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Behavioral and neural influences of interoception and alexithymia on emotional empathy in autism spectrum disorder
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Behavioral and neural influences of interoception and alexithymia on emotional empathy in autism spectrum disorder
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Content
BEHAVIORAL AND NEURAL INFLUENCES OF INTEROCEPTION AND ALEXITHYMIA ON
EMOTIONAL EMPATHY IN AUTISM SPECTRUM DISORDER
by
Christiana Dodd Butera
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
(OCCUPATIONAL SCIENCE)
August 2021
Copyright 2021 Christiana Dodd Butera
ii
ACKNOWLEDGEMENTS
First, I would like to thank my advisor Dr. Lisa Aziz-Zadeh for her expertise, warm
encouragement, and guidance over the last five years. In addition to my advisor, I also am
grateful to the other members of my committee: Dr. Jonas Kaplan, Dr. Sharon Cermak, Dr.
Marian Williams, and Dr. Grace Baranek for providing me with your thoughtful feedback,
technical expertise, inspiration, mentorship, and for the opportunities you afforded me to grow
as a researcher.
I would also like to thank my friends and colleagues at the Brain and Creativity Institutive
and at the Department of Occupational Science for being my academic and professional homes
throughout this process. Your friendship, collaboration, encouragement, and leading by example
in your own work has always been the most important motivation and nourishment for me.
I am indebted to my wonderful family, and to my chosen family/tribe of close friends,
who have always been my cheerleaders and my champions. I can say with certainty that I would
not have made it here without you. My community was always there to remind me who I was,
and why I came here in the first place. Finally, I need to acknowledge my partner, Zach, who
has been my absolute sanctuary and has kept me grounded, laughing, determined, loved, and
well-fed through all the ups and downs of this process.
iii
TABLE OF CONTENTS
Acknowledgments ii
List of Tables vi
List of Figures vii
Abstract viii
Chapter 1. Brief Dissertation Introduction 1
Specific Aims 2
Occupational Science Lens 3
Chapter 2. Review: Behavioral and Neurobiological Influences of Interoception and
Alexithymia on Emotional Empathy in Autism Spectrum Disorder 4
Abstract 4
Introduction 4
Neural Mechanisms of Emotional Empathy 6
Interoception: Definitions, Mechanisms, and Measures 11
Alexithymia: Definitions, Mechanisms, and Measures 13
Emotional Empathy in ASD 15
Interoception, Alexithymia, and Empathy in ASD 23
Conclusions, Limitations, & Future Directions 24
Chapter 3. Relationships Between Alexithymia, Interoception, and Components of
Emotional Empathy in Autism Spectrum Disorder 27
Abstract 27
Introduction 28
Interoception, Alexithymia, and Emotional Empathy 29
Methods 31
Participants 31
Assessments 32
Analysis 35
Results 36
Group Differences – ASD & TD 36
Regression Analysis 42
emBody Interview 43
Discussion 45
Interoception, Alexithymia and Emotional Empathy in ASD 45
Personal Distress and Empathic Concern 47
Empathy in ASD 48
Limitations and Future Directions 49
Conclusions 50
Chapter 4. Neural Correlates of Alexithymia, Interoception, and Emotional Empathy in
Autism Spectrum Disorder: An fMRI Study 52
Abstract 52
Introduction 54
Neural Mechanisms of Emotional Empathy 54
iv
Emotional Empathy in ASD: Behavioral 55
Emotional Empathy in ASD: Neural Correlates 56
Alexithymia 57
Interoception 58
Alexithymia, Interoception and Empathy 59
Methods 61
Participants 61
Assessments 62
Behavioral Data Analysis 65
fMRI Analysis 65
Results 69
Behavioral Group Differences – Hypothesis 1 69
fMRI Group Differences – Hypothesis 2 70
ROI Analysis: Partial Correlations – Hypothesis 3 & 4 74
Stepwise Linear Regression Analysis in ASD – Hypothesis 3 & 4 75
Hierarchical Regressions – Hypothesis 5 77
Discussion 79
Empathy & Alexithymia: Behavioral 80
Emotional Empathy Neural Regions of Interest 81
Interoception 82
Influences on Emotional Empathy in ASD 85
Limitations and Future Directions 85
Conclusions 86
Chapter 5. Alexithymia, Interoception, Emotional Empathy and Task-Based Functional
Neural Connectivity Autism Spectrum Disorder 88
Abstract 88
Introduction 91
Neural Mechanisms of Emotional Empathy 91
Emotional Empathy in ASD: Neural 92
Alexithymia, Interoception and Empathy 98
Methods 99
Participants 99
Assessments 100
Behavioral Data Analysis 103
fMRI Analysis 104
Results 108
Behavioral Group Differences 108
Behavioral Partial Correlations 109
Connectivity Group Differences & Correlations: Whole Brain Analysis 110
Connectivity Group Differences & Correlations: ROI Analysis 113
Hierarchical Regressions – Hypothesis 5 114
Discussion 114
Connectivity Group Differences 115
Emotional Empathy 116
Interoception 116
Alexithymia 118
Autism Severity 119
Anterior Insula & IFGop 119
Limitations and Future Directions 120
v
Conclusions 121
Chapter 6. General Discussion: Neural and Behavioral Relationships of Alexithymia,
Interoception, and Emotional Empathy in Autism Spectrum Disorder 124
Introduction 124
Summary of Findings 125
Study 1: Behavioral 125
Study 2: Neural Activity Correlates 132
Study 3: Neural Functional Connectivity 138
Conclusions From Studies 1-3 134
Alexithymia 134
Interoception and Embodied Measures 136
Emotional Empathy in ASD 137
Proposed Model of Emotion Responding in ASD 139
ADHD 142
Discussion, Limitations, & Future Directions 142
Defining Emotional Empathy in ASD Research 142
Therapeutic Implications 143
Limitations 145
References 149
Supplementary Data 196
fMRI procedure, acquisition, preprocessing 196
vi
LIST OF TABLES
Chapter 3
Table 1. Descriptive Statistics and Group Comparisons of Behavioral Data ………………….…37
Table 2. Partial Correlations With Emotional Empathy Scores...................................................39
Chapter 4
Table 1. Descriptive Statistics and Group Comparisons of Behavioral Data………………….....71
Table 2. Descriptive Statistics and Group Comparisons of Anatomical ROI Parameter
Estimates..……………………………...……………………………………………………......……...73
Table 3. Summary of Hierarchical Regression Analysis for IRI Personal Distress Across
Groups……………………………………………………………………………………………….......78
Table 4. Summary of Hierarchical Regression Analysis for Right Amygdala Activity During
Observation of Emotional Faces Across Groups………………………………………………….…79
vii
LIST OF FIGURES
Chapter 2
Figure 1. Theories of Emotional Empathy Disruption in Autism Spectrum Disorders.................22
Chapter 3
Figure 1. Emotional Empathy Subscales: Scatter Plots…………………………….......................40
Figure 2. Emotional Empathy Results Summary Across all Methods.........................................44
Figure 3. Commonalities in Personal Distress Relationships in TD and ASD.............................45
Chapter 4
Figure 1. Stimulus and task design……………………………………..............………………..…197
Figure 2. Regions of Interest.......................................................................................................68
Figure 3. Main Effect of Facial Expression Observation Task……….……………...…..…………72
Figure 4. ROI Group Differences...............................................................……………………….74
Figure 5. Scatter Plots of Significant Correlations in the ASD Group..……………………………76
Chapter 5
Figure 1. Neural Regions of Interest From Neurosynth Empathy Meta-Analysis Map..............107
Figure 2. Whole Brain PPI with Left Ventral Anterior Insula......................................................111
Figure 3. Scatterplots of Significant Correlations: Behavior and Left AI Connectivity...............112
Chapter 6
Figure 1. Emotional Empathy Results Summary Across all Behavioral Analyses in Study 1....127
Figure 2. ROI Group Differences... ………………...........................................................……...130
Figure 3. Study 2 Scatter Plots of Significant Correlations in the ASD Group……………….…131
Figure 4. Whole Brain PPI with Left Ventral Anterior Insula.....…………………………………..134
Figure 5. Summary of Neural and Behavioral Relationships in Chapters 3 & 4………...……...138
Figure 6. Proposed Reactive-Responsive Profile Model in ASD...............................................141
ABSTRACT
viii
Background: Social deficits in Autism Spectrum Disorder (ASD) commonly involve
abnormalities in intention understanding and empathy. There is substantial evidence to suggest
that individuals with ASD have difficulties with cognitive empathy (for review see Frith and
Happé, 2005); however, emotional empathy deficits are unclear. The presence of co-occurring
alexithymic tendencies in ASD (Bird, et al., 2010) and alterations in interoceptive processing
(Fukushima, Terasawa, & Umeda, 2011; Grynberg & Pollatos, 2015) are associated with
reductions in empathic ability. The purpose of this study was to answer the question: how do
emotional empathy, alexithymia, and interoception interact with 1) each other, 2) neural activity,
and 3) neural connectivity of mechanisms of emotion processing during facial expression
observation in TD and ASD youth.
Methods: Self-report and interview data were collected to explore relationships between
interoceptive sensibility, alexithymia, and emotional empathy in 35 high-functioning youth with
ASD and 40 typically developing (TD) controls (ages 8-17). A sub-sample also completed an
imaging study (TD; n = 37, ASD n = 28). fMRI data was collected inside a 3-T Siemens
MAGNETOM Prisma scanner, where participants observed video clips of facial expressions
presented in a block design. Both groups were entered into multivariate linear regression
models for the task for exploring main effects and between-group comparisons. Anatomically
and functionally defined neural regions of interest (ROIs) included the ACC, AI, amygdala and
IFGop. Psycho-physiological interaction (PPI) analysis was performed to determine task-based
functional connectivity between left ventral AI seed region, and the rest of the brain. Parameter
estimates of predetermined ROIs were extracted. All data were analyzed using two-tailed
independent sample t-tests, Pearson partial correlation, stepwise multiple linear regression, and
hierarchical linear regression.
ix
Results: Major behavioral findings include: 1) The ASD sample had increased
alexithymia and physiological hyperarousal, but no significant differences on any measures of
interoception or emotional empathy skill in comparison to TD participants; 2) In ASD and TD,
alexithymia in ASD is associated with lower empathic concern, and higher personal distress; 3)
In the TD group (with low incidence of anxiety), those with higher interoceptive sensibility tended
to have lower personal distress; and 4) in the ASD group, greater alexithymia severity, and
higher personal distress to others’ emotions is associated with reduced reporting of bodily
sensations during negative emotions (BSE-neg); 5) support for the alexithymia hypothesis in
personal distress.
Major neural activity findings include: 1) support for the alexithymia hypothesis in the
right amygdala 2) reduced left IFGop activation in individuals with ASD compared to TD
individuals; 3) empathic concern as the strongest predictor of left IFGop activation and ADHD
symptom severity as the strongest predictor of right IFGop activation in ASD; 4) opposite
relationships in left and right amygdala with alexithymia 5) opposite patterns of relationships
between interview vs self-report questionnaires of interoceptive information with ROI’s.
Major connectivity findings included 1) alexithymia is associated with reduced left AI-left
precuneus connectivity, and reduced right dorsal AI-left ventral AI connectivity during facial
expression observation; 2) inverse relationships between BPQ-VSF and interoceptive interview
variables (BSE, BSE-neg) with behavioral and neural outcomes of alexithymia and emotional
empathy; 3) in the ASD group, higher connectivity between left ventral AI-right lateral prefrontal
cortex during facial expression observation.
Conclusions: Behaviorally we show that interoception and emotional empathy ability are
intact in ASD, and that alexithymia severity is higher in ASD. We find inverse patterns of
relationships with emotional empathy and alexithymia, where alexithymia is positively correlated
with personal distress and negatively correlated with empathic concern. We show support for
x
the alexithymia hypothesis only in the personal distress domain, as alexithymia predicted higher
personal distress above and beyond the influence of group status (ASD). For bodily awareness
variables we observed that BPQ-VSF may be indexing maladaptive empathy outcomes,
(reduced emotional empathy) and may measure something more similar to physiological
hyperarousal, which was positively correlated with alexithymia.
Our data indicate a dynamic interplay between: 1) the amygdala, which is strongly
involved in emotional empathy processes and affected by alexithymia presence, differentially in
right and left hemisphere, above and beyond ASD diagnosis; 2) the anterior insula, which is
strongly involved in visceral and interoceptive sensory processing, empathic concern
processing, and alexithymia; 3) the IFGop, which is involved in empathic concern,
attentional/inhibitory, and social and motor processing, interoceptive sensibility, and shows
differences when comparing groups based on ASD diagnosis. Across the dissertation, in
behavior, neural activity, and neural connectivity certain traits consistently clustered together to
reflect associations between patterns of embodied responding, emotion understanding, and
emotional empathy style. Here, I attempt to characterize these two styles observed in ASD as a
“reactive” and a “responsive” style to bodily cues, emotions, and emotional empathy.
Therapeutic implications and future directions are discussed.
1
Chapter 1. Brief Dissertation Introduction
This dissertation is comprised of a review chapter, three studies, and a discussion
chapter that investigate emotional empathy processing in autism spectrum disorder (ASD). All
three studies compared children and adolescents with ASD to a second to a group of typically
developing youth (TD). The overarching questions addressed in this dissertation are: 1) is
emotional empathy reduced in ASD; 2) what traits impact emotional empathy in ASD; and 3)
what neural mechanisms are related to these traits?
Evidence for emotional empathy difficulties in ASD are mixed. Some studies find that
individuals with ASD have reduced emotional empathy ability (Bos & Stokes, 2018; Kasari et
al.,1990; Lombardo et al., 2007; Mathersul et al., 2013; Minio-Paluello et al., 2009; Peterson et
al., 2014; Schulte-Rüther et al., 2011; Shamay-Tsoory et al., 2002; Sigman et al., 1992;
Sucksmith et al., 2013; Yirmiya et al., 1992) while others have not found support for any
emotional empathy differences (Bellebaum et al., 2014; Deschamps et al., 2014; Dziobek et al.,
2008; Hadjikhani et al., 2014; Jones et al., 2010; Markram et al., 2007; Pouw et al., 2013;
Rogers et al., 2007; Rueda et al., 2015; Schwenck et al., 2012; Smith, 2009). Some evidence
suggests deficits in empathy in ASD may be attributed to co-occurring alexithymia rather than
ASD symptomatology alone (Bird & Cook, 2013), while others suggest interoception
impairments are responsible for inconsistent reductions seen in emotional empathy in ASD
(Quattrocki & Friston, 2014). In this study, I will focus on how one prominent sensory feature
(interoceptive sensibility) and one prominent emotional feature (alexithymia) may differentially
impact the neural mechanisms associated with emotional empathy in youth with ASD and their
TD peers. To my knowledge, to date, only one study (Mul et al., 2018) has investigated how
both alexithymia and interoception interact together with emotional empathy, and this study did
not have a neural component measured.
2
To address this scientific gap, this dissertation assesses how alexithymia and interoception
relate to empathy through a behavioral study and two functional imaging studies. A set of three
experiments was performed across ASD and TD participants. The aims of each study are described
below.
Specific Aims
Aim one was to assess whether interoceptive sensibility and alexithymia are related to
emotional empathy skills in TD and ASD youth. Initial hypotheses were that interoceptive
sensibility deficits and alexithymia severity would be negatively correlated with emotional
empathy skills in ASD and TD youth. Aim two was to assess whether individual differences in
interoceptive sensibility, alexithymia and emotional empathy in TD and ASD children correlated
with neural activity brain regions associated with empathy while viewing videos of facial
expressions. Initial hypotheses were that interoceptive sensibility impairment, alexithymia traits,
and emotional empathy impairment would be negatively correlated with activation in neural
regions associated with empathic processing (insula, amygdala, inferior frontal gyrus pars
opercularis, and anterior cingulate cortex) during facial expression observation in both
groups. Lastly, aim three was to assess whether individual differences in interoceptive
sensibility, alexithymia and emotional empathy in TD and ASD youth correlated with neural
connectivity between brain networks associated with empathy while viewing videos of facial
expressions. Initial hypotheses were that interoceptive sensibility impairment, alexithymia traits,
and emotional empathy impairment would be negatively correlated with connectivity between
neural regions associated with empathic processing (insula, amygdala, inferior frontal gyrus
pars opercularis, and anterior cingulate cortex) during facial expression observation in both
groups.
Occupational Science Lens
3
Occupational science (OS) has many definitions, but the one I use for the purpose of this
dissertation is the study of everyday living, concerned with understanding humans as
occupational beings, and the activities they engage in that influence their health and wellbeing
(Wilcock, 2005). Occupational performance and engagement can be constrained when
challenges are present for full participation (Wilcock, 2006). Given this, one of the original
purposes of OS was as a basic science to create an evidence base in support of occupational
therapy practice (Clark et al., 1991; Yerxa, 1990), and that is the goal of this work. While the
goal of neuroscience is to understand the structure and function of the brain, my goal as an
occupational scientist is to use neuroscience and psychology tools to understand appropriate
ways occupation may be used to promote health by intervening in atypical functioning. Placing
value and attention on the wholistic unity between the mind and body in humans and their
recovery is a primary focus that distinguishes occupational therapy practice from other health
care professions (Bing, 1981; Kielhofner, 1995; Wood, 1998. As described above, my work
explores mechanisms of how the body is an active agent in producing feeling states, and in
understanding the feelings of others. Further, I consider empathy ability to be a neccesary
embodied component of social belonging in our environments and occupations, similar to how
transactional occupation is characterized by Aldrich & Cutchin (2013).
4
Chapter 2. Behavioral and Neurobiological Influences of Interoception and Alexithymia
on Emotional Empathy in Autism Spectrum Disorder
Abstract
Social deficits in Autism Spectrum Disorder (ASD) commonly involve abnormalities in intention
understanding and empathy. There is substantial evidence to suggest that individuals with ASD
have difficulties with cognitive empathy (for review see Frith and Happé, 2005); however,
emotional empathy deficits are unclear. To understand discrepant findings of emotional
empathy ability, three prominent theories of the neurobiology of emotional empathy disruption in
ASD will be reviewed: 1) the Broken Mirror Hypothesis (Iacaboni & Dapretto, 2006;
Ramachandran & Oberban, 2006), 2) the Oxytocin Interoceptive Deficit (Quattrocki & Friston,
2014) 3) the Alexithymia Hypothesis (Bird & Cook, 2013). Findings suggest that control and
mediating variables of alexithymia and interoceptive ability should be utilized in neuroimaging
studies in ASD. Understanding the mechanisms and role these symptoms play in social and
emotional functioning in ASD will further inform neurobiological relationships with
symptomatology, and may implicate future therapeutic targets for individuals with ASD who
experience these comorbidities.
Keywords: alexithymia, autism, interoception, empathy
Autism spectrum disorder (ASD) is a condition comprised of two categories of core
impairments: 1) social communication and social interaction, and 2) restricted or repetitive
behaviors and interests (American Psychiatric Association, 2013). Although social deficits are a
core feature of ASD, the biological mechanisms for these deficits are not yet fully understood.
One skill necessary for adaptive social functioning is the ability to empathize with others.
