - -0.025, p =
.030, but no significant correlation between training oscillatory power and change in right LC
contrast, r(74) = -0.085, 95% CI = -0.305 - 0.143, p = .463. In older participants, training power
was not correlated with change in either left LC contrast, r(34) = 0.024, 95% CI = -0.307 - 0.35,
p = .889, or right LC contrast, r(34) = 0.076, 95% CI = -0.259 - 0.395, p = .660. We note that the
negative association between training power and left LC contrast change in younger adults did
not emerge in a linear mixed effects analysis testing the fixed effects of training power, age
group, hemisphere, and their interactions on LC contrast; specifically, this analysis indicated no
significant fixed effects of training power or interaction effects involving training power (p’s >
.05; Appendix A, Section 2.3).
3.3. The association between training oscillatory power and change in left LC contrast was
more negative in males
Pre- and post-training LC contrast for younger males and females is shown in Figure 3A.
A linear mixed effects analysis testing fixed effects of timepoint, condition, hemisphere, sex and
their interactions in younger adults indicated no significant timepoint x condition x sex or
timepoint x condition x hemisphere x sex interactions on LC contrast (p’s > 0.05; Appendix A,
Section 2.4). We note that this analysis indicated a significant fixed effect of sex on LC contrast,
p = .007, d = 0.650, driven by greater LC contrast for females than males, as well as a significant
timepoint x condition interaction on LC contrast, p = .032, d = -0.290, in line with what we
observed above (Section 3.1). When we next added sex as a fixed effect to the model testing the
31
effects of training oscillatory power and hemisphere on change in LC contrast (Table 3), we
found a marginally significant interaction between training power and sex, p = .050, d = 0.470.
This was driven by younger males having a more negative association between training power
and change in LC contrast than younger females (Figure 3B). Notably, this analysis also
indicated a significant fixed effect of sex, p = .048, d = -0.473, with females exhibiting greater
decreases in LC contrast relative to males, and a significant fixed effect of training oscillatory
power on change in LC contrast, p = .036, d= -0.505, after accounting for the effects of sex and
hemisphere.
Table 3
Results of linear mixed effects analysis testing the fixed effects of training oscillatory power,
hemisphere, and sex on change in LC MRI contrast in younger participants
Predictor Estimate SE 95% CI t p
Intercept 0.080 0.039 0.004, 0.156 2.050 .044
Training power -0.012 0.006 -0.024, -0.001 -2.142 .036
Hemisphere 0.041 0.049 -0.055, 0.137 0.838 .405
Sex -0.157 0.078 -0.309, -0.004 -2.008 .048
Training power x Hemisphere -0.008 0.007 -0.022, 0.006 -1.132 .261
Training power x Sex 0.023 0.012 0.000, 0.046 1.993 .050
Hemisphere x Sex -0.027 0.098 -0.218, 0.165 -0.273 .785
Training power x Hemisphere x Sex 0.003 0.015 -0.025, 0.032 0.215 .830
Note. Models included random intercepts for participants. Factors were coded as: hemisphere
(left = 0.5, right = -0.5), sex (female = 0.5, male = -0.5). CI = confidence interval.
32
Figure 3
Effect of HRV biofeedback training on LC MRI contrast in younger participants, stratified by sex
Note. (A) LC contrast at the pre- and post-training timepoints is shown for younger participants
in the Osc+ and Osc- conditions, stratified by sex (top = females, bottom = males). (B)
Scatterplots depict associations between change in LC contrast and training oscillatory power
among younger participants, stratified by sex (top = females, bottom = males). Linear regression
lines with 95% confidence bands are shown in gray.
3.4. Decreases in left LC contrast were associated with decreases in CREB activity
Results of RNA sequencing in younger participants with available blood-based measures
indicated a significant interaction between timepoint and condition on expression of genes
regulated by the sympathetic nervous system-responsive CREB transcription factor, bootstrap z =
-3.30, p = .001. Younger participants in the Osc- condition showed what appears to be a secular
trend, with increased CREB activity from pre- to post-training, bootstrap z = 2.70, p = .008,
33
whereas participants in the Osc+ condition were buffered against that trend, showing no
significant change over time, bootstrap z = -0.45, p = .650.
We also found that greater change in LC contrast was associated with greater change in
CREB activity (Figure 4), selectively for left LC contrast, bootstrap z = 1.97, p = .049, with no
significant effect for right LC contrast, bootstrap z = 0.63, p = .530. In other words, participants
with larger decreases in left LC contrast had larger decreases in CREB activity.
Figure 4
CREB activity changes that were associated with changes in LC MRI contrast
Figure 4. Figure depicts fold-difference metrics reflecting pre- to post-training elevation in
CREB activity in genes that showed >1.5 fold-differential expression per standard deviation of
left and right LC contrast change. Crossbar central lines indicate the mean fold-differences, with
a mean fold-difference of 1 corresponding to no pre- to post-training difference in CREB
activity. Upper and lower bounds of crossbars extend reflect standard errors derived from
bootstrap resampling of linear model residual vectors in underlying gene expression data. Figure
reflects data from a subset of 39 younger participants for whom both blood-based measures and
LC contrast values were available.
34
4. Discussion
In recent years, much has been learned about how LC MRI contrast, a proxy for LC
structural integrity, relates to cognition across the lifespan (Betts et al., 2019b; Elman et al.,
2021a; Liu et al., 2020). However, no published studies have examined factors that influence LC
contrast across time. Here, we found that in younger adults, performing 5 weeks of HRV
biofeedback training decreased LC contrast. This effect was larger for the left LC and scaled
with the extent to which participants increased their heart rate oscillations during training. We
also found that among younger participants with available blood-based measures, decreases in
left LC contrast were coupled with decreases in activity of the CREB transcription factor that
mediates sympathetic nervous system signaling through beta-adrenergic receptors (Cole et al.,
2010; Mayr & Montminy, 2001). On the contrary, among older adults who completed
biofeedback training, we did not observe training effects on LC contrast. Thus, for younger
adults, using biofeedback to increase heart rate oscillations in daily training sessions affected LC
contrast.
Why might HRV biofeedback training have decreased LC contrast in younger adults? The
beneficial effects of HRV biofeedback involving slow breathing are thought to occur through
multiple mechanisms, including stimulation of the vagus nerve (Huang et al., 2018; Lehrer &
Gevirtz, 2014). The vagus nerve is a major component of the parasympathetic nervous system
and sends inputs to the LC via the medullary nucleus tractus solitarii (Badran et al., 2018; Fornai
et al., 2011). The nucleus tractus solitarii is affected by respiration, with its cell firing suppressed
during inhalation and facilitated during exhalation (Miyazaki et al., 1998). Thus, the balance of
respiratory phases may affect LC activity. In support of this idea, exhalation-gated auricular
vagal afferent nerve stimulation elicits greater responses in the nucleus tractus solitarii and the
35
LC compared to inhalation-gated stimulation in humans (Garcia et al., 2017; Sclocco et al.,
2019). One possibility is thus that the repeated practice of slow, paced breathing leads to more
phasic and less tonic stimulation of the nucleus tractus solitarii and LC. In addition, a cluster of
neurons in the medullary preBötzinger complex serves as a major breathing rhythm generator
and provides excitatory input to the LC; when breathing is slow, the preBötzinger cluster
provides less excitatory input to the LC, promoting lower tonic levels of arousal (Yackle et al.,
2017). These slow-breathing effects on the nucleus tractus solitarii and preBötzinger neurons
would have the net effect of shifting LC activity to a higher phasic and lower tonic level, which
would manifest as lower LC MRI signal contrast and reduced sympathetic activity. The
association we observed between decreases in LC contrast and decreases in activity of the CREB
transcription factor are consistent with the notion of decreased LC contrast in younger adults
reflecting decreased cumulative noradrenergic activity during the intervention time frame.
More broadly, our effects may be accounted for by an overall shift to parasympathetic
dominance that occurs with the repeated practice of HRV biofeedback training. The LC receives
projections from the medulla’s nucleus paragigantocellularis (Aston-Jones et al., 1986; Aston-
Jones et al., 1991), which itself receives widespread autonomic inputs and has been implicated in
the regulation and control of sympathetic activity and respiration (Van Bockstaele & Aston-
Jones, 1995). Parasympathetic/sympathetic balance is then expected to directly impact the LC.
As correlational evidence for this idea in humans, we previously found that HRV was negatively
associated with LC contrast in younger adults (Mather et al., 2017). In addition, LC efferent
projections provide excitatory control over preganglionic sympathetic neurons and inhibitory
control over the parasympathetic dorsal motor vagal nucleus and nucleus ambiguus (Samuels &
Szabadi, 2008). Having relatively lower LC structural integrity would therefore give rise to less
36
excitatory input to sympathetic centers and reduced inhibition of parasympathetic centers, as
well as reduced excitatory input to the central nucleus of the amygdala by LC neurons, which
also contribute to sympathetic activation (Wood et al., 2017).
We found that the effects of biofeedback were larger for the left than the right LC.
Decreases in LC contrast for participants in the Osc+ condition were greater for the left than the
right LC, and significant associations with training oscillatory power and CREB activity were
observed for the left, but not the right, LC. Previous studies have reported higher LC contrast in
the left compared to the right LC (Betts et al., 2017; Dahl et al., 2019; Liu et al., 2019). Our
findings are also in line with reports of more positive associations between LC contrast and
cortical thickness for left relative to right LC (Bachman et al., 2021), as well as hemispheric
differences in functional connectivity of the LC (Jacobs et al., 2018).
We also observed sex differences in how training oscillatory power related to change in
LC contrast among younger participants, with males exhibiting a more negative association
between training power and change in LC contrast than females. Relative to that of males, the
female LC exhibits morphological and functional differences: LC neurons are more sensitive to
corticotropin-releasing factor (Bangasser et al., 2016) and exhibit greater dendritic density and
branching (Bangasser et al., 2011; Ross & Van Bockstaele, 2020) in females. In line with
previous reports of higher LC contrast in females relative to males (Bachman et al., 2021;
Riphagen et al., 2020), we found that younger females had higher LC contrast than males across
conditions, hemispheres and timepoints. Our findings of change being more coupled with
training oscillatory power in younger males than females suggests that there are sex differences
in the factors that shape LC contrast over time, which warrants further investigation.
37
Although we observed differential effects of the two HRV biofeedback training
conditions on LC contrast among younger participants, this was not the case among older
participants. Instead, among older participants, there was an overall increase in LC contrast from
pre- to post-training. One possibility is that our study was not sufficiently powered to detect
condition-specific effects in older adults; data collection for the older cohort was terminated
early due to the COVID-19 pandemic. Another possibility is that among older adults, LC
contrast reflects neurodegeneration more so than stress, whereas the opposite is true in younger
adults. This means that an intervention affecting the autonomic nervous system would be more
likely to change younger adults’ LC contrast levels than those of older adults. Finally, the similar
changes seen across the two conditions raise the possibility that older adults’ LC contrast levels
were sensitive to an aspect of the intervention not explored here and present in both training
conditions.
There are several other limitations to note. First, RNA sequencing analyses included only
a subset of younger participants as we started collecting blood samples after some participants
had completed the study. Second, participants in this study included mostly university students,
limiting the external validity of results and potentially introducing a secular trend towards greater
sympathetic nervous system activity as the 7-week study progressed; we aimed to avoid semester
breaks in the study and therefore, across conditions, enrolled most younger participants at the
beginning of semesters, when there are usually fewer exams and deadlines relative to later weeks
in the semester. Third, our study only encompassed 5 weeks of HRV biofeedback training, but
training over longer time periods may yield larger effects on LC contrast in both hemispheres
and in younger and older participants. Finally, a limitation to the study is our limited
understanding of the stability of the LC contrast measure over time. Our findings would be better
38
contextualized by future studies which assess the stability of LC contrast in younger and older
adults over time.
In this study, we assessed the effect of performing 5 weeks of heart rate variability
biofeedback training on LC contrast, a measure that has been linked to cognition in older adults
and arousal and negative affect in younger adults. We found that training decreased left LC
contrast among younger participants and this effect scaled with the extent to which participants
increased their heart rate oscillations during training. Furthermore, decreases in left LC contrast
were related to decreases in CREB activity, a marker of sympathetic nervous system activity.
These results provide novel evidence that among younger adults, LC contrast can be changed
through the daily practice of increasing heart rate oscillations.
39
Chapter 2: Associations between locus coeruleus MRI contrast and physiological responses
to acute stress in younger and older adults
1. Introduction
Acute stress is a recurring feature of daily life, occurring in response to various
psychosocial and environmental stressors. The nervous system’s highly conserved response to
acute stress is designed to promote behaviors and processes that facilitate survival in the face of
such stressors (Johnson et al., 1992; Monaghan & Spencer, 2014). At the same time, there is
substantial variability in how individuals respond to stress (Rab & Admon, 2021; Sapolsky,
2015; Zänkert et al., 2019), with exaggerated or prolonged stress responses associated with
adverse health outcomes. Individuals with excessive responses to and impaired recovery from
acute stress are at elevated risk for atherosclerosis, hypertension, myocardial infarction, and
cardiovascular disease mortality (Chida & Steptoe, 2010; Panaite et al., 2015; Treiber et al.,
2003). Furthermore, stress is a documented contributor to dementia risk (Justice, 2018; Lyons &
Bartolomucci, 2020; Yuede et al., 2018), which may in part be mediated by noradrenergic
modulation of beta-amyloid and tau production and clearance (Mather, 2021). Thus,
characterizing factors that may protect against stress vulnerability across the adult lifespan is an
important aim of psychophysiological research.
Acute stress responses engage both the hypothalamic-pituitary-adrenal axis and the
brain’s noradrenergic system, the hub of which is the locus coeruleus. The LC is a nucleus within
the pons of the brainstem that releases noradrenaline throughout the brain and spinal cord
(Dahlström & Fuxe, 1964; Schwarz & Luo, 2015). Noradrenergic projections from the LC reach
cortical regions implicated in attention, learning and memory (Sara, 2009), but the LC also sends
projections to preganglionic sympathetic neurons in the spinal cord, which coordinate peripheral
40
arousal responses (Jones & Yang, 1985). Besides releasing noradrenaline to the brain and spinal
cord, the LC is innervated by brain regions including the central nucleus of the amygdala, the
paraventricular nucleus of the hypothalamus, and the nucleus paragigantocellularis (Aston-Jones
et al., 1986; Curtis et al., 2002; Mather, 2020; Samuels & Szabadi, 2008; Van Bockstaele &
Aston-Jones, 1995). These inputs provide visceral feedback signals which the LC integrates to
adaptively regulate noradrenaline release and, in turn, arousal levels (Morris et al., 2020a).
As an arousal hub region within the nervous system, the LC is robustly activated in
response to diverse stressors (Morilak, 2007; Valentino & Van Bockstaele, 2008). During acute
stress, in tandem with hypothalamic-pituitary-adrenal axis activation, corticotropin-releasing
factor is released on the LC by the paraventricular nucleus, the central nucleus of the amygdala,
and the bed nucleus of the stria terminalis (Johnson et al., 1992; Valentino & Van Bockstaele,
2005). Corticotropin-releasing factor increases the rate of tonic, or basal, noradrenaline discharge
by LC neurons while decreasing the frequency of phasic, stimulus-evoked responses (Curtis et
al., 1997; Valentino & Foote, 1988). A shift to higher tonic LC activity increases cortical levels
of noradrenaline (Kawahara et al., 2000) and promotes adaptive behavioral and physiological
shifts that subserve threat detection and avoidance, such as the reorienting of attention and
cardiovascular reactivity (Bremner et al., 1996; Sara & Bouret, 2012; Wood & Valentino, 2017).
The LC’s short-term response to acute stress is adaptive, promoting behaviors that
complete the stress cycle. Yet stress experienced over the longer term may have maladaptive
consequences for the LC. Corticotropin-releasing factor exposure due to chronic stress causes
morphological changes to LC neurons, increasing both dendritic arborization and the number of
primary processes (Borodovitsyna et al., 2018). Stress also affects the activity of LC neurons,
with LC neurons from rodents exposed to chronic stress exhibiting higher excitability and
41
sensitivity relative to those from controls, as well as anxiety-like behaviors (Jedema & Grace,
2003; Mana & Grace, 1997; McCall et al., 2015). Together, these findings suggest that structure
of the LC may be closely intertwined with physiological responses to acute stress.
Despite the LC’s involvement in the stress response, little is known about how LC
structure is related to physiological responses in humans. This may be due in part to limitations
of studying the LC in vivo in humans due to the LC’s small size and location. Recently, the
development of specialized MRI protocols, including high-resolution TSE and magnetization
transfer sequences (Betts et al., 2019b; Sasaki et al., 2006), has made quantifying LC structure in
vivo possible. In these sequences, the LC appears as hyperintense regions bordering the fourth
ventricle, with signal intensity contrast of the LC relative to that of surrounding pontine tissue
thought to reflect LC structural integrity (Keren et al., 2009). A recent study using such a
protocol found that LC volume was higher in younger adults with anxiety disorders relative to
healthy controls, and that, across the sample of younger adults, LC volume was positively
correlated with levels of anxious arousal and general distress (Morris et al., 2020b). In separate
studies, younger and older adults with higher LC MRI contrast had lower HRV, a measure of
parasympathetic control over heart rate, during a fear conditioning task (Mather et al., 2017), and
younger adults with higher contrast of the LC’s caudal region had lower average cortical
thickness (Bachman et al., 2021). Both HRV and cortical thickness are lower in individuals with
stress- and anxiety-related disorders relative to healthy controls (Chalmers et al., 2014; Molent et
al., 2018), and dysregulation of noradrenergic signaling is feature of such disorders (Hendrickson
& Raskind, 2016; Ressler & Nemeroff, 2000). Together, this evidence suggests that in younger
adults, structure of the LC may be associated with poorer stress- and anxiety-related outcomes.
Yet there has been little work directly examining associations between MRI-assessed LC
42
structure and comprehensive measures of physiological arousal during acute stress, despite the
LC’s projections to and innervation from sympathetic and parasympathetic arousal centers.
In contrast to the reports described above, studies of older adults have indicated that
having higher LC MRI contrast is associated with better cognitive performance across domains
(Dahl et al., 2019; Liu et al., 2020), higher cortical thickness (Bachman et al., 2021), and lower
risk of developing mild cognitive impairment (Elman et al., 2021a) in older adulthood. The LC is
the first brain region where tau pathology accumulates in the progression of Alzheimer’s disease
(Braak et al., 2011), and older adults with Alzheimer’s disease have lower LC integrity relative
to healthy controls (Betts et al., 2019a; Takahashi et al., 2015). On the surface, these seemingly
discrepant findings – LC contrast being associated with better outcomes later in adulthood but
poorer outcomes earlier in adulthood – suggest that LC structure may be more influenced by
stress in younger adults and more by neurodegeneration in older adults. However, just as for
younger adults, no studies to date have assessed whether LC structure in older adulthood is
related to aspects of the parasympathetic and sympathetic response to stress.
In the present study, we attempted to fill these gaps in the literature by examining how
physiological responses to acute stress were related to LC MRI contrast in a sample of 102
younger and 51 older adults. We assessed multiple measures of physiological arousal during rest,
acute stress, and acute stress recovery, and we used TSE MRI scans to assess LC contrast, an in
vivo measure of LC structural integrity, along the LC’s rostrocaudal extent. Pairwise correlation
and partial least squares correlation analyses were applied to assess how LC contrast was
associated with multiple measures of physiological arousal in each age group. In line with
previous findings of LC volume being positively correlated with levels of anxious arousal in
younger adults and LC contrast being associated with lower parasympathetic control over heart
43
rate (Mather et al., 2017; Morris et al., 2020b), we predicted that younger adults with higher LC
contrast would have higher-magnitude responses to acute stress. In terms of predictions for older
adults, in line with studies linking MRI-assessed LC integrity to better cognitive and neural
outcomes in aging (Bachman et al., 2021; Dahl et al., 2019; Elman et al., 2021b; Liu et al.,
2020), we originally expected that older adults with higher LC contrast would have lower-
magnitude acute stress responses, reflecting reduced potential impacts of stress. However,
another perspective is that acute stress reactivity in aging is beneficial, reflecting a responsive,
flexible autonomic system in the context of normal sympathetic tone. From this perspective, a
competing possibility was that older adults with higher LC contrast would have larger-magnitude
acute stress responses.