Empathy is a multifaceted construct that refers to the ability to understand and experience the
feelings of others (Dvash & Shamay-Tsoory, 2014). Empathy can be further divided into
cognitive and emotional/affective categories. Cognitive empathy (CE) is the ability to mentally
5
imagine how another person is thinking or feeling (de Waal & Preston, 2017), and emotional
empathy (EE) refers to the capacity for sharing emotions others are experiencing (Davis et al.,
1994). Evidence suggests that individuals with ASD have difficulties with CE (for review see
Frith and Happé, 2005); however, EE deficits are unclear. Some studies find that individuals
with ASD have impaired EE skills (Bos & Stokes, 2018; Kasari et al.,1990; Lombardo et al.,
2007; Mathersul et al., 2013; Minio-Paluello et al., 2009; Schulte-Rüther et al., 2011; Shamay-
Tsoory et al., 2002; Sigman et al., 1992; Sucksmith et al., 2013; Yirmiya et al., 1992) while
others have not found support for an EE deficit (Bellebaum et al., 2014; Deschamps et al., 2014;
Dziobek et al., 2008; Hadjikhani et al., 2014; Markram et al., 2007; Rogers et al., 2007; Rueda
et al., 2015; Schwenck et al., 2012; Smith, 2009).
The heterogeneity in ASD creates a complex problem for determining the underlying
factors that contribute to core deficits. Some have argued that it is not ASD per se, but the
presence of co-occurring alexithymia (trouble recognizing, describing, and distinguishing
emotions in oneself; Bird, et al., 2010) or disrupted interoception (internal sense of the
physiological condition of the body; Fukushima et al., 2011; Grynberg & Pollatos, 2015) that are
associated with reductions in empathic ability. In order to provide context to these hypotheses,
the neural mechanisms of EE, and the definitions, mechanisms, and measures of interoception
and alexithymia in research will first be discussed. Then we will explore the contributions of
interoceptive and emotion processing differences on EE in ASD across behavioral and neural
functioning with a focus on: 1) the Broken Mirror Hypothesis (Ramachandran & Oberban, 2006);
2) the Oxytocin-Mediated Interoceptive Deficit (Quattrocki & Friston, 2014); and 3) the
Alexithymia Hypothesis (Bird & Cook, 2013). Findings from each will be integrated and
recommendations will be made for implications to consider in future research studies.
Neural Mechanisms of Emotional Empathy
6
Although both cognitive and affective components are involved in empathizing in typical
social interactions (Zaki & Ochsner, 2012), it is important to distinguish EE from related
constructs, particularly sympathy or compassion. EE involves feeling another person’s
emotional experience, and is often called emotional resonance. By contrast, CE, or sympathy,
involves cognitively understanding another person’s emotional experience. EE can result in
responses of sympathy and compassion but is a separate phenomenon, which does not require
prosocial action (Hein & Singer, 2008; Singer & Klimecki, 2014). EE is thought to be a result of
representing others emotional states through embodied simulation; an automatic process of
evocation of internal representations of body states associated with an observed emotion.
(Gallese & Sinigaglia, 2011). Emotional contagion occurs when an observer implicitly resonates
another’s affective state. Once emotional contagion is experienced, the perceiver must
recognize that affective state as being experienced by the other to qualify as EE (Bird & Viding,
2014).
Distinct neural systems have been associated with the processing of CE and EE,
indicating that they can be thought of as separate constructs. The network of neural
mechanisms that contribute to the process of EE are thought to be the inferior frontal gyrus
(IFG), anterior cingulate cortex (ACC), amygdala, and insula (Dvash & Shamay-Tsoory, 2014).
If these regions are damaged, patients demonstrate deficits in EE and emotion recognition
(Shamay-Tsoory et al., 2009). These regions are commonly active when experiencing emotion
and when observing another having an emotional experience (see Hein & Singer, 2008 for
review). Initial evidence of emotion simulation comes from a body of work on processing other
people’s experience of pain, which we describe below.
Pain Matrix
The “pain matrix” is a network of brain regions comprised of the ACC, middle cingulate
cortex (MCC), insula, and the somatosensory cortices (SI and SII) (Jackson et al., 2006). There
7
is a large body of evidence that the pain matrix, particularly the ACC and insula, responds to
pain in the self and observing pain in others (see Hein & Singer, 2008 for review). This network
is active for both physical and social pain (Eisenberger & Lieberman, 2004) and is more strongly
elicited as the perceived intensity of pain increases (Saarela et al., 2007). This simulating
response occurs for observing both strangers and familiar individuals experiencing pain (Singer
et al., 2006). Individuals with alexithymia (trouble recognizing, describing, and distinguishing
emotions in oneself) have lower activation in the insula for others’ pain (Bird et al., 2010). Higher
scores on empathic self-report scales correspond with greater activation in the insula and MCC
when perceiving others pain (Singer et al., 2006). However, pain sharing can fluctuate
depending on many factors. Reduced neural responses to other’s pain can be seen in
physicians with frequent exposure to others’ pain (Decety et al., 2015) or when the individual
experiencing pain is perceived as a member of a social outgroup (Xu et al., 2009) or of superior
social status (Feng et al., 2016). Some studies find the opposite pattern, where individuals
activate the pain matrix more when they observe members of an unlikeable social outgroup
experiencing pain as compared to neutral individuals (Fox et al., 2013), indicating that context
and meaning are primary factors in modulating these responses. Furthermore, this data
indicates that empathic processing may involve larger networks in addition to the pain matrix.
Indeed, there is some evidence that emotional resonance may include the orbitofrontal cortex
(OFC), which is recruited during vicarious pleasant touch (Lamm et al., 2015). Evidence
indicates that pain processing is embodied, but top-down mechanisms, and contextual factors
can modulate these responses, and regions beyond the pain matrix may be involved (Aziz-
Zadeh et al., 2018).
Affective Processing
In addition to empathy for pain, there is evidence for shared emotion networks for
processing feeling states. The Self to Other Model of Empathy (SOME; Bird & Viding, 2014)
8
proposes that empathy includes a much larger network. It hypothesizes that four neural
networks are necessary for EE: (1) a situation understanding system; (2) an affective cue
classification system; (3) an affective representation system; and (4) a self to other switching
mechanism. In this model the situation understanding system processes the situation another
individual is in using the Theory of Mind Network (ToM; temporoparietal junction (TPJ), medial
prefrontal cortex, and precuneus; Frith & Frith, 2006). The affective cue classification system
analyzes emotional cues like facial expression and biological motion using areas including the
fusiform gyrus, the superior temporal sulcus (STS), and the amygdala. The affective cue
classification system influences the affective representation system either directly through
emotion contagion, or via the mirror neuron system, as a result of automatic mimicry. The
affective representation system, localized to the insula and ACC provides a representation of
the self’s affective state. As a self to other switch is applied, likely by the TPJ (Santiesteban et
al., 2012; Spengler von Cramon, & Brass, 2009), the affective state of the self becomes tuned to
the perceived state of the other. This theory proposes that the TOM network cognitively
understands the emotion of another, that emotion is felt in the self via the affective
representation system (insula, ACC) and the mirror neuron system, and then the TPJ projects
that emotion back to the observed individual to produce empathy.
Activation of the anterior insula (AI) has been noted in a number of different feeling
stimuli presentations such as happiness, disgust, fear, anger and sadness (e. g. Jabbi et al.,
2007; Seara-Cardoso et al., 2016). In a meta-analysis of fMRI studies for empathy of these
basic emotions, consistent activation in medial orbitofrontal cortex, the ACC, MCC, and insular
cortex was observed regardless of stimulus and task type (Fan et al., 2011). Although less
consistently reported, the amygdala also is thought to participate in EE (Adolphs, 2010),
particularly for reading emotion in the eyes (Baron-Cohen et al., 1999) and responding to
auditory cues of emotion, such as laughing or crying (Sander et al., 2007). In another study,
9
when participants were asked to rate the valence of their own feeling states while observing
emotional faces in an fMRI, participants recruited the AI, dACC, IFG, and the amygdala.
Further, both amygdala and AI activation was modulated by self-reported empathic responses
(Seara-Cardoso et al., 2016). Evidence suggests that EE is a simulated process of emotional
experiencing of others’ affective states primarily employing the ACC, amygdala, insula, and the
IFG, though other networks may also be involved.
Mirror Neuron System
The human mirror neuron system (MNS) is a set of brain regions which activate when
performing actions and when observing others perform the same action (Rizzolatti & Craighero,
2004). Mirror neurons were originally discovered in the macaque monkey (di Pellegrino et al.,
1992), and the human MNS is thought to consist of the IFG and the inferior parietal lobule (IPL).
Additional fMRI work suggests a broader mirroring network, commonly referred to as the Action
Observation Network (AON) that includes the premotor cortex, supplementary motor area
(SMA), and superior temporal sulcus (Caspers et al., 2010). Although there is some controversy
around the role of this system in humans (see Hickok, 2009 for review), there is much support
for a similar system in humans (for review see Rajmohan & Mohandas, 2007; Rizzolatti &
Craighero, 2004). The involvement of the MNS in empathy models has been implicated in both
CE (Kilroy & Aziz-Zadeh, 2017) and EE (Dvash & Shamay-Tsoory, 2014) functioning. The
overlapping classification of this system is due to its potential role in recognizing and
understanding the intentions of others’ motor actions (Avenanti et al., 2013; Iacoboni et al.,
2005; Rizzolatti & Craighero, 2004), and its proposed role as a neural mechanism for emotional
contagion (Keysers & Gazzola, 2006).
Similar to other models of EE, the MNS is thought to be involved in processing the
intentions of others through embodied simulation, or mapping their actions onto motor
representations for performing those actions. Processing motor representations of perception
10
and action in the IFG, IPL and premotor areas may be important for understanding the
intentions behind others actions in social contexts (Gallese et al., 2004; Keysers & Gazzola,
2006). The simulation theory of emotion is supported by a large body of work indicating that the
MNS is engaged in ‘mirroring’ for both basic and complex emotions (Blakemore et al., 2005;
Ebisch et al., 2008; Jabbi et al., 2007; Morrison et al., 2004; Singer et al., 2006). There is also
evidence for an auditory mirror system, where the sounds of actions activate regions of the
premotor cortex for performing the action (Aziz-Zadeh et al., 2004; Gazzola et al., 2006; Kohler
et al., 2002). This system could be particularly important for empathizing with emotional
vocalizations (e.g., cries, laughs; Aziz-Zadeh et al., 2010). Individual differences in trait empathy
have been correlated with activity in the MNS (Aziz-Zadeh et al., 2010; Jackson et al., 2005;
Kaplan & Iacoboni, 2006; Gazzola et al., 2006). For example, Saarela et al. (2007) reported that
scores on the Interpersonal Reactivity Index (IRI; Davis, 1980) empathic concern subscale
correlated with IFG activity during painful face observation. In another study, Jabbi et al. (2007)
observed correlations between self-reported empathy on the IRI (Davis, 1980) and activation of
the inferior operculum and insula when participants observed disgusted facial expressions. They
found that empathic ability of both a cognitive and an emotional subscale were correlated with
the strongest activation of the insula when observing others experience disgust. Despite a
significant body of work suggesting MNS involvement in empathic processes, its participation in
these processes has not always been consistent (Fan et al., 2011), and could indicate that other
networks may work together with the MNS for empathic processing. Other shared circuits may
also be involved when observing and empathizing with others being touched (Keysers et al.,
2004) or experiencing pain (Singer et al., 2006).
Interoception: Definitions, Mechanisms, and Measures
Interoception is the internal sense of the physiological condition of all the tissues in the
body (Craig, 2003). This information is thought to be carried by the homeostatic afferent
11
pathway, originating in sensory afferent fibers that innervate tissues and organs. Information
from the body travels via lamina I neurons to caudal medulla, trigeminal dorsal horns, the
nucleus of the solitary tract (NTS), to the hypothalamus, insula, and ACC (Craig, 2003).
Embodied simulation theories of emotion processing posit that autonomic body states lead to
learning and predictive influence on felt emotion (ie James, 1884; Schachter & Singer, 1962;
Damasio, 1994). The somatic marker hypothesis (Damasio et al., 1996) posits that experiences
induce peripheral responses in the body (e.g., increased heart rate), and these physical states
are interpreted by emotion-related brain regions and produce feeling states. The relationship
between somatic reactions, memories of these body states, and situational cues may then
modulate empathic behavior (Damasio et al., 1996). The insula is considered the main region
for interoceptive processing (Critchley, 2005); and is involved in integrating bodily sensations to
be processed as feelings (Berthoz et al., 2002; Craig, 2014; Frewen et al., 2008; Karlsson et al.,
2008). Although it has been posited that interoception plays a fundamental role in ToM due to
the influence of one’s emotional and physical states in predicting another’s mental states
(Ondobaka et al., 2017), the data do not seem to support this hypothesis. Instead, interoceptive
ability only seems to impact understanding of affective states rather than cognitive mental states
(Shah et al., 2017). In this study interoceptive accuracy predicted performance only in situations
where understanding emotion was crucial for others’ mental state representation. Accuracy on
questions where individuals were asked how someone was ‘feeling’, rather than what they were
‘thinking’, were correlated with interoceptive accuracy. The results suggest that interoception is
not necessary for the representation of all mental states, but it contributes to representation of
mental states in situations where emotional information must be used (Shah et al., 2017).
Two common methods are used to measure interoceptive ability. The first is
interoceptive sensibility (IS; Garfinkel et al., 2015), which is a term used to describe an
individual’s evaluation of their ability to perceive afferent information from their body. IS is
12
assessed through self-report questionnaires about awareness of sensations such as heart rate,
hunger, or respiration rate. Some measures focus solely on awareness of internal physical body
states (Body Awareness Questionnaire, Shields et al., 1989; Body Perception Questionnaire,
Porges, 1993; Interoceptive Awareness Questionnaire, Bogaerts et al., 2018) while other
multidimensional measures include constructs like emotional distress, attention, or self-
regulation in response to interoceptive cues (Multidimensional Assessment of Interoceptive
Awareness, Mehling et al., 2012). The criticisms of self-report measures of interoception are the
presence of bias in subjective thresholds and the need for strong metacognitive skills to
evaluate one’s own performance (Garfinkel et al., 2015).
Interoceptive accuracy (Iac) methods are experimental measures of an individual’s
ability to perceive internal body signals (Garfinkel, et al., 2015). The most common task
employed to measure Iac is a silent heartbeat counting task done at varying time intervals (25,
35, and 45 seconds) while the number of heartbeats is recorded (Heartbeat Tracking; Schandry,
1981). Another common Iac task is heartbeat discrimination, which requires participants to
report timing of heartbeats by tapping or discriminating between heartbeats and external stimuli
(Whitehead et al., 1977). Criticisms of this form of measurement are its reliance on potentially
confounding processes of complex sensory integration and sustained attention. In the heartbeat
discrimination task, most neurotypical individuals perform close to chance level, making it
difficult to compare typical development to pathologies in interoceptive ability (Knapp-Kline &
Kline, 2005). The heartbeat discrimination task relies on cardiac, auditory, and motor
integration, and an ability to discriminate between simultaneous asynchronous rhythms.
Heartbeat tracking has previously been related to the ability to estimate time (Meissner &
Wittmann, 2011), indicating that sustained attention to temporal cues can influence performance
on heartbeat tracking.
13
These types of Iac methods may be particularly problematic for testing interoceptive
ability in children with ASD. Altered resting-state connectivity in the salience network is
consistently seen in ASD (Uddin et al., 2013). The salience network is thought to be the
mechanism to choose which internal and external stimuli to attend to, and when to switch
between them (Menon & Uddin, 2010). Despite disruption of the salience network in ASD, when
these individuals are asked to attend to specific stimuli, they can overcome salient stimuli
through attentional modulation, and restore typical neural patterns (Green et al., 2018).
Considering this, the nature of Iac tasks may facilitate attentional control, and overlook
spontaneous interoceptive difficulties. Given this, one variable that may account for the
discrepant EE findings in ASD is the varying presence of interoceptive deficits (Fukushima et
al., 2011; Grynberg & Pollatos, 2015).
Alexithymia: Definitions, Mechanisms, and Measures
Alexithymia is a condition characterized by trouble recognizing, describing, and
distinguishing emotions in oneself (Sifneos et al., 1977). It is associated with deficits in the
regulation of physical and emotional arousal (Cox et al., 1995; Laloyaux et al., 2015) and is
related to poor emotional awareness (Da Silva et al., 2017). Alexithymia is thought to be a
disorder of affect regulation that is associated with impaired interoceptive ability (Herbert et al.,
2011) and somatic conditions (Kano & Fukudo, 2013).
There are several theories regarding the mechanisms responsible for alexithymia.
Failure to identify and describe emotions in alexithymia may result from altered autonomic,
endocrine, and immune activity (Taylor et al., 1997). Although findings are mixed, there is
evidence of greater resting sympathetic and cardiovascular activity, as well as poor immune
status in individuals with alexithymia (Lumley et al., 2007). Two theories are based on principles
of hemispheric dominance (Buchanan et al., 1980). The first proposes a disruption in
interhemispheric communication through the corpus callosum, causing a disconnect between
14
emotion processing and language processing and resulting in an impairment in ascribing
language to felt emotions (Hoppe & Bogen, 1977; Larsen et al., 2003; Wingbermühle et al.,
2012). Others propose a general right hemisphere deficit as the cause of alexithymia, with a
hypoactivation in the right and hyperactivation of the left hemisphere, as the right hemisphere is
thought to be more dominant in emotion processing (Bermond et al., 2005; Kano et al., 2003;
Sifneos, 1988). Beyond hemispheric dominance, others theorize that alexithymia is caused by a
primary deficit of bodily information not reaching the ACC to be processed as feeling (Lane et
al., 1997), and impaired communication between the hippocampus and amygdala (Aleman,
2005). Meta-analysis of fMRI studies in individuals with alexithymia report that when
participants engage in emotion processing tasks for both the self and others, both hyper- and
hypo- activation of the bilateral ACC and consistent hypoactivation in right insula, bilateral
amygdala, left dorsomedial prefrontal cortex (dmPFC), and bilateral precuneus (van der Velde
et al., 2013) have been observed. In addition, another study found hyper activation in frontal and
parietal MNS regions (Moriguchi et al., 2009). Thus the neural basis of alexithymia stands to be
better understood.
Alexithymia is traditionally measured by emotion projection tests, observational
assessment, and self-report scales. The Rorschach Alexithymia Scale (RAS; Porcelli & Mihura,
2010) is a condensed version of the original Rorschach comprehensive system (inkblots), which
uses items from the comprehensive test that are strongly associated with alexithymic
tendencies on the Toronto Alexithymia Scale (TAS-20; Bagby et al., 1994). Another commonly
used assessment is the Toronto Structured Interview for Alexithymia (Bagby et al., 2006). This
24-item structured interview is composed of two domain scales (affect awareness, and
operative thinking) and four facets within those domains (difficulty identifying feelings, difficulty
describing feelings, externally oriented thinking, and imaginal processing). It has acceptable
inter-rater, internal, and retest reliability. The interview consists of questions like: “When you
15
experience stressful situations do you find it difficult to describe how you feel? How do you
experience this difficulty? Give me some examples” (Bagby et al., 1994). The scoring ranges
from ‘0’ (no difficulty) to ‘2’ (significant difficulty) in describing feelings. The most frequently used
self-report questionnaire is the TAS-20 (Bagby et al., 1994). The TAS-20 has a three-factor
structure consistent with alexithymia symptom domains, and has demonstrated high internal
consistency and test-retest reliability. There are seven items in the difficulty identifying feelings
factor (e.g., “I am often confused about what emotion I am feeling”), eight items in the externally
oriented thinking factor (e.g., “I prefer to analyze problems rather than just describe them”), and
five items in the difficulty describing feelings factor (e.g., “It is difficult for me to reveal my
innermost feelings, even to close friends”). Evidence of convergent, discriminant, and
concurrent validity of the TAS-20 was demonstrated through correlations with other
psychological scales and outside medical observer-ratings of alexithymia (Bagby et al., 1994).
Emotional Empathy in ASD
The data on whether individuals with ASD have a deficit in EE is mixed. Several studies
suggest that individuals with ASD have impaired EE.
Children with ASD show less prosocial
behavior towards both familiar and unfamiliar adults in distress (Bacon et al., 1998). Similarly,
adults with high-functioning ASD have demonstrated reduced EE scores on several
standardized self-report measures (Mathersul et al., 2013), and adults with ASD show reduced
electromyography (EMG) neurophysiological response when observing others’ pain (Minio-
Paluello et al., 2009).
Other studies support intact EE functioning in individuals with ASD (Bellebaum et al.,
2014; Deschamps et al., 2014; Dziobek et al., 2008; Hadjikhani et al., 2014; Markram et al.,
2007; Rogers et al., 2007; Rueda et al., 2015; Schwenck et al., 2012; Smith, 2009). Dziobek et
al. (2008) found no significant differences between adults with ASD and typically developing
(TD) individuals on EE portions of a self-report empathy measure. In children diagnosed with
16
ASD, recognition of emotional facial video clips (Schwenck et al., 2012) and emotional
contagion with characters in a story task (Deschamps et al., 2014) did not differ from TD
participants. Finally, a neuroimaging study by Hadjikhani et al. (2014) assessed responses to
observing other people in pain and found no significant differences between adolescents and
adults diagnosed with ASD and TDs in the activation of brain areas involved in shared pain
experiences (“the pain matrix”), although it should be noted that activity in the pain matrix is not
necessarily the same as empathic processing (see for example, Aziz-Zadeh et al., 2018; Fox et
al., 2013).
It is possible that inconsistent findings may be a result of the heterogeneity of ASD
etiology (Happe et al., 2006). In the next section, we will review three prominent theories of EE
disruption in ASD, and the neural correlates associated with them including: 1) the Broken
Mirror Hypothesis (Ramachandran & Oberban, 2006), 2) The Oxytocin (Interoceptive) Deficit
(Quattrocki & Friston, 2014), and 3) the Alexithymia Hypothesis (Bird & Cook, 2013). We note
that these theories are not necessarily mutually exclusive and acknowledge the possibility that
subgroups of ASD may have deficits more in line with one theory than the other. See Figure 1
for a review of the similarities and differences in the models.
Broken Mirror Theory (BMT)
The broken mirror theory (BMT) poses that disruption of the MNS is a causal factor of
some of the social deficits in ASD. As discussed previously, the MNS is involved in
understanding and imitating other people’s actions (Rizzolatti & Singaglia, 2010). There is
evidence that individuals with ASD have deficits in imitation (see Edwards, 2014 for review).
These deficits may be caused by MNS dysfunction, and result in motor difficulties and disrupted
self-other mapping (Gallese et al., 2009; Williams et al., 2001). Beyond action imitation deficits,
some researchers theorize that the MNS is involved in simulation of other’s emotions and
mental states, and when impaired, can cause problems in mentalizing, empathic processing,
17
and language (Dapretto et al., 2006; Ramachandran & Oberman, 2006). Aberrant neural
functioning of the MNS in ASD has been demonstrated in fMRI, electroencephalography (EEG)
and EMG studies (Cattaneo et al., 2007; Dapretto et al., 2006; Oberman et al., 2005).
Consistent with the BMT, there is fMRI evidence of abnormal activation of the IFG in ASD
during observation of hand movements (Martineau et al., 2010), imitating and observing
emotional expressions (Dapretto et al., 2006), and observing and experiencing disgusting tastes
(Bastiaansen et al., 2011). Some have found that imitation and MNS functioning may be spared
in ASD (Fan et al., 2011; Hamilton et al., 2007; Press et al., 2010; Spengler et al., 2010);
however, aberrant findings may be a result of varying methodological approaches,
heterogeneity in the ASD group, and diagnostic criteria used for ASD group assignment
(Fakhoury, 2015), all of which need to be further explored.
The Oxytocin-Mediated Interoceptive Deficit
As reviewed above, it has been posited that affective experiences are linked to the
perception of changes in internal body states (Damasio, 1994; James, 1884; Schachter &
Singer, 1962) via the Homeostatic Afferent Pathway (Craig, 2014). Thus, awareness of changes
in the body are highly linked to emotion processing (Barrett et al., 2004). Interoceptive
sensibility, one’s aptitude to consciously perceive internal sensory experiences, has also been
linked to empathic ability in TD individuals (Fukushima et al., 2011; Grynberg & Pollatos, 2015).
Individuals with ASD may have alterations in internal sensory processing (for review DuBois et
al., 2016), particularly in childhood and adolescence (Fallia et al., 2019; Nicholson et al., 2019).
The AI is cited as a common neural mechanism for both interoception (Critchley, et al., 2005),
and emotion processing (Berthoz et al., 2002; Frewen et al., 2008; Karlsson et al., 2008). A
meta-analysis of 39 studies concluded that the AI has consistently been shown to have reduced
activation in ASD in social vs. non-social processing, including facial expression observation (Di
Martino et al., 2009). Given the role of AI to generate feeling states from bodily representations,
18
previously reported abnormalities in the AI may underscore emotion processing deficits in ASD.
It was recently demonstrated in adults that posterior insula responses during a cardiac
interoception task were associated with self-reported autism social severity scores (Failla et al.,
2019). A difficulty in processing internal signals may reduce accuracy in emotional and social
processing in ASD, explaining research linking ASD to alexithymia and empathy deficits.
One potential mechanism for interoception disturbances seen in certain individuals with
ASD is oxytocin dysfunction (Quattrocki & Friston, 2014). Oxytocin contributes to regulation of
parasympathetic functions in the nervous system (Ropper & Samuels, 2009). Oxytocin is
involved in the transmission of interoceptive signals, including temperature (Uvnas-Moberg,
1997), thirst (James et al., 1995), appetite (Leng et al., 2008), pain (Rash et al., 2014), itch
(Yaksh et al., 2014), and nausea (Seng et al., 2013). In ASD, there is evidence of ~45%
reduction in plasma oxytocin (Alabdali et al., 2014; Modahl et al., 1998), higher baseline levels
of serum oxytocin (Jansen et al., 2006) and increases in unprocessed oxytocin peptides (Green
et al., 2001). In TD populations, oxytocin administration improves emotional face recognition
(Domes et al., 2007; Rimmele et al., 2009; Savaskan et al., 2008), and increases gaze directed
at the eyes (Guastella et al., 2007). In ASD, oxytocin administration improves emotional face
recognition (Domes et al., 2014), social behavior (Andari et al., 2010; Hollander et al., 2007),
and neural activity in face perception networks (Domes et al., 2014). The oxytocin hypothesis
may be compatible with accounts of MNS dysfunction or co-occurring alexithymia. A previous
study reveals that oxytocin administration increases activation in mothers’ insula and IFG in
response to infant crying (Riem et al., 2011); further illustrating its connection to theories of
affect representation and embodied simulation through interoceptive integration. Brewer, Cook,
and Bird (2016) suggest that oxytocin dysfunction can account for interoceptive deficits and that
interoceptive failure leads to alexithymia, which may then cause primary social impairments. In
support of this hypothesis, Murphy, Catmur, and Bird (2018) found that those with high
19
alexithymic traits relied less on interoceptive cues for modulating respiratory output, muscular
exertion, and taste sensitivity, when controlling for co-morbid conditions like autism or anxiety.
This may be a promising way to explore underlying mechanisms of neural responses to
emotional stimuli in ASD.
Alexithymia Hypothesis
The Alexithymia hypothesis suggests that EE difficulties seen in ASD are accounted for
by co-morbid alexithymia rather than a primary feature of the ASD phenotype (Bird & Cook,
2013). This hypothesis posits that impaired EE is a result of dysfunction in the neural affective
representation system (Bird & Viding, 2014). In this theory, impaired CE in ASD is a result of
disruption to mentalizing neural mechanisms, and unrelated to alexithymia. Alexithymia is
common in individuals with high-functioning ASD (50%; Hill et al., 2004; Kinnaird et al., 2019),
while the incidence of alexithymia in the typical population is estimated between 5-10% (Berthoz
et al., 2011; Kinnaird et al., 2019). In a sample of adults with ASD observing face videos, degree
of alexithymia scores predicted gaze fixation on the mouth instead of the eyes (Bird et al.,
2011). Co-occurring alexithymia has also been related to difficulty in recognizing emotions of
faces in ASD (Cook et al., 2013; Milosavljevic et al., 2016) and in lower prosocial interactions
(Gerber et al., 2019).
Imaging studies suggest a relationship between alexithymia and emotion processing in
ASD at the neural level. Activity of the AI during emotional picture rating was negatively related
to alexithymia scores in ASD (Silani et al., 2008). Bird and colleagues (2010) investigated
alexithymia’s role in ASD through an empathy for pain task. Participants lay in the scanner with
a partner in a chair next to them, and each received pain stimulation in a ‘self pain’ or ‘other
pain’ condition. For each stimulus, an arrow was projected on a large screen indicating which
person would receive the painful stimulation next. After each electrical pain stimulus,
participants rated the level of pleasantness. In this study, the presence of alexithymia accounted
20
for both individual differences in trait empathy and differences in AI activation between ASD and
control groups during others’ pain (Bird et al., 2010). Additionally, the degree of insula activity
during the empathy for pain task described above was predictive of degree of alexithymia
across all participants. In another study, alexithymia scores were positively associated with AI
activation when watching another person experience physical pain, but negatively associated
with AI activation when watching another person experience emotional pain (Li et al., 2018).
Authors interpreted this to mean that higher levels of alexithymia caused inappropriate
hyperactivation of AI responses to physical pain, and under-responsivity of AI for affective pain
(Li et al., 2018). Increased severity of both autism and alexithymia traits combined was
correlated with increased neural activation (AI, mid-cingulate) during perceived physical pain,
whereas reduced responses to affective pain were correlated only to alexithymia severity (Li et
al., 2018). However, this investigation did not control for anxiety, which is thought to be both
associated with increased responses of the AI to physical pain paradigms (Alvarez et al., 2015)
and highly co-morbid in both ASD (van Steensel et al., 2011) and alexithymia (Zeitlin & McNally,
1993). In fact, in a recent fMRI study of pain and disgust observation, group differences in brain
activation between TD and ASD participants were lost when either alexithymia or anxiety were
controlled for (Lassalle et al., 2019), indicating that anxiety may be an important factor to
consider in these investigations.
Evidence implicates a relationship between alexithymia and EE brain regions, however
further investigation of alexithymia’s influence on empathy subtypes is needed in ASD. It is
important to note that there is some disagreement about the independence of alexithymia from
core symptoms of ASD, with some authors suggesting that they are overlapping and share too
much variance to be distinct (Fitzgerald & Bellgrove, 2006). Other groups have argued that
these critiques have been addressed by the fact that no study has found that all participants
with ASD should be categorized as alexithymic (Hill & Berthoz, 2006), that alexithymia is not
21
necessary for an ASD diagnosis, and that individuals can show severe alexithymia symptoms
without demonstrating other ASD symptoms (Bird & Cook, 2013). However, the
counterargument is that children with ASD who seem to not have alexithymia may only appear
this way due to use of scripted language learned in therapeutic contexts (Cascio, personal
communication). Further research is needed to better understand the degree of independence
between alexithymia and ASD.
22
Figure 1
Theories of Emotional Empathy Disruption in Autism Spectrum Disorders
Note. Each circle describes the main hypothesized deficits in ASD that cause impairments in
emotional empathy for the Oxytocin-Mediated Deficit, the Alexithymia Hypothesis, and the
Broken Mirror Theory. Proposed neural mechanisms are given for each theory, and common
proposed mechanisms are represented in the areas of overlap between the theories. SON =
supraoptic nucleus, SI = primary somatosensory cortex, SII = secondary somatosensory cortex,
IPL = inferior parietal lobule, IFG = inferior frontal gyrus, ACC = anterior cingulate cortex.
23
Interoception, Alexithymia, and Empathy in ASD
Although different theories posit primary deficits of either simulation in the MNS
(Ramachandran & Oberban, 2006), interoceptive processing (Quattrocki & Friston, 2014), or
alexithymia (Shah et al., 2016), there is overlap in all the models of a theorized breakdown of
the embodied process of emotion simulation in ASD (Bird & Cook, 2013; Bird & Viding, 2014;
Dapretto et al., 2006; Gallese et al., 2009; Ramachandran & Oberman, 2006; Williams et al.,
2001). Based on the embodied mechanisms of emotion processing, evidence may be stronger
for the argument that alexithymia is a reflection of a general impairment in interoception (Brewer
et al., 2016) or irregular changes in the autonomic nervous system (Taylor et al., 1997).
Alexithymia in the absence of an ASD diagnosis is associated with difficulty regulating physical
and emotional arousal (Cox et al., 1995, Laloyaux et al., 2015) and impaired interoceptive ability
(Herbert et al., 2011). Damage to the AI is associated with both interoceptive impairment and
alexithymia (Ibañez et al., 2010).
Alexithymia and interoception deficits in ASD have been associated with difficulties in
empathic social processing tasks including: recognition of emotional expression in faces (Cook
et al., 2013), recognition of vocal affect (Heaton et al., 2012), skin conductance responses to
emotional pictures (Gaigg et al., 2018), moral decision-making (Brewer et al., 2015), and eye
fixation in social scenes (Bird et al., 2011). In the brain, alexithymia in ASD is related to
structural differences in affective networks from the insula to posterior regions (Bernhardt et al.,
2014) and abnormal functional responses to emotional stimuli in the AI (Bird et al., 2010; Silani
et al., 2008). The high incidence of co-occurrence in interoceptive problems and alexithymia in
ASD, along with overlapping neural substrates, suggests a relationship between the two
features. Some studies show that alexithymia can explain interoceptive difficulties in ASD, and
that interoceptive deficits should be considered a symptom of alexithymia, rather than ASD per
se (Shah et al., 2016, 2017). However, recent work has not provided evidence for interoceptive
24
accuracy impairments in adults with ASD, whether or not co-occurring alexithymia was
considered (Nicholson et al., 2018).
There are a very small number of studies that have examined the effect of interoceptive
deficits and alexithymia in ASD on specific subtypes of empathy functioning. In a sample of
adults with ASD, alexithymia predicted emotion understanding outcomes, and when controlling
for alexithymia, ASD predicted performance on tests of theory of mind (ToM) but not emotion
understanding (Oakley et al., 2016). Bernhardt and colleagues (2014) found that in self-report
measures, individuals with ASD had decreased CE scores when compared to controls, and no
deficits in EE scores regardless of alexithymia, despite structural neural differences in EE
networks for alexithymic participants. In contrast, when a study compared two groups of ASD
participants, one with and one without alexithymia, those with alexithymia demonstrated lower
EE and CE than ASD participants without alexithymia. In the same study, ASD participants
showed reduced IS and Iac, and alexithymia scores mediated the relationship between Iac and
empathy (Mul et al., 2018). Given these discrepant findings, further studies are needed to
better understand the relationships between empathy subtypes, alexithymia, and interoception
in ASD.
Conclusions, Limitations, & Future Directions
Additional research is needed to better define the nature of EE in ASD and its
relationship to neural networks involved in social processing. Some theories of empathy
disruption in ASD argue that deficits in EE are due to an impairment in neural systems important
for simulation (the mirror system and other shared networks). One variable that may account for
the discrepant EE impairment findings is the presence of interoceptive processing deficits.
Individuals with ASD show alterations in interoceptive processing (for review DuBois et al.,
2016). The role of AI to integrate emotional states with interoceptive sensory information may
underscore core symptoms and emotion processing deficits in ASD. Difficulty in processing
25
internal signals may reduce accuracy in emotion processing in individuals with ASD, explaining
research linking ASD to alexithymia (Bird et al., 2010).
Although these are promising findings, there are some limitations to consider. First, there
are issues with measuring these constructs in ASD. Some of the measures used to capture
interoception, alexithymia, and empathy ability, are self-report measures. This can be
particularly challenging in a population known to struggle with metacognitive skills and self-
referential insight. Particularly, alexithymia can only be measured in those individuals who have
verbal ability, which means that the prevalence of alexithymia can only be known in individuals
with ASD who are verbal. Secondly, not all individuals with ASD would necessarily be able to
tolerate an fMRI scanning environment, as it can be claustrophobic, and very loud for any
individual who experiences sensory sensitivity. It should be noted that all types of imaging and
neurophysiology methods should be considered for use in future research studies in order to
represent a large portion of the population of individuals with ASD. Lastly, empathy is a
multifaceted skill which requires a dynamic interaction of cognitive and emotional functions.
Although CE and EE have been separated to clarify specific processes’ contributions to
empathy, it is important to acknowledge the bidirectional communication that occurs between
these networks in empathy functioning. Deficits in CE seem to be consistent in the ASD
literature, and deficits in EE seem to be more variable, therefore it is important that future
research consider the holistic interactions of behavioral and neural functions in both the CE and
EE systems, and how they impact symptomatology in ASD.
Relevant control and mediating variables often are missing in many neuroimaging
studies in ASD, which makes it difficult to understand the complexities of the neurobiological
relationships with symptomatology. Further neural research is needed to identify mechanisms of
EE disruption in ASD. Importantly, including interoception and alexithymia measures in social
and affective neuroscience studies may be necessary in order to understand the mechanisms
26
and role these symptoms play in social and emotional functioning in ASD. Having a clear
understanding of neurobiological relationships with interoceptive and emotional symptomatology
may implicate future therapeutic targets in a clinical setting for individuals with ASD who
experience these comorbidities.
27
Chapter 3. Relationships Between Alexithymia, Interoception, and Components of
Emotional Empathy in Autism Spectrum Disorder
Abstract
Some studies suggest that individuals with Autism Spectrum Disorder (ASD) have reduced
emotional empathy (e.g. Bos & Stokes, 2018; Sucksmith et al., 2013)
while others have not
found evidence for this deficit (e.g. Bellebaum et al., 2014; Deschamps et al., 2014). The
presence of co-occurring alexithymic tendencies in ASD (Bird, et al., 2010 ) and alterations in
interoceptive processing (Fukushima, Terasawa, & Umeda, 2011; Grynberg & Pollatos, 2015)
are associated with reductions in empathic ability. Self-report and interview data were collected
to explore relationships between Interoceptive sensibility, alexithymia, and emotional empathy in
35 high-functioning youth with ASD and 40 typically developing (TD) controls (ages 8-17). Data
were analyzed using two-tailed independent sample t-tests, Pearson partial correlation, and
stepwise multiple linear regression. Independent sample t-tests indicated that the ASD sample
has increased alexithymia and physiological hyperarousal, but no significant differences on any
measures of interoception or emotional empathy skill in comparison to TD participants.
Alexithymia severity was correlated with higher personal distress in both TD and ASD groups,
and lower empathic concern in the ASD group. In the TD group higher interoception scores
predicted lower personal distress scores. In the ASD group, interview responses describing
embodied experiences of emotion show that higher reports of bodily sensation during negatively
valenced emotional experience (disgust, sadness, anger, fear) is related to lower personal
distress, and lower alexithymia. These results suggest that interoceptive sensibility during
emotion experience may be related to alexithymia and emotional empathy outcomes in ASD,
even if general interoceptive sensibility is not impaired overall. Implications regarding
terminology used to characterize empathy ability in ASD are discussed.