2. Methods
2.1. Participants
Data were collected as part of a clinical trial testing the effects of HRV biofeedback
training on emotion regulation brain networks (Nashiro et al., 2021). For the present analyses,
only data from the pre-intervention measurement timepoint - that is, before participants learned
about or started the intervention - were considered. Specifically, we considered data from all
participants who completed an MRI session including a TSE scan and an acute stress induction
task at the pre-intervention timepoint. This included 115 younger and 59 older participants (data
collection for the older cohort was terminated prematurely due to the COVID-19 pandemic).
These participants were MRI-eligible individuals without major medical, neurological,
psychiatric, or cardiac conditions. Individuals who engaged in regular relaxation, biofeedback or
breathing techniques were excluded from participation, as were individuals taking psychoactive
medications. Individuals taking antidepressants or anti-anxiety medications were eligible to
44
participate so long as the medication had been taken for at least three months prior to study
participation. Older adults who scored lower than 16 on the TELE, a brief cognitive assessment
administered over the telephone (Gatz et al., 1995), were excluded from participation for
possible dementia.
Of those who completed the acute stress induction task and an MRI session, scans from
18 participants were excluded from LC delineation due to severe motion artifact (n = 15),
susceptibility artifact overlapping the LC or pons (n = 2), and incorrect scan resolution (n = 1).
Following LC delineation, 1 older participant was excluded from analysis due to incorrect
placement of the LC search space, and 2 participants (1 younger, 1 older) were missing complete
physiological recordings from the stress task due to recording errors and were therefore excluded
from analysis. The final sample for analysis included 153 participants (102 younger, 51 older)
and is described in Table 1. The study protocol was approved by the University of Southern
California Institutional Review Board. All participants provided written, informed consent and
received monetary compensation for their participation.
45
Table 1
Sample characteristics
Younger
(N = 102)
Older
(N = 51)
Comparison,
p-value
a
Age, mean (SD) 22.66 (2.89) 64.61 (6.31) <.001
Age, range 18 - 31 55 - 80
N (%) females 55 (53.9%) 34 (66.7%) .183
Education, mean (SD) 15.9 (2.16) 16.51 (2.44) .131
Education, range 12 - 24 12 - 25
CESD, mean (SD) 13.95 (7.54) 9.93 (7.09) .002
CESD, range 1.5 - 39.5 0 - 28.5
TAI, mean (SD) 42.15 (9.97) 35.3 (10.54) <.001
TAI, range 21.5 - 65.5 20 - 67
Note. Age and education are presented in years. During their lab visits for the acute stress
induction protocol and MRI session, participants completed the Center for Epidemiological
Studies Depression Scale (CES-D; Radloff, 1977) and Trait Anxiety Inventory (TAI; Spielberger
et al. 1983). A single score for each assessment for each participant was calculated by averaging
over their scores from both visits.
a
Comparison p-values reflect results of independent samples Welch’s t-tests and, for comparing
the proportion of females in each age group, a chi-squared test.
2.2. Data collection
2.2.1. Acute stress induction protocol. During the first week of the study, participants
completed a computerized acute stress induction procedure based on a standardized protocol
known to elicit a robust acute physiological stress response (Crowley et al., 2011). The task
consisted of a 4-minute baseline resting phase, a cognitive challenge phase, and a 4-minute
recovery resting phase (Figure 1).
46
Figure 1
Acute stress induction protocol
Note. The protocol consisted of 3 phases (A): a baseline resting phase, a cognitive challenge
phase, and a recovery resting phase. The challenge phase consisted of two tasks: the Paced
Auditory Serial Addition Task (PASAT; B) and a Stroop color-word matching task (C). Younger
adults completed both tasks, whereas older adults completed only the Stroop task.
During the baseline phase, participants sat with their feet resting flat on the ground and
their hands resting in a supine position on a flat tabletop. Participants viewed a black screen with
a white fixation cross centered on the screen and were instructed to refrain from talking and to
breathe normally during this phase. Following the baseline phase, participants completed a
challenge phase which consisted of two cognitive tasks for younger adults and one task for older
adults (piloting indicated that the first task was excessively frustrating for older participants). To
47
increase the socially evaluative nature of the challenge phase, participants were told that their
performance would be evaluated by the experimenter and compared with that of other
participants. The first task, completed only by younger adults, was a computerized version of the
Paced Auditory Serial Addition Task (PASAT; Figure 1B; Tombaugh, 2006) in which
participants were presented with a series of digits and instructed to add each digit to the digit that
came directly before it. Participants were instructed to enter the resulting sum on the keyboard
using their dominant hand, and potential responses were never greater than 20. The task
consisted of 30 trials in which participants had 3 seconds to respond to each digit; the task lasted
approximately 160 seconds.
The second task, completed by both younger and older adults, was a Stroop color-word
matching task (Figure 1C; MacLeod, 1991), in which a color word (`RED`, `BLUE`, or
`GREEN`) was presented on a computer screen in a color incongruent with its meaning (either
red, green or blue). Participants were instructed to use their dominant hand to press a key
corresponding to either the color in which the word was presented, or the meaning of the word,
based on an instruction which appeared directly before the word. The Stroop task consisted of 20
trials and lasted approximately 120 seconds.
During both tasks, auditory feedback was provided to participants on each trial: A bell
sound was played in response to correct responses, whereas a buzzer sound was played after
missing or incorrect responses to increase the socially-evaluative nature of the tasks (Dickerson
& Kemeny, 2004). Before beginning the tasks, participants were provided with instructions and
practice trials for both tasks. Following the cognitive challenge phase, participants completed a
4-minute recovery resting phase that was identical to the baseline resting phase.
48
Physiological signals were recorded throughout the acute stress induction protocol at a
sampling rate of 2000 Hz using a BIOPAC MP160 system (Goleta, CA). Electrocardiogram
(ECG) signals were collected with a standard Lead II configuration with disposable, pre-gelled
Ag/AgCl electrodes (EL501) and transmitted using a wireless BioNomadix transmitter system.
Respiration was measured with the Biopac Respiratory Effort Transducer, which involved a belt
being placed around the lower rib cage to measure changes in chest circumference, and signals
were transmitted using the BioNomadix system. Continuous blood pressure was recorded on
each participant’s non-dominant arm using a BIOPAC noninvasive blood pressure monitoring
system (NIBP100D).
2.2.2. MRI. Approximately one week after completing the stress induction task,
participants returned to the laboratory for an MRI session. Sequences of interest for the present
analyses included a three-dimensional, T1-weighted MPRAGE anatomical scan and a TSE scan.
Parameters of the MPRAGE sequence were as follows: TR = 2300 ms, TE = 2.26 ms, flip angle
= 9°, field of view = 256 mm, voxel size = 1.0 x 1.0 x 1.0 mm, 175 volumes collected. Based on
the MPRAGE scan, a two-dimensional, multi-slice TSE scan covering the entire pons was
collected. The TSE sequence had the following parameters: TR = 750 ms, TE = 12 ms, flip angle
= 120°, bandwidth = 287 Hz/pixel, voxel size = 0.43 x 0.43 x 2.5 mm, gap between slices =
1.0mm, 11 axial slices.
2.3. Physiological data analysis
Physiological signals collected during the acute stress induction protocol were first split
into segments for each participant, with segments corresponding to the various parts of the
protocol: baseline phase, PASAT, Stroop task, and recovery phase. Quality control checks and
preprocessing were performed for each segment separately, using the steps described below. For
49
5 older participants, we included only baseline segments for preprocessing and analysis, because
these participants completed a pilot version of the protocol that included 2 cognitive challenge
tasks. Processing steps described in this section were performed using MATLAB (Version
R2021b).
2.3.1. Preprocessing and quality control.
ECG signals. To remove baseline wander and high-frequency noise, ECG segments were
filtered with a finite impulse response bandpass filter with a passband between 0.5 and 40 Hz.
ECG signals were assessed for quality in two steps. ECG segments were first visually inspected
for signal quality and noise; 12.3% (n = 68) of all segments demonstrating excessive noise or
abnormalities such that QRS complexes were not discernible were excluded from analyses of
heart rate and HRV. Second, during r-peak delineation and HRV analysis (see Section 2.3.2), an
average signal quality index from 0-1 reflecting a comparison between r-peak annotations
performed by two algorithms, jqrs and wqrs (Behar et al., 2014; Johnson et al., 2014), was
calculated for each segment. We excluded an additional 5.0% (n = 24) of ECG segments for
having an average signal quality index of below 0.7. Further segments were excluded for atrial
fibrillation being detected (6.4%, n = 31) and having more than 20% of peaks missing (0.8%, n =
4).
Respiration signals. Respiration segments were resampled to 50 Hz and filtered with a
finite impulse response filter with a passband between 0.05 and 1 Hz. Preprocessed segments
were then visually inspected for signal quality and were overlaid with detected peaks
corresponding to inhalations, for visual inspection of peak detection accuracy. Respiration
segments with poor quality and/or inaccurate peak detection in the majority of the segment were
50
excluded from all analyses. This led to 6.4% (n = 35) of respiration segments being excluded
from analysis of breathing rate.
Continuous blood pressure signals. For removal of high-frequency noise from
continuous arterial blood pressure segments, we applied a lowpass finite impulse response filter
with a cutoff frequency of 40 Hz. Raw segments were visually inspected for abnormalities; those
in which regular systolic peaks were not detectable were excluded from analyses of systolic
blood pressure. This led to 3.6% (n = 20) of segments being excluded from analysis. For another
37 segments, the blood pressure monitor re-calibrated mid-way through data collection; these
segments were also excluded from analysis.
2.3.2. Calculation of arousal measures.
Preprocessed physiological data segments were next used to compute segment-wise
measures of physiological arousal. We first calculated average values for each arousal measure
across the baseline, challenge, and recovery phases for each participant, for the purposes of
visualization and for testing whether the acute stress induction task effectively modulated arousal
in each age group (see Section 2.5.2).
Then, to quantify the greatest magnitude of physiological reactivity to the stressor for
each measure, we performed an additional set of calculations. Specifically, we combined
segments from the PASAT and Stroop tasks for younger adults into a single challenge segment
(older adults’ challenge phase segment reflected only the Stroop task) and then computed the
rolling average of each arousal measure across the challenge phase, with a window size of 20
seconds and step size of 1 second. We then calculated the maximum (or, for RMSSD, the
minimum) of the rolling average for each measure. These peak (or minimum) values were used
51
for subsequent calculations of stress reactivity as described in Section 2.5.3
1
. Calculation of
arousal measures was performed using MATLAB (Version R2021b).
Heart rate and heart rate variability. The PhysioNet Cardiovascular Signal Toolbox
(Version 1.0.2; Vest et al., 2018), an open-source toolbox designed to address issues of
validation, standardization, and reproducibility in HRV signal processing, was used for QRS
complex detection and to calculate time- and frequency-domain measures of HRV from
preprocessed ECG signals. QRS detection was performed with the jqrs beat detector (Behar et
al., 2014; Johnson et al., 2014). Parameters for HRV calculation are described in Appendix B
(Section 1.1). Based on delineated r-peaks, we calculated a value of mean heart rate for baseline
and recovery segments. For challenge phase segments, we computed a 20-second rolling average
of heart rate and extracted the peak value across the entire phase.
Time-domain HRV analysis was performed on resulting rr-intervals, yielding a measure
of root mean square of the successive differences (RMSSD) for each segment. For baseline and
recovery segments, this was performed across the entire segment, but for challenge phase
segments, RMSSD was computed over 20-second intervals, and the minimum RMSSD value
across the challenge phase was saved (Munoz et al., 2015). Frequency-domain analysis was
performed using the Lomb periodogram method for calculating power spectral density, which is
the default in PhysioNet because it can handle losses of up to 20% of data (Clifford, 2002). This
yielded measures of low-frequency (LF) and high-frequency (HF) spectral power for each
segment. For challenge phase segments, LF and HF power were not computed with a 20-second
1
We also computed stress reactivity using average values of each measure during the challenge phase and examined
resulting associations with LC contrast. The pattern of results was very similar as when we used peak metrics, with
the following differences: the positive correlation between caudal LC contrast and systolic blood pressure reactivity
was no longer significant, r(30) = 0.31, p = .088; RMSSD reactivity was no longer a stable contributor to the caudal
LC-arousal PLS latent variable (bootstrap ratio = -1.96).
52
rolling average but rather calculated for the entire challenge phase. Frequency bands of 0.04 -
0.15 and 0.15 and 0.4 Hz were used for calculating LF and HF power, respectively.
Breathing rate. Peaks corresponding to inhalations were identified on resampled, filtered
respiration signals using MATLAB’s `findpeaks` function with a minimum peak width of 500
milliseconds. Identified peaks were used to calculate a single value of breathing rate for baseline
and recovery segments. For challenge phase segments, we calculated a 20-second rolling average
of breathing rate and extracted the peak value across the entire challenge phase.
Systolic and diastolic blood pressure. Beat-to-beat systolic and diastolic blood pressure
were extracted from preprocessed continuous blood pressure segments using the algorithm from
the CRSIDLab toolbox (da Silva & Oliveira, 2020). This method uses rr-intervals in the
corresponding ECG segment to identify systolic peaks and dicrotic notches within each cardiac
cycle (Parati et al., 1995). For this method, a systolic/diastolic threshold of 80 was specified (the
default in CRSIDLab). When rr-intervals were unavailable due to a low-quality ECG segment,
an alternative algorithm based on the continuous blood pressure waveform was used (Li et al.,
2010). For both methods, we specified that successive maxima and successive minima could be a
minimum of 0.375 and a maximum of 2 seconds apart. Identified peaks were used to calculate a
value of mean systolic and diastolic blood pressure for each baseline and recovery segment. For
challenge phase segments, systolic and diastolic blood pressure were calculated with a 20-second
rolling average, and the peak value across windows was selected.
Sympathetic tone. We assessed sympathetic tone using the neuECG method, an approach
to quantify skin sympathetic nerve activity from ECG signals (Kusayama et al., 2020). In this
approach, raw ECG segments were first high-pass filtered with a finite impulse response filter
with cutoff frequency of 500 Hz. Filtered ECG signals were then full-wave rectified and
53
integrated with a leaky integrator (time constant: 0.1 seconds). We then computed the average
voltage of the resulting signal across each segment (aSKNA), a measure that has been shown to
increase during sympathetic-activating manipulations (Kusayama et al., 2020). For baseline and
recovery segments, we computed aSKNA across entire segments, but for challenge segments, we
computed a 20-second rolling average of aSKNA and identified the peak value.
2.4. LC delineation
We used a validated, semi-automated approach to delineate the LC on TSE scans (Figure
2). The method is fully described in Chapter 1 and is based on approaches by Dahl et al. (2019)
and Ye et al. (2021). As a summary, LC delineation entailed aligning all MPRAGE and TSE
scans across participants, and then warping TSE scans to MNI152 linear 0.5mm standard space.
LC delineation steps were performed using Advanced Normalization Tools (ANTs; Version
2.3.4; Avants et al., 2011), and visualization steps were performed using ITK-SNAP (Version
3.6.0; Yushkevich et al., 2006).
54
Figure 2
LC delineation procedure
Note. (A) The TSE template warped to MNI152 (linear) 0.5mm standard space is shown in blue
and overlaid on the MPRAGE template, also warped to standard space. (B) Detailed view of
pons within the TSE template in standard space, including hyperintensities bordering the 4th
ventricle. (C) LC meta-map from Dahl et al. (2022b) is shown in pink, overlaid on the TSE
template in standard space. Central pontine reference map, also from Dahl et al. (2022b), is
shown in yellow. (D) Sagittal (left) and coronal (right) views of LC meta-map (pink) overlaid on
the TSE template in standard space.
55
To isolate potential LC voxels on warped TSE scans, we applied the Dahl et al. (2022b)
meta-map as a mask; this mask was selected because it aggregates across many published LC
maps. Within the masked region on each scan, we extracted the intensity value and coordinates
of the peak-intensity voxel in each slice in the z dimension for left and right LC. As a validation
step, two raters delineated the LC on native TSE scans (see Appendix B, Section 1.2). LC
intensities determined through the semi-automated approach were found to have high
correspondence between LC intensities from manual delineation, based on intra-class correlation
coefficients calculated using consistency two-way models (left LC: ICC(3,1) = 0.948, 95% CI =
0.929 - 0.962, p < .001; right LC: ICC(3,1) = 0.938, 95% CI = 0.915 - 0.955, p < .001). We
likewise applied the Dahl et al. (2022b) central pontine reference map as a mask on each warped
TSE scan and extracted the peak intensity within the masked region in each z-slice. Values of
left and right contrast for each participant and z-slice were then calculated as a ratio (Liu et al.,
2017):
𝐿𝐶 𝑐𝑜𝑛𝑡𝑟𝑎𝑠𝑡 =
,𝑖𝑛𝑡𝑒𝑛𝑠𝑖𝑡𝑦
!"#$ &'
−𝑖𝑛𝑡𝑒𝑛𝑠𝑖𝑡𝑦
!"#$ (")"("*+"
1
𝑖𝑛𝑡𝑒𝑛𝑠𝑖𝑡𝑦
!"#$ (")"("*+"
Contrast ratios for left and right LC were averaged within each z-slice, and in a final step,
the peak ratio across all z-slices was used for statistical analysis (these are henceforth referred to
as peak LC contrast values). Based on evidence that rostral LC exhibits greater neuronal loss in
aging and Alzheimer’s disease relative to caudal LC (Manaye et al., 1995; Zarow et al., 2003), as
well as reports of spatially confined associations with contrast along the LC’s rostrocaudal axis
(Bachman et al., 2021; Dahl et al., 2019), we also calculated rostral and caudal LC contrast
values for each participant. This entailed first calculating percentiles of slices along the LC’s
rostrocaudal axis where we previously found age differences in LC contrast and associations
56
with cortical thickness in younger versus older adults (Bachman et al., 2021). These percentiles
were applied to the z-range of slices included in the LC meta-map (z = 85-112) to identify ranges
of slices corresponding to rostral and caudal LC (rostral: MNI z = 87-95; caudal: MNI z = 101-
104). For each participant, contrast ratios were then averaged across the caudal and rostral
clusters of slices to obtain values of rostral and caudal LC contrast, respectively.
2.5. Statistical analysis
2.5.1. Data transformation and outlier removal. RMSSD, LF power and HF power
values were determined to exhibit severe non-normality and were therefore log-transformed prior
to analysis. Outliers for each arousal measure were then identified using the mean absolute
deviation-median rule for each age group separately and treated as missing values for all
analyses (Wilcox, 2011). Outlier detection was performed prior to the detection of peak (or
minimum) arousal metrics for each challenge segment. A summary of identified outliers for each
measure is included in Appendix B (Section 1.3).
For peak, rostral and caudal LC contrast values, we tested for outliers for younger and
older adults separately according to the mean absolute deviation-median rule. One older
participant was an outlier for peak LC ratios and excluded from relevant analyses.
57
2.5.2. Analysis of effectiveness of the acute stress induction protocol. To assess
whether the acute stress induction protocol was effective at modulating each measure of
physiological arousal in younger and older adults, we fit a series of linear mixed effects models.
These models tested the fixed effects of phase (baseline/challenge/recovery), age group, and their
interaction on each arousal measure (heart rate, breathing rate, systolic blood pressure, diastolic
blood pressure, sympathetic tone, RMSSD, LF power and HF power), using a separate model for
each measure. For these analyses, we used average values of each measure from each phase. We
used a repeated contrast coding scheme for the phase factor to test two contrasts of interest:
challenge vs. baseline and recovery vs. challenge (Schad et al., 2020). Age group was sum coded
(younger = 0.5, older = -0.5). Models were fit with the `lmer4` R package (Version 1.1-27.1;
Bates et al., 2015) and parameter significance was assessed with the `lmerTest` package using
Satterthwaite’s method (Version 3.1-3; Kuznetsova et al., 2017). Planned comparisons of each
measure for each phase contrast were performed with Bonferroni corrections for multiple
comparisons, using the `emmeans` package (Version 1.7.0; Lenth, 2021).
We also examined performance on the PASAT and Stroop tasks by computing each
participant’s mean accuracy and reaction time on the tasks. As only younger adults completed
the PASAT, we reported PASAT performance as the mean and standard deviation of accuracy
and reaction time across participants. For the Stroop task, we used independent-samples Welch’s
t-tests to compare accuracy and reaction times by age group.