Keywords: alexithymia, autism, interoception, empathy, personal distress
28
Introduction
Autism spectrum disorder (ASD) is a condition defined by difficulties in social
communication and social interaction, and restricted or repetitive behaviors and interests
(American Psychiatric Association, 2013). Individuals with ASD also may have impairments in
intention understanding and empathic processing, which are important skills for adaptive social
emotional functioning. Although all components of empathy are involved in typical social
interactions (Zaki & Ochsner, 2012), empathy can be divided into two dimensions 1) cognitive
empathy and 2) emotional empathy. Cognitive empathy is a top-down ability to imagine how
another person is thinking or feeling (de Waal & Preston, 2017), and emotional empathy
describes the ability to share and experience the feelings of others (Davis et al., 1994) through
embodied simulation (Gallese & Sinigaglia, 2011).
While there is considerable evidence that individuals with ASD have reduced situation
understanding, mentalizing, and cognitive empathy ability (e.g. Castelli et al., 2002; Frith &
Happé, 2005; Jolliffe & Baron-Cohen, 1997; Zalla et al., 2009), there is no consensus on how
emotional empathy is impacted. Some studies suggest that individuals with ASD have reduced
emotional empathy ability (Bos & Stokes, 2018; Kasari et al., 1990; Lombardo et al., 2007;
Mathersul et al., 2013; Mazza et al., 2014; Minio-Paluello et al., 2009; Schulte-Rüther et al.,
2011; Shamay-Tsoory et al., 2002; Sigman et al., 1992; Sucksmith et al.; 2013; Trimmer et al.,
2017; Yirmiya et al., 1992) while other studies have found intact emotional empathy functioning
in ASD (Bellebaum et al., 2014; Deschamps et al., 2014; Dziobek et al., 2008; Hadjikhani et al.,
2014; Markram et al., 2007; Smith, 2009; Rogers et al., 2007; Rueda et al., 2015; Schwenck et
al., 2012). Inconsistent findings regarding emotional empathy in individuals with ASD may be
understood from the perspective of two prominent theories: 1) the alexithymia hypothesis (Bird,
et al., 2010), or 2) the interoception deficit hypothesis (Fukushima et al., 2011; Grynberg &
Pollatos, 2015).
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Interoception, Alexithymia, and Emotional Empathy
Interoception is the sense of the physiological condition of all internal tissues in the body
(Craig, 2003). Embodied simulation theories of emotion processing suppose that changes in
bodily states produce feeling states in the brain, which can modulate empathic behavior (i.e.,
James, 1884; Schachter & Singer, 1962; Damasio, 1994). Interoceptive ability is not
fundamentally necessary for the representation of all mental states (or Theory of Mind; ToM),
but has been shown to contribute to representation of affective mental states where emotional
information must be used (Shah et al., 2017). For these reasons, interoceptive ability is more
frequently hypothesized to be related to emotional empathy, rather than cognitive empathy
(ref). Thus, the presence of interoception deficits is one variable that may account for discrepant
emotional empathy findings in ASD. Individuals with ASD show alterations in interoceptive
sensory processing (see DuBois et al., 2016 for review), but not all studies have found evidence
for interoceptive differences in ASD (Nicholson et al., 2018). Other studies suggest that
emotional empathy reductions observed in ASD can be accounted for by comorbid alexithymia
(Bird & Cook, 2013). Alexithymia – or trouble recognizing, describing, and distinguishing one’s
own emotions – is also linked to difficulties in regulation of physical and emotional arousal (Cox
et al. 1995) and interoceptive ability (Herbert et al., 2011), and is highly comorbid in ASD (50%;
Hill et al., 2004). Co-occurring alexithymia in ASD has accounted for difficulties in emotion
processing tasks including: neural responses to empathy paradigms (Bird et al., 2010), skin
conductance responses to emotional pictures (Gaigg et al., 2018), identification of emotions in
faces (Cook et al., 2013; Milosavljevic et al., 2016), understanding of vocal affect (Heaton et al.,
2012), moral decision-making (Brewer et al., 2015), and eye gaze fixation patterns of faces (Bird
et al., 2011). Two studies have specifically looked at the relationship between alexithymia and
emotional empathy and cognitive empathy functioning, separately. In a sample of adults with
ASD, alexithymia scores predicted an emotional empathy outcome, but not scores on a ToM
task (Oakley et al., 2016). However, another study using self-report measures in an ASD
30
sample observed that those with and without alexithymia had no difference in emotional
empathy scores compared with controls, despite structural neural differences in emotional
empathy networks for alexithymic participants irrespective of ASD (Bernhardt et al., 2014).
The co-occurrence of interoception abnormalities and alexithymia in ASD, the
interdependence of bodily information and interpretation of feeling states, and shared neural
substrates for these features (anterior insula), suggest a potentially important relationship to
investigate. Some argue that alexithymia is the result of a general impairment in interoception
(Brewer et al., 2016), while others support that interoceptive difficulties are a symptom of
alexithymia, rather than of ASD (Shah et al., 2016, 2017). To my knowledge, only one study to
date has examined both interoception and alexithymia with the specific domain of emotional
empathy in ASD. Mul and colleagues (2018), directly compared two groups of adults with ASD,
with and without alexithymia; those with alexithymia demonstrated lower emotional empathy
than ASD participants without alexithymia, although importantly anxiety symptoms were not
measured. ASD participants showed reduced interoceptive sensibility (self-report) and accuracy
(heartbeat tracking) than controls, and alexithymia scores mediated the relationship between
interoceptive sensibility and emotional empathy (Mul et al., 2018).
Given the discrepant findings of alexithymia and interoception influences on emotional
empathy in ASD, and the dearth of research dedicated to this question, further studies are
needed to better understand these relationships. The purpose of the present study is to: 1)
compare groups on levels of interoceptive sensibility, alexithymia, and empathy; and 2) examine
which behavioral variables most strongly influence emotional empathy ability, with a particular
focus on interoception and alexithymia; in TD and ASD youth. Initial hypotheses were that 1)
compared to TD controls, participants with ASD would have lower interoceptive sensibility,
higher alexithymia, and lower cognitive and emotional empathy ability, 2) that interoceptive
sensibility would be positively associated with empathy ability, and alexithymia severity would
31
be negatively associated with empathy ability and 3) that alexithymia may account for
differences in emotional empathy ability seen in the ASD group.
Methods
Participants
Study participants included youth ages 8 to 17 years (mean age 11.90 ± 2.16) who were
typically developing (TD; n = 40, 12 female, mean age = 11.86 ± 2.17) or had a diagnosis of
ASD (n = 35, 7 female, mean age = 11.95 ± 2.19). A measure of interoceptive sensibility (26
TD, 20 ASD) and an interview measure (25 TD, 24 ASD) were only collected in a subset of
individuals from the total group because these measures were added after the study began.
Participants were recruited through advertisements and contacts in community centers, social
media sites, parent group website postings, healthcare clinics, and schools. Inclusion criteria for
all groups included: IQ > 80 on at least one composite score (Verbal Comprehension Index,
Perceptual Reasoning Index, Full Scale IQ–2, or the Full Scale IQ–4) as assessed by a trained
staff member on the Wechsler Abbreviated Scale of Intelligence, 2
nd
edition (WASI-II; Wechsler,
2011); and English fluency of child and parent. As this was part of a larger imaging study, all
participants were additionally screened for MRI compatibility, had no history of loss of
consciousness greater than five minutes, were right-handed, and were born after 36 weeks of
gestation. Each family was informed about study procedures in accordance with the protocol
approved by the University of Southern California’s Institutional Review Board. Participants and
caregivers then provided their written child assent and parental consent.
TD participants were excluded if they had any psychological or neurological disorder,
including attention deficit hyperactivity disorder (ADHD) and generalized anxiety disorder. They
were also excluded if they had a first degree relative with an ASD diagnosis, or scored above a
T-score of 60 on the Social Responsiveness Scale, Second Edition (SRS-2; Constantino &
Gruber, 2012) indicating higher ASD symptom severity including reduced social awareness,
reciprocal social interaction, or presence of restricted or repetitive behaviors. Eligible ASD
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participants had a previous diagnosis through clinical diagnostic interview or diagnostic
assessment, and met criteria on the Autism Diagnostic Observation Schedule, Second Edition
(ADOS-2; Lord et al., 2000), the Autism Diagnostic Interview-Revised (ADI-R; Lord et al., 1994),
or both, which were administered at the time of the study. Individuals in the ASD group were
excluded if they had another diagnosis of neurological or psychological disorder with the
exception of Developmental Coordination Disorder, ADHD, or generalized anxiety disorder, due
to their high comorbidity with ASD (Mazzone et al., 2012). Twelve ASD participants had
prescriptions for psychotropic medication at the time of the study (ADHD and anxiety), and no
TD participants reported any prescription medication use.
Assessments
Anxiety and ADHD Measures
The 20-item version of the parent-report Childhood Adolescent Symptom Inventory
(CASI-Anx; Sprafkin et al., 2002), was used to assess symptoms of the six DSM-IV defined
anxiety disorders. The 10-item Conners third edition (Conners-3AI; Conners, 2008), a parent
questionnaire for children ages 6-18 years, was used to characterize levels of ADHD symptoms.
Both the CASI-Anx and the Conners-3AI are rated on a four-point Likert scale from “never” to
“very often” with higher scores indicating greater symptoms.
Empathy Measures
Empathy ability was assessed using the self-report Interpersonal Reactivity Index (IRI;
Davis, 1983). Questions are rated on a five-point Likert scale from “does not describe me well”
to “describes me very well”. The IRI is a 28-item self-report measure consisting of four 7-item
subscales: 1) Perspective Taking (IRI PT) a tendency to adopt and understand the viewpoint of
others, 2) Empathic Concern (IRI EC) feelings of sympathy and concern for others, 3) Personal
Distress (IRI PD) feelings of personal unease in tense interpersonal settings, and 4) Fantasy
(IRI FS) a tendency to transpose oneself into the feelings of fictional characters. The IRI PT and
IRI FS subscale index the cognitive aspects of empathy, and the IRI EC and IRI PD domains
33
measure affective aspects of empathy (Baron-Cohen & Wheelwright, 2004). All IRI subscales
are reported in initial group comparisons, and subsequent analyses focus on the two IRI
aspects of emotional empathy (IRI EC and IRI PD).
Trained research staff administered two subtests, which are part of the Developmental
Neuropsychological Assessment (NEPSY-II; Korkman et al., 2007) Social Perception Domain.
Subtests used captured (1) Theory of Mind (ToM) that assesses visual and verbal ability to
comprehend the perspectives, experiences, and beliefs of others, and (2) Affect Recognition
(AR) that assesses the ability to identify and discriminate between facial expressions. The
NEPSY-II ToM subscale was used as an additional measure of cognitive empathy ability
beyond the self-report IRI.
Alexithymia Measure
Alexithymia was measured using the 20-item self-report Alexithymia Questionnaire for
Children (AQC; Rieffe et al., 2006). Items were rated on a three-point Likert scale from “not true”
to “often true”, and consisted of questions such as, “I am often confused about the way I am
feeling inside.” Two subscales are used to assess alexithymia: difficulty identifying feelings
(AQC ID), and difficulty describing and communicating feelings (AQC COMM). The third
subscale, externally oriented thinking (AQC EOT) was not used in this study due to an
unacceptably low Cronbach’s alpha in the original psychometric paper, and because alexithymia
can be reliably assessed in youth without the eight items rating the AQC EOT dimension (Loas
et al., 2017). Considering this, the 2 factor total was calculated instead and used as the AQC
total in this study.
Embodiment Measures: Interoception, Physiological Hyperarousal, Sensation of Emotion
For interoceptive sensibility, the 12-item self-report Body Perception Questionnaire-Body
Awareness Very Short Form (BPQ-VSF; Porges, 1993, Cabrera et al., 2018) was used to
assess the subjective awareness of the function and reactivity of target organs and structures
that are innervated by the autonomic nervous system. The BPQ-VSF only probes aspects of the
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body awareness subscale of the original BPQ, with items such as, “During most situations, I am
aware of how fast I am breathing.” Questions are rated on a five-point Likert scale from “never”
to “always.”
Another measure used to capture bodily experience, the Physiological Hyperarousal
Scale for Children (PH-C; Laurent et al., 2004) is a brief self-report measure of frequency of
physical experiences associated with physiological hyperarousal. This 18-item scale asks
questions such as, “How often have you felt or experienced being unable to catch your breath in
the last two weeks?”. Questions are rated on a five-point Likert scale from “never” to “all of the
time.” It is important to note that while the BPQ-VSF and the PH-C capture similar bodily
information, the BPQ-VSF is more focused on general awareness of bodily sensations i.e. “how
fast my heart is beating,” and the PH-C is probing the frequency of physiological experiences
associated with hyperarousal i.e. “heart-pounding.”
As an additional measure of interoceptive and emotional awareness, a semi-structured
interview using the emBODY tool (Nummenmaa et al., 2014) was administered. This interview
assesses participants’ ability to identify somatotopic patterns of physical feeling associated with
each of the six basic emotions (i.e., anger, fear, disgust, happiness, sadness, and surprise).
Participants were asked to draw on a picture of human bodies where they felt increasing or
decreasing activation while experiencing different emotions. After the drawing task was
completed, a qualitative interview was carried out by a trained staff member probing what types
of situations elicit the given emotion, where participants feel that emotion in their body, and what
type of sensation occurs within physical localizations. The interview consists of questions such
as: “What kinds of things make you feel afraid?; Can you tell me how you feel when you’re
afraid?; Can you show me where in your body you feel fear, and describe that sensation?”
Interviews were transcribed and coded by two raters, and a third tie-break rater for use of
physical descriptions of felt emotion. All instances describing the body were coded for use of
embodied language mentioning an action (e.g., “I know I am angry in my hand because I want
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to hit someone with it”), a physical sensation (e.g., “I know I am angry because I feel my head
getting hot and chest-pounding”), or a literal physiological description (e.g., “I know I am angry
because my brain sends a signal to my heart to pump blood”). Each embodied language code
was calculated for 1) individual emotions, 2) collapsed across all negatively valenced emotions
(anger, fear, disgust, sadness), and 3) collapsed across all emotions.
Analysis
Group differences in behavioral and interview data variables between TD and ASD
groups were assessed using two-tailed independent samples t-tests, or chi-square tests of
independence. Pairwise Pearson partial correlation coefficients were computed to assess
relationships between interoception, physiological hyperarousal, alexithymia, and emotional
empathy in all participants, the ASD group alone, and the TD group alone, while controlling for
age, gender, and VCI. In order to assess previously documented influence of alexithymia in
relationships between interoception and emotional empathy variables in ASD (Mul et al., 2018),
additional analyses were run, which included AQC total as a control variable in Pearson partial
correlations. Due to the high incidence of comorbidity between anxiety and ASD (Simonoff et
al., 2008), the association between anxiety and increased IS (Ehlers & Breuer, 1992), and the
lack of relationship between anxiety and performance accuracy on a heartbeat tracking task
(Garfinkel et al., 2016), anxiety symptoms also were controlled for in secondary analyses in the
ASD group. Scatter plots were visually analyzed for the presence of outliers to confirm validity of
correlations.
Stepwise multiple linear regression analysis was used to estimate model fit of predictors
on emotional empathy scores (both IRI EC and IRI PD). Data was centered and the following
behavioral variables were entered into a stepwise multiple linear regression model: gender,
WASI-II: VCI, age, CASI-Anx, AQC ID, AQC COMM, PH-C, Conners-3AI, SRS-2, NEPSY-II:
AR, BPQ-VSF. Due to the reduced sample size of participants with available BPQ-VSF scores,
if that variable was not a significant predictor in the initial model, the regression was run a
36
second time in the larger sample of participants with the BPQ-VSF removed as a model
predictor. The criterion for entering a variable into the model was set at F = 0.07, with a removal
probability of F = 0.1. Homoscedasticity and normality of residuals were assessed, and the
variance inflation (VIF) and tolerance statistics were used to assess independent variables for
multicollinearity.
Results
Group Differences – ASD & TD
As expected, significant group differences were observed on the WASI-II: VCI, SRS-2,
Conners 3AI, and CASI-Anx (p’s < .05; Table 1). Differences in empathy measures were only
present in the cognitive empathy domain. For the IRI, the cognitive empathy subscale of IRI PT
approached significance (p = .052), and the NEPSY-II ToM total was significantly different
between groups (p = .006; Table 1). For additional variables of interest, significant differences
were observed (Table 1) in the NEPSY-II AR (p = .035), AQC total (p = .045), and in PH-C total
(p = .012). In summary, the ASD group had reduced VCI, facial affect recognition, and CE/ToM;
increased social impairment, alexithymia severity, ADHD symptomatology, anxiety, and
physiological hyperarousal; and intact interoception and emotional empathy skill. Due to the fact
that cognitive empathy differences in ASD are well established in the literature, and the results
in this paper echo those findings, moving forward, all analysis was performed solely on
relationships in the emotional empathy domain, as there is not consensus on emotional
empathy ability in ASD and that is the intended scope of this study.
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Table 1
Descriptive Statistics and Group Comparisons of Behavioral Data
Variable TD ASD t p
M SD M SD
Age 11.86 2.17 11.95 2.17 -0.18 0.859
WASI-II: FSIQ-4 114.85 12.9 109.29 17.76 1.57 0.122
WASI-II: VCI 114.95 12.29 105.8 19.12 2.43 .018*
SRS-2 46.63 5.00 75.83 8.89 -17.79 < .001*
Conners-3AI 47.65 9.01 84.91 8.45 -18.22 < .001*
AQC ID 0.49 0.39 0.62 0.47 -1.43 0.156
AQC COMM 0.64 0.49 0.85 0.49 -1.9 0.061
AQC total 6.33 4.26 8.44 4.64 -2.04 .045*
IRI PT 15.4 5.44 12.86 5.7 1.98 0.052
IRI FD 17.43 5.54 16.57 5.74 0.655 0.515
IRI EC 18.33 5.04 17.23 5.71 0.883 0.38
IRI PD 12.55 5.08 13.94 5.25 -1.17 0.247
IRI Total 63.95 13.78 60.6 14.57 1.02 0.31
NEPSY-II AR 11.15 2.48 9.88 2.51 2.15 .035*
NEPSY-II ToM Total 25.08 1.65 23.17 3.06 2.98 .005*
CASI-Anx 24.9 4.91 36.97 8.59 -7.08 < .001*
PH-C 25.68 5.88 30.81 9.5 -2.62 .012*
BPQ-VSF 27.46 13.11 26.95 11.68 0.137 0.891
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Note. Group differences between TD and ASD groups WASI-II: FSIQ-4 = Wechsler Abbreviated
Scale of Intelligence, Second Edition: Full Scale IQ; WASI-II: VCI = Wechsler Abbreviated Scale
of Intelligence, Second Edition: Verbal Comprehension Index; WASI-II: PRI = Wechsler
Abbreviated Scale of Intelligence, Second Edition: Perceptual Reasoning Index; SRS-2 = Social
Responsiveness Scale, Second Edition; Conners-3AI = Conners 3 ADHD Index; AQC ID =
Alexithymia Questionnaire for Children Identifying Emotions Subscale; AQC COMM =
Alexithymia Questionnaire for Children Communicating Emotions Subscale ; AQC total =
Alexithymia Questionnaire for Children 2 factor total; IRI PT = Interpersonal Reactivity Index
Perspective Taking Subscale; IRI FS = Interpersonal Reactivity Index Fantasy Subscale; IRI EC
= Interpersonal Reactivity Index Empathic Concern Subscale; IRI PD = Interpersonal Reactivity
Index Personal Distress Subscale; IRI Total = Interpersonal Reactivity Index Total Score;
NEPSY-II AR = The Developmental Neuropsychological Assessment Affect Recognition
Subscale; NEPSY-II ToM Total = The Developmental Neuropsychological Assessment Theory
of Mind Subscale; CASI-Anx = Childhood Adolescent Symptom Inventory Total; PH-C =
Physiological Hyperarousal Scale for Children Total; BPQ-VSF = Body Perception
Questionnaire-Body Awareness Very Short Form Total. *p < .05
Partial Correlation: Self-Report Data
Across all participants, alexithymia was related to empathy in both emotional empathy
domains, but not always in the hypothesized direction. When controlling for age, gender, and
VCI, across all participants, alexithymia scores were positively correlated with an emotional
empathy subscale, IRI PD (AQC ID: r = .365, p = .002; AQC COMM: r = .352, p = .002; AQC
total: r = .444, p < .001). This same positive relationship with IRI PD was seen in the TD group
with the AQC total (r = .355, p = .031; Table 2) and in the ASD group, with all the AQC variables
(AQC ID: r = .457, p = .016; AQC COMM: r = .403, p = .022; AQC total: r = .519, p = .003; Table
2). These relationships suggest that greater alexithymia severity was associated with higher
emotional empathy in the personal distress to others pain domain. However, an opposite pattern
emerges in another measure of emotional empathy, the IRI EC subscale. Across all
participants, AQC COMM was negatively correlated with IRI EC (r = -.351, p = .003). This
relationship was observed again in the ASD group alone (r = -.471, p = .007; Table 2), but not in
the TD group alone. The presence of alexithymia was associated with lower empathic concern,
but higher personal distress to others' pain (Figure 1).