58
2.5.3. Calculation of measures of acute stress reactivity and recovery. We then
calculated measures of acute stress reactivity by computing the change in each physiological
measure from baseline to the challenge phase (Llabre et al., 1991). For these calculations, we
used peak values of each measure from the challenge phase:
𝑚𝑒𝑎𝑠𝑢𝑟𝑒
,-(",, ("#+-./.-0
= 𝑚𝑒𝑎𝑠𝑢𝑟𝑒
!"#$ +1#22"*3"
−𝑚𝑒𝑎𝑠𝑢𝑟𝑒
4#,"2.*"
To calculate measures of acute stress during recovery, we likewise computed the
difference in each measure from baseline to the recovery phase:
𝑚𝑒𝑎𝑠𝑢𝑟𝑒
,-(",, ("+5/"(0
= 𝑚𝑒𝑎𝑠𝑢𝑟𝑒
("+5/"(0
−𝑚𝑒𝑎𝑠𝑢𝑟𝑒
4#,"2.*"
Larger-magnitude values of reactivity were therefore expected to reflect greater stress
reactivity, whereas larger-magnitude values of recovery would reflect higher arousal during
stress recovery.
2.5.4. Analysis of LC MRI contrast in the sample.
Prior to testing associations between LC contrast and arousal, we examined whether peak
LC contrast differed in younger and older adults using an independent-samples Welch’s t-test.
Based on previous findings of age differences in contrast according to LC topography (Dahl et
al., 2019), we also performed a separate 2x2 mixed-design analysis of variance (ANOVA),
implemented with the R package `afex` (Version 1.0-1; Singmann et al., 2021), testing the
effects of age group (younger, older) and topography (rostral, caudal) on LC contrast.
59
2.5.5. Analysis of associations between LC MRI contrast and physiological arousal.
We first assessed associations between LC contrast and physiological arousal by computing,
separately for younger and older participants, a Pearson correlation matrix reflecting pairwise
correlations between all measures of arousal (baseline, reactivity and recovery) and all measures
of LC contrast (peak, rostral and caudal). For this step, all available pairwise observations were
used.
To further probe associations between LC contrast and arousal using a multivariate
framework, we then used a series of partial least squares (PLS) correlation analyses. The aim of
PLS is to identify latent variables that express a maximal amount of covariance between a set of
predictors and an outcome variable (Krishnan et al., 2011; Mcintosh et al., 1996; McIntosh &
Lobaugh, 2004). In this case, our goal was to identify patterns of arousal measures whose
relation with LC contrast differed across age groups.
Because PLS required the data to be restricted to complete cases, we removed breathing
rates from this set of analyses to boost the number of available complete cases to reflect 53
younger and 23 older participants. Then, all measures were centered and normalized.
Physiological arousal measures reflecting rest, stress reactivity and stress recovery were stored in
a matrix 𝑋, with rows reflecting individual participants and columns containing the various
measures. LC contrast values were stored in a single-column matrix 𝑌, with rows reflecting
individual participants. The cross-correlation map 𝑅 = 𝑌
6
𝑋 was computed for each age group
and after arranging the maps in a matrix, the matrix was subjected to singular value
decomposition: 𝑅 = 𝑈𝑆𝑉
6
. The resulting left singular vectors (𝑈) reflect the LC contrast profiles
that best characterized the correlation matrix (also termed “LC saliences”), the right singular
vectors (𝑉) reflect the physiological profiles that best characterized the correlation matrix (also
60
termed “physiological saliences”), and 𝑆 is a matrix of singular values. The original data 𝑋 and 𝑌
were then projected onto their respective singular vectors, yielding latent variables of 𝑋
(“physiological scores”; 𝐿
7
= 𝑋𝑉) and latent variables of 𝑌 (“LC scores”; 𝐿
8
= 𝑌𝑈) for each
participant.
To test the reliability of identified latent variable(s), a permutation test with 10,000
samples was conducted. This entailed randomly shuffling the rows of 𝑋 but not 𝑌 and using the
distribution of singular values from all permutation samples for testing the null hypothesis of no
reliable latent variables (Krishnan et al., 2011; McIntosh & Lobaugh, 2004). Latent variable(s)
identified as reliable were then tested for stability through bootstrapping (Krishnan et al., 2011).
Specifically, for each of 10,000 bootstrap samples, 𝑋 and 𝑌 were sampled with replacement, and
standard errors were calculated based on physiological saliences across all bootstrap samples.
Physiological saliences were divided by their standard errors, yielding a bootstrap ratio for each
physiological arousal measure, with each ratio reflecting how much the given arousal measure
showed a stable association with LC contrast in the latent variable of interest. Bootstrap ratios
with absolute values greater than 2 were considered significantly stable (Krishnan et al., 2011).
The procedure described above was performed three times, once with each LC contrast
measure (peak, rostral or caudal) comprising 𝑌. PLS correlation analyses were performed in
MATLAB using the `PLScmd` toolbox (Mcintosh et al., 1996). All other analyses were
performed in R (Version 4.0.4; R Core Team, 2021). Effect sizes for analyses other than PLS
were calculated using the R package `effectsize` (Version 0.5; Ben-Shachar et al., 2020) and
reported as partial r.
61
3. Results
3.1. Effectiveness of the acute stress induction protocol
Average measures of arousal during each phase of the stress induction task are presented
in Figure 3. Linear mixed-effects analyses were used to assess whether during the acute stress
induction protocol, average measures of arousal differed during the baseline and challenge
phases, and during the challenge and recovery phases (Table 2). Results of all planned, pairwise
comparisons of each measure for each phase contrast and age group are presented in Appendix B
(Section 2.1). For heart rate, breathing rate, systolic blood pressure, and diastolic blood pressure,
we found significant elevations from the baseline to the challenge phase (ps <= .012; Table 2),
and significant decreases from the challenge to the recovery phase (ps < .001; Table 2).
Sympathetic tone did not increase significantly from baseline to the challenge phase (p = .244;
Table 2) but decreased significantly from challenge to recovery (p = .002; Table 2). For systolic
blood pressure, we found a significant phase (recovery-challenge) x age group interaction (p =
.006), which was driven by greater challenge-to-recovery decreases in blood pressure for older
compared to younger participants, although challenge-to-recovery changes were significant in
both age groups (Appendix B, Section 2.1).
62
Figure 3
Average measures of physiological arousal during each phase of the stress induction protocol
Note. Crossbars reflect standard errors of the mean. LF = low-frequency; HF = high-frequency;
RMSSD = root mean square of the successive differences.
63
Table 2
Results of linear mixed effects analyses testing that during the acute stress induction protocol,
average heart rate (A), breathing rate (B), systolic blood pressure (C), diastolic blood pressure
(D), and sympathetic tone (E) differed during the baseline and challenge phases and during the
challenge and recovery phases
Predictor Estimate SE 95% CI t r p
A. Heart rate
Intercept 73.53 0.978
71.614,
75.445
75.187 0.988 <.001
Challenge - Baseline 4.452 0.433
3.608,
5.297
10.282 0.577 <.001
Recovery - Challenge -3.238 0.435
-4.086, -
2.389
-7.443 -0.456 <.001
Age group -6.511 1.956
-10.344, -
2.68
-3.329 -0.276 .001
Challenge - Baseline
x Age group
-0.398 0.866
-2.088,
1.291
-0.460 -0.032 .646
Recovery - Challenge
x Age group
-0.654 0.87
-2.35,
1.044
-0.751 -0.052 .453
B. Breathing rate
Intercept 17.368 0.272
16.835,
17.901
63.869 0.982 <.001
Challenge - Baseline 3.542 0.252
3.050,
4.035
14.041 0.645 <.001
Recovery - Challenge -3.56 0.253
-4.053, -
3.066
-14.075 -0.649 <.001
Age group -0.058 0.544
-1.124,
1.007
-0.106 -0.009 .916
Challenge - Baseline
x Age group
0.604 0.505
-0.381,
1.590
1.198 0.072 .232
64
Predictor Estimate SE 95% CI t r p
Recovery - Challenge
x Age group
0.216 0.506
-0.771,
1.205
0.428 0.026 .669
C. Systolic blood pressure
Intercept 123.436 1.410
120.674,
126.198
87.548 0.990 <.001
Challenge - Baseline 3.359 0.833
1.733,
4.983
4.034 0.243 <.001
Recovery - Challenge -4.937 0.843
-6.581, -
3.291
-5.859 -0.345 <.001
Age group 15.806 2.820
10.282,
21.331
5.605 0.418 <.001
Challenge - Baseline
x Age group
1.528 1.665
-1.724,
4.776
0.917 0.057 .360
Recovery - Challenge
x Age group
-4.703 1.686
-7.991, -
1.409
-2.790 -0.173 .006
D. Diastolic blood pressure
Intercept 72.595 0.888
70.854,
74.335
81.706 0.989 <.001
Challenge - Baseline 1.411 0.556
0.325,
2.497
2.536 0.156 .012
Recovery - Challenge -4.278 0.563
-5.378, -
3.178
-7.593 -0.430 <.001
Age group 8.795 1.777
5.312,
12.274
4.949 0.376 <.001
Challenge - Baseline
x Age group
1.097 1.112
-1.074,
3.270
0.986 0.061 .325
Recovery - Challenge
x Age group
-0.100 1.127
-2.298,
2.102
-0.089 -0.006 .929
E. Sympathetic tone
Intercept 0.109e-1 4.950e-4
1.084e-1,
1.103e-1
220.874 0.999 <.001
65
Predictor Estimate SE 95% CI t r p
Challenge - Baseline 6.867e-4 5.878e-4
-4.601e-4,
1.833e-3
1.168 0.077 .244
Recovery - Challenge -1.826e-3 5.922e-4
-2.983e-3,
-6.719e-4
-3.084 -0.202 .002
Age group 4.299e-3 9.900e-4
2.359e-3,
6.239e-3
4.343 0.350 <.001
Challenge - Baseline
x Age group
-1.153e-6 1.176e-3
-2.295e-3,
2.291e-3
-0.001 0.000 .999
Recovery - Challenge
x Age group
-1.938e-4 1.184e-3
-2.502e-3,
2.123e-3
-0.164 -0.011 .870
Note. Models tested the fixed effects of each phase contrast of interest (Challenge - Baseline,
Recovery - Challenge), age group and their interaction effects. All models included random
intercepts for participants. CI = confidence interval.
Examining measures of HRV during the stress task, we found that RMSSD decreased
significantly from the baseline to the challenge phase (p = .034; Table 3) and increased
significantly from the challenge to the recovery phase (p = .017; Table 3). Although both HF and
LF power exhibited the same numeric pattern, the only significant phase contrast was an increase
in LF power from challenge to recovery (p < .001; Table 3). For LF power, we also found a
significant phase (challenge-baseline) x age group interaction (p = .012; Table 3) and a
marginally significant phase (recovery-challenge) x age group interaction effect (p = .066; Table
3); pairwise comparisons indicated significant baseline-to-challenge decreases and challenge-to-
recovery increases in LF power for younger but not older participants (Appendix B, Section 2.1).
66
Table 3
Results of linear mixed effects analyses testing that during the acute stress induction protocol,
average RMSSD (A), HF power (B), and LF power (C) differed during the baseline and
challenge phases and during the challenge and recovery phases
Predictor Estimate SE 95% CI t r p
A. RMSSD
Intercept 3.315 0.047 3.223, 3.407 70.604 0.987 <.001
Challenge - Baseline -0.066 0.031 -0.127, -0.006 -2.136 -0.147 .034
Recovery - Challenge 0.075 0.031 0.014, 0.136 2.412 0.166 .017
Age group -0.556 0.094 -0.740, -0.372 -5.918 -0.460 <.001
Challenge - Baseline
x Age group
0.016 0.062 -0.105, 0.137 0.257 0.018 .797
Recovery - Challenge
x Age group
0.071 0.062 -0.051, 0.192 1.132 0.079 .259
B. HF power
Intercept 5.869 0.088 5.696, 6.042 66.570 0.986 <.001
Challenge - Baseline -0.058 0.072 -0.198, 0.082 -0.811 -0.056 .418
Recovery - Challenge 0.092 0.071 -0.048, 0.231 1.285 0.089 .200
Age group -1.473 0.176 -1.818, -1.128 -8.354 -0.589 <.001
Challenge - Baseline
x Age group
0.131 0.143 -0.149, 0.411 0.916 0.063 .361
Recovery - Challenge
x Age group
0.069 0.143 -0.210, 0.347 0.481 0.033 .631
C. LF power
Intercept 6.367 0.073 6.224, 6.510 87.135 0.992 <.001
Challenge - Baseline -0.037 0.067 -0.168, 0.094 -0.551 -0.038 .582
Recovery - Challenge 0.361 0.068 0.228, 0.493 5.315 0.347 <.001
Age group -1.115 0.146 -1.401, -0.828 -7.629 -0.556 <.001
67
Predictor Estimate SE 95% CI t r p
Challenge - Baseline
x Age group
0.343 0.135 0.081, 0.606 2.549 0.173 .012
Recovery - Challenge
x Age group
-0.251 0.136 -0.516, 0.014 -1.847 -0.127 .066
Note. Models tested the fixed effects of each phase contrast of interest (Challenge - Baseline,
Recovery - Challenge), age group and their interaction effects. All models included random
intercepts for participants. CI = confidence interval; LF = low-frequency; HF = high-frequency;
RMSSD = root mean square of the successive differences.
In terms of performance on the cognitive challenge tasks, accuracy and reaction times on
the tasks are visualized in Appendix B (Section 2.2). On the PASAT, which was not completed
by older participants, younger participants had mean accuracy of 84.3% (SD = 14.8%) and
reaction time of 0.9 seconds (SD = 0.2). On the Stroop task, younger participants were
significantly more accurate than older participants, t(57.42) = 6.41, p < .001, r = 0.65 (Myounger =
83.7%, SDyounger = 12.2%, Molder = 62.8%, SDolder = 20.0%). Younger participants also had faster
reaction times on the Stroop task compared to older participants, t(76.04) = -7.04, p < .001, r = -
0.63 (Myounger = 1.0, SDyounger = 0.2, Molder = 1.3, SDolder = 0.2 seconds).
3.2. LC MRI contrast in the sample
Peak, rostral and caudal LC contrast values in the sample are presented in Figure 4. A
Welch’s t-test indicated a trend toward higher peak LC contrast in older relative to younger
participants, t(114.47) = 1.88, p = .063, r = 0.173. A 2x2 mixed ANOVA to examine effects of
topography (rostral/caudal) and age group indicated a significant main effect of age group on LC
contrast, F(1, 151) = 4.05, p = .046, r = 0.162, driven by higher ratios in older relative to younger
participants, but no significant effect of topography, F(1, 151) = 1.16, p = .283, r = 0.087, or age
group x topography interaction on LC contrast, F(1, 151) = 2.48, p = .117, r = 0.127.
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Figure 4
LC MRI contrast in the sample
Note. Crossbars reflect standard errors of the mean.
3.3. Associations between LC MRI contrast and arousal during the stress induction task
Visualizations of pairwise Pearson correlations between LC contrast values and all
arousal measures are presented in Figure 5. Pearson’s correlation analyses indicated that in
younger participants, peak LC contrast was significantly negatively correlated with breathing
rate during stress recovery, r(95) = -0.21, p = .038, and rostral LC contrast was positively
correlated with sympathetic tone during stress recovery, r(64) = 0.31, p = .010.
In older participants, peak LC contrast was significantly positively correlated with LF
power at baseline, r(41) = 0.31, p = .045. Caudal LC contrast was significantly positively
correlated with systolic blood pressure increases during stress reactivity, r(37) = 0.42, p = .007,
and there were was a marginally significant negative correlation between caudal LC contrast and
HF power during stress recovery in older participants, r(33) = -0.32, p = .058. No other arousal
measures were significantly correlated with LC contrast.
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Figure 5
Visualizations of correlation matrices reflecting pairwise Pearson correlations between LC MRI
contrast and arousal during the stress induction protocol
Note. + p < .05, ++ p < .01, +++ p < .001.
70
As a multivariate approach to quantify associations between LC contrast and arousal
during the stress induction protocol, we performed a series of PLS correlation analyses. We note
that these analyses were performed with only participants with no missing values (53 younger,
23 older), whereas the correlations presented above reflected all available sets of pairwise
observations. PLS analyses examining associations of peak and rostral LC contrast, respectively,
with arousal yielded no reliable latent variables. The final PLS analysis, examining associations
between caudal LC contrast and arousal, indicated 1 marginally reliable latent variable (p =
.053). Bootstrap ratios reflecting the contribution of each arousal measure to this latent variable,
as well as the correlation between physiological scores for this latent variable and peak LC
contrast values, are shown in Figure 6. Physiological scores on this latent variable were highly
correlated with caudal LC contrast in older participants, r(21) = 0.66, p < .001, but not in
younger participants, r(51) = 0.04, p = .781. Furthermore, higher physiological scores for this
latent variable reflected greater systolic blood pressure increases and RMSSD decreases during
stress reactivity, higher systolic blood pressure during stress recovery, and lower RMSSD and
HF power during stress recovery.
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Figure 6
Results of partial least squares correlation analyses examining the association between caudal
LC MRI contrast and physiological arousal during the stress induction protocol
Note. These analyses indicated a marginally reliable latent variable reflecting an association
between caudal LC contrast and arousal for older participants. The left panel depicts bootstrap
ratios which reflect how much each arousal measure contributed to the latent variable (bootstrap
ratios with absolute value greater than 2, indicated in red, were considered stable contributors).
The right panel depicts associations between physiological scores - reflecting the projection of
each respective latent variable onto the original physiological arousal data - and LC contrast
values.
4. Discussion
As an arousal hub region in the brain, the LC plays a major role in the central nervous
system’s response to acute stress, releasing noradrenaline throughout the brain and spinal cord to
promote behaviors that facilitate stressor avoidance or elimination (Bremner et al., 1996; Sara &
Bouret, 2012; Wood & Valentino, 2017). Studies using MRI to assess the LC’s structure in vivo
have suggested that having a more structurally intact LC in later adulthood is associated with
better cognitive outcomes and reduced risk of cognitive decline (Elman et al., 2021a; Liu et al.,
2020), but it is unclear whether LC MRI contrast is related to acute stress responses. Limited
evidence in small studies of mostly younger adults suggests that higher LC contrast is associated
with greater arousal levels, with anxious arousal being positively correlated with LC volume
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(Morris et al., 2020b) and LC contrast being negatively correlated with parasympathetic control
over heart rate (Mather et al., 2017). Here, we tested how LC contrast was associated with
arousal at rest, during reactivity to acute stress, and during recovery from acute stress in both
younger and older adults. Across univariate and multivariate analyses, we found that for older
adults, having higher caudal LC contrast was associated with higher stress-related increases in
systolic blood pressure and lower HRV during stress recovery. Together, these findings suggest
that having a more structurally intact caudal LC in older adulthood is associated with more
pronounced physiological responses to acute stress.
In response to acute psychosocial stressors, sympathetic arousal increases and HRV
generally decreases, reflecting parasympathetic withdrawal (Rab & Admon, 2021). Aging is
associated with changes to the autonomic system that may impact acute stress responses (Kaye &
Esler, 2008). In general, older adults exhibit higher mean levels of cortisol than younger adults,
reflecting higher tonic activation of the hypothalamic-pituitary-adrenal axis in later adulthood
(Lupien et al., 2009). Sympathetic nervous system activity tends to also increase in aging (Fagius
& Wallin, 1993; Seals & Esler, 2000), and consistent with this pattern, we found that older
participants had higher values of a measure of skin sympathetic nerve activity quantified from
ECG signals, relative to younger participants. In addition to older age being associated with
elevated sympathetic tone, vagal control of heart rate and HRV decline with age (Jandackova et
al., 2016). Evidence for age changes in parasympathetic responses to stress is scarce, but here,
we found that older participants had significantly smaller decreases in LF power relative to
younger participants during acute stress. Thus an autonomic system that is relatively less affected
by age-related dysregulation might be expected to feature more dynamic responses to acute
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stress - specifically, greater sympathetic increases and greater parasympathetic withdrawal in
response to stress.