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Table 2
Partial Correlations With Emotional Empathy Scores
Variable Empathic Concern Personal Distress
TD ASD TD ASD
r p r p r p r p
SRS-2 .206 .222 -.017 .925 -.047 .784 .075 .684
Conners-3AI .064 .706 .116 .535 .126 .457 -.267 .146
AQC ID -.036 .833 -.102 .577 .233 .165 .405 .022*
AQC COMM -.251 .133 -.471 .007* .320 .054 .403 .022*
AQC total -.158 .349 -.203 .273 .355 .021* .519 .003*
NEPSY-II AR .079 .648 .069 .721 .190 .266 .001 .996
CASI-Anx .221 .188 .183 .343 .278 .096 .293 .124
PH-C .133 .466 .162 .411 .373 .027* .041 .836
BPQ-VSF -.009 .966 .024 .927 -.164 .455 -.295 .250
Note. Partial correlations with Interpersonal Reactivity Index emotional empathy subscales
when controlling for age, sex, and VCI. SRS-2 = Social Responsiveness Scale, Second Edition;
Conners-3AI = Conners 3 ADHD Index; AQC ID = Alexithymia Questionnaire for Children
Identifying Emotions Subscale; AQC COMM = Alexithymia Questionnaire for Children
Communicating Emotions Subscale; AQC total = Alexithymia Questionnaire for Children two
factor total; NEPSY-II AR = The Developmental Neuropsychological Assessment Affect
Recognition Subscale; CASI-Anx = Childhood Adolescent Symptom Inventory Total; PH-C =
Physiological Hyperarousal Scale for Children Total; BPQ-VSF = Body Perception
Questionnaire-Body Awareness Very Short Form Total. *p < .05
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Figure 1
Emotional Empathy Subscales: Scatter Plots
A. ASD: IRI PD and AQC total
B. TD: IRI PD and AQC total
C. ASD: IRI PD and BSE D. ASD: IRI EC & AQC COMM
Note. Scatter plots for significant partial correlations with emotional empathy subscales
controlling for age, sex, and VCI A. IRI PD with AQC total in ASD: r = .519, p = .003; B. IRI PD
with AQC total in TD: r = .355, p = .031; C. IRI PD with BSE in ASD: r = -.464. p = .026; D. IRI
EC with AQC Comm in ASD: r = -.471, p = .007.
There were no significant relationships with the BPQ-VSF with AQC or IRI emotional
empathy variables across all participants or in either individual group (p’s > .05). When AQC
total was controlled for in the partial correlation, there were no significant relationships between
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interoceptive sensibility and emotional empathy. Across all participants, PH-C was positively
correlated with AQC ID (r = .286, p = .020), and AQC total (r = .289, p = .019), suggesting that
higher physiological arousal was related to more severe alexithymia across all. In the TD group,
the AQC total was also positively correlated with physiological hyperarousal (PH-C; r = .379, p =
.027; Table 2). In ASD, there were no significant relationships with PH-C and emotional
empathy variables, and this remained true if the AQC total was controlled for in the partial
correlation (p’s > .05).
A particular variable of interest that may influence these relationships is the presence of
anxiety. There were no significant correlations between CASI-Anx scores and empathy,
alexithymia, or interoception in the TD group. However, in the ASD group CASI-Anx was
positively correlated with main variables of interest including AQC ID (r = .412, p = .026), and
the AQC total (r = .411, p = .030). To further understand the relationships between empathy,
alexithymia, interoception, and physiological hyperarousal in the ASD group, partial correlations
were run with the addition of the CASI-Anx as a control variable. After controlling for anxiety, the
relationships between alexithymia and emotional empathy variables remained significant. The
AQC COMM subscale was still negatively correlated with IRI EC (r = -.554, p = .002), and the
AQC total was still positively correlated with IRI PD (r = .457, p = .016). These results suggest
that anxiety in the ASD group does not account for the differing relationships observed between
alexithymia and the two emotional empathy subscales. Additionally, no significant relationships
between BPQ-VSF and emotional empathy outcomes in ASD were observed if anxiety or
alexithymia were controlled for. However, if both anxiety and alexithymia are controlled for
together in the ASD sample, a significantly negative relationship was observed between IRI PD
and the BPQ-VSF (r = -.589, p = .034). Results suggest that only when controlling for both
anxiety and alexithymia those with higher interoceptive sensibility experience lower personal
distress in the ASD sample. This is the only relationship we found between the BPQ-VSF and
any emotional empathy variables in ASD.
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Regression Analysis
Personal Distress Domain
A stepwise linear regression model was run in each group to predict emotional empathy
scores. For IRI PD, in the TD group, a significant regression equation was observed (F(2, 22) =
5.592, p = .001), with an R
2
of 0.337. TD participants predicted IRI PD score was equal to 0.492
- 0.138 (BPQ-VSF) + 0.479 (PH-C). In other words, in the TD group physiological hyperarousal
and interoceptive body awareness scores predicted 33% of the variance in empathy personal
distress scores, with higher physiological hyperarousal scores predicting higher personal
distress scores, and higher interoception scores predicting lower personal distress scores. For
the ASD group, the BPQ-VSF was not a significant predictor in the initial model, so the
regression was run in the complete sample with the BPQ-VSF removed as a predictor. A
significant regression equation for IRI PD was observed (F(1, 25) = 6.103, p = .021), with an R
2
of 0.196. ASD participants predicted IRI PD score was equal to 0.209 + 5.078 (AQC ID).
Results indicated that in the ASD group alexithymia identifying emotions severity scores
predicted 20% of the variance in empathy personal distress scores, with higher alexithymia
scores predicting higher personal distress scores.
Empathic Concern Domain
A stepwise linear regression model was run in each group to predict emotional empathy
scores. For both the TD and ASD group, the BPQ-VSF was not a significant predictor in the
initial model, so the regression was run in the complete sample with the BPQ-VSF removed as
a predictor. In the TD group, none of the included variables significantly predicted the variance
in empathic concern scores. For the ASD group, a significant regression equation for IRI EC
was observed (F(2, 24) = 7.857, p = .002), with an R
2
of 0.396. ASD participants predicted IRI
EC score was equal to 18.055 - 8.246 (AQC COMM) + .119 (WASI-II: VIQ). Therefore, in the
ASD group alexithymia communicating emotions severity scores, and VCI predicted 40% of the
variance in empathic concern scores, with higher alexithymia severity scores predicting lower
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empathic concern scores, and higher VCI scores predicting higher empathic concern scores.
emBODY Interview
As an additional measure of interoceptive and emotional awareness, a semi-structured
interview using the emBODY tool (Nummenmaa, Glerean, Hari, & Heitenan, 2014) was
administered. In a combined total code of all feeling states (anger, fear, disgust, happiness,
sadness, surprise), there were no significant differences in use of embodied language to
describe action, sensation, or physiological responses between TD and ASD (p’s > .05). This
suggests that the ASD group was using embodied language at the same rate as our TD sample
to describe their experiences of emotion overall. No individual emotion categories, or combined
emotion categories by valence had group differences between TD and ASD.
There were no correlations found in the TD group between interview codes and
behavioral variables of alexithymia, emotional empathy, interoceptive sensibility, or
physiological arousal. In the ASD group, use of sensation words to describe emotional
experience was significantly negatively correlated with IRI PD (r = -.464. p = .026; Figure 1).
When the emotions were grouped into a negative valence category (disgust, sadness, anger,
and fear) a negative correlation was observed with both IRI PD (r = -.462, p = .040) and the
AQC total (r = -.462, p = .046). Results suggest that in the ASD group, greater alexithymia
severity, and higher personal distress to others’ emotions was associated with using fewer
descriptions of physical sensations in the body when experiencing emotions, particularly
negatively valenced emotions. This points to links between personal distress, alexithymia, and
interoceptive sensibility in a subgroup of children in the ASD group.
These qualitative interview results echo some of the quantitative findings in that children
with ASD could describe bodily experiences of emotion, suggesting that interoceptive sensibility
was not impaired in this ASD sample. Although the ASD group was not significantly reduced in
their descriptions of embodied emotional experiences, greater alexithymia severity, and higher
personal distress to others’ emotions were associated with using fewer descriptions of physical
44
sensations in the body when experiencing negatively valenced emotions. These results
combined across all three methods (correlations, regression, and interview) are summarized in
Figure 2 & Figure 3.
Figure 2
Emotional Empathy Results Summary Across all Methods
Note. Figure summarizes the major findings for both groups in the two emotional empathy
subscales of the Interpersonal Reactivity Index (personal distress and empathic concern) across
the three analysis methods used in the study. TD = typically developing; ASD = autism
spectrum disorder.
45
Figure 3
Commonalities in Personal Distress Relationships in TD and ASD
Note. Figure summarizes the major findings for both groups in the Interpersonal Reactivity Index
Personal Distress subscale across the three analysis methods used in the study. TD = typically
developing; ASD = autism spectrum disorder.
Discussion
Interoception, Alexithymia and Emotional Empathy in ASD
In this study, no significant differences in interoceptive sensibility were observed
between TD and ASD youth. These results are inconsistent with others who find reduced
interoceptive sensibility (Elwin et al., 2012; Fiene & Brownlow, 2015), or increased interoceptive
sensibility in ASD (Garfinkel et al., 2016), although these studies were done in adults.
Additionally, no relationships between interoceptive sensibility and empathy in ASD were found,
even if alexithymia alone, or anxiety alone was controlled for. However, if age, sex, VCI, and
both anxiety and alexithymia were controlled for in the ASD sample, there was a significantly
negative relationship between IRI PD and the BPQ-VSF, suggesting that only when controlling
for both anxiety and alexithymia, those with higher interoceptive sensibility experienced lower
personal distress. It is important to keep in mind the reduced number of participants who
46
received the BPQ-VSF, and the multiple control variables that reduce degrees of freedom in this
comparison, so these results are cautiously interpreted. Even so, results from the TD group, and
qualitative interviews support the implications of these findings.
Regression analysis in the TD group revealed that physiological hyperarousal and
interoceptive body awareness scores predicted the greatest amount of variance in empathy
personal distress scores. Importantly, for TD participants, a group with significantly lower
alexithymia scores than the ASD group, we again saw a relationship between higher
interoceptive sensibility and lower personal distress. In emBODY interviews, youth with ASD
were able to describe bodily experiences of emotion at the same rate as TD youth, supporting
the notion that interoceptive sensibility was not impaired in this ASD sample. Also in alignment
with the self-report data, greater alexithymia severity and higher personal distress to others’
emotions were associated with using fewer descriptions of physical sensations in the body when
experiencing emotion, specifically for negative emotions. So although there may not be
reductions in interoceptive sensibility in ASD, there is evidence that reduced awareness of
interoceptive information, during actual emotion experience specifically, was related to both
alexithymia severity and increased personal distress in our ASD sample.
A study by Mul and colleagues (2018) was done in an adult sample, but used a similar
protocol for defining ASD clinical diagnosis and IQ inclusion criteria. In their study, ASD
participants showed reduced interoceptive sensibility, and alexithymia scores mediated the
relationship between interoceptive sensibility and emotional empathy (Mul et al., 2018). In the
current study, these findings were not supported, as there was no relationship with IRI EC and
interoceptive sensibility in the ASD sample when controlling for alexithymia, and only if both
anxiety and alexithymia were controlled for, higher interoceptive sensibility was associated with
lower personal distress. The crucial difference in defining emotional empathy as a whole, or
separately in the two domains of empathic concern and personal distress are discussed in the
next section.
47
Personal Distress and Empathic Concern
In this study, no significant differences between TD and ASD in either emotional
empathy subscore were observed. An unexpected outcome of this study was the two inverse
patterns we saw with alexithymia across the different domains of emotional empathy. A key
difference between the current study and the work of Mul and colleagues (2018), was the
separation of emotional empathy into the two components of personal distress and empathic
concern. Mul and colleagues (2018) demonstrate that those with alexithymia have lower
emotional empathy than ASD participants without alexithymia. So while correlation analyses in
this paper support previous work that alexithymia is related to reduced emotional empathy in
ASD in the empathic concern domain, the opposite pattern is true in the personal distress
portion of emotional empathy, which has not been previously reported. The divergent patterns in
IRI PD and IRI EC could account for previous discrepant findings regarding emotional empathy
ability in ASD. The results point to a measurement and operational definition issue when
combining the two subscales as part of one construct. Some studies that refer to emotional
empathy in ASD are only referring to the empathic concern domain (e.g. Dziobek et al., 2008;
Trimmer et al., 2017), while other studies consider characteristics of both personal distress and
empathic concern together as a measure of emotional empathy ability on measures like the
Multifaceted Empathy Test (e.g. Mazza et al., 2014).
Personal distress is an index of emotional empathy that refers to the tendency to feel
personal pain when exposed to the tension, pain, or suffering of others. While this is an aspect
of greater emotional empathy, it is also associated with maladaptive outcomes such as
ruminative coping, neuroticism, depression, self-criticism, and negative self-concept (Kim &
Han, 2018). Similar to the results of the current study, empathic concern shows an opposite
pattern of relationships with those maladaptive outcomes than personal distress does; authors
suggest that personal distress could block empathic interaction and prosocial behavior by
encouraging avoidance of overwhelm from others’ suffering (Kim & Han , 2018). Although, to my
48
knowledge, the relationship between alexithymia and personal distress has not been previously
explored in ASD specifically, the link between alexithymia severity and higher personal distress
has been observed in: non-clinical samples with schizotypy and autism traits (Aaron et al.,
2015), TD participants (Grynberg et al., 2010; Moriguchi et al., 2007), and individuals diagnosed
with major depressive disorder (Banzhaf et al., 2018), and eating disorders (Brewer et al.,
2019). Consistent with these findings, the current results suggest that alexithymia is marked by
reduced ability to engage in other-oriented empathic concern, and an increased tendency
towards self-oriented personal distress when exposed to another’s suffering. These findings
reveal the need for specificity about what aspects of emotional empathy are being measured in
ASD, and to a larger conversation regarding the narrative used to characterize empathy ability
in ASD, which is reviewed below.
Empathy in ASD
The current study found intact emotional empathy ability and reduced cognitive empathy
or ToM ability in a sample of ASD youth. In a recent editorial written by Fletcher-Watson and
Bird (2019), the authors argue that significant effort should be made to separate the social
attentional, emotion processing and normative social behavioral processes that surround the
phenomenon of empathy. Conflation of these terms can result in the belief that reduced capacity
for the felt experience of emotional empathy is a central feature of autism. Based on this study
and previous studies, atypical emotional empathy may actually be a feature of alexithymia and
not ASD (e.g. Bird & Cook, 2013; Brewer et al., 2015; Oakley et al., 2016). Additionally, the
results of the current study suggest that even for those with alexithymia, there is not necessarily
a lack of empathic feeling, but perhaps an actual increase in personal distress to others' pain
causing avoidance, rather than an other-oriented empathic concern that is prosocially
expressed.
A major point reviewed by Fletcher-Watson and Bird (2019) is how ingroup-outgroup
status of individuals with ASD impacts the way empathy ability in ASD is conceptualized. What
49
has been described as the “double empathy problem” (Milton, 2012) is supported by evidence
that TD individuals less accurately judge emotional expressions of individuals with ASD (Edey et
al., 2016; Sheppard et al., 2016) and that interactions of two individuals with ASD are rated
higher in terms of rapport than interactions with ASD/TD pairs, by both the participants and
diagnosis-blind observers (Crompton et al., 2020). Normative behavioral responses to emotional
signaling of others are by definition dictated by societal expectations defined by the non-ASD
majority. In situations of others’ emotional display, individuals with ASD may appear to lack
feelings of empathy, when in reality they are indeed experiencing felt empathy, but not following
the same rules of behavioral responding as a TD person (Fletcher-Watson & Bird, 2019). In fact,
writings by individuals on the spectrum describe intense empathic hyperarousal (Elcheson et al.,
2018; Williams, 1998), and other work indicates that object personification in ASD could cause
empathic responses to a wider distribution of targets than empathy in TD participants (White &
Remington, 2019). The consequences of these misattributions can cause misunderstanding and
have harmful effects on the ASD community. The belief of lack of empathy has facilitated work
associating autism with extremist terrorism (Palermo, 2013), and with experiences of
dehumanization in individuals with ASD (Yergeau, 2013). The findings of this study are
supported by many of the issues raised by Fletcher-Watson and Bird (2019) about emotional
empathy in ASD, and implicate a need for clear definitions, greater use of qualitative and
experimental measures that probe individual experiences of empathy, and require reflection on
the part of the researchers responsible for disseminating this work.
Limitations and Future Directions
There are many limitations to consider when interpreting the current study’s results.
First, individuals diagnosed with ASD make-up a very heterogeneous population; the relatively
small sample size and homogeneity of the participants in this study limit generalization to the
whole spectrum of ASD. This study included participants ages 8-18 years with ASD who are
verbal, and have at least one WASI-II IQ composite above 80; therefore the results may or may
50
not apply to individuals on the spectrum with less verbal and intellectual ability or in other age
groups. Some of the main variables of interest in the study can only be measured in individuals
who are verbal, future studies should further investigate the influence of VCI on the relationships
reported here. Additionally, the AQC was used as a gold standard tool for measuring
alexithymia in youth, however, like the TAS-20 (Bagby et al., 1994) this questionnaire captures
more cognitive aspects of alexithymia including identifying emotions and communicating
emotions, but does not measure additional emotionalizing aspects which should also be
explored in ASD like: “When friends around me argue, I become emotional”.
Secondly, many of the measures used to capture interoception, alexithymia, and
empathy ability, are self-report measures. Accurate self-report can be particularly challenging
for individuals with ASD who may have difficulty with metacognition and self-referential insight.
For this reason, there was an experimenter-led interview which also measured some of the
main variables in the self-report data. Future studies should examine these questions in a larger
and more heterogeneous sample of ASD participants, should employ more observational and
standardized measures when possible, and should include the multidimensional emotional
aspects of alexithymia including emotionalizing and fantasizing.