We found that older participants with more pronounced physiological responses to acute
stress - that is, greater sympathetic increases and parasympathetic withdrawal in response to
stress - had higher caudal LC contrast. The LC is a component of the central autonomic network,
the set of brain regions that coordinates neuroendocrine, visceromotor, and behavioral responses
to situational demands such as acute stress (Benarroch, 1993). In particular, the LC contains
excitatory projections to the rostral ventrolateral medulla, inhibitory projections to
parasympathetic nuclei, and bidirectional connections with C1 neurons that coordinate
cardiovascular responses (Lamotte et al., 2021). According to the neurovisceral integration
model, the ability to modulate heart rate on a moment-to-moment basis reflects the central
autonomic network’s capacity for brain-heart integration (Thayer & Lane, 2000). With MRI-
assessed LC integrity thought to reflect neurodegeneration in aging (Betts et al., 2019b), our
results suggest that the structural integrity of the caudal LC may be important for allowing
messages from the brain to reach the heart and coordinate effective physiological responses.
These findings add to a growing body of literature linking higher LC integrity to better cognitive
and neural outcomes in aging (Bachman et al., 2021; Dahl et al., 2019; Elman et al., 2021b; Liu
et al., 2020). Furthermore, current research suggests a role of the LC in the progression of
Alzheimer’s disease (Jacobs et al., 2021), with lower MRI-indexed LC integrity being associated
with elevated risk for mild cognitive impairment (Elman et al., 2021a) and LC integrity being
lower in individuals with Alzheimer’s disease relative to healthy controls (Betts et al., 2019a;
Takahashi et al., 2015). Mild cognitive impairment and dementia are characterized by autonomic
dysfunction, including hypothalamic-pituitary-adrenal axis hyperactivation (Justice, 2018) and
74
reductions in HRV (Collins et al., 2012; da Silva et al., 2017). Our findings provide novel
evidence that greater neurodegeneration of the caudal LC in aging may be associated with
dysregulated physiological responses to stress.
Our results furthermore highlight a potential role of the LC’s caudal aspect in
neurovisceral integration. We previously found that in aging, associations with episodic memory
and gray matter integrity were greater for rostral compared to caudal LC contrast (Bachman et
al., 2021; Dahl et al., 2019). Consistent with the rostral LC being important for age-related
outcomes, the rostral LC undergoes relatively more cell loss than the caudal LC in aging
(Manaye et al., 1995) and Alzheimer’s disease (Zarow et al., 2003). So why might the caudal LC
be more relevant for physiological arousal in aging? Hirschberg et al. (2017) identified two
populations of LC neurons: One population consisted of more rostrally-originating neurons
projecting to the prefrontal cortex, and another was clustered in the caudal LC and consisted of
neurons projecting primarily to the spinal cord. At the spinal cord, noradrenergic neurons from
the LC synapse onto sympathetic preganglionic neurons, promoting downstream peripheral
arousal responses (Clark & Proudfit, 1991). Based on current evidence, it is unclear whether
medullary and parasympathetic projections from the LC also originate predominantly in the LC’s
caudal aspect. Yet our results suggest that the caudal LC may play a role in the pathway linking
neural appraisals of the world to physiological arousal responses.
We found associations between LC contrast and patterns of acute stress responding in
older participants, but we did not find the expected relationships between LC contrast and
arousal in younger participants. Specifically, PLS did not indicate a pattern of associations
between LC contrast and arousal, and pairwise correlation analyses also did not reveal a
consistent pattern: We found that peak LC contrast was positively correlated with lower
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breathing rate during stress recovery, while rostral LC contrast was negatively correlated with
sympathetic tone during stress recovery. Although few studies have investigated associations
with LC structure in younger adults, the largely null findings in our younger sample are
inconsistent with reports of higher LC volume being associated with higher anxious arousal
(Morris et al., 2020b) and of caudal LC contrast being negatively associated with cortical
thickness (Bachman et al., 2021) in younger adults. We previously reported that LC contrast was
negatively correlated with HRV during a fear conditioning task in both younger and older adults
(Mather et al., 2017). Our current findings offer an alternative explanation for the previous
findings in older adults (more pronounced acute stress responses in individuals with higher LC
contrast), but they are inconsistent with the previous findings in younger adults. Based on these
and other previous findings linking LC structure to arousal in younger adults, we predicted that
LC contrast would be associated with larger physiological arousal responses to acute stress
among younger participants. In this case, a potential reason that we did not observe expected
associations in younger adults is that the LC contrast measure does not reflect functional acute
stress responses in younger adults. Related to this, it is possible that the imaging method used
may be less reliable in younger than older adults (Hämmerer et al., 2018). A different
explanation, and another limitation of the study, is that the cognitive challenge tasks may not
have reliably induced acute stress for younger participants. Qualitatively, older participants
reported the Stroop task to be very challenging, whereas this was not common feedback from
younger participants. Heart rate, breathing rate, systolic and diastolic blood pressure and
sympathetic tone all increased reliably for younger participants during the challenge phase.
However, because we did not measure salivary cortisol levels throughout the acute stress
induction task, we cannot be sure that this task elicited an acute stress response in participants or
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simply cognitive load. Further research will be needed to examine whether robust acute stress
responses in younger and older adults are indeed related to MRI-indexed LC structure.
Another important limitation of this study is that the PLS analyses used only cases with
all available physiological measures, constraining the number of participants included for these
analyses. (The older sample available for analysis was also smaller in size than the younger
sample, due to early termination of data collection at the start of the COVID-19 pandemic.) To
combat this limitation associated with PLS, we also reported pairwise correlations between
measures of LC contrast and physiological arousal and considered whether the results were
consistent with the pattern of associations that emerged using PLS. The latent variable reflecting
associations between caudal LC contrast and arousal indicated that participants with higher
caudal LC contrast had higher systolic blood pressure increases and RMSSD decreases in
response to stress, higher systolic blood pressure during stress recovery, and lower RMSSD and
HF power during stress recovery. Pairwise correlation analyses likewise indicated that older
participants with higher caudal LC contrast had greater systolic blood pressure increases in
response to stress, and the pattern of associations between caudal LC contrast and HRV during
recovery was also negative, albeit not significant. These findings suggest the interesting
possibility that older adults with caudal LC contrast have more pronounced physiological
responses to acute stress, but further work is needed to replicate these findings in a larger sample
of older adults.
To conclude, we examined how LC MRI contrast, an in vivo measure of LC structure,
was related to physiological arousal at rest, during acute stress reactivity, and during recovery
from acute stress. In younger adults, LC contrast was largely unrelated to physiological arousal,
although this may be explained by the task being challenging, but not reliably stressful, for
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younger participants. In older participants, caudal LC contrast was associated with greater stress-
related increases in systolic blood pressure and decreases in HRV, as well as lower HRV during
stress recovery. These results suggest that caudal LC structure is associated with more
pronounced stress responses in aging and implicate the caudal LC in neurovisceral integration.
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Chapter 3: Isometric handgrip exercise speeds working memory responses in younger and
older adults
1. Introduction
Physiological arousal affects attention, perception, and memory (Berridge & Waterhouse,
2003; Sara, 2009). These effects are mediated by the brain’s locus coeruleus (LC), a small
nucleus in the brainstem that serves as the brain’s primary source of noradrenaline (Schwarz &
Luo, 2015). Many studies testing arousal’s effects on cognition have employed manipulations
with emotion- and/or stress-inducing components, with fewer studies testing how isolated effects
of physiological arousal and LC activity affect cognitive performance. An influential model of
LC function posits a U-shaped relationship between tonic LC activity and cognitive
performance, with performance peaking at moderate levels of tonic LC activity and being worse
at high and low levels of tonic LC activity (Aston-Jones & Cohen, 2005).
Isometric handgrip exercise is one manipulation that affects arousal and noradrenergic
activity, with recent studies reporting that performing short bouts of handgrip temporarily
increases salivary alpha amylase levels, heart rate, systolic blood pressure, and blood flow
velocity (Nielsen & Mather, 2015; Washio et al., 2021), without concomitant activation of the
hypothalamic-pituitary-adrenal axis (Finke et al., 2018). Handgrip also increases pupil diameter,
a non-invasive proxy of LC activity (Joshi et al., 2016; Murphy et al., 2014; Nielsen & Mather,
2015). Critically, there are also effects on arousal and cognition immediately after handgrip. We
reported that while isometric handgrip increased concurrent pupil diameter, in the period after
handgrip, it reduced tonic pupil diameter, enhanced phasic pupillary responses, and decreased
reaction times during an auditory oddball task (Mather et al., 2020). Post-handgrip benefits on
cognition have been reported in other domains, with isometric handgrip exercise enhancing
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subsequent memory retention (Nielson et al., 1996) and speeding reaction times on a subsequent
go/no-go task (Washio et al., 2021). These results suggest that a brief period of static exercise
may temporarily reduce subsequent tonic levels of arousal and LC activity to a point that
optimizes task performance (Aston-Jones & Cohen, 2005).
In addition to lower tonic pupil diameter and enhanced phasic pupillary responses after
handgrip, we found that handgrip led to greater engagement of the brain’s right frontoparietal
network during a subsequent oddball task (Mather et al., 2020). Frontoparietal brain regions are
densely innervated by noradrenergic projections from the LC (Pickel et al., 1974; Schwarz &
Luo, 2015), and during arousal, phasic signals from the LC promote neural gain within
frontoparietal brain regions, facilitating selective attention and task performance (Arnsten et al.,
1996; Aston-Jones & Cohen, 2005; Corbetta et al., 2008). Therefore, changes in arousal may be
particularly relevant for tasks that engage frontoparietal brain regions, including working
memory, the short-term storage and maintenance of information in memory (Baddeley, 2003).
How arousal affects cognition may also change with age. We reported that during a fear
conditioning task, arousal increased functional connectivity between the LC and frontoparietal
network less in older compared to younger adults (Lee et al., 2018). One explanation is that older
adults have higher baseline levels of norepinephrine, yielding a reduced dynamic range within
which arousal can affect cognitive performance (Mather, 2020). On the other hand, we found
that isometric handgrip benefited younger and older women similarly in terms of frontoparietal
network activation, raising the possibility that handgrip-induced arousal has similar effects for
younger and older adults (Mather et al., 2020).
In this study, we tested the effects of isometric handgrip on subsequent working memory
performance in younger and older adults. We randomly assigned participants to one of two
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groups: a handgrip or a control group. Participants completed short bouts of isometric handgrip
exercise (handgrip group), or a control task of equal duration (control group), alternating with
rounds of an auditory n-back task to probe working memory performance. Based on reports of
beneficial effects of isometric handgrip exercise on subsequent cognitive task performance
(Mather et al., 2020; Washio et al., 2021), we predicted that both younger and older participants
in the handgrip group would have higher accuracy and faster reaction times on the working
memory task relative to those in the control group. Furthermore, we expected that levels of tonic
arousal would be reduced and that phasic pupillary responses would be enhanced after handgrip.
2. Methods
2.1. Participants
Younger participants were recruited from the University of Southern California
Psychology Subject Pool, and older participants were recruited from the Los Angeles and USC
communities. Eligible participants were fluent English speakers with corrected or normal-to-
corrected vision who were not taking psychoactive or beta-blocker medications and who did not
self-identify as having any chronic illness or cognitive impairment. Prior to participation, older
adults were screened for hearing impairment over the phone using the Hearing Handicap
Inventory for the Elderly-Screening Version (Yueh et al., 2003); individuals who scored greater
than 8 on this assessment, corresponding to a probability of hearing impairment greater than or
equal to 50%, were not eligible for participation. Prior to taking part in the study, participants
were asked to adhere to the following requirements: no consumption of alcohol, caffeine, or
nicotine for 24 hours prior, no cardiovascular exercise for 24 hours prior, no prescription or over-
the counter medications for 12 hours prior, awake for at least 3 hours prior, and no eating,
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brushing teeth, chewing gum, or eating mints for 1 hour prior. Compliance with these
requirements was assessed before participants began the study.
A total of 57 younger and 56 older adults participated in the study. Of these, 2 participants
(1 younger, 1 older) were excluded from all analyses after disclosing non-compliance with at
least 3 pre-study requirements during the experiment. An additional 2 participants in the
handgrip group were excluded from all analyses for not having greater mean integrated
electromyogram (EMG) signal during the squeeze relative to the rest phases during handgrip
runs (Appendix C, Section 1.1). The sample included for analysis (n = 109) is described in Table
1. The study was approved by the University of Southern California Institutional Review Board,
and all participants provided written, informed consent prior to participation. Younger
participants received course credit for participation, and older participants received monetary
compensation.
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Table 1
Sample characteristics
Age
group
Group N
Age,
mean
(SD)
Age,
range
N (%)
female
Edu,
mean
(SD)
Edu,
range
MMSE,
mean
(SD)
MMSE,
range
Younger Handgrip 28
20.11
(1.73)
18-26
18
(0.64)
13.89
(1.13)
12-16 NA NA
Younger Control 28
20.21
(2.06)
18-29
13
(0.46)
13.82
(0.87)
13-16 NA NA
Older Handgrip 25
73.92
(5.28)
65-85
13
(0.52)
17.42
(2.62)
14-23
27.68
(2.19)
22-30
Older Control 28
72.77
(5.58)
66-83
15
(0.54)
16.71
(1.46)
14-20
26.89
(1.95)
23-30
Note. Age and education are expressed in years; MMSE = Mini-Mental State Exam.
2.2. Procedure
2.2.1. Study overview. The study consisted of a single experimental session lasting
between 90 and 120 minutes (Figure 1A). Participants were randomly assigned to either a
handgrip or a control group. Blinded to group assignment, participants were told that the purpose
of the study was to measure the effects of muscle exertion on cognition. Baseline physiological
signals were first recorded for an initial 4-minute resting period, during which participants sat
with their feet resting flat on the ground and their palms facing down on a flat surface.
Participants then completed runs of either an isometric handgrip protocol (handgrip group) or
control protocol (control group), with each run followed by a run of an auditory working
memory (n-back) task. Physiological recordings were performed continuously to measure
changes in physiological arousal, and saliva samples were collected periodically to assess
sympathetic arousal and stress.
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Figure 1
Overview of experiment, handgrip protocol, and auditory n-back task
Note. (A) Overview of experiment. After completing a 4-minute baseline resting period,
participants completed alternating runs of a handgrip (or control) protocol and an auditory n-
back task. (B). Handgrip and control protocols. Participants in the handgrip and control groups
heard the same sounds through headphones but performed different tasks: upon hearing “LEFT”
or “RIGHT” (squeeze phase), participants in the handgrip group squeezed a therapy ball with
their left or right hand, respectively, whereas participants in the control group placed their left or
right palm facing down on a flat surface, respectively. Upon hearing “BREAK” (rest phase),
participants in both groups rested their hands. (C) Auditory n-back task. As a measure of
working memory performance, participants completed a task in which they heard a series of
digits through headphones and indicated with the keyboard when the digit they heard matched
the digit they heard n digits before. Figure displays the 2-back load, but multiple working
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memory loads were tested (0-, 1-, 2-, and 3-back; older adults did not complete 3-back). Each n-
back run consisted of multiple blocks, each testing a different working memory load. Each block
consisted of 18 trials preceded by an 8-second fixation period and a display of the working
memory load for that block.
2.2.2. Isometric handgrip protocol. The isometric handgrip protocol (Figure 1B) was
based on a finding from our laboratory that a protocol consisting of alternating 18-second
squeeze and 60-second rest phases elicited greater increases in salivary alpha amylase and pupil
diameter compared to a protocol consisting of a constant 3-minute squeeze period (Nielsen &
Mather, 2015). In the present study, each handgrip run consisted of a 10-second initial rest
period, followed by 4 repetitions of the following sequence: an 18-second squeeze phase
followed by a 30-second rest phase (the rest phase was shortened from 60s as in Nielsen et al.
(2015) to reduce the overall duration of the experiment). During each run, participants in the
handgrip group held two identical therapy balls, one in each hand, while resting their forearms
on a flat surface. They heard sounds through headphones telling them to either squeeze with their
left hand (“LEFT”), squeeze with their right hand (“RIGHT”), or to rest their hands in a resting
position until the next sound (“BREAK”).
Instead of handgrip, participants in the control group performed a task of equivalent
length (Figure 1B). Followed by an initial 10-second rest period, participants then heard sounds
through the headphones telling them to either rest their left palm facing down on the table
(“LEFT”), rest their right palm facing down on the table (“RIGHT”), or to rest with their hands
in a resting position (“BREAK”). The sounds used in the control protocol were identical to those
used in the handgrip protocol. To avoid handedness or laterality effects, participants in both
conditions used alternating hands such that they squeezed (or placed their palm facing down)
with each hand twice during a single run. The hand with which the first squeeze (or palm
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placement) was performed was counterbalanced across runs within individual participants, as
well as across participants.
2.2.3. Auditory n-back task. After each handgrip (or control) run, participants performed
1 run of an auditory n-back task (Figure 1C). Within each n-back run, participants completed
multiple blocks, each testing a different working memory load. The number of blocks per run
differed by age group, with younger participants completing 4 blocks per run (0-, 1-, 2- and 3-
back) and older participants completing 3 blocks per run (0-, 1- and 2-back). The 3-back load
was not tested for older participants as we anticipated that it could be stress-inducing. Within
each run, the order of n-back loads was randomized, and across runs, the order of n-back loads
was never the same for an individual participant.
During the task, participants were instructed that they would hear a series of digits
through headphones. They were instructed to press the J key when the number they heard
matched the number they heard some number of trials before, referred to by the experimenter as
the “delay.” They were instructed that this delay would vary across blocks of trials, and that they
would be shown the delay on screen before starting each block. At the beginning of each block,
before participants were shown the delay number, we included an 8-second resting period for the
purpose of assessing tonic pupil diameter periodically throughout the task. The delay number
was subsequently shown on screen for 5 seconds, followed by another 1-second rest, and then
participants began the task.
Each block consisted of 18 trials, and the number of target trials - on which a response
was expected - depended on working memory load. Placement of target trials within n-back
blocks is detailed in Appendix C (Section 1.2). Prior to all n-back and handgrip runs, participants
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reviewed examples of each condition with the experimenter. Participants then practiced 1 block
of each working memory load in ascending order (e.g., 0-, 1-, 2-, 3-back).
2.2.4. Physiological recordings. To assess physiological arousal and handgrip effort
during the experiment, electrocardiogram (ECG) and forearm EMG signals were collected using
a BIOPAC MP150 system (Goleta, CA) at a sampling rate of 2000 Hz. The ECG setup involved
a standard Lead I configuration with disposable, pre-gelled Ag/AgCl electrodes; signals were
transmitted to the MP150 system using a wireless BioNomadix transmitter (BN-RSPEC-T).
EMG signals were collected from the right and left anterior forearm using an EMG100C module
with leads connected to disposable, pre-gelled Ag/AgCl electrodes at the following locations: on
the flexor digitorum radialis and distal to elbow joint (positive), on the flexor digitorum
superficialis and medial to the positive electrode (negative), and just distal to wrist joint
(ground). Integrated EMG signals for each arm were computed online from raw EMG signals.
ECG and EMG signals were recorded in Acqknowledge (Version 5.0).
Pupil diameter was recorded continuously from both eyes using an SMI RED eyetracker
at a sampling rate of 120 Hz. Throughout the experiment, participants rested their head in a
chinrest placed at a fixed distance from the experimental computer screen. All tasks in which we
planned to analyze pupil diameter involved only auditory stimuli, with a black fixation cross of
40x40 pixels in size displayed on a gray background throughout the tasks.
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2.2.5. Salivary assays. To assess sympathetic arousal changes due to handgrip, salivary
alpha amylase was assessed immediately before the first handgrip (or control) run and
immediately after the first handgrip (or control) run. To ensure that the tasks were not stress-
inducing, salivary cortisol was assessed from samples taken immediately after the baseline
resting period and after all handgrip and n-back runs. The first saliva sample was collected at
least 10 minutes after participants arrived in the lab and consumed 8 ounces of water.
Samples were collected using Salimetrics, LLC (State College, PA) Oral Swabs. Swabs
were frozen and shipped to Salimetrics’ SalivaLab (Carlsbad, CA). Samples were assayed using
the Salimetrics Salivary Alpha-Amylase Assay Kit (Cat. No 1-1902) and the Salimetrics Salivary
Cortisol Assay Kit (Cat. No 1-3002), both without modifications to the manufacturers’ protocol.
2.3. Analysis
2.3.1. Physiological data processing and analysis. Prior to preprocessing and analysis,
physiological signals for each participant were split into 7 segments corresponding to the various
tasks: baseline, handgrip runs 1-3, and n-back runs 1-3.