Conclusions
The current study found intact emotional empathy ability and interoceptive sensibility,
increased alexithymia severity, and reduced CE/ToM ability in a sample of ASD youth. Major
findings include: 1) alexithymia in ASD is associated with lower empathic concern, and higher
personal distress in both TD and ASD; 2) In the TD group, those with higher interoceptive
sensibility tended to have lower personal distress, and this was true in the ASD group only when
controlling for both anxiety and alexithymia; and 3) in the ASD group, greater alexithymia
severity, and higher personal distress to others’ emotions is associated with fewer physical
sensations in the body when experiencing negatively valenced emotions. An important
takeaway from this work is the need for exploring the two domains of empathic concern and
51
personal distress separately when examining alexithymia and interoception in ASD. The
divergent patterns in personal distress and empathic concern could account for previous
discrepant findings regarding emotional empathy ability in ASD. Future work should be cautious
of conflation of empathy with other social cognitive processes, and dissemination of work should
use care when reporting the implications of the results for the ASD community. The results of
the current study suggest that ASD youth with concomitant alexithymia do not lack empathic
feelings, but rather experience an increase in personal distress to others' pain; this may result in
avoidance rather than prosocial action, but should not be characterized as an absence of a
capacity for empathy.
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Chapter 4. Neural Correlates of Alexithymia, Interoception, and Emotional Empathy in
Autism Spectrum Disorder: An fMRI Study
Abstract
Background: There is evidence to suggest that individuals with ASD have difficulties with
cognitive empathy, however, emotional empathy deficits are unclear. Here I focus on how two
common comorbidities of ASD – disturbances in interoceptive sensibility and presence of
alexithymia – may differentially impact neural mechanisms of emotional empathy in ASD.
Methods: Study participants included youth ages 8 to 17 (mean 12.04 ± 2.20) who were
typically developing (TD; n = 37, 11 female) or had a diagnosis of high-functioning ASD (n = 28,
6 female). Participants completed Interpersonal Reactivity Index (IRI; Davis, 1983), Empathy
Questionnaire for Children and Adolescents (EmQue-CA; Rieffe et al., 2010), Alexithymia
Questionnaire for Children (AQC; Rieffe et al., 2006), and Child and Adolescent Symptom
Inventory (CASI-Anx; Sprafkin et al., 2002), self-report questionnaires. A measure of self-
reported interoceptive sensibility (Body Perception Questionnaire, BPQ-VSF; 25 TD, 18 ASD)
and an interview measure of bodily sensation during emotion experience (25 TD, 23 ASD) were
also collected in a subset of individuals. fMRI data was collected inside a 3-T Siemens
MAGNETOM Prisma scanner, where participants observed video clips of facial expressions
presented in a block design. Anatomically defined neural regions of interest (ROIs) included the
ACC, AI, amygdala and IFGop. Whole brain and ROI pairwise group comparisons were
conducted using two-tailed independent samples t-tests (TD vs ASD and Pearson partial
correlation coefficients were computed to assess relationships between behavioral variables
and ROI parameter estimates (all, TD, ASD). To better understand which features were the
strongest predictors of neural activation in the ASD group in particular, stepwise multiple linear
regression analysis was used to estimate model fit of predictors on ROI activation during the
fMRI task. To further test the alexithymia hypothesis, hierarchical linear regression models were
run across all participants to assess whether alexithymia explained variance in emotional
53
empathy outcomes and activation of ROIs during emotional facial expression observation above
and beyond the influence of diagnostic group. Results: There were no significant differences
between TD and ASD groups in empathic concern, personal distress, interoceptive sensibility,
or bodily sensation experienced during emotions. Significant differences were observed
between TD and ASD groups in AQC communicating emotions (p = .016). The TD group had
significantly higher activation than the ASD group in the left IFGop (p = .039), and the strongest
predictor of left IFGop activation in the ASD group was empathic concern. Inconsistent with our
hypotheses, emotional empathy and the two interoceptive measures (not alexithymia scores)
were the strongest predictors of AI activation in the ASD group. Across all participants,
hierarchical regression models including: age, sex, WASI-II: VCI, and AQC total positively
predicted higher personal distress, and higher right amygdala activation in emotional facial
expression observation above and beyond the influence of group status (ASD). Conclusions:
Thus, our data indicate a dynamic interplay between: 1) the amygdala, which is strongly
involved in emotional empathy processes and affected by alexithymia presence, differentially in
right and left hemisphere, above and beyond ASD diagnosis; 2) the anterior insula, which is
strongly involved in visceral and interoceptive sensory processing, and empathic concern
processing; and 3) the IFGop, which is involved in empathic concern, attentional/inhibitory, and
social and motor processing, and shows differences when comparing groups based on ASD
diagnosis. These results help clarify previous inconsistencies in the literature – namely activity
in some regions commonly found to be hypoactive in ASD (IFGop, insula) are related to
empathic concern, whereas activity in other regions (amygdala) are more related to presence of
alexithymia than an ASD diagnosis and are involved in processing related to emotional empathy
– personal distress.
54
Introduction
Empathy, the ability to understand and experience the feelings of others, is one
important social skill that may be compromised in ASD (Dvash & Shamay-Tsoory, 2014).
Cognitive empathy involves mentally imagining how another person is thinking or feeling (de
Waal & Preston, 2017), while emotional empathy involves sharing the emotions others are
experiencing (Davis et al., 1994). There is a large body of research that suggests that
individuals with ASD have difficulties with cognitive empathy (for review see Frith & Happé,
2005). However, evidence for emotional empathy difficulties are mixed. Some studies find that
individuals with ASD display reduced emotional empathy (Bos & Stokes, 2018; Kasari et
al.,1990; Lombardo et al., 2007; Mathersul et al., 2013; Minio-Paluello et al., 2009; Peterson et
al., 2014; Schulte-Rüther et al., 2011; Shamay-Tsoory et al., 2002; Sigman et al., 1992;
Sucksmith et al., 2013; Yirmiya et al., 1992) while others have not found support for any
emotional empathy differences (Bellebaum et al., 2014; Deschamps et al., 2014; Dziobek et al.,
2008; Hadjikhani et al., 2014; Jones et al., 2010; Markram et al., 2007; Pouw et al., 2013;
Rogers et al., 2007; Rueda et al., 2015; Schwenck et al., 2012; Smith, 2009). Some evidence
suggests deficits in empathy in ASD may be attributed to co-occurring alexithymia rather than
ASD symptomatology alone (Bird & Cook, 2013), while others suggest interoception
impairments are responsible for inconsistent reductions seen in emotional empathy in ASD
(Quattrocki & Friston, 2014). In this study, I will focus on how one prominent sensory feature
(interoceptive sensibility) and one prominent emotional feature (alexithymia) may differentially
impact the neural mechanisms associated with emotional empathy in youth with ASD and their
typically developing (TD) peers.
Neural Mechanisms of Emotional Empathy
The network of neural mechanisms involved in emotional empathy include the inferior
frontal gyrus pars opercularis (IFGop), anterior cingulate cortex (ACC), amygdala, and the
anterior insula (AI, Dvash & Shamay-Tsoory, 2014). These regions (AI, ACC, amygdala, IFGop)
55
are commonly active when experiencing emotion and when observing, or even hearing, another
person having an emotional experience (e.g., Adolphs, 2010; Fan et al., 2011; Hein & Singer,
2008; Jabbi et al., 2007; Sander et al., 2007; Seara-Cardoso et al., 2016). If these regions are
damaged with lesions, patients may demonstrate deficits in emotional empathy and emotion
recognition (Shamay-Tsoory et al., 2009). Further, individual differences in trait empathy have
been correlated with increased activity in the IFGop (e.g. Aziz-Zadeh et al., 2010; Gazzola et al.,
2006; Jackson et al., 2005; Kaplan & Iacoboni, 2006; Saarela et al., 2007), AI (e.g. Seara-
Cardoso et al., 2016; Singer et al., 2006), amygdala (e.g. Seara-Cardoso et al., 2016), and the
ACC (Masten et al., 2011) in TD individuals.
Emotional Empathy in ASD: Behavioral
In some studies, adults and children with high-functioning ASD have demonstrated
reduced emotional empathy scores on standardized self-report measures (Bos & Stokes, 2018;
Lombardo et al., 2007; Mathersul et al., 2013; Schulte-Rüther et al., 2011; Shamay-Tsoory et
al., 2002; Sucksmith et al., 2013; Trimmer et al., 2017) and teacher-report measures (Peterson
et al., 2014), while other studies report typical emotional empathy scores on self-report
measures (Bellebaum et al., 2014; Dziobek et al., 2008; Pouw et al., 2013; Rogers et al., 2007;
Rueda et al., 2015). Attenuated emotional empathy outcomes in individuals with ASD compared
to TD peers has been reported on experimental tasks using videos of emotional experiences
(Yirmiya et al., 1992), affective sharing during joint attention paradigms (Kasari et al.,1990), and
when observing others’ pain (Minio-Paluello et al., 2009). However, other studies find no
difference between ASD and TD in recognition of emotional facial video clips (Schwenck et al.,
2012), emotional contagion with characters in a story task (Deschamps et al., 2014), and
concern about the feelings of a victim of aggressive actions (Jones et al. 2010). Even within
individual studies, there are inconsistent results depending on which methodology was used, as
in Trimmer et al. (2016), where adults with ASD self-report lower levels of perceived negative
mood to emotionally distressing video clips, however respond similarly to controls on
56
physiological arousal and facial affect to emotionally salient stimuli. Similarly, other work finds
mixed results depending on the valence of emotion stimuli. For example, Mazza and colleagues
(2014) found that adolescents with ASD are able to report shared emotional experience of other
people in emotionally charged photographs for positive valenced emotions, but display reduced
emotion sharing for negative valenced emotions on the Multifaceted Empathy Test.
Emotional Empathy in ASD: Neural Correlates
Numerous studies have investigated neural responses of empathy for pain in ASD (Bird
et al., 2010; Fan et al., 2014; Gu et al., 2015; Hadjikhani et al., 2014; Krach et al., 2015;
Lassalle et al., 2018; Li et al., 2018), with mixed results. Krach et al. (2015) found no TD vs ASD
differences in activity in the ACC and AI to viewing other people experiencing pain in their limbs.
However, when viewing facial expressions of pain, Hadjikhani et al. (2014) found higher
activation in the ASD group in some emotion-related brain areas (e.g. ACC, temporoparietal
junction, supramarginal gyrus). Similarly, Gu et al. (2015) found in ASD evidence of increased
activation in the AI when viewing others experience pain in their limbs. On the other hand, Fan
et al. (2014) found participants with ASD had decreased neural responses of the ACC and AI
when viewing others experiencing injury to body parts. Similarly, Lassalle and colleagues (2018)
found in ASD reduced activation of the IFG and thalamus when participants viewed other
people’s limbs in painful situations, but not when they viewed faces showing a painful
expression.
Other studies have not focused on pain, and instead have measured empathic
functioning using brain responses to the facial expressions of others. Numerous studies have
demonstrated individuals with ASD show decreased activation in the IFG to facial expressions
(Dapretto et al., 2006; Greimel et al., 2010; Kilroy et al., 2020; Klapwijk et al., 2016; Schulte-
Rüther et al., 2011) and when observing others experiencing disgusting tastes (Bastiaansen et
al., 2011). A meta-analysis of 39 studies concluded that the AI has also consistently been
57
shown to have reduced activation in ASD in social vs. non-social processing, including facial
expression observation (Di Martino et al., 2009).
Alexithymia
It has been hypothesized that emotional empathy difficulties seen in ASD are accounted
for by co-morbid alexithymia rather than a primary feature of the ASD phenotype (Bird & Cook,
2013). Alexithymia (trouble recognizing, describing, and distinguishing emotions in oneself) is
common in individuals with high-functioning ASD (50%; Hill et al., 2004; Kinnaird et al., 2019),
while the incidence of alexithymia in the typical population is estimated between 5-10% (Berthoz
et al., 2011; Kinnaird et al., 2019). Alexithymia in ASD has been associated with difficulties in
empathic social processing tasks including: recognition of emotional expression in faces (Cook
et al., 2013), recognition of vocal affect (Heaton et al., 2012), skin conductance responses to
emotional pictures (Gaigg et al., 2018), moral decision-making (Brewer et al., 2015), and eye
fixation in social scenes (Bird et al., 2011).
At the structural neural level, the alexithymia hypothesis is supported by the findings
that alexithymia (and not an ASD diagnosis) is predictive of reduced covariance of frontal
affective networks from the insula to posterior regions (Bernhardt et al., 2014), whereas ASD is
predictive of covariance in networks crucial for theory of mind skill centered on dorsomedial
prefrontal cortex and temporoparietal junction. In that study, the ASD group had decreased
perspective taking but similar empathic concern to TD participants. Functionally, Silani et al.
(2008) found that in both ASD and TD participants, alexithymia scores and an Interpersonal
Reactivity Index (IRI: Davis, 1983) score (perspective taking and empathic concern combined)
were significantly correlated with activity in the AI, and were also correlated with activity in the
left amygdala in the ASD group during an emotion introspection task. Bird et al. (2010) followed
up this work and found activation of AI to others limbs in pain correlated with alexithymia scores,
and when alexithymia was equal between TD and ASD groups, there were no differences in
empathy scores on the IRI. However, other studies have suggested that alexithymia’s role is not
58
completely accounting for empathic brain activation in ASD, and it may depend on stimulus type
and compounding effects of additional symptoms. For example, Li et al. (2018) found that in TD
(undiagnosed) participants while alexithymia was actually associated with increased AI
responses to perceived physical pain of others’ limbs, and decreased AI responses during
viewing of painful facial expressions. Another study found when viewing pictures depicting
hands and feet in painful or disgusting situations, neural differences between TD and ASD
groups disappeared if either alexithymia or anxiety were controlled for (Lassalle et al., 2019).
Taken together, results suggest that mechanisms of emotional empathy may be
impacted in ASD, but that this association is complex and may depend on comorbid alexithymia.
Given that alexithymia was not measured or accounted for in many of the previously cited
studies, a difference in alexithymia severity among participants with ASD could potentially
explain discrepant findings in emotional empathy related neural regions.
Interoception
Interoceptive sensibility, one’s aptitude to consciously perceive internal sensory
experiences, has also been linked to empathic ability (Fukushima et al., 2011; Grynberg &
Pollatos, 2015). Individuals with ASD may have alterations in interoceptive sensibility, and
interoceptive accuracy (e.g. heart rate tracking; for review DuBois et al., 2016), particularly in
childhood and adolescence (Failla et al., 2019; Nicholson et al., 2019). The AI is cited as a
common neural mechanism for both interoception (Critchley et al., 2005), and emotion
processing (Berthoz et al., 2002; Frewen et al., 2008; Karlsson et al., 2008), and the amygdala
is also thought to be a key region in a large network supporting allostasis and interoceptive
processing (Kleckner et al., 2017). Given the role of the AI and amygdala in generating feeling
states from bodily representations, previously reported neural abnormalities in these areas may
underscore emotion processing deficits in ASD. In fact, insular responses during a cardiac
interoception task have been associated with self-reported autism social severity scores in
adults (Failla et al., 2019). Brewer et al. (2016) suggest that interoceptive failure leads to
59
alexithymia, which may then cause primary social impairments. In support of this hypothesis,
Murphy et al. (2018) found that those with high alexithymic traits relied less on interoceptive
cues for modulating respiratory output, muscular exertion, and taste sensitivity, when controlling
for comorbid conditions like ASD or anxiety. Some studies show that alexithymia can explain
interoceptive difficulties in ASD, and that interoceptive deficits should be considered a symptom
of alexithymia, rather than ASD per se (Shah et al., 2016, 2017). However, recent work has not
provided evidence for interoceptive accuracy impairments in a sample of adults with ASD,
whether or not co-occurring alexithymia was considered (Nicholson et al., 2018).
Alexithymia, Interoception and Empathy
To my knowledge, to date, only one study has investigated how both alexithymia and
interoception interact together with subtypes of empathy, and this study did not have a neural
component measured. Mul et al. (2018) compared a TD group with two groups of adults with
ASD, one group with and one without alexithymia. ASD participants with high alexithymia
demonstrated lower emotional empathy and lower cognitive empathy than ASD participants with
low alexithymia (Mul et al., 2018). In the same study, across groups ASD participants showed
both reduced interoceptive sensibility and interoceptive accuracy, and alexithymia scores
mediated the relationship between interoceptive sensibility and emotional empathy (Mul et al.,
2018). Given these discrepant findings, further studies are needed to better understand the
relationships between emotional empathy, alexithymia, and interoception in ASD.
The current paper will address some of the gaps in this literature in the following ways.
First, unlike Mul and colleagues (2018), the current study will separate two major components of
emotional empathy (personal distress and empathic concern). Some studies that refer to
emotional empathy in ASD are only referring to the empathic concern domain (e.g. Dziobek et
al., 2008; Trimmer et al., 2017), while other studies consider characteristics of both personal
distress and empathic concern combined together as a measure of emotional empathy ability on
measures like the Multifaceted Empathy Test (e.g., Mazza et al., 2014). Personal distress is an
60
aspect of emotional empathy associated with maladaptive outcomes such as ruminative coping,
neuroticism, depression, self-criticism, and negative self-concept, while empathic concern
shows an opposite pattern of relationships with those maladaptive outcomes (Kim & Han, 2018).
Therefore, the two aspects of emotional empathy should be observed separately.
Secondly, the influence of anxiety also will be included in the investigation of emotional
empathy outcomes in ASD. Only one of the studies reviewed above (Lassalle et al., 2019)
investigated the influence of anxiety on the relationship between emotional empathy and neural
responses in ASD. In addition to being highly comorbid with ASD (van Steensel et al., 2011),
anxiety is thought to be associated with increased interoceptive sensibility (Ehlers & Breuer,
1992), alexithymia (Zeitlin & McNally, 1993), and decreased empathy (Todd et al., 2015).
Anxiety is also related to increased responses of the AI when processing emotion related
content such as physical pain (Alvarez et al., 2015).
Lastly, to my knowledge there is no study to date that has explored the functional
imaging relationship between alexithymia, interoception, and emotional empathy in ASD
together. Further, the studies that have examined either alexithymia or interoception’s role alone
in empathic neural processing have focused on empathy for pain. Here we instead investigate
the interplay between alexithymia and/or interoception during a task where participants view
dynamic videos of a wide range of other people producing facial expressions (happy, sad,
neutral, angry, disgust, fear, surprise). We used this task because prior studies have shown that
viewing dynamic facial expressions activates emotion-related brain regions, which are
commonly thought to be involved in empathic processing. Further, this task is more relevant to
many forms of emotional empathy (sharing the joys and sorrows of others), beyond prior studies
focus on empathy for pain. It was hypothesized that: 1) overall, participants with ASD compared
to TD controls would have lower interoceptive sensibility, higher alexithymia, and lower
emotional empathy ability; 2) participants with ASD compared to TD controls would have lower
neural activation in emotion-related brain regions of interest (ROIs: insula, amygdala, IFG, and
61
ACC) during facial expression observation; 3) across participants and within each group (ASD,
TD), interoceptive sensibility and emotional empathy would be positively correlated and
alexithymia severity would be negatively correlated with neural activity in ROIs; 4) within the
ASD group anxiety may contribute to relationships observed between both emotional empathy
and interoceptive sensibility with ROI activation during facial expression observation 5) across
participants, higher alexithymia will predict lower emotional empathy, and lower activity in neural
ROIs during facial expression observation.