ECG. ECG and EMG data from 10 participants was missing due to recording errors.
Available ECG data segments were visually inspected for quality by two trained researchers. A
total of 16 segments (0.02%) with noise or artifact such that QRS complexes were not detectable
by either researcher were excluded from further analysis. A summary of excluded ECG segments
is provided in Appendix C (Table C1). QRS detection on raw ECG segments was then performed
using the jqrs algorithm as implemented in the PhysioNet Cardiovascular Signal Toolbox
(Version 1.0.2; Vest et al., 2018; parameters described in Appendix C, Section 1.4). At this
stage, an additional 7 ECG segments were excluded from analysis because they contained too
little high-quality data for QRS detection. Following r-peak delineation, we used the R package
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RHRV (Version 4.2.6; Rodriguez-Linares et al., 2020) to calculate an instantaneous heart rate
signal from heartbeat positions in each segment. Instantaneous heart rate signals were filtered to
eliminate outliers and spurious points using the default parameters in RHRV and then
interpolated to a sampling rate of 4 Hz. Mean heart rate for events was calculated from the
resulting interpolated signals. All heart rate values were baseline-corrected using mean heart rate
from the initial baseline period.
To calculate a measure of sympathetic tone, we used the neuECG method described by
Kusayama et al. (2020), implemented in MATLAB (Version R2021b). Specifically, we
calculated the aSKNA measure, which increases during sympathetic-activating manipulations
(Kusayama et al., 2020). In this approach, raw ECG segments deemed to be high-quality were
high-pass filtered with a digital finite impulse response filter with a cutoff frequency of 500 Hz.
Following full-wave rectification, we applied a leaky integrator with a time constant of 100
milliseconds and calculated the average of the resulting signal (aSKNA) for events of interest.
All aSKNA values were baseline-corrected using mean aSKNA from the initial baseline period.
EMG. For analysis of forearm muscle activity during handgrip, we took integrated left
and right arm EMG signals from the handgrip (or control) segments, and computed the mean
signal for each squeeze and rest phase. For squeeze phases, we used data from the relevant arm
(depending on whether participants heard “LEFT” or “RIGHT”). For rest events, we averaged
signals over the left and right arms. Resulting mean values were baseline-corrected using the
mean integrated EMG value across both forearms from the initial baseline period.
Eyetracking. Eyetracking data from 6 participants was not recorded due to failure of the
eyetracker to calibrate (n = 5) and being unable to position the participant in the chinrest (n = 1).
Available eyetracking data segments were downsampled to 60 Hz and subsequently
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preprocessed using the R package `gazeR` (Version 0.1; Geller et al., 2020). First, samples
marked as invalid by iView were marked as missing (NA). Blinks were identified using a
velocity-based algorithm from the `saccades` R package (Version 0.2-1; von der Malsburg,
2019); in this approach, x- and y-coordinates were smoothed using a moving window average
with window size 3 prior to detection and a tuning parameter (lambda) of 6 was applied. Pupil
diameter values during blink events were treated as NA, and NAs were extended 100ms before
and after blink events. Pupil diameter data were linearly interpolated and smoothed using a 10-
point moving average. The fraction of missing samples after blink detection and extension for
each event of interest was calculated; events with more than 70% of samples missing were
excluded from relevant analyses (a summary of excluded events is provided in Appendix C,
Section 1.5). Pupil diameter values during handgrip were baseline-corrected using mean pupil
diameter from the 4-minute baseline period. Baseline correction for n-back segments is described
below. For all analyses, left and right pupil diameter data were preprocessed and baseline-
corrected separately, then averaged.
2.3.2. Statistical analysis.
Effect of handgrip on concurrent physiological arousal. To examine how handgrip
affected concurrent physiological arousal, we calculated baseline-corrected, average measures of
pupil diameter, heart rate, and sympathetic tone during the squeeze and rest phases of the
handgrip protocol (for control group participants, we computed average measures for the
corresponding control and rest phases). Mixed-design ANOVAs were performed to test the
effects of group (handgrip/control), phase (squeeze/rest) and age group (younger/older) on each
arousal measure. ANOVAs were supplemented with planned comparisons of squeeze-rest
differences for each measure, group and age group.
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We then tested whether handgrip effort was associated with arousal increases during the
handgrip protocol. Handgrip effort was calculated as the difference between each participant’s
mean integrated EMG signal across all squeeze phases relative to that across all rest phases. Each
arousal measure’s increase during handgrip was calculated as the difference between each
participant’s mean value across squeeze phases relative to that across all rest phases. Pearson
correlation analyses were used to test for relationships between handgrip effort and increases in
each arousal measure.
Effect of handgrip on subsequent n-back performance. N-back accuracy was computed
for each participant and working memory load as the percentage of target trials correctly
identified as targets. 1 older participant was excluded from all n-back analyses for not making
any correct responses on the n-back task. Each participant’s reaction times were trimmed to
include only values falling within 2.5 standard deviations of their mean reaction time on target
trials. The mean reaction time for each participant and working memory load, on target trials
only, was calculated. Because significant deviations from normality were observed for
aggregated accuracy data (younger: W = 0.829, p < .001; older: W = 0.858, p < .001) and
reaction time data (younger: W = 0.943, p < .001; older: W = 0.980, p = .021), we used permuted
mixed-design ANOVAs to test the effects of group and working memory load (younger: 0-/1-/2-
/3-back; older: 0-/1-/2-back) on accuracy and reaction time. Because younger and older
participants completed different working memory loads, these analyses were carried out for each
age group separately. Planned comparisons of each performance measure for the handgrip versus
control group were performed for each age group and working memory load.
Effect of handgrip on subsequent salivary amylase and cortisol. Next, we tested whether
handgrip affected salivary alpha amylase levels, using a permuted mixed-design ANOVA with
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timepoint relative to the first handgrip run (pre-handgrip/post-handgrip) as a within-subjects
factor, and group and age group as between-subjects factors. To confirm that the handgrip and n-
back tasks did not elicit a stress response, we also tested for an interaction between timepoint
(pre-baseline/after all handgrip and n-back runs), group and age group on salivary cortisol levels.
Planned, pairwise comparisons of salivary alpha amylase and cortisol by timepoint were
performed for each group and age group. Outliers for salivary measures were identified for each
age group separately using the mean absolute deviation-median rule (Wilcox, 2011) and
removed prior to analysis. Permuted ANOVAs were used after Shapiro-Wilk tests indicated
deviations from normality after outlier removal for salivary alpha amylase, W = 0.834, p < .001,
and cortisol levels, W = 0.961, p < .001.
Effect of handgrip on subsequent physiological arousal. We subsequently tested
whether physiological arousal levels during the n-back task differed between the handgrip and
control groups. As a measure of tonic pupil diameter, we calculated mean pupil diameter during
the initial fixation period of each n-back block for each participant. As separate measures of
tonic arousal, we calculated mean heart rate and sympathetic tone for each participant during
each n-back block. All arousal measures were baseline-corrected using mean values from the
initial baseline resting period. Previous work in our laboratory suggested that the effect of
isometric handgrip on post-handgrip tonic arousal is time-dependent, diminishing as time after
handgrip increases (Mather et al., 2020). To take this into account and also test for handgrip
effects on post-handgrip tonic arousal, we performed mixed-design ANOVAs testing the effects
of group and n-back block number relative to handgrip offset (1-4 for younger participants; 1-3
for older participants) on each tonic arousal measure. Because the number of blocks in each n-
back run differed by age group, these analyses were performed for younger and older
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participants separately. We performed planned comparisons of each tonic arousal measure for
the handgrip versus control groups, for each block and age group.
We then examined whether handgrip affected phasic pupillary responses during the n-
back task. To assess phasic pupillary responses, we computed the maximum pupil diameter on
each n-back trial. To avoid these values being contaminated by local fluctuations in tonic
arousal, values were baseline-corrected using mean pupil diameter during the initial fixation
period at the beginning of the respective n-back block. We also computed the onset time of the
maximum pupil diameter within each trial. Then, to test whether phasic n-back pupillary
responses or their onset times were affected by handgrip and whether these effects depended on
working memory load, we performed mixed-design ANOVAs with group as a between-subjects
factor and working memory load as a within-subjects factor, for each age group separately. To
avoid the influence of trials in which participants may have been distracted, ANOVAs included
only trials on which participants correctly responded or correctly withheld a response. Planned,
pairwise comparisons of maximum pupil diameter and onset times for the handgrip versus
control groups were performed for each working memory load and age group separately.
Statistical analyses were performed in R (Version 4.0.4; R Core Team, 2021). ANOVAs
were performed with the `afex` R package (Version 1.0-1; Singmann et al., 2021) with
Greenhouse-Geisser sphericity corrections automatically applied. Permuted ANOVAs were
performed with the `permuco` (Version 1.1.0; Frossard & Renaud, 2019) package and 10,000
permutations. For parametric ANOVAs, comparisons of estimated marginal means were
performed with the emmeans package (Version 1.7.0; Lenth, 2021). For permuted ANOVAs, a
bootstrapping procedure with 1000 bootstrap samples was used for pairwise comparisons. For
each set of pairwise comparisons, p-values were adjusted with a Bonferroni correction.
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3. Results
3.1. Handgrip increased concurrent physiological arousal
Arousal measures during the handgrip protocol are shown in Figure 2A. Using a mixed-
design ANOVA, we found a significant group x phase interaction (p < .001; Table 2A), as well
as a significant group x age group x phase interaction on pupil diameter during the handgrip
protocol (p < .001; Table 2A). The 3-way interaction reflected a larger group x phase interaction
in younger compared to older participants, although pairwise comparisons indicated that for both
age groups, pupil diameter was significantly higher during squeeze compared to rest for the
handgrip group (ps <= .002; Appendix C, Table C4) but not for the control group (ps >= 0.109;
Appendix C, Table C4). Separate ANOVAs indicated significant group x phase interactions on
heart rate (p < .001; Table 2B) and sympathetic tone (p < .001; Table 2C) during the handgrip
protocol. These interactions were driven by each measure being significantly higher during
squeeze relative to rest for the handgrip but not the control group, for both younger and older
participants (Appendix C, Tables C5-6). There were no significant group x age group x phase
interactions on either heart rate or sympathetic tone (ps >= .056).
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Figure 2
Pupil diameter, heart rate and sympathetic tone and their associations with handgrip effort
during the handgrip protocol
Note. (A) Pupil diameter, heart rate and sympathetic tone are shown across the course of the
handgrip protocol. During squeeze phases, participants in the handgrip group squeezed a therapy
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ball, whereas participants in the control protocol turned their hand from a supine to a prone
position. Pupil diameter, heart rate and sympathetic tone values were baseline-corrected using
mean values from a 4-minute baseline resting period at the beginning of the experiment. Error
bars indicate standard errors of the mean. (B) Scatterplots depict associations between handgrip
effort and increases in pupil diameter, heart rate, and sympathetic tone during the handgrip
protocol. Handgrip effort was computed as the difference between each participant’s mean
integrated EMG signal from the relevant arm during all squeeze phases and that across both
forearms during all rest phases. Change in each arousal measure during handgrip was computed
as the difference between each participant’s mean value during all squeeze phases and the mean
value during all rest phases. 95% confidence bands are shown in gray. Figures A and B reflect
data averaged across all 3 runs of the handgrip protocol.
Table 2
Results of mixed-design ANOVAs testing the effects of group, phase, and age group on pupil
diameter (A), heart rate (B), and sympathetic tone (C) during the handgrip protocol
Effect df F η
2
p p
A. Pupil diameter
Group 1, 92 3.44 .036 .067
Age group 1, 92 0.00 <.001 .983
Group x Age group 1, 92 0.77 .008 .383
Phase 1, 92 75.08 .449 <.001
Group x Phase 1, 92 23.67 .205 <.001
Age group x Phase 1, 92 6.94 .070 .010
Group x Age group x Phase 1, 92 11.62 .112 <.001
B. Heart rate
Group 1, 92 2.87 .030 .093
Age group 1, 92 9.81 .096 .002
Group x Age group 1, 92 1.14 .012 .288
Phase 1, 92 41.39 .310 <.001
Group x Phase 1, 92 36.98 .287 <.001
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Effect df F η
2
p p
Age group x Phase 1, 92 6.93 .070 .010
Group x Age group x Phase 1, 92 3.74 .039 .056
C. Sympathetic tone
Group 1, 92 0.05 <.001 .831
Age group 1, 92 2.01 .021 .159
Group x Age group 1, 92 0.03 <.001 .855
Phase 1, 92 11.54 .111 .001
Group x Phase 1, 92 22.47 .196 <.001
Age group x Phase 1, 92 0.80 .009 .372
Group x Age group x Phase 1, 92 1.42 .015 .236
Note. Pupil diameter, heart rate, and sympathetic tone values were baseline-corrected prior to
analysis based on mean values from a 4-minute initial baseline period.
Mean integrated EMG signals, reflecting forearm muscle activity during squeeze and rest
phases during the handgrip protocol, are visualized in Appendix C (Figure C1). Associations
between handgrip effort and increases in each arousal measure during the handgrip protocol are
depicted in Figure 2B. Across participants, we found that handgrip effort was significantly
correlated with increases in pupil diameter, r(86) = 0.500, p < .001, heart rate, r(86) = 0.620, p <
.001, and sympathetic tone, r(86) = 0.546, p < .001.
3.2. Handgrip speeded n-back reaction times but did not affect accuracy
Mean accuracy and reaction times on the n-back task are presented in Figure 3. A
permuted ANOVA indicated an expected significant main effect of load on accuracy in both age
groups (permuted ps < .001; Table 3A), but no significant main effects of group (younger:
permuted p = .769; older: permuted p: = .730; Table 3A) or group x load interactions (younger:
permuted p = .680; older: permuted p: = .351; Table 3A) on accuracy. However, examining
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reaction times on the n-back task, in addition to an expected significant main effect of working
memory load for both age groups (permuted ps < .001; Table 3B), we found a significant main
effect of group on reaction times for both younger and older adults (younger: permuted p = .046;
older: permuted p = .017; Table 3B), which was driven by participants in the handgrip group
having faster reaction times than those in the control group. There were no significant load x
group interaction effects in either age group (younger: permuted p = .838; older: permuted p =
.274; Table 3B). Pairwise comparisons of accuracy and reaction time by group, for each
condition and age group separately, are presented in Appendix C (Tables C7-8).
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Figure 3
Mean accuracy and reaction times on the n-back task
Note. Figure shows mean accuracy (A) and reaction times (B) on the n-back task, for each
working memory load. Crossbars indicate standard errors of the mean. Plots reflect data
averaged across all 3 n-back task runs. Older participants did not complete the 3-back task.
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Table 3
Results of permuted, mixed-design ANOVAs testing the effects of group and working memory
load on n-back accuracy (A) and reaction times (B)
Age group Effect df F η
2
p Parametric p Permutation p
A. Accuracy
Younger Group 1, 54 0.08 .002 .772 .769
Younger Load 3, 162 77.62 .590 <.001 <.001
Younger Group x Load 3, 162 0.50 .009 .683 .680
Older Group 1, 50 0.13 .003 .724 .730
Older Load 2, 100 59.08 .542 <.001 <.001
Older Group x Load 2, 100 1.05 .021 .354 .351
B. Reaction times
Younger Group 1, 54 4.29 .074 .043 .046
Younger Load 3, 162 61.33 .532 <.001 <.001
Younger Group x Load 3, 162 0.28 .005 .839 .838
Older Group 1, 50 6.31 .112 .015 .017
Older Load 2, 100 22.32 .309 <.001 <.001
Older Group x Load 2, 100 1.33 .026 .269 .274
Note. ANOVAs were performed separately for each age group because older participants did not
complete the 3-back working memory load.
3.3. Handgrip did not affect subsequent salivary alpha amylase or cortisol levels
Salivary alpha amylase levels immediately before and after the first handgrip run are
presented in Figure 4A. Examining these values with a permuted mixed-design ANOVA, we did
not find a significant timepoint x group (permuted p = .648; Table 4A) or timepoint x group x
age group (permuted p = .953; Table 4A) interaction on salivary alpha amylase levels. Instead,
there was a significant main effect of age group (permuted p < .001; Table 4A), driven by higher
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salivary alpha amylase levels in older relative to younger participants, as well as a significant
main effect of timepoint (permuted p = .029; Table 4A), driven by lower levels after versus
before the first handgrip run.
Figure 4
Salivary alpha amylase and cortisol levels during the experiment
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Note. Salivary alpha amylase levels immediately before and after the first handgrip run are
shown in A. Salivary cortisol levels before the initial baseline resting period and following all
handgrip and n-back task runs are shown in B. Crossbars indicate standard errors of the mean.
Table 4
Results of permuted, mixed-design ANOVAs testing the effects of timepoint, group and age group
on salivary alpha amylase (A) and cortisol (B) levels
Effect df F η
2
p
Parametric
p
Permutation
p
A. Salivary alpha amylase
Group 1, 102 0.01 <.001 .909 .906
Age group 1, 102 31.03 .233 <.001 <.001
Group x Age group 1, 102 0.09 .001 .766 .769
Timepoint 1, 102 4.84 .045 .030 .029
Timepoint x Group 1, 102 0.21 .002 .644 .648
Timepoint x Age group 1, 102 0.59 .006 .445 .450
Timepoint x Group x Age group 1, 102 0.00 <.001 .952 .953
B. Salivary cortisol
Group 1, 102 1.82 .018 .180 .178
Age group 1, 102 27.09 .210 <.001 <.001
Group x Age group 1, 102 0.80 .008 .372 .369
Timepoint 1, 102 3.07 .029 .083 .082
Timepoint x Group 1, 102 0.27 .003 .602 .607
Timepoint x Age group 1, 102 4.93 .046 .029 .025
Timepoint x Group x Age group 1, 102 0.42 .004 .517 .514
Note. For (A), salivary alpha amylase levels were assessed at two timepoints: immediately prior
to and immediately following the first handgrip run. For (B), salivary cortisol levels were
assessed at two points: prior to the initial baseline resting period, and following all handgrip and
n-back runs.
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We also tested whether the tasks elicited a stress response in either group by assessing
salivary cortisol before the baseline resting period and after all handgrip and n-back runs (Figure
4B). A permuted mixed-design ANOVA indicated there was no significant timepoint x group
(permuted p = .607; Table 4B) or timepoint x group x age group (permuted p = .514; Table 4B)
interaction on salivary cortisol levels. Instead, there was a main effect of age group, driven by
higher salivary cortisol in younger compared to older participants (permuted p < .001; Table 4B),
and a significant timepoint x age group interaction (permuted p = .025; Table 4B). This
interaction effect was driven by greater decreases in cortisol for younger relative to older
participants. Results of all pairwise comparisons of salivary alpha amylase and cortisol levels are
presented in Appendix C (Tables C9-10).
3.4. Handgrip had time-dependent effects on subsequent tonic pupil diameter but did not
affect subsequent heart rate or sympathetic tone
Next, we tested the effects of group and block relative to handgrip offset on tonic
measures of arousal during the n-back task (Figure 5). We found a significant group x block
interaction effect on fixation pupil diameter for both younger and older participants (younger: p
= .041; older: p = .021; Table 5A). For younger participants, this was driven by lower fixation
pupil diameter in the handgrip versus control group in all but the first n-back block after
handgrip, although pairwise comparisons indicated that handgrip-control differences were not
significant in any blocks (p’s >= .122; Appendix C, Table C11). For older participants, this was
driven by lower fixation pupil diameter in the handgrip relative to the control group across
blocks, although pairwise comparisons indicated the group difference was only significant in the
first block after handgrip (p = .039; remaining comparisons described in Appendix C, Table
C11). There were no significant main effects of group on fixation pupil diameter (younger: p =
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.649; older: p = .209; Table 5A), but we did find significant main effects of block relative to
handgrip, driven by decreases in fixation pupil diameter as block number relative to handgrip
offset increased (younger: p < .001; older: p < .001; Table 5A).