Methods
Participants
Study participants included youth ages 8 to 17 (mean 12.04 ± 2.20) who were typically
developing (TD; n = 37, 11 female) or had a diagnosis of ASD (n = 28, 6 female). A measure of
interoceptive sensibility (25 TD, 18 ASD) and an interview measure of bodily sensation during
emotion experience (25 TD, 23 ASD) were collected in a subset of individuals from the total
group due to being added later in the study. Participants were recruited through advertisements
and contacts in community centers, social media sites, parent group website postings,
healthcare clinics, and schools. Inclusion criteria for all groups included: IQ > 80 on at least one
composite score (Verbal Comprehension Index, Perceptual Reasoning Index, Full Scale IQ–2,
or the Full Scale IQ–4) assessed by a trained staff member using the Wechsler Abbreviated
Scale of Intelligence 2nd edition (WASI-II; Wechsler, 2011); and English fluency of child and
parent. As this was part of a larger imaging study, all participants were additionally screened for
MRI compatibility, had no history of loss of consciousness greater than five minutes, were right-
handed, and were born after 36 weeks of gestation. Each family was informed about study
procedures in accordance with the protocol approved by the University of Southern California’s
Institutional Review Board. Participants and parents then provided their written child assent and
parental consent.
62
TD participants were excluded if they had any psychological or neurological disorder,
including ADHD and generalized anxiety disorder. They were also excluded if they had a first
degree relative with an ASD diagnosis, or scored above a t-score of 60 on the Social
Responsiveness Scale, Second Edition (SRS-2; Constantino & Gruber, 2012); indicating a risk
for ASD symptoms including reduced social awareness, reciprocal social interaction, or
presence of restricted or repetitive behaviors. Eligible ASD participants had a previous
diagnosis through clinical diagnostic interview or diagnostic assessment, and met criteria on the
Autism Diagnostic Observation Schedule, Second Edition (ADOS-2; Lord et al., 2000), the
Autism Diagnostic Interview-Revised (ADI-R; Lord et al., 1994), or both administered by a
trained researcher. Individuals in the ASD group were excluded if they had another diagnosis of
neurological or psychological disorder with the exception of Developmental Coordination
Disorder, attention deficit hyperactivity disorder (ADHD), or generalized anxiety disorder, due to
their high prevalence in ASD (Mazzone et al., 2012). Eleven ASD participants had established
prescriptions for psychotropic medication at the time of the study (for ADHD and anxiety), and
no TD participants reported any prescription medication use.
Assessments
Anxiety and ADHD Measures
In addition to the inclusion criteria measures, the 20-item version of the parent-report
Child and Adolescent Symptom Inventory (CASI-Anx; Sprafkin et al., 2002), was used to assess
symptoms of the six DSM-IV defined anxiety disorders. The 10-item Conners third edition
(Conners-3AI; Conners, 2008), a parent measure for children ages 6-18 years, was used to
characterize levels of ADHD symptoms. Both the CASI-Anx and the Conners-3AI are rated on a
four-point Likert scale from “never” to “very often.”
Empathy Measures
Empathy ability was assessed using the self-report Interpersonal Reactivity Index (IRI;
Davis, 1983). Questions are rated on a five-point Likert scale from “does not describe me well”
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to “describes me very well”. The IRI is a 28-item self-report measure consisting of four 7-item
subscales: 1) Perspective Taking (IRI PT) a tendency to adopt and understand the viewpoint of
others, 2) Empathic Concern (IRI EC) feelings of sympathy and concern for others, 3) Personal
Distress (IRI PD) feelings of personal unease in tense interpersonal settings, and 4) Fantasy
(IRI FS) a tendency to transpose oneself into the feelings of fictional characters. All IRI
subscales are reported in initial group comparisons, and subsequent analyses focus on the two
IRI aspects of emotional empathy (IRI EC and IRI PD).
The Empathy Questionnaire for Children and Adolescents (EmQue-CA; Rieffe et al.,
2010) is a 20-item self-report measure, represents three facets of empathy observable in
children: (1) affective empathy (closely related to empathic concern in this study) (2) cognitive
empathy and (3) intention to comfort. Questions are rated on a three-point Likert scale from “not
true” to “often true”. According to Overgaauw et al. (2017) Cronbach’s alphas indicated good
internal consistencies with affective empathy, cognitive empathy, and intention to comfort; and
the affective and cognitive empathy scales were positively correlated with the corresponding IRI
scales. Cognitive and emotional empathy EmQue-CA subscales are reported in initial group
comparisons, and subsequent analyses focus on the affective empathy subscale (EmQue-CA
AE).
Additionally, trained research staff administered two subtests which comprise the
Developmental Neuropsychological Assessment (NEPSY-II; Korkman et al., 2007) Social
Perception Domain. Subtests used captured (1) Theory of Mind (ToM) that assesses visual and
verbal ability to comprehend the perspectives, experiences, and beliefs of others, and (2) Affect
Recognition (NEPSY-II AR) that assesses the ability to identify and discriminate between facial
affects. The NEPSY-II ToM subscale is an additional measure of CE ability beyond the self-
report IRI.
Alexithymia Measure
64
Alexithymia was measured using the 20-item self-report Alexithymia Questionnaire for
Children (AQC; Rieffe et al., 2006). Items were rated on a three-point Likert scale from “not true”
to “often true”, and consisted of questions like “I am often confused about the way I am feeling
inside.” Two subscales are used to assess alexithymia: difficulty identifying feelings (AQC ID),
and difficulty describing and communicating feelings (AQC COMM). The third subscale,
externally oriented thinking (AQC EOT) was not used in this study due to an unacceptably low
Cronbach’s alpha in the original psychometric paper, and because alexithymia can be reliably
assessed in youth without the eight items rating the AQC EOT dimension (Loas et al., 2017).
Considering this, the two-factor total was calculated instead and used as the AQC total in this
study.
Embodiment Measures: Interoception and Bodily Sensation of Emotion
For interoceptive sensibility, the 12-item self-report Body Perception Questionnaire-Body
Awareness Very Short Form (BPQ-VSF; Porges, 1993; Cabrera et al., 2018) was used to
assess the subjective awareness of the function and reactivity of target organs and structures
that are innervated by the autonomic nervous system. The BPQ-VSF only probes aspects of the
body awareness subscale of the original BPQ, with items like, “During most situations, I am
aware of how fast I am breathing.” Questions are rated on a five-point Likert scale from “never”
to “always.”
Another measure used to capture bodily experience, the Physiological Hyperarousal
Scale for Children (PH-C; Laurent et al., 2004) is a brief self-report measure of frequency of
physical experiences associated with physiological hyperarousal. This 18-item scale asks
questions such as, “How often have you felt or experienced being unable to catch your breath in
the last two weeks?”. Questions are rated on a five-point Likert scale from “never” to “all of the
time.” It is important to note that while the BPQ-VSF and the PH-C capture similar bodily
information, the BPQ-VSF is more focused on general awareness of bodily sensations i.e. “how
65
fast my heart is beating,” and the PH-C is probing the frequency of physiological experiences
associated with hyperarousal i.e. “heart-pounding.”
As an additional measure of interoceptive and emotional awareness, a semi-structured
interview using the emBODY tool (Nummenmaa et al., 2014) was administered. This interview
assesses participants' ability to identify somatotopic patterns of physical feeling associated with
each of the six basic emotions (i.e., anger, fear, disgust, happiness, sadness, and surprise).
Participants were asked to draw on a picture of human bodies where they felt increasing or
decreasing activation while experiencing different emotions. After the drawing task was
completed, a qualitative interview was carried out by a trained staff member probing what types
of situations elicit the given emotion, where participants feel that emotion in their body, and what
type of sensation occurs within physical localizations. The interview consists of questions such
as: “What kinds of things make you feel afraid?; Can you tell me how you feel when you're
afraid?; Can you show me where in your body you feel fear, and describe that sensation?”
Interviews were transcribed and coded by two raters and a third tie-break rater for use of
physical descriptions of felt emotion. All instances describing the body were coded for use of
embodied language mentioning a physical sensation (e.g., “I know I am angry because I feel my
head getting hot and chest-pounding”) and collapsed across all emotions (BSE) and across the
negative emotions (BSE-neg).
Behavioral Data Analysis
Group Comparisons: Testing Hypothesis 1
To test hypothesis 1, that overall, participants with ASD compared to TD controls would
have lower interoceptive sensibility, higher alexithymia, and lower emotional empathy ability,
pairwise group comparisons between TD and ASD groups were conducted using two-tailed
independent samples t-tests on all behavioral measure outcome variables.
fMRI Analysis
Procedure, Acquisition & Preprocessing
66
fMRI procedure, task stimuli, fMRI acquisition, and data preprocessing were done
following the protocol previously published in Kilroy et al. (2020; see supplementary data for
details). Prior to scanning, all participants completed a practice session in a mock scanner to
prepare for the scanning environment and to ensure minimal head motion. Participants
completed an action observation task in the scanner (Kilroy et al., 2020). There were no
significant differences in head motion between the two groups.
Whole Brain Analysis – Testing Hypothesis 2
Experimental stimulus conditions were each modeled with a separate regressor derived
from a convolution of the task design and a double gamma function to represent the
hemodynamic response, and the temporal derivative of each task regressor was also included
as an additional regressor. Subject-specific motion correction parameters were entered as
nuisance regressors.
To test hypothesis 2, that overall participants with ASD compared to TD controls would
have lower neural activation during facial expression observation, both groups (TD & ASD) were
entered into multivariate linear regression models for the task for exploring main effects and
between-group comparisons. In all whole-brain analyses, mean-centered age, sex and WASI-II
VCI were entered as covariates in the model. Individual participants' statistical images were
entered into the higher level mixed-effects analyses using FSL’s FLAME Stage 1 algorithm.
Resulting group level images for all models were thresholded using FSL’s cluster probability
algorithm, with a cluster-forming threshold of Z > 3.1, a cluster size probability threshold of p <
.001. To identify networks elicited by facial observation compared to rest, two stimulus
conditions (emotional facial expressions, non-emotional facial expressions) were collapsed into
one facial expression condition to determine the main effect of all stimuli compared to resting
baseline. Similar analyses were performed for each individual stimulus condition (emotional
facial expressions, non-emotional facial expressions).
Region of Interest Analysis
67
Regions of interest (ROIs) included the ACC, AI, amygdala and IFGop as defined by a
hand-drawn anatomical delineation (IFGop) or by the Harvard-Oxford Dictionary’s delineation
(all other ROIs; Figure 2). Percent signal change was extracted from each ROI using the
featquery function in FSL. For all ROI analyses, extreme outlier data was removed from
participants whose mean percent signal change in the queried ROI were more than 3 times the
interquartile range from the first and third quartile relative to the entire group.
ROI Group Comparisons: Testing Hypothesis 2. To further test hypothesis 2 in the
specified ROI’s, group differences between TD and ASD groups were assessed using two-tailed
independent samples t-tests.
ROI Correlations & Stepwise Regressions: Testing Hypothesis 3 – 5. To test
hypothesis 3, that across participants and within each group (ASD, TD), interoceptive sensibility
would be positively associated with neural activation in emotional empathy ROIs, and
alexithymia severity would be negatively associated with neural activation in emotional empathy
ROIs, two methodologies were used.
1) Pearson partial correlation coefficients were computed to assess relationships
between interoception, alexithymia, and emotional empathy with ROI parameter estimates in all
participants, the ASD group alone, and the TD group alone, while controlling for age, gender,
and VCI. Correlation analyses were conducted in the combined facial expression condition to
increase the number of data points and statistical power. Scatter plots were visually analyzed
for the presence of outliers to confirm validity of correlations.
2) In the ASD group only, our main clinical group of interest, stepwise multiple linear
regression analysis was used to estimate model fit of strongest predictors on ROIs in the
combined facial expression condition. Data was centered for each group and the following
behavioral variables were entered into a stepwise multiple linear regression model: gender,
WASI-II: VCI, age, CASI-Anx, AQC ID, AQC COMM, Conners-3AI, SRS-2, PH-C, BPQ-VSF,
BPE. Due to the reduced sample size of participants with available BPQ-VSF scores, if that
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variable was not a significant predictor in the initial model, the regression was run a second time
in the larger sample of participants with the BPQ-VSF removed as a model predictor. The
criterion for entering a variable into the model was set at F = 0.05, with a removal probability of
F = 0.10. Homoscedasticity and normality of residuals were assessed, and the variance inflation
(VIF) and tolerance statistics were used to assess independent variables for multicollinearity.
Figure 2
Regions of Interest
Note. Pink = anterior cingulate cortex; red = inferior frontal gyrus pars opercularis; blue =
anterior insula; yellow = amygdala.
Due to the high incidence of comorbidity with anxiety (Simonoff et al., 2008) in ASD, and
the association between anxiety and increased interoceptive sensibility (Ehlers & Breuer, 1992),
anxiety symptoms also were controlled for in secondary analyses in the ASD group. To test
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hypothesis 4, that within the ASD group anxiety may contribute to relationships observed
between emotional empathy and interoceptive sensibility, Pearson partial correlation coefficients
were computed to assess relationships between interoception and emotional empathy with ROI
parameter estimates while controlling for age, gender, VCI, and anxiety in each model. The
relationships were compared to previous partial correlations observed when anxiety was not
added as a covariate in the model.
To specifically test the alexithymia hypothesis -- that differences between TD and ASD in
emotional empathy are actually due to differences in trait alexithymia (Hypothesis 5; Bird &
Cook, 2013; Bird et al., 2010; Bird et al., 2011; Brewer et al., 2015; Cook et al., 2013; Gaigg et
al., 2018; Heaton et al., 2012; Mul et al., 2018; Shah et al., 2016, Shah et al., 2017; Sivathasan
et al., 2020) a hierarchical multiple linear regression model was used across all participants. In
order to probe the impact of alexithymia on emotional empathy (IRI PD, IRI EC, and EmQue-CA
AE) and ROI activation during emotional face processing (ACC, IFGop, amygdala, AI), nuisance
regressors of age, VCI, and sex were entered into the model before alexithymia and group.
Data was centered and behavioral variables were entered into a hierarchical multiple linear
regression model in the following order: age, sex, WASI-II: VCI, AQC total, group. Significance
of alexithymia as a predictor was assessed before and after the addition of group to the model.
Homoscedasticity and normality of residuals were assessed, and the VIF and tolerance
statistics were used to assess independent variables for multicollinearity.
Results
Behavioral Group Differences – Hypothesis 1
Independent samples t-tests resulted in significant differences between TD and ASD
groups on the SRS-2, Conners-3AI, and CASI-Anx (p’s < .05; Table 1). There were no
significant differences in affect recognition (NEPSY-II AR) or empathy measures, although in the
cognitive empathy domain, the NEPSY-II ToM total approached significance (p = .052; ASD <
TD; Table 1). With regards to alexithymia, significant differences were observed in the AQC
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COMM (p = .016; Table 1), while the AQC total score approached significance (p = .058). On
the emBODY interview, using a combined total code of all feeling states, and on negative
valenced feeling states there were no significant differences in responses between TD and ASD
(BSE: p = .992; BSE-neg: p = .500). There was a significant difference between TD and ASD on
the PH-C (p = .032). In summary, the ASD group had increased social impairment, alexithymia
severity, ADHD symptomatology, physiological hyperarousal, and anxiety, and a nearly
significant decrease in ToM. By contrast, no significant differences from TD were found in the
ASD group for self-reported interoceptive sensibility or emotional empathy skills. Due to the fact
that ToM differences in ASD are well established in the literature, all further analysis was
performed on relationships in the emotional empathy domain as this is the intended scope of the
current study.
fMRI Group Differences – Hypothesis 2
As expected, during observation of all facial expressions as compared to rest, both
groups showed significant activation in key nodes of the Action Observation Network (IFG,
premotor regions, superior temporal sulcus) along with regions typically involved in empathic
and emotional processing (amygdala, insular cortex) and other regions (e.g., visual areas;
Figure 3). When comparing groups using whole brain contrasts, there were no significant
differences between TD and ASD (Z = 3.1, cluster corrected) for any of the conditions. When
comparing mean percent signal change of all hypothesized ROI’s (ACC, AI, amygdala, IFGop),
the TD group had significantly higher activation than the ASD group only in the left IFGop during
observation of non-emotional facial expressions (Table 2, Figure 4; p = .039).
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Table 1
Descriptive Statistics and Group Comparisons of Behavioral Data
Category Variable TD ASD t p
M SD M SD
Demographic
&
Behavioral
Age 11.927 2.176 12.199 2.277 -0.488 0.627
WASI-II: FSIQ-4 114.57 13.02 112.82 17.036 0.469 0.641
WASI-II: VCI 115.43 11.915 110.82 17.226 1.214 0.231
CASI-Anx 4.05 3.535 10.21 4.717 -6.022 < .001**
Conners 3AI 45.34 2.94 84.11 8.997 -21.521 < .001**
SRS-2 45.68 5.202 75.82 9.056 -15.757 < .001**
Alexithymia
AQC ID 0.459 0.342 0.561 0.429 -1.064 0.291
AQC COMM 0.638 0.461 0.921 0.453 -2.472 0.016*
AQC total 6.405 4.219 8.536 4.623 -1.934 0.058
Bodily
Sensation
BPQ-VSF 27.96 13.123 25.889 10.414 0.555 0.582
PH-C 25.81 6.02 30.20 8.55 -2.217 0.032*
BSE 3.29 1.92 3.28 2.22 0.010 0.922
Affect
Recognition
NEPSY-II AR 11.05 2.460 10.00 2.236 1.758 0.084
Empathy
EmQue-CA AE 7.568 2.433 7.577 3.668 -0.011 0.991
EmQuE-CA CE 7.324 1.733 6.846 2.292 0.943 0.349
IRI PT 15.81 5.021 13.71 5.032 1.665 0.101
IRI FS 17.11 5.522 16.57 5.392 0.392 0.696
IRI EC 18.3 5.211 16.68 4.997 1.262 0.212
IRI PD 12.51 4.823 13.68 5.806 -0.883 0.38
IRI Total 64 14.3 60.64 13.666 0.955 0.343
NEPSY-II ToM
Total
24.97 1.951 23.65 2.939 2.000 0.052
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Note. Group differences between TD and ASD groups. TD = typically developing; ASD = autism
spectrum disorder; WASI-II: FSIQ-4 = Wechsler Abbreviated Scale of Intelligence, Second
Edition: Full Scale IQ; WASI-II: VCI = Wechsler Abbreviated Scale of Intelligence, Second
Edition: Verbal Comprehension Index; CASI-Anx count = Childhood Adolescent Symptom
Inventory Symptom Count Total; Conners-3AI = Conners 3 ADHD Index; SRS-2 = Social
Responsiveness Scale, Second Edition; AQC ID = Alexithymia Questionnaire for Children
Identifying Emotions Subscale; AQC COMM = Alexithymia Questionnaire for Children
Communicating Emotions Subscale; AQC total = Alexithymia Questionnaire for Children 2 factor
total; BPQ-VSF = Body Perception Questionnaire-Body Awareness Very Short Form Total;
BSE= bodily sensation experienced duting emotion; NEPSY-II AR = The Developmental
Neuropsychological Assessment Affect Recognition Subscale; EmQue-CA AE = Empathy
Questionnaire for Children and Adolescents Affective Empathy Subscale; EmQue-CA CE =
Empathy Questionnaire for Children and Adolescents Cognitive Empathy Subscale; IRI PT =
Interpersonal Reactivity Index Perspective Taking Subscale; IRI FS = Interpersonal Reactivity
Index Fantasy Subscale; IRI EC = Interpersonal Reactivity Index Empathic Concern Subscale;
IRI PD = Interpersonal Reactivity Index Personal Distress Subscale; IRI Total = Interpersonal
Reactivity Index Total Score; NEPSY-II ToM Total = The Developmental Neuropsychological
Assessment Theory of Mind Subscale. *p < .05; **p < .001.
Figure 3
Main Effect of Facial Expression Observation Task
Note. Z = 3.1.