Testing the effects of group and block relative to handgrip on other measures of arousal
during the n-back task, there were no significant main effects of group or significant group x
block interactions on either heart rate or sympathetic tone (ps >= .065; Tables 5B and 5C). There
was a marginally significant group x block interaction on sympathetic tone for older participants
(p = .065; Table 5C), driven by sympathetic tone being lower in the handgrip relative to the
control group and this difference diminishing with increasing block number relative to handgrip
offset (although no handgrip-control pairwise comparisons were significant, ps >= .454; see
Appendix C, Table C13). In younger participants we found a significant main effect of block
relative to handgrip offset on sympathetic tone (p < .001; Table 5C), which was driven by
decreases in sympathetic tone with increasing block number relative to handgrip offset; this
effect was only marginally significant in older adults (p = .064; Table 5C). Results of planned,
pairwise comparisons of heart rate and sympathetic tone are reported in the Appendix C (Tables
C12-13).
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Figure 5
Pupil diameter during n-back fixation periods, heart rate during n-back blocks, and sympathetic
tone during n-back blocks
Note. Each measure is plotted by n-back block number relative to handgrip offset, with 1
reflecting the first n-back block after handgrip in a given run of the task. Crossbars indicate
standard errors of the mean. Plots reflect data averaged across all 3 runs of the n-back task.
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Table 5
Results of mixed-design ANOVAs testing the effects of group and n-back block number relative to
handgrip offset on pupil diameter during n-back fixation periods (A), heart rate during n-back
blocks (B), and sympathetic tone during n-back blocks (C)
Age group Effect df F η
2
p p
A. Pupil diameter during n-back pre-block fixation periods
Younger Group 1, 45 0.21 .005 .649
Younger Block 2.55, 114.78 117.46 .723 <.001
Younger Group x Block 2.55, 114.78 3.01 .063 .041
Older Group 1, 37 1.64 .042 .209
Older Block 1.95, 72.22 44.17 .544 <.001
Older Group x Block 1.95, 72.22 4.13 .100 .021
B. Heart rate during n-back blocks
Younger Group 1, 46 0.25 .005 .622
Younger Block 1.77, 81.37 1.37 .029 .259
Younger Group x Block 1.77, 81.37 0.62 .013 .523
Older Group 1, 44 0.53 .012 .469
Older Block 1.66, 72.95 0.22 .005 .764
Older Group x Block 1.66, 72.95 1.86 .041 .169
C. Sympathetic tone during n-back blocks
Younger Group 1, 47 1.15 .024 .289
Younger Block 2.35, 110.24 8.23 .149 <.001
Younger Group x Block 2.35, 110.24 0.11 .002 .926
Older Group 1, 44 0.06 .001 .815
Older Block 1.52, 66.88 3.12 .066 .064
Older Group x Block 1.52, 66.88 3.10 .066 .065
Note. Because younger and older participants completed a different number of blocks in each n-
back run, these analyses were performed for each age group separately. Pupil diameter, heart
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rate, and sympathetic tone values were baseline-corrected prior to analysis based on mean values
from a 4-minute initial baseline period.
3.5. Effects of handgrip on phasic pupillary responses depended on working memory load
We then examined how handgrip affected phasic pupillary responses on n-back trials on
which participants responded correctly. Pupil diameter time courses, maximum pupil diameter,
and onset times of maximum pupil diameter during n-back trials are presented in Figure 6.
Across the handgrip and control groups, there were expected significant effects of working
memory load on maximum pupil diameter and maximum pupil diameter onset time for both
younger and older participants (ps <= .040; Tables 6A and 6B). We did not find significant main
effects of group, or group x working memory load interaction effects, on either maximum pupil
diameter (ps >= .070; Table 6A) or the onset time of maximum pupil diameter during n-back
trials (ps >= .111; Table 6B) in either age group. There was a marginally significant group x load
interaction effect on maximum pupil diameter for older participants (p = .070; Table 6A), which
was driven by participants in the handgrip group having significantly higher maximum pupil
diameter than those in the control group, for the 1-back load only (p = .015; Appendix C, Table
C14). Planned pairwise comparisons indicated that for younger participants, the handgrip group
had significantly lower maximum pupil diameter (p = .044) and significantly faster maximum
pupil diameter onset (p = .007) relative to the handgrip group during the 3-back load only
(comparisons detailed in Appendix C, Tables C14-15).
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Figure 6
Smoothed mean pupil diameter time courses, maximum pupil diameter during n-back trials, and
onset time of maximum pupil diameter during n-back trials
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Note. Correct trials were those in which participants made a correct response or correctly
withheld a response, and incorrect trials were those in which participants incorrectly made a
response or incorrectly withheld a response. Mean time courses and their 95% confidence bands
shown in A were visualized by downsampling individual time courses to 10Hz and applying a
loess smoothing function. In B and C, crossbars indicate standard errors of the mean. Plots
reflect data averaged across all blocks and runs of the n-back task.
Table 6
Results of mixed-design ANOVAs testing the effects of group and working memory load on
maximum pupil diameter (A) and the onset time of maximum pupil diameter (B) during n-back
trials
Age group Effect df F η
2
p p
A. Maximum pupil diameter on n-back trials
Younger Group 1, 46 2.40 .050 .128
Younger Load 2.86, 131.56 64.46 .584 <.001
Younger Group x Load 2.86, 131.56 1.78 .037 .156
Older Group 1, 39 0.87 .022 .358
Older Load 1.79, 69.92 27.07 .410 <.001
Older Group x Load 1.79, 69.92 2.85 .068 .070
B. Onset time of maximum pupil diameter on n-back trials
Younger Group 1, 45 2.02 .043 .162
Younger Load 2.89, 129.94 6.05 .118 <.001
Younger Group x Load 2.89, 129.94 2.06 .044 .111
Older Group 1, 39 0.11 .003 .747
Older Load 1.6, 62.55 3.67 .086 .040
Older Group x Load 1.6, 62.55 0.27 .007 .713
Note. Because older participants did not complete the 3-back load, these analyses were
performed for each age group separately. Pupil diameter values during each trial were baseline-
corrected prior to analysis based on the mean pupil diameter in the fixation period at the start of
the respective block, before participants were aware of the working memory load tested on that
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block. ANOVAs included only trials for which participants made a correct response or correctly
withheld a response.
4. Discussion
Decades of animal and pharmacological research have indicated that working memory
depends on noradrenaline (Arnsten, 2000; Chamberlain et al., 2006; Robbins & Arnsten, 2009).
Such findings suggest that behavioral states that vary in their noradrenergic activity levels should
show corresponding differences in working memory, but there has been relatively little non-
pharmacological work in humans linking working memory with fluctuations in activity of the
noradrenergic system. In this study, we examined how isometric handgrip exercise, a
manipulation known to engage the noradrenergic system, affected subsequent working memory
performance in younger and older adults. In line with previous studies demonstrating beneficial
effects of handgrip on cognitive performance (Mather et al., 2020; Nielson et al., 1996; Washio
et al., 2021), we found that, compared to participants who performed a control procedure,
participants who performed handgrip had faster reaction times on a subsequent n-back task that
assessed working memory performance. Handgrip-speeded reaction times on the n-back task
were observed for younger and older participants and across working memory loads. These
results link working memory to the noradrenergic system in humans and provide novel evidence
that short bursts of isometric exercise can temporarily improve working memory performance in
both younger and older adults.
Examining multiple measures of physiological arousal during handgrip (pupil diameter,
heart rate and sympathetic tone), we found that handgrip increased concurrent arousal. This is
consistent with previous studies demonstrating that handgrip temporarily increases pupil
diameter, heart rate, systolic blood pressure, and blood flow velocity (Nielsen & Mather, 2015;
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Washio et al., 2021), with the coronary circulation effects of handgrip mediated by beta-
adrenergic receptors (Prodel et al., 2021). In addition, we found time-dependent effects of
handgrip on tonic pupil diameter in the period after handgrip. These results are in line with our
previous finding of post-handgrip reductions in tonic pupil diameter that diminish after a short
period of time (Mather et al., 2020). A post-handgrip reduction in tonic noradrenergic activity
may be due to noradrenergic depletion, which has been documented to occur in animals
following arousing events (Maynert & Levi, 1964; Shinba et al., 2010). One possibility is
therefore that during the handgrip period, tonic LC activity is temporarily elevated, and in the
period after handgrip, tonic LC activity is reduced to a point that optimizes working memory, in
line with the U-shaped relationship between tonic noradrenergic activity and performance
(Arnsten, 1998; Aston-Jones & Cohen, 2005). However, it remains to be determined whether the
effects of handgrip on working memory performance are driven by increased noradrenergic
activity during handgrip or reduced noradrenergic in the post-handgrip period. Further studies
testing these potential mechanisms in humans are warranted.
Regardless of the mechanism at work, our findings provide evidence for a link between
the noradrenergic system and working memory in humans and suggest testable neural substrates
of this effect. Working memory engages the brain’s prefrontal cortex (D’Esposito & Postle,
2015), and ascending projections from the LC reach a number of frontal and parietal brain
regions (Pickel et al., 1974; Schwarz & Luo, 2015). Phasic release of noradrenaline from the LC
promotes neural gain within frontoparietal brain regions, affecting selective attention and task
performance (Arnsten et al., 1996; Aston-Jones & Cohen, 2005; Corbetta et al., 2008). Tonic-
phasic tradeoffs in LC activity are also reflected in frontoparietal brain regions: For instance,
spontaneous fluctuations in pupil diameter at rest, thought to reflect tonic LC activity, have been
111
associated with activity in frontoparietal brain regions (Breeden et al., 2017; Schneider et al.,
2016), and individuals with greater resting-state oscillatory activity in frontoparietal brain
regions, reflecting tonic frontoparietal activity, had dampened phasic pupillary responses on a
working memory task (Elman et al., 2017). We previously reported that participants who
performed handgrip exhibited greater activity of frontoparietal brain regions, lower tonic pupil
diameter, and better performance during a post-handgrip oddball task (Mather et al., 2020),
implicating frontoparietal brain regions in handgrip’s effects. Future studies should test whether
the benefits of isometric exercise for working memory are also mediated by LC-frontoparietal
interactions.
That we observed handgrip-speeded working memory reaction times is especially relevant
for older adults, since working memory and processing speed exhibit general declines in aging
(Rypma & D’Esposito, 2000; Salthouse, 2000; Verhaeghen & Salthouse, 1997). Although one
possibility was that due to elevated baseline levels of noradrenaline in aging (Mather, 2020),
older participants would exhibit smaller effects of handgrip on working memory performance,
we found comparable effects of handgrip on working memory reaction times in both age groups.
This finding may furthermore reflect an important link between the noradrenergic system and the
frontoparietal network in aging (Bachman et al., 2021; Robertson, 2013). One hypothesis is that
a right-lateralized frontoparietal network facilitates the effects of cognitive reserve, an index of
the brain’s protection against age-related decline (Robertson, 2014). In particular, the ability of
the noradrenergic system to effectively modulate the frontoparietal network may be important for
the maintenance of cognitive faculties such as attentional control and working memory in aging.
There are several limitations to note. First, although we demonstrated that handgrip
increased concurrent physiological arousal, we did not find group differences in salivary alpha
112
amylase levels immediately before and after the first handgrip run. This is inconsistent with a
previous report of handgrip increasing concurrent sympathetic arousal (Nielsen & Mather, 2015).
One possibility is that because we did not measure salivary alpha amylase during the squeeze
phase but rather after the fourth rest phase of the first handgrip run (approximately 30 seconds
after the last squeeze event), group differences in levels due to handgrip could have disappeared
by the time of measurement. Second, based on a previous finding that handgrip enhanced phasic
pupillary responses on a subsequent auditory oddball task (Mather et al., 2020), we predicted that
post-handgrip reductions in tonic pupil diameter would be coupled with enhanced phasic
pupillary responses. Yet we found only limited evidence for handgrip effects on phasic pupillary
responses. One explanation is that phasic pupillary responses may be more pronounced in an
oddball task compared to the n-back task used here, in which working memory load load may
have complex influences on phasic pupillary responses. Finally, we note the limitations of
drawing inferences about LC activity based on pupil diameter. Recent studies in mice reported
that pupil diameter does not accurately reflect LC spiking (Megemont et al., 2022; Yang et al.,
2021), and in humans, baseline pupil diameter was not associated with task-evoked pupil
diameter (Martin et al., 2022), raising the question of whether pupil diameter accurately reflects
tonic and phasic LC activity.
In conclusion, we tested how isometric handgrip, a manipulation that temporarily engages
the noradrenergic system, affected working memory performance. We found that in both
younger and older participants, handgrip decreased reaction times on a subsequent working
memory task. Probing mechanisms of handgrip’s effects, we found that handgrip increased
concurrent levels of pupil diameter, heart rate and sympathetic tone and led to subsequent, time-
dependent reductions in tonic pupil diameter. Our results provide novel evidence that handgrip
113
affects working memory in humans and offer testable possibilities for the mechanism underlying
handgrip’s effects on performance. Furthermore, our findings suggest that isometric exercise
represents a strategy to temporarily improve working memory in both younger and older adults.
114
Concluding Remarks
In summary, this dissertation provided evidence for behavioral and physiological
correlates of LC MRI contrast, an in vivo measure of LC structure, and LC function in younger
and older adults. In Chapter 1, we provided the first evidence to date that LC contrast can be
changed over time. Specifically, we demonstrated that performing daily HRV biofeedback
training to increase heart rate oscillations decreased LC contrast and sympathetic nervous system
signaling in younger adults. These findings suggest that early in adulthood, LC structure may be
influenced by sympathetic activity. Furthermore, they suggest that LC structure can be changed
in early adulthood by an intervention targeting parasympathetic/sympathetic balance in the
autonomic nervous system. Future studies testing whether long-term HRV biofeedback-related
changes in LC contrast map onto changes in self-reported stress and anxiety, as well as central
nervous system noradrenergic signaling, are warranted to better understand potential
consequences of changes in LC contrast. The finding that HRV biofeedback decreased LC
contrast and sympathetic signaling also has implications for the treatment of stress- and anxiety-
related disorders; a possibility is that HRV biofeedback could reduce the noradrenergic
hyperactivity and associated symptoms that are common in post-traumatic stress disorder (Blase
et al., 2021; Tan et al., 2011).
Critically, we did not find effects of five weeks of HRV biofeedback on LC structure in
aging. Further research is needed to examine whether a longer-term, regular practice of
increasing heart rate oscillations affects LC structure in older adults, but the current finding does
support the notion that different factors contribute to LC structure in older compared to younger
adulthood. Tau accumulation and LC neuronal loss are thought to be primary influences on LC
structure later in life. The LC has garnered attention across disciplines for its potential role in
115
neurodegenerative diseases including Alzheimer’s disease, with LC neurodegeneration having
broad effects on central noradrenergic signaling and cognitive performance (Beardmore et al.,
2021; Matchett et al., 2021; Mather & Harley, 2016; Weinshenker, 2018). Identifying strategies
to slow LC neurodegeneration in aging therefore remains an important goal with implications for
cognitive and brain health in later life.
In Chapter 2, we then attempted to address an open question in the literature regarding
peripheral arousal correlates of LC contrast across the lifespan. We found that in older adults,
contrast of the caudal LC was associated with larger physiological responses to acute stress. In
combination with previous work linking LC structure to cognition and brain structure in aging
(Bachman et al., 2021; Dahl et al., 2019; Elman et al., 2021a; Elman et al., 2021b; Liu et al.,
2020), these findings suggest that a more structurally intact caudal LC may index an autonomic
nervous system that is less affected by age-related dysregulation. While confirming this pattern
in larger samples of older adults is warranted, these findings suggest that the caudal LC is
important for neurovisceral integration in aging. As we did not find expected associations
between peripheral arousal responses to acute stress and LC contrast in younger adults, these
findings also raise the question of when in the adult lifespan LC contrast becomes an informative
index of LC neurodegeneration, a question that would be best served with longitudinal studies.
Finally, in Chapter 3, we demonstrated the relevance of not only LC structure but also LC
function across the lifespan. In particular, we reported that isometric handgrip exercise, known to
affect noradrenergic activity, temporarily improved processing speed on a subsequent working
memory task in both younger and older adults. Using non-invasive measures of physiological
arousal, we found that arousal increased during handgrip, but after handgrip, there was a
decrease in tonic pupil diameter. Future studies investigating the neural basis of post-handgrip
116
effects on arousal and cognitive performance are warranted. Because phasic signals from the LC
modulate the frontoparietal attention network to promote selective attention (Corbetta et al.,
2008), future studies could use functional MRI to probe whether interactions between the LC and
frontoparietal attention network contribute to handgrip's effects on cognitive performance. More
generally, these results suggest that performing short rounds of isometric exercise can be
beneficial for adults of all ages looking to boost their cognitive performance in daily life.
In conclusion, this dissertation sheds light on how the autonomic nervous system and
physiological arousal are closely linked to non-invasive measures of LC structure and function,
with broader implications for the treatment of stress- and anxiety-related disorders and healthy
cognitive aging. We have demonstrated potential benefits of two interventions targeting LC
structure and function, respectively: the daily practice of increasing heart rate oscillations and
short bursts of isometric exercise. Together, these findings add to a growing body of work
implicating the LC in arousal and cognition across the lifespan.
117
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Appendix A: Chapter 1 Supplementary Material
1. Supplementary Methods
1.1. Details of included and excluded TSE scans
A total of 115 younger (60 Osc+, 55 Osc-) and 60 older (31 Osc+, 29 Osc-) participants
completed pre- and/or post-training MRI assessments including a TSE scan. Of these, 15
younger and 9 older participants did not finish the study and thus did not complete a post-
training MRI assessment. The proportion of participants who did not complete the study was
higher for older than for younger adults because we terminated the study early due to the
COVID-19 pandemic; at that point, 6 older participants were unable to complete the study. There
was 1 older adult participant whose pre-training MRI assessment did not include a TSE scan. A
breakdown of MRI assessments that included MPRAGE and TSE scans, according to age group,
training condition and timepoint, is provided in Table A1.
Table A1
Description of MRI assessments that included MPRAGE and TSE scans
Age group Training condition Timepoint Number of assessments
Younger Osc+ Pre-training 60
Younger Osc+ Post-training 52
Younger Osc- Pre-training 55
Younger Osc- Post-training 48
Older Osc+ Pre-training 31
Older Osc+ Post-training 26
Older Osc- Pre-training 28
Older Osc- Post-training 25
Note. Osc+ = increase-oscillations condition; Osc- = decrease-oscillations condition.
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Available TSE scans were visually inspected by one trained researcher for quality and
artifact. Scans with incorrect positioning (n = 3), different resolution (n = 1), or susceptibility
artifact overlapping the LC or central pons (n = 5) were excluded from LC delineation. Of
remaining scans, 29 were excluded due to excessive motion. As a validation step, we compared
whether scans were included or excluded with qualitative information provided by two raters
during manual LC delineation (see Appendix A, Section 1.3); specifically, in some cases, one or
both raters reported not being able to manually identify or delineate the LC due to artifact. Scans
that could not be rated by either rater were considered non-rateable, whereas scans rated by at
least one rater were considered rateable. We then found 93.7% agreement between whether scans
had been flagged as included or excluded based on visual inspection, and their rateability.
Because raters were told to prioritize delineating the LC even if the surrounding image contained
excessive motion or artifact, inclusions and exclusions from visual inspection were taken as final
decisions.
1.2. LC delineation parameters and validation
We first upsampled available MPRAGE and FSE scans to twice their native resolution.
For this step, the ResampleImage ANTs routine was used, using linear interpolation and pixel
type set to ‘float.’
We then generated an initial, whole-brain template using a subset of 134 upsampled
MPRAGE scans whose fields of view were spatially well-aligned, specifically with qoffset_x,
qoffset_y, and qoffset_z values falling within 1 standard deviation of the mean across scans.
The subset included scans from 88 younger and 46 older participants. These scans were used as
inputs to a run of the antsMultivariateTemplateConstruction.sh routine with the following
parameters: gradient step size = 0.25, iteration limit = 6, max iterations = 1x0x0, modality
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weights used in similarity metric = 1, number of modalities = 1, N4 bias field correction on, rigid
body registration of inputs on, registration similarity metric = cross-correlation, transformation
model type = greedy-SyN, update template with full affine transform on, no initial template. The
resulting template was used as the initial template for a full template-building run of the
antsMultivariateTemplateConstruction.sh routine, using all 287 (191 younger, 96 older)
MPRAGE scans as inputs. This template-building run had the same parameters as the initial
template-building run, except for the following: max iterations = 30x90x20, rigid body
registration of inputs off. The result was a full, whole-brain (MPRAGE) template, as well as each
inputted MPRAGE scan coregistered to whole-brain template space.
Coregistration of upsampled TSE scans to corresponding whole-brain template-
coregistered MPRAGE scans was performed using antsRegistrationSyNQuick.sh, with the
following parameters: transform type = rigid, affine and deformable SyN (‘s’), histogram bins =
32, spline distance = 26, precision type = double, transform type = SyN, histogram matching on.
Coregistered TSE scans were then used to create a TSE template. Parameters for this template-
building procedure were the same as that for the whole-brain template building procedure, except
that all coregistered TSE scans were used for both the initial and full template-building runs.
We then coregistered the full TSE template with the whole-brain template, and we
coregistered the whole-brain template with the MNI152 0.5mm (linear) brain. For both
coregistration steps, the antsRegistrationSyN.sh routine was used with the following parameters:
transform type = rigid, affine and deformable SyN (‘s’), radius = 4, spline_distance = 26,
precision type = double, histogram matching on, collapse output transforms on. For
coregistration of the TSE template to MPRAGE template space, a binarized lesion mask was
used which was constructed by thresholding the TSE template at intensity 1. Finally, we warped
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upsampled TSE scans to MNI152 0.5mm (linear) space, using the antsApplyTransforms.sh
routine, with interpolation type set to linear. The antsApplyTransforms.sh, with the same
parameters, was used to apply relevant transforms to warp the TSE template to MNI152 space.
As a validation step, hyperintensities on the TSE template that had been warped to
MNI152 space were compared with the locations of a publicly available LC meta-map (Dahl et
al., 2022b). These hyperintensities are displayed alongside the meta-map coordinates in Figure
A1.
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Figure A1
Comparison of signal hyperintensity locations on the TSE template, LC meta-map from Dahl et
al. (2022b), and their overlap
Note. The TSE template was warped to MNI152 0.5mm (linear) standard space. Locations of
hyperintensity were all voxels in the dorsal pons that survived thresholding based on intensities
within the central pontine reference region. Specifically, the Dahl et al. (2022b) reference map
was applied as a mask to the TSE template. For each slice in the z-direction (z= 85-112), an
intensity threshold was computed as the mean reference intensity in that slice plus 3.5 times the
standard deviation of the reference intensity in that slice. Then, voxels within the same slice of
the dorsal pons (x = 164-196, y = 174-182) where intensities were greater than the threshold
value were classified as hyperintensities. Left panel shows graphical comparison of the Dahl et
al. (2022b) meta-mask, template hyperintensities, and their overlap. Right panel shows the same
comparison overlaid on the TSE template in MNI space (x = 187, y = 179, z = 98).
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1.3. Manual LC delineation
As another validation step, peak LC locations were also manually delineated on native-
resolution TSE scans by two trained raters. Raters were blind to training condition, age and
timepoint. Manual LC delineation was performed using ImageJ (Version 1.5.2; Schneider et al.,
2012; https://imagej.nih.gov) according to the protocol described by Dahl et al. (2019). In
summary, raters identified three 2x2 voxel regions of interest in the left and right hemispheres
which exhibited peak intensity and overlapped the expected location of the LC. This procedure
was performed by each rater for each z-slice in which the left and/or right LC was visible.
For each rater, the voxel with peak LC intensity was then selected across z-slices, for each
hemisphere separately. We then used two-way mixed-effects intra-class correlation analyses
based on absolute agreement to assess correspondence between peak left and right LC intensity
values across raters. These analyses indicated high correspondence between raters for the left
LC, ICC(3, 1) = 0.966, 95% CI = 0.951 - 0.975, p < .001, and right LC, ICC(3, 1) = 0.968, 95%
CI =0.958 - 0.976, p < .001. Therefore, we averaged left and right LC intensity values across
raters. The resulting averaged values were used to assess correspondence between intensity
values determined manually and using the semi-automated method (Chapter 1, Section 2.3.1).
1.4. Training pulse data collection and calculation of training oscillatory power
During each biofeedback training session, pulse was measured using HeartMath emWave
Pro software with an infrared pulse plethysmograph ear sensor with a sampling rate of 370 Hz.
Interbeat interval data for each session were extracted from pulse data after the elimination of
ectopic beats or other sources of artifact through a built-in process in emWave Pro.
As a measure of how much each participant increased their heart rate oscillations on
average during biofeedback training, we computed a measure of training oscillatory power as
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follows. Kubios HRV Premium (Version 3.1; Tarvainen et al., 2014;
https://www.kubios.com/hrv-premium/) was used to calculate autoregressive spectral power
from the interbeat interval data from each biofeedback training session. The following
parameters were applied during analysis with Kubios: automatic artifact correction, 500-lambda
smoothing priors detrending method, autoregressive order = 16, no autoregressive factorization,
and all other parameters set to default values. For each training data segment, we summed power
values within the frequency range from 0.063 - 0.0125 Hz, which corresponded to the range of
participants’ potential breathing paces (8-16s per breath). A single value of training power for
each participant was computed by averaging across power values from all training sessions.
Training power values were log transformed prior to statistical analysis.
1.5. Testing for sex differences in effects on LC contrast
To test for sex differences in how HRV biofeedback training affected LC contrast in
younger adults, we used a linear mixed effects analysis, specifying fixed effects of training
condition, timepoint, hemisphere, sex and their interactions and LC contrast as the dependent
variable. To test for sex differences in the association between LC contrast change and training
oscillatory power, we performed another analysis, specifying fixed effects of training power,
hemisphere, sex, and their interactions and change in LC contrast as the dependent variable. Sex
was coded as female = 0.5, male = -0.5.
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2. Supplementary Results
2.1. Analysis of distance between pre- and post-training peak LC locations
Figure A2
Peak LC signal intensity at the pre- and post-training timepoints
Note. (A) Coordinates of peak LC signal intensity are shown for the pre- and post-training
timepoints in MNI152 0.5mm linear standard space. Coordinates range from x = 192 (left) to 168
(right), y = 172 (front) to 182 (back), z = 85 (bottom) to 112 (top). (B) Density plots depict the
distribution of 3-dimensional distances in millimeters between each participant’s peak LC
intensity coordinates at the pre- and post-training timepoints. Osc+ = increase-oscillations
condition; Osc- = decrease-oscillations condition.
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Table A2
Results of a linear mixed effects analysis testing the fixed effects of training condition,
hemisphere and age group on 3-dimensional distance between pre- and post-training peak LC
intensity locations
Predictor Estimate SE 95% CI t p
Intercept 1.648 0.052 1.545, 1.750 31.513 <.001
Condition -0.050 0.105 -0.255, 0.155 -0.476 .635
Hemisphere 0.048 0.095 -0.139, 0.235 0.507 .613
Age group -0.144 0.105 -0.349, 0.061 -1.380 .170
Condition x Hemisphere -0.186 0.191 -0.559, 0.188 -0.975 .332
Condition x Age group 0.001 0.209 -0.409, 0.411 0.006 .995
Hemisphere x Age group 0.133 0.191 -0.241, 0.506 0.697 .488
Condition x Hemisphere x
Age group
0.500 0.381 -0.247, 1.247 1.311 .193
Note. Model included random intercepts for participants. Factors were coded as: condition (Osc+
= 0.5, Osc- = -0.5), age group (older = 0.5, younger = -0.5), hemisphere (left = 0.5, right = -0.5).
Distance values were square root transformed prior to analysis as a correction for non-normality,
which was indicated by a significant Shapiro-Wilk test, W = 0.904, p < .001. The significant
intercept term indicates that distances differed from 0 across conditions, age groups and
hemispheres. CI = confidence interval.
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2.2. Analysis of training effects on LC contrast
Table A3
Results of a linear mixed effects analysis testing the fixed effects of timepoint, training condition,
hemisphere, and age group on LC contrast
Predictor Estimate SE 95% CI t p
Intercept 0.051 0.004 0.042, 0.059 11.903 <.001
Timepoint 0.007 0.004 -0.002, 0.015 1.607 .109
Condition 0.004 0.009 -0.012, 0.021 0.511 .611
Hemisphere 0.050 0.004 0.041, 0.058 11.463 <.001
Age group 0.031 0.009 0.015, 0.048 3.679 <.001
Timepoint x Condition -0.008 0.009 -0.025, 0.009 -0.895 .371
Timepoint x Hemisphere -0.006 0.009 -0.023, 0.011 -0.690 .491
Condition x Hemisphere 0.012 0.009 -0.005, 0.029 1.338 .182
Timepoint x Age group 0.015 0.009 -0.002, 0.032 1.736 .084
Condition x Age group 0.007 0.017 -0.026, 0.04 0.419 .676
Hemisphere x Age group -0.008 0.009 -0.025, 0.009 -0.931 .353
Timepoint x Condition x
Hemisphere
-0.005 0.017 -0.039, 0.029 -0.267 .790
Timepoint x Condition x
Age group
0.026 0.017 -0.008, 0.06 1.505 .133
Timepoint x Hemisphere x
Age group
0.017 0.017 -0.017, 0.051 0.971 .332
Condition x Hemisphere x
Age group
0.002 0.017 -0.032, 0.036 0.096 .924
Timepoint x Condition x
Hemisphere x Age group
0.016 0.035 -0.052, 0.084 0.469 .639
Note. Model included random intercepts for participants. Factors were coded as: timepoint (pre-
training = 0.5, post-training = -0.5), condition (Osc+ = 0.5, Osc- = -0.5), age group (older = 0.5,
younger = -0.5), hemisphere (left = 0.5, right = -0.5). CI = confidence interval.
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2.3. Analysis of associations between training power and change in LC contrast
Table A4
Results of a linear mixed effects analysis testing fixed effects of training oscillatory power, age
group and hemisphere on change in LC contrast
Predictor Estimate SE 95% CI t p
Intercept 0.031 0.027 -0.022, 0.083 1.147 .254
Training power -0.003 0.004 -0.012, 0.005 -0.823 .412
Hemisphere 0.029 0.035 -0.04, 0.098 0.830 .408
Age group -0.059 0.054 -0.164, 0.046 -1.104 .272
Training power x
Hemisphere
-0.005 0.006 -0.016, 0.006 -0.970 .334
Training power x Age group 0.011 0.008 -0.005, 0.028 1.351 .179
Hemisphere x Age group -0.023 0.071 -0.162, 0.115 -0.332 .741
Training power x
Hemisphere x Age group
0.006 0.011 -0.016, 0.027 0.505 .614
Note. Model included random intercepts for participants. Factors were coded as: age group (older
= 0.5, younger = -0.5), hemisphere (left = 0.5, right = -0.5). CI = confidence interval.
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2.4. Analysis of sex differences in LC contrast change
Table A5
Results of a linear mixed effects model testing the fixed effects of timepoint, training condition,
hemisphere, and sex on LC contrast
Predictor Estimate SE 95% CI t p
Intercept 0.034 0.004 0.025, 0.043 7.554 <.001
Timepoint -0.001 0.005 -0.011, 0.008 -0.218 .828
Condition -0.003 0.009 -0.021, 0.014 -0.373 .710
Hemisphere 0.052 0.005 0.042, 0.061 10.714 <.001
Sex 0.025 0.009 0.007, 0.042 2.794 .007
Timepoint x Condition -0.021 0.010 -0.04, -0.002 -2.159 .032
Timepoint x Hemisphere -0.014 0.010 -0.033, 0.005 -1.463 .145
Condition x Hemisphere 0.011 0.010 -0.008, 0.03 1.138 .256
Timepoint x Sex -0.003 0.010 -0.022, 0.016 -0.306 .760
Condition x Sex 0.007 0.018 -0.028, 0.042 0.391 .697
Hemisphere x Sex -0.017 0.010 -0.036, 0.002 -1.771 .078
Timepoint x Condition x
Hemisphere
-0.012 0.019 -0.05, 0.026 -0.613 .541
Timepoint x Condition x
Sex
0.016 0.019 -0.022, 0.054 0.821 .412
Timepoint x Hemisphere x
Sex
-0.006 0.019 -0.044, 0.032 -0.290 .772
Condition x Hemisphere x
Sex
0.061 0.019 0.023, 0.099 3.165 .002
Timepoint x Condition x
Hemisphere x Sex
0.000 0.039 -0.076, 0.076 0.010 .992
Note. Model included random intercepts for participants. Factors were coded as: timepoint (pre-
training = 0.5, post-training = -0.5), condition (Osc+ = 0.5, Osc- = -0.5), age group (older = 0.5,
younger = -0.5), hemisphere (left = 0.5, right = -0.5), sex (female = 0.5, male = -0.5). Only data
from younger adults was used for this analysis. CI = confidence interval.
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Appendix B: Chapter 2 Supplementary Material
1. Supplementary Methods
1.1. Parameters for HRV analysis
For HRV analysis with the PhysioNet Cardiovascular Signal Toolbox (Version 1.0.2;
Vest et al., 2018), we applied all default settings and parameter values except for the following.
Because we performed manual quality control of ECG segments and applied our own post-
analysis thresholds for ECG signal quality based on the average sqijw metric for each segment,
we set the low-quality threshold for signal quality to be 0 and the rejection threshold to be 1.
This led all segments to be analyzed and allowed us to subsequently exclude segments and
record detailed metrics on which segments were excluded. In addition, to reduce the number of
signals not analyzed, we increased the fraction of data allowed to be missing from a window to
0.20 (missing data occurred after the removal of physiologically implausible r-peaks). Finally,
during HRV analysis, we set the window length to the length of the segment being analyzed.
1.2. Manual LC delineation
To validate LC intensities determined with the semi-automated approach, we used a manual
approach to delineate the LC on native TSE scans. Two trained raters performed the manual
delineation protocol described below. On each scan, each rater identified three 2x2 voxel regions
of interest (probable LC locations) in each hemisphere in the dorsal pons, near the floor of the
fourth ventricle, with maximum signal intensity. This was repeated in each slice in the z-
direction where hyperintense voxels near the fourth ventricle were visible. Then, for each
hemisphere, the intensity of the maximum-intensity voxel across all slices was extracted. This
yielded values of peak left and right LC intensity for each scan, for each rater.
168
Using intra-class correlation coefficients (two-way models based on absolute agreement),
we found high correspondence between LC intensities determined by each rater (left LC:
ICC(3,1) = 0.974, 95% CI = 0.964 - 0.981, p < .001; right LC: ICC(3,1) = 0.967, 95% CI = 0.953
- 0.976, p < .001). Therefore, we averaged LC intensities for each rater for the purposes of
comparing manual intensities with those determined using the semi-automated approach.
1.3. Summary of identified outliers for arousal measures
Table B1
Summary of outliers identified for average values of each arousal measure
Arousal measure Age group N (%) Outliers
Heart rate Younger 3 (1%)
Breathing rate Younger 2 (0.52%)
Systolic blood pressure Younger 6 (1.55%)
Sympathetic tone (aSKNA) Younger 0 (0%)
RMSSD Younger 3 (1%)
log HF power Younger 1 (0.33%)
log LF power Younger 1 (0.33%)
Heart rate Older 1 (0.82%)
Breathing rate Older 0 (0%)
Systolic blood pressure Older 12 (9.52%)
Sympathetic tone (aSKNA) Older 1 (0.82%)
RMSSD Older 5 (4.1%)
log HF power Older 5 (4.1%)
log LF power Older 5 (4.1%)
Note. Outliers were identified using the mean absolute deviation-median rule, for each age group
separately. HF = high-frequency; LF = low-frequency; RMSSD = root mean square of the
successive differences.
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Table B2
Summary of outliers identified from rolling average values of each arousal measure during the
challenge phase
Arousal measure Age group N (%) Outliers
Heart rate Younger 275 (1.29%)
Breathing rate Younger 321 (1.28%)
Systolic blood pressure Younger 0 (0%)
Sympathetic tone (aSKNA) Younger 130 (0.6%)
RMSSD Younger 134 (0.69%)
Heart rate Older 0 (0%)
Breathing rate Older 28 (0.62%)
Systolic blood pressure Older 0 (0%)
Sympathetic tone (aSKNA) Older 0 (0%)
RMSSD Older 216 (5.8%)
Note. Before choosing the peak value from 20-second rolling averages of each measure during
the challenge phase, we identified outliers using the mean absolute deviation-median rule, for
each age group separately. After outlier removal, the peak value during the challenge phase was
identified for each measure and participant. For RMSSD, the minimum value across the
challenge phase was identified. RMSSD = root mean square of the successive differences.
2. Supplementary Results
2.1. Pairwise comparisons of physiological arousal measures by phase, for each age group,
during the acute stress task
To test that the acute stress task affected each measure of physiological arousal, we
performed planned, pairwise comparisons of each measure for each phase contrast of interest
(challenge - baseline, recovery - baseline), for each age group separately. For each set of
comparisons, a Bonferroni correction for multiple comparisons was applied. Results of these
comparisons for each measure are presented in Tables B3 - B10.
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Table B3
Results of pairwise comparisons of heart rate during the acute stress induction task, for each
phase contrast of interest and age group
Age Group Contrast Estimate SE t r p
Younger Challenge - Baseline 4.65 0.50 9.23 0.54 <.001
Younger Recovery - Challenge -2.91 0.51 -5.71 -0.37 <.001
Younger Recovery - Baseline 1.74 0.51 3.39 0.23 .005
Older Challenge - Baseline 4.25 0.70 6.04 0.38 <.001
Older Recovery - Challenge -3.56 0.71 -5.05 -0.33 <.001
Older Recovery - Baseline 0.69 0.68 1.01 0.07 1.000
Table B4
Results of pairwise comparisons of breathing rate during the acute stress induction task, for
each phase contrast of interest and age group
Age Group Contrast Estimate SE t r p
Younger Challenge - Baseline 3.24 0.27 11.79 0.58 <.001
Younger Recovery - Challenge -3.67 0.28 -13.24 -0.63 <.001
Younger Recovery - Baseline -0.43 0.27 -1.56 -0.09 .717
Older Challenge - Baseline 3.84 0.42 9.08 0.48 <.001
Older Recovery - Challenge -3.45 0.42 -8.15 -0.44 <.001
Older Recovery - Baseline 0.39 0.42 0.94 0.06 1.000
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Table B5
Results of pairwise comparisons of systolic blood pressure during the acute stress induction task,
for each phase contrast of interest and age group
Age Group Contrast Estimate SE t r p
Younger Challenge - Baseline 2.59 0.84 3.07 0.19 .014
Younger Recovery - Challenge -2.59 0.87 -2.98 -0.18 .019
Younger Recovery - Baseline 0.01 0.87 0.01 0.00 1.000
Older Challenge - Baseline 4.12 1.44 2.87 0.18 .026
Older Recovery - Challenge -7.29 1.44 -5.04 -0.30 <.001
Older Recovery - Baseline -3.17 1.44 -2.21 -0.14 .169
Table B6
Results of pairwise comparisons of diastolic blood pressure during the acute stress induction
task, for each phase contrast of interest and age group
Age Group Contrast Estimate SE t r p
Younger Challenge - Baseline 0.86 0.56 1.53 0.10 .769
Younger Recovery - Challenge -4.23 0.58 -7.29 -0.42 <.001
Younger Recovery - Baseline -3.37 0.58 -5.78 -0.34 <.001
Older Challenge - Baseline 1.96 0.96 2.04 0.13 .252
Older Recovery - Challenge -4.33 0.97 -4.48 -0.27 <.001
Older Recovery - Baseline -2.37 0.96 -2.47 -0.15 .085
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Table B7
Results of pairwise comparisons of sympathetic tone during the acute stress induction task, for
each phase contrast of interest and age group
Age Group Contrast Estimate SE t r p
Younger Challenge - Baseline 6.87e-4 6.80e-4 1.01 0.07 1.000
Younger Recovery - Challenge -1.73e-3 6.82e-4 -2.53 -0.17 .072
Younger Recovery - Baseline -1.04e-3 6.99e-4 -1.49 -0.10 .825
Older Challenge - Baseline 6.86e-4 9.60e-4 0.72 0.05 1.000
Older Recovery - Challenge -1.92e-3 9.69e-4 -1.99 -0.13 .290
Older Recovery - Baseline -1.24e-3 9.33e-4 -1.33 -0.09 1.000
Table B8
Results of pairwise comparisons of RMSSD during the acute stress induction task, for each
phase contrast of interest and age group
Age Group Contrast Estimate SE t r p
Younger Challenge - Baseline -0.07 0.04 -2.09 -0.14 .228
Younger Recovery - Challenge 0.04 0.04 1.11 0.08 1.000
Younger Recovery - Baseline -0.03 0.04 -0.94 -0.07 1.000
Older Challenge - Baseline -0.06 0.05 -1.15 -0.08 1.000
Older Recovery - Challenge 0.11 0.05 2.17 0.15 .189
Older Recovery - Baseline 0.05 0.05 1.06 0.07 1.000
173
Table B9
Results of pairwise comparisons of HF power during the acute stress induction task, for each
phase contrast of interest and age group
Age Group Contrast Estimate SE t r p
Younger Challenge - Baseline -0.12 0.08 -1.52 -0.10 .781
Younger Recovery - Challenge 0.06 0.08 0.70 0.05 1.000
Younger Recovery - Baseline -0.07 0.08 -0.80 -0.05 1.000
Older Challenge - Baseline 0.01 0.12 0.06 0.00 1.000
Older Recovery - Challenge 0.13 0.12 1.08 0.07 1.000
Older Recovery - Baseline 0.13 0.11 1.17 0.08 1.000
Table B10
Results of pairwise comparisons of LF power during the acute stress induction task, for each
phase contrast of interest and age group
Age Group Contrast Estimate SE t r p
Younger Challenge - Baseline -0.21 0.08 -2.70 -0.18 .044
Younger Recovery - Challenge 0.49 0.08 6.27 0.39 <.001
Younger Recovery - Baseline 0.28 0.08 3.50 0.23 .003
Older Challenge - Baseline 0.13 0.11 1.22 0.08 1.000
Older Recovery - Challenge 0.24 0.11 2.11 0.14 .215
Older Recovery - Baseline 0.37 0.11 3.46 0.23 .004
174
2.2. Performance on the cognitive challenge tasks
Figure B2
Accuracy and reaction times on the cognitive challenge tasks
Note. Figure shows mean accuracy and reaction times on the Paced Auditory Serial Addition
Task (PASAT; A) and Stroop color-word matching task (B).