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Table 2
Descriptive Statistics and Group Comparisons of Anatomical ROI Parameter Estimates
Category Variable TD ASD t p
M SD M SD
Emotional
Facial
Expressions
Anterior Cingulate Cortex 0.052 0.166 -0.017 0.18 1.591 0.117
Left Amygdala 0.235 0.175 0.182 0.282 0.886 0.381
Right Amygdala 0.225 0.181 0.199 0.235 0.510 0.612
Left Anterior Insula 0.056 0.135 0.032 0.162 0.633 0.529
Right Anterior Insula 0.052 0.151 0.045 0.17 0.169 0.866
Left IFG pars opercularis 0.113 0.190 0.088 0.184 0.530 0.598
Right IFG pars opercularis 0.145 0.171 0.098 0.201 1.018 0.312
Non-Emotional
Facial
Expressions
Anterior Cingulate Cortex 0.027 0.182 -0.004 0.187 0.660 0.512
Left Amygdala 0.194 0.202 0.206 0.226 -0.217 0.829
Right Amygdala 0.253 0.200 0.258 0.259 -0.076 0.940
Left Anterior Insula 0.101 0.162 0.033 0.145 1.714 0.092
Right Anterior Insula 0.085 0.161 0.055 0.172 0.706 0.483
Left IFG pars opercularis 0.146 0.189 0.05 0.166 2.105 0.039*
Right IFG pars opercularis 0.178 0.17 0.109 0.175 1.626 0.109
All Facial
Expressions
Anterior Cingulate Cortex 0.045 0.15 -0.034 0.212 1.75 0.085
Left Amygdala 0.235 0.163 0.203 0.264 0.576 0.568
Right Amygdala 0.266 0.162 0.234 0.254 0.580 0.565
Left Anterior Insula 0.087 0.144 0.023 0.140 1.794 0.078
Right Anterior Insula 0.075 0.141 0.043 0.162 0.844 0.402
Left IFG pars opercularis 0.146 0.192 0.067 0.169 1.721 0.090
Right IFG pars opercularis 0.178 0.166 0.103 0.184 1.726 0.089
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Note. TD = typically developing; ASD = autism spectrum disorder; IFG = inferior frontal gyrus; *p
< .05.
Figure 4
ROI Group Differences
Note. TD and ASD group percent signal change in left inferior frontal gyrus pars opercularis
during non-emotional facial expression condition.
ROI Analysis: Partial Correlations – Hypothesis 3 & 4
Alexithymia
There were no correlations between any alexithymia variables (AQC ID, AQC COMM, or
AQC total) with activation in any ROI during observation of facial expressions across all
participants or within any group. In the ASD group, when anxiety is included in the model, all
relationships between alexithymia variables remain non-significant (p’s > .05).
Interoceptive sensibility
Across all participants as well as within the TD group, there were no significant
relationships between BPQ-VSF, BSE, or BSE-neg, and neural ROIs (p’s > .05). In the TD
75
group, the PH-C was negatively correlated with right amygdala activation during the all facial
expression condition (r = -.409, p = .018). In the ASD group, left amygdala activation and left
IFGop were negatively correlated with the BPQ-VSF during all facial expressions (Figure 5; r = -
.627, p = .012; r = -.563, p = .029, respectively). The inclusion of anxiety in the model did not
change either of the significant relationships in the ASD group.
Emotional Empathy
There were no significant correlations between activation level in any of the ROI’s and
emotional empathy scores in the TD group. EmQue-CA AE was positively associated with right
AI activation during facial expression observation across all participants, (r = .300, p = .020),
and within the ASD group (Figure 5; r = .413, p = .047). In the ASD group, when including
anxiety in the model, EmQue-CA AE was no longer positively associated with right AI activation
at a significant level during facial expression observation (r = .383, p = .078).
Stepwise Linear Regression Analysis in ASD – Hypothesis 3 & 4
Anterior Cingulate Cortex
In the ASD group, none of the included variables significantly predicted the variance in
ACC activation with or without the BPQ-VSF in the model.
Anterior Insula
A significant regression equation for the left AI was observed (F(3, 11) = 11.584, p =
.001), with an R
2
of 0.760. ASD participants predicted left AI activity was equal to -0.051 - .005
(BPQ-VSF) + .023 (BSE) + .011 (IRI EC). Results indicated that in the ASD group interoceptive
sensibility, bodily sensation during emotion, and empathic concern scores predicted 76% of the
variance in left AI activation. Specifically lower BPQ scores, higher sensation during emotion,
and higher IRI EC predict higher AI activation during facial expression observation. A significant
regression equation was also observed in the right AI (F(1, 13) = 6.122, p = .028), with an R
2
of
0.320. ASD participants predicted right AI activity was equal to -0.027 - .007 (BPQ-VSF).
Results indicated that in the ASD group interoceptive sensibility predicted 32% of the variance
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in right AI activation, with lower BPQ scores predicting higher AI activation during facial
expression observation.
Figure 5
Scatter Plots of Significant Correlations in the ASD Group
Note. A. Empathy Questionnaire for Children and Adolescents Affective Empathy subscore with
right anterior insula percent signal change during facial expression observation (partial
correlation significance with age, VCI, and gender: r = .413, p = .047) B. Body Perception
Questionnaire-Very Short Form with left amygdala percent signal change during facial
expression observation (partial correlation significance with age, VCI, and gender; r = -.627, p =
.012).
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Amygdala
A significant regression equation for the left amygdala was observed (F(1, 20) = 5.294, p
= .032), with an R
2
of 0.209. ASD participants predicted left amygdala activity was equal to -
0.004 - .283 (AQC COMM). Results indicated that in the ASD group AQC COMM predicted
20.9% of the variance in left amygdala activation, with less severe AQC COMM scores
predicting higher left amygdala activation during facial expression observation. In the ASD
group, none of the included variables significantly predicted the variance in right amygdala
activation with or without the BPQ-VSF in the model.
Inferior Frontal Gyrus Pars Opercularis
A significant regression equation for the left IFGop was observed (F(1, 20) = 5.511, p =
.029), with an R
2
of 0.216. ASD participants predicted left IFGop activity was equal to -0.030 +
.018 (IRI EC). Results indicated that in the ASD group IRI EC predicted 21.6% of the variance in
left IFGop activation, with higher IRI EC scores predicting higher left IFGop activation during
facial expression observation. A significant regression equation was also observed for the right
IFGop (F(1, 20) = 9.691, p = .005), with an R
2
of 0.326. ASD participants predicted right IFGop
activity was equal to -0.013 + .011 (Conners-3AI). Results indicated that in the ASD group,
Conners-3AI scores predicted 32.6% of the variance in right IFGop activation, with higher
Conners-3AI severity predicting higher right IFGop activation during facial expression
observation.
Hierarchical Regressions – Hypothesis 5
Hierarchical linear regression models were run across all participants to assess whether
alexithymia explained variance in emotional empathy outcomes and activation of ROIs during
emotional facial expression observation above and beyond the influence of diagnostic group.
Variance in IRI EC and EmQue-CA AE were not significantly explained by the model, or any of
its individual predictors. However, the model including: age, sex, WASI-II: VCI, and AQC total
explained 27.2% of the variance in personal distress across participants. The addition of group
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to this model did not explain any additional variance in personal distress (Table 3). Variance in
AI, IFGop, and ACC were not significantly explained by the models including alexithymia.
However, in the right amygdala, the model including: age, sex, WASI-II: VCI, and AQC total
explained 16.5% of variance in activation during emotional facial expression observations. The
addition of group to this model increased the R
2
by .036, and accounted for 20.1% of the
variance in right amygdala activation during emotional facial expression observation, but this
was not a significant change (p = .109, Table 4) suggesting that the inclusion of group does not
add to our understanding of changes in right amygdala activation. Thus, to summarize, an
unexpected finding was that across groups, greater alexithymia behaviorally predicted higher
personal distress, and neurally predicted higher right amygdala activation in emotional facial
expression observation above and beyond the influence of group status (ASD).
Table 3
Summary of Hierarchical Regression Analysis for IRI Personal Distress Across Groups
Model 1 Model 2
Variable B SE B β B SE B β
Age
-.154 .270 -.065 -.155 .273 -.065
Sex
3.219 1.340 .271 3.258 1.359 .274
WASI-II: VCI
-.022 .041 -.061 -.020 .042 -.055
AQC total
.511 .133 .437 .501 .140 .428
Group
.318 1.239 .030
R
2
.272
.273
Change in R
2
.001
Note. WASI-II: VCI = Wechsler Abbreviated Scale of Intelligence, Second Edition: Verbal
Comprehension Index; AQC = Alexithymia Questionnaire for Children.
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Table 4
Summary of Hierarchical Regression Analysis for Right Amygdala Activity During Observation of
Emotional Faces Across Groups
Model 1 Model 2
Variable B SE B β B SE B β
Age
-.008 .011 -.088 -.008 .011 -.084
Sex
.007 .056 .016 -.003 .055 -.006
WASI-II: VCI
-.005 .002 -.366 -.006 .002 -.405
AQC total
.012 .006 .268 .015 .006 .324
Group
-.082 .051 -.201
R
2
.165
.201
Change in R
2
.036
Note: WASI-II: VCI= Wechsler Abbreviated Scale of Intelligence, Second Edition: Verbal
Comprehension Index; AQC = Alexithymia Questionnaire for Children.
Discussion
This study found that hypothesis 1 was partially supported, specifically, participants with
ASD demonstrated higher alexithymia (in difficulty communicating emotions), however,
interoceptive sensibility and emotional empathy scores were not different between ASD and TD
groups. Hypothesis 2, testing between group neural differences in regions commonly involved in
emotional empathy, was partially supported; in only one ROI, the IFGop, we found lower activity
for the ASD compared to the TD participants when viewing others' facial expressions.
Hypothesis 3 was supported in the ASD group, with less severe alexithymia scores predicting
higher left amygdala activation, higher empathic concern scores predicting higher AI and IFGop
activation, and higher BSE predicting higher AI activation during observation of facial
expressions. Hypothesis 3 was also supported across all participants, as emotional empathy
scores were correlated with AI activation. However, contrary to hypothesis 3, a negative
relationship between BPQ-VSF and IFGop, amygdala, and AI was observed. Hypothesis 4 was
partially supported; adding anxiety to the model made a previous positive relationship between
EmQue-CA AE and AI activation in the ASD group no longer significant. However, contrary to
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hypothesis 4, the significance of relationships in the ASD group between interoceptive
sensibility and activity in ROIs did not change if anxiety was added as covariates to the models.
Hypothesis 5 was partially supported, alexithymia, more so than group membership (ASD, TD),
predicted maladaptive emotional empathy functioning (greater personal distress), . However,
contrary to the hypothesis, alexithymia did not predict reduced empathic concern, nor did it
neuronally predict activity in the ACC, IFGop, or AI. Instead, alexithymia was strongly predictive
of positive amygdala activation, more so than the effect group membership (ASD, TD). The
implications of these findings are discussed in depth below.
Empathy & Alexithymia: Behavioral
In this sample, the ASD group had increased social impairment, alexithymia severity,
ADHD symptomatology, and anxiety; and intact affect recognition, and self-reported
interoception and emotional empathy skills. This study did not find support for a behavioral
reduction in emotional empathy in ASD. As suggested by previous work (e.g. SOME model; Bird
& Viding, 2014) the ASD group performed (near significantly, p = .052) worse than the TD group
on the ToM measure, but not emotional empathy measures. Mechanisms unique to ASD may
be more relevant to cognitive aspects of empathy like ToM, whereas mechanisms related to
alexithymia may be more related to emotion processing aspects of empathy.
Hierarchical regression demonstrated that alexithymia predicted personal distress above
and beyond ASD group membership. Although this is the opposite direction of the original
alexithymia hypothesis (higher alexithymia, lower emotional empathy), it does give support for
the idea that alexithymia is associated with potentially maladaptive forms of empathy. Personal
distress is associated with outcomes such as ruminative coping, neuroticism, depression, self-
criticism, and negative self-concept, while empathic concern shows an opposite pattern of
relationships with those outcomes (Kim & Han, 2018). As regressions do not test causality, it
may also be the case that the more strongly an individual feels personal distress by the
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experiences of others, the more overwhelmed by emotional experiences they are, and this may
impact their ability to identify and communicate their emotions (alexithymia).
Emotional Empathy Neural Regions of Interest
IFGop
Consistent with prior studies, when comparing between groups we found reduced IFGop
activity in individuals with ASD (Dapretto et al., 2006; Greimel et al., 2010; Kilroy et al., 2020;
Klapwijk et al., 2016; Schulte-Rüther et al., 2011) during facial expression observation.
Additionally, the strongest predictor of left IFGop activation in the ASD group was empathic
concern. Thus in individuals with ASD, reduced IFGop activation may relate to difficulties with
empathic concern. Consistent with this notion, prior studies show that damage to this region
(lesions) results in deficits in emotional empathy and emotion recognition (Shamay-Tsoory et
al., 2009). Further individual differences in trait empathy (personal distress, empathic concern,
and perspective taking) have previously been positively correlated with activity in the IFGop
(e.g. Aziz-Zadeh et al., 2010; Gazzola et al., 2006; Kaplan & Iacoboni, 2006; Saarela et al.,
2007). Importantly, alexithymia was not correlated with IFGop activation, it did not come out as
a significant predictor of IFGop in the stepwise regression model in ASD, or the hierarchical
regression model of IFGop across groups. These results suggest that empathic concern may be
impacted in ASD regardless of presence of alexithymia comorbidity/severity. Although we find
no behavioral differences in empathic concern in the ASD group, these results indicate that
some of the neural regions important for its processing may be compromised in ASD and
alternate neural routes may be utilized instead. Therefore, the underlying neural processing of
empathic concern may be altered in ASD.
Amygdala
Our findings in the amygdala show different patterns in the left and right hemisphere. In
the left hemisphere, in the ASD group we find that scores on the alexithymia communicating
emotions subscale are the strongest predictor of reduced left amygdala activity. This is
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consistent with previous literature linking left amygdala activation and gray matter volume to
reduced alexithymia severity (Goerlich-Dobre et al., 2015; Ihme et al., 2013), particularly in ASD
(Silani et al., 2008). However, in the right hemisphere we find the opposite pattern: across
groups, alexithymia total score predicts increased activity in the right amygdala, above and
beyond the influence of group. This is consistent with some previous studies, that suggest that
the right amygdala is involved in increased reactive/impulsive aggression in individuals with
alexithymia (Farah et al., 2018), and with increased responses to displays of anger in individuals
with alexithymia (Hadjikhani et al., 2017). Taken together with the findings here linking
alexithymia and personal distress; in the non-speaking right hemisphere, increased emotion
processing of others in the amygdala may lead to overwhelming emotions, and be related to
higher alexithymia. In the language-capable left hemisphere, in individuals with ASD, ability to
speak about one’s emotions (lower alexithymia for communicating emotions) is instead linked
with higher emotion resonance with others (higher activity in left amygdala).
The amygdala may be important for functions of emotional salience, processing facial
expressions, producing facial expressions of emotion, detection of emotional significance in
faces and speech, and the generation of emotional feelings (Bermond et al., 2006, Goerlich-
Dobre et al., 2014, Kano & Fukudo, 2013, Larsen et al., 2003, Moriguchi & Komaki, 2013,
Taylor & Bagby, 2004, Wingbermühle et al., 2012). Importantly, the amygdala and insula are
connected in automatic rapid evaluation of environmental stimuli for emotional information
(Carretié, 2014). Reduced activation in the left and increased activation in the right in response
to others facial expressions in individuals with ASD and alexithymia could interfere with
computation of affective meaning, salience, and value, causing individuals with alexithymia to
miss and therefore not process or react to subtle emotional signals and thus interfere with social
interactions.
Interoception
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This study did not find behavioral evidence for a reduction in self-reported interoceptive
sensibility in youth with ASD. However, an unexpected finding was the negative relationships
between BPQ-VSF and activity in the amygdala and IFGop. Similarly, a negative correlation
was found with the PH-C and the amygdala in the TD group. Further, interoceptive sensibility
was significantly negatively predictive of variance in the left and right AI in the regression
analyses in individuals with ASD. These results may be reflective of: 1) compensatory
mechanisms of increased neural activation in those with ASD who have reduced interoceptive
ability; 2) generally poor interoceptive ability may result in hypervigilance to bodily signals and
have an inverse relationship with interoceptive accuracy in ASD (Garfinkel et al., 2016).
Importantly, interoceptive accuracy and interoceptive sensibility may be independent from each
other (Garfinkel et al., 2015), and do not always agree in the directionality of their relationships
behaviorally (Pollotos & Georgiou, 2016), particularly in ASD (Garfinkel et al., 2016). Further,
while interoceptive accuracy ability (e.g. heart rate tracking paradigms) is associated with higher
activation and connectivity of emotional empathy regions (Kleint et al., 2015; Sedeño et al.,
2014; Wang et al., 2019) self-reported interoceptive sensibility has, in some cases, been
associated with lower neural activation and connectivity in emotional empathy regions (Stern et
al., 2017; Wang et al., 2020). While many studies have found no significant differences in
cardiac interoceptive accuracy between TD and ASD groups (Mash et al., 2017; Nicholson et
al., 2018; Schauder et al., 2015; Shah et al., 2016a; Shah et al., 2016b), several reports suggest
differences in self-reported interoceptive sensibility, reflecting diminished attention to and
interpretation of interoceptive cues (Fiene et al., 2018; Fiene & Brownlow, 2015; Garfinkel et al.,
2016; Mul et al., 2018). Garfinkel et al., 2016, found that individuals with ASD actually had an
inverse relationship between self-reported interoceptive sensibility and experimentally measured
interoceptive accuracy on a heart-beat tracking task; where the more confident a rater was
about their interoceptive sensibility, the worse they performed on an interoceptive accuracy
task. These results indicate that individuals with ASD may have a particular difficulty in
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accurately reporting their interoceptive ability when asked in questionnaires. This discrepancy
could impact social–emotional functioning by increasing the likelihood of incorrect interpretation
of one’s own interoceptive cues (Failla et al., 2020).
Self-reported global interoceptive sensibility may be related to impaired multisensory
integration and cognitive-affective control, resulting in increased hyper-vigilance toward bodily
signals (Wang et al., 2020). Further, Stoica & Depue (2020) found that in TD individuals,
emotional empathy (personal distress in particular) and an interoceptive sensibility subscale
were negatively related to each other, and increased resting state BOLD variability in the right
inferior frontal operculum (rIFO) was associated with increased emotional empathy and a
reduced interoceptive sensibility. Authors suggest that directing attention towards bodily
sensations may relieve personal distress, but not necessarily allow for perspective taking or
prosocial empathic behavior with the individual experiencing pain without employing additional
cognitive strategies in the mentalizing/ToM network (Stoica & Depue, 2020).
Interestingly, in the interview assessment, which uses experimenter-led questioning
about frequency, location, and intensity of physical sensation experienced during multiple
emotional states, we actually see a positive association with AI activation (the expected
direction). The assessment format itself (probing from the interviewer to recall situations that
elicited emotions), and the linkage to specific emotionally salient experiences rather than global
body awareness, may allow for easier and more accurate reporting of awareness of bodily
signals. Further illustrating a mismatch between the questionnaire data and interview data,
these two variables are not correlated in the ASD group. So, although children with ASD who
have higher AI activation to facial expressions may report a lower general sense of
interoception, these same people do report higher instances of experiencing bodily sensations
during emotional experiences. Given that the interview data aligns with previous literature of AI
functioning and interoceptive processing, I will refrain from interpreting the directionality of the
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BPQ-VSF results in the discussion, and