175
Appendix C: Chapter 3 Supplementary Material
1. Supplementary Methods
1.1. Exclusion of handgrip participants based on EMG
To confirm that participants in the handgrip group squeezed as instructed and rested as
instructed during handgrip runs, we computed the mean of each participant’s integrated EMG
signal (computed during data collection in Acqknowledge) during each squeeze and rest event,
for the left and right arms separately. At an individual level, we tested whether the mean EMG
signal from the relevant arm during all squeeze phases was numerically greater than mean EMG
averaged across both arms during all rest phases. This resulted in 2 older participants, for whom
mean EMG during all squeeze events was not greater than mean EMG during all rest events,
being excluded from all analyses.
1.2. Details of target trial placement in n-back task blocks
Each n-back block consisted of 18 trials, with the number of target trials per block
differing by working memory load: 0-back blocks contained 6 target trials each, 1-back blocks
contained 6 target trials, 2-back blocks contained 5 target trials, and 3-back blocks contained 4
target trials. Constraints placed on target trial placement within blocks are described below.
0-back blocks. Of 6 target trials per block, 3 occurred in the first half of the block, and 3
occurred in the second half of the block. Target trials could occur back-to-back, but only a
maximum of 2 times.
1-back blocks. Of 6 target trials per block, 3 occurred in the first half of the block, and 3
occurred in the second half of the block. There were no back-to-back target trials.
176
2-back blocks. Of 5 target trials per block, 2-3 target trials occurred in the first half of the
block, and the remaining 2-3 occurred in the second half of the block. Target trials could occur
back-to-back, but only once per block.
3-back blocks. Of 4 target trials per block, 2 occurred in the first half of the block, and 2
occurred in the second half of the block. There were no back-to-back target trials.
1.3. Details of excluded ECG segments
Table C1
Summary of ECG data segments included based on visual inspection
Age group Task N (%) segments excluded
Younger Baseline 2 (0.003%)
Younger Handgrip 2 (0.003%)
Younger N-back 5 (0.006%)
Older Baseline 0 (0%)
Older Handgrip 3 (0.004%)
Older N-back 4 (0.005%)
Note. Signals with artifact and/or noise such that QRS complexes were not visually
distinguishable were excluded from analysis.
1.4. Parameters for QRS detection on ECG segments
QRS detection on ECG data segments was performed with the PhysioNet Cardiovascular
Signal Toolbox (Version 1.0.2; Vest et al., 2018). All parameters were set to the default values
except that we changed the window length for QRS detection to 10 seconds to avoid missing
values that sometimes arose for the SQIjw index with longer windows. In addition, because we
177
performed ECG quality control outside of PhysioNet, we changed the rejection threshold to 1
and the low-quality threshold to 0, so that QRS detection would be performed for all segments.
1.5. Details of pupil diameter data preprocessing and exclusions
While preprocessing eyetracking data segments, we recorded the fraction of missing
samples within each event of interest. Events with greater than 70% missing samples were
excluded from analysis. A total of 2 participants had greater-than-threshold missingness in
baseline eyetracking data segments, thus 4 (0.02%) segments (1 segment per eye, per participant)
were excluded from analysis. Handgrip and n-back fixation pupil data segments for these
participants were also excluded from analysis. For all remaining handgrip segments, excluded
events are described in Table C2. Excluded events in remaining n-back pupil segments are
described in Table C3.
Table C2
Summary of events excluded from analysis, for eyetracking data segments from handgrip runs
Event N (%) excluded
Rest1 100 (0.16%)
Rest2 102 (0.17%)
Rest3 136 (0.22%)
Rest4 142 (0.23%)
Squeeze1 110 (0.18%)
Squeeze2 126 (0.2%)
Squeeze3 132 (0.21%)
Squeeze4 154 (0.25%)
178
Table C3
Summary of events excluded from analysis, for eyetracking data segments from n-back runs
Event N (%) excluded
Pre-block fixation: block 1 relative to handgrip offset 146 (0.24%)
Pre-block fixation: block 2 relative to handgrip offset 96 (0.16%)
Pre-block fixation: block 3 relative to handgrip offset 126 (0.2%)
Pre-block fixation: block 4 relative to handgrip offset 64 (0.19%)
Trials: 0-back 2678 (0.24%)
Trials: 1-back 2446 (0.22%)
Trials: 2-back 2400 (0.21%)
Trials: 3-back 1388 (0.23%)
179
2. Supplementary Results
2.1. Results of pairwise comparisons of arousal measures during the handgrip protocol
We performed pairwise comparisons of each arousal measure (pupil diameter, heart rate,
and sympathetic tone) for the squeeze compared to the rest phases, for each group and age group
separately. Results of these comparisons for pupil diameter are shown in Table C4, for heart rate
in Table C5, and for sympathetic tone in Table C6.
Table C4
Results of pairwise comparisons of pupil diameter during squeeze versus rest phases of the
handgrip protocol, by group and age group
Age group Group Contrast Estimate SE df t p
Younger Handgrip Squeeze - Rest 0.830 0.080 92 10.410 <.001
Younger Control Squeeze - Rest 0.128 0.085 92 1.518 .530
Older Handgrip Squeeze - Rest 0.317 0.088 92 3.595 .002
Older Control Squeeze - Rest 0.194 0.086 92 2.245 .109
180
Table C5
Results of pairwise comparisons of heart rate during squeeze versus rest phases of the handgrip
protocol, by group and age group
Age group Group Contrast Estimate SE df t p
Younger Handgrip Squeeze - Rest 4.711 0.551 92 8.544 <.001
Younger Control Squeeze - Rest 0.290 0.540 92 0.537 1.000
Older Handgrip Squeeze - Rest 2.193 0.563 92 3.893 <.001
Older Control Squeeze - Rest -0.096 0.551 92 -0.174 1.000
Table C6
Results of pairwise comparisons of sympathetic tone during squeeze versus rest phases of the
handgrip protocol, by group and age group
Age group Group Contrast Estimate SE df t p
Younger Handgrip Squeeze - Rest 0.002 0.001 92 3.922 <.001
Younger Control Squeeze - Rest 0.000 0.001 92 0.381 1.000
Older Handgrip Squeeze - Rest 0.002 0.001 92 4.129 <.001
Older Control Squeeze - Rest -0.001 0.001 92 -1.716 .358
181
2.2. Mean integrated EMG during the handgrip protocol
Figure C1
Mean integrated EMG signals across the course of the handgrip protocol
Note. Values were baseline corrected using mean values from a 4-minute baseline period at the
beginning of the experiment. Figure reflects data averaged over all 3 runs of the handgrip
protocol.
2.3. Results of pairwise comparisons of n-back accuracy and reaction times
We performed pairwise comparisons of each n-back performance measure (mean
accuracy, mean reaction time) for the handgrip versus control group, for each working memory
load and age group separately. Results of comparisons of n-back accuracy are shown in Table C7
and those of n-back reaction times in Table C8.
182
Table C7
Results of pairwise comparisons of mean accuracy on the n-back task for the handgrip versus
control group, by working memory load and age group
Age
group
Contrast Load
Bootstrapped
estimate
95% CI
Bootstrapped
p
Bootstrapped
p, adjusted
Younger
Handgrip
- Control
0-back 0.026
0.008 -
0.053
.002 .008
Younger
Handgrip
- Control
1-back 0.022
-0.029 -
0.098
.470 1.000
Younger
Handgrip
- Control
2-back -0.018
-0.09 -
0.068
.675 1.000
Younger
Handgrip
- Control
3-back -0.014
-0.117 -
0.092
.807 1.000
Older
Handgrip
- Control
0-back -0.030
-0.101 -
0.014
.276 .827
Older
Handgrip
- Control
1-back 0.029
-0.042 -
0.106
.456 1.000
Older
Handgrip
- Control
2-back 0.033
-0.056 -
0.126
.464 1.000
Note. Because mean accuracy data deviated from normality, pairwise comparisons were
performed using a bootstrapping procedure. 95% confidence intervals (CIs) and p-values were
calculated from bootstrap samples.
183
Table C8
Results of pairwise comparisons of mean reaction times on the n-back task for the handgrip
versus control group, by working memory load and age group
Age
group
Contrast Load
Bootstrapped
estimate
95% CI
Bootstrapped
p
Bootstrapped
p, adjusted
Younger
Handgrip
- Control
0-back -0.079
-0.156 -
-0.001
.048 .192
Younger
Handgrip
- Control
1-back -0.070
-0.142 -
0.005
.064 .256
Younger
Handgrip
- Control
2-back -0.106
-0.202 -
-0.006
.040 .160
Younger
Handgrip
- Control
3-back -0.094
-0.218 -
0.027
.144 .575
Older
Handgrip
- Control
0-back -0.056
-0.121 -
0.002
.066 .264
Older
Handgrip
- Control
1-back -0.077
-0.16 -
0.014
.092 .368
Older
Handgrip
- Control
2-back -0.114
-0.191 -
-0.031
.008 .032
Note. Because mean reaction time data deviated from normality, pairwise comparisons were
performed using a bootstrapping procedure. 95% confidence intervals (CIs) and p-values were
calculated from bootstrap samples.
184
2.4. Results of pairwise comparisons of salivary alpha amylase and cortisol levels
We performed pairwise comparisons of each salivary assay (alpha amylase, cortisol)
across relevant timepoints, for each group and age group separately. Results of comparisons of
salivary alpha amylase are shown in Table C9 and those for comparisons of salivary cortisol in
Table C10.
Table C9
Results of pairwise comparisons of salivary alpha amylase levels before and after the first
handgrip (control) run, for each group and age group
Age group Group Contrast
Boot-
strapped
estimate
95% CI
Boot-
strapped
p
Boot-
strapped
p,
adjusted
Younger Handgrip
Post-handgrip #1
- Pre-handgrip #1
-11.866
-24.243
- 0.765
.058 .232
Younger Control
Post-handgrip #1
- Pre-handgrip #1
-10.066
-21.075
- -0.451
.040 .160
Older Handgrip
Post-handgrip #1
- Pre-handgrip #1
-19.403
-46.918
- 6.077
.132 .527
Older Control
Post-handgrip #1
- Pre-handgrip #1
-11.883
-41.951
-
11.745
.364 1.000
Note. Because salivary alpha amylase values deviated from normality, pairwise comparisons
were performed using a bootstrapping procedure. 95% confidence intervals (CIs) and p-values
were calculated from bootstrap samples.
185
Table C10
Results of pairwise comparisons of salivary cortisol levels before the baseline period and after
all n-back and handgrip (control) runs, for each group and age group
Age group Group Contrast
Boot-
strapped
estimate
95% CI
Boot-
strapped
p
Boot-
strapped
p,
adjusted
Younger Handgrip
Post-tasks -
Pre-baseline
-0.031
-0.056 -
-0.006
.020 .080
Younger Control
Post-tasks -
Pre-baseline
-0.026
-0.047 -
-0.002
.028 .112
Older Handgrip
Post-tasks -
Pre-baseline
0.000
-0.01 -
0.01
.975 1.000
Older Control
Post-tasks -
Pre-baseline
0.005
-0.007 -
0.016
.442 1.000
Note. Because salivary cortisol values deviated from normality, pairwise comparisons were
performed using a bootstrapping procedure. 95% confidence intervals (CIs) and p-values were
calculated from bootstrap samples.
2.5. Results of pairwise comparisons of tonic arousal measures during the n-back task
We performed pairwise comparisons of arousal measures during the n-back task (fixation
pupil diameter, heart rate, and sympathetic tone) for the handgrip versus control group, for each
n-back block relative to handgrip offset and age group separately. Results of comparisons of
fixation pupil diameter are shown in Table C11, those of heart rate are shown in Table C12, and
those of sympathetic tone are shown in Table C13.
186
Table C11
Results of pairwise comparisons of pupil diameter during n-back fixation periods for the
handgrip versus control group, for each n-back block relative to handgrip offset and age group
Age
group
Contrast
Block, relative
to handgrip
offset
Estimate SE df t p
Younger Handgrip - Control 1 -0.338 0.323 45 -1.045 .302
Younger Handgrip - Control 2 0.066 0.290 45 0.227 .821
Younger Handgrip - Control 3 0.432 0.274 45 1.575 .122
Younger Handgrip - Control 4 0.291 0.302 45 0.966 .339
Older Handgrip - Control 1 0.695 0.326 37 2.135 .039
Older Handgrip - Control 2 0.086 0.304 37 0.283 .778
Older Handgrip - Control 3 0.326 0.311 37 1.049 .301
Table C12
Results of pairwise comparisons of heart rate during n-back task blocks for the handgrip versus
control group, for each n-back block relative to handgrip offset and age group
Age
group
Contrast
Block, relative
to handgrip
offset
Estimate SE df t p
Younger Handgrip - Control 1 0.648 1.459 46 0.444 .659
Younger Handgrip - Control 2 1.176 1.338 46 0.879 .384
Younger Handgrip - Control 3 0.679 1.215 46 0.559 .579
Younger Handgrip - Control 4 0.026 1.480 46 0.018 .986
Older Handgrip - Control 1 -1.986 1.839 44 -1.080 .286
Older Handgrip - Control 2 -0.516 1.668 44 -0.309 .758
Older Handgrip - Control 3 -0.898 1.301 44 -0.691 .493
187
Table C13
Results of pairwise comparisons of sympathetic tone during n-back task blocks for the handgrip
versus control group, for each n-back block relative to handgrip offset and age group
Age
group
Contrast
Block, relative
to handgrip
offset
Estimate SE df t p
Younger Handgrip - Control 1 0.001 0.001 47 1.077 .287
Younger Handgrip - Control 2 0.001 0.001 47 1.003 .321
Younger Handgrip - Control 3 0.001 0.001 47 0.940 .352
Younger Handgrip - Control 4 0.001 0.001 47 0.895 .375
Older Handgrip - Control 1 -0.001 0.001 44 -0.755 .454
Older Handgrip - Control 2 0.000 0.001 44 -0.366 .716
Older Handgrip - Control 3 0.001 0.001 44 0.391 .698
2.6. Results of pairwise comparisons of phasic pupillary responses during the n-back task
We performed pairwise comparisons of maximum pupil diameter and the onset time of
maximum pupil diameter on n-back trials for the handgrip versus control group, for each
working memory load and age group separately. Results of comparisons of maximum pupil
diameter are shown in Table C14, and results of comparisons of onset time of maximum pupil
diameter are shown in Table C15.
188
Table C14
Results of pairwise comparisons of maximum pupil diameter during n-back trials for the
handgrip versus control group, for each working memory load and age group
Age group Contrast Load Estimate SE df t p
Younger Handgrip - Control 0-back -0.384 0.241 46 -1.590 .119
Younger Handgrip - Control 1-back 0.142 0.215 46 0.662 .511
Younger Handgrip - Control 2-back -0.063 0.255 46 -0.247 .806
Younger Handgrip - Control 3-back -0.551 0.266 46 -2.073 .044
Older Handgrip - Control 0-back -0.048 0.197 39 -0.245 .808
Older Handgrip - Control 1-back 0.559 0.220 39 2.543 .015
Older Handgrip - Control 2-back -0.119 0.264 39 -0.453 .653
Table C15
Results of pairwise comparisons of onset time of maximum pupil diameter during n-back trials
for the handgrip versus control group, for each working memory load and age group
Age group Contrast Load Estimate SE df t p
Younger Handgrip - Control 0-back -0.007 0.041 45 -0.159 .874
Younger Handgrip - Control 1-back -0.035 0.044 45 -0.786 .436
Younger Handgrip - Control 2-back -0.025 0.040 45 -0.635 .529
Younger Handgrip - Control 3-back -0.108 0.039 45 -2.802 .007
Older Handgrip - Control 0-back -0.007 0.053 39 -0.136 .892
Older Handgrip - Control 1-back 0.002 0.046 39 0.035 .972
Older Handgrip - Control 2-back -0.033 0.047 39 -0.703 .486
Abstract (if available)
Abstract
There is growing evidence that the locus coeruleus (LC), a small nucleus in the brainstem, is important for cognitive aging. As the brain’s primary source of the neurotransmitter noradrenaline, the LC serves as an arousal hub region in the brain, integrating signals from many brain regions and releasing noradrenaline to adaptively regulate behavior and cognition. Recent advances in magnetic resonance imaging (MRI) have allowed for in vivo assessment of LC structure, and many studies suggest that having a more structurally intact LC in later life is associated with better cognition and brain health.
Beyond its protective role in aging, the LC is bidirectionally connected to the autonomic nervous system and helps orchestrate the central nervous system’s response to stress. During acute stress, the LC promotes adaptive responses that help complete the stress cycle. Chronic stress, on the other hand, has maladaptive effects on LC function and structure. Understanding how the LC’s structure and function contribute to cognition and arousal responses across the lifespan will be important for understanding why the LC is important for cognitive aging.
The aim of this dissertation was to identify physiological and behavioral correlates of LC structure and function across the lifespan. In Chapter 1, we tested whether LC MRI contrast, an in vivo measure of LC structure, can be changed through heart rate variability biofeedback, an intervention targeting the autonomic nervous system. We found that LC MRI contrast decreased in younger participants who performed daily biofeedback training and that decreases in left LC MRI contrast were related to decreases in sympathetic nervous system signaling. These findings provide novel evidence that LC structure can be changed over time in younger adults.
In Chapter 2, we attempted to fill a gap in the literature regarding potential functional correlates of LC MRI contrast by testing associations between LC MRI contrast and physiological arousal during an acute stress induction task. We found that in older adults, having higher MRI contrast of the caudal LC, which sends relatively more noradrenergic projections to the spinal cord compared to the rostral LC, was associated with more pronounced physiological responses to acute stress. These results suggest that having a more intact LC in aging is associated with better neurovisceral integration.
Finally, in Chapter 3, we built upon animal and pharmacological research linking working memory to noradrenaline and tested whether changes in LC activity, induced via isometric handgrip exercise, affected subsequent working memory performance. We found that both younger and older adults who performed handgrip had faster working memory reaction times, and we found that handgrip both increased concurrent arousal and decreased subsequent tonic pupil diameter. Our findings suggest that performing short bouts of isometric handgrip exercise can temporarily improve working memory, and they implicate the noradrenergic system in handgrip’s effects on performance. Collectively, these findings link non-invasive measures of LC structure and function to cognition and physiological arousal across the lifespan.
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Bachman, Shelby Lane
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Structure and function of the locus coeruleus across the lifespan
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Leonard Davis School of Gerontology
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Gerontology
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2022-08
Publication Date
05/23/2022
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acute stress
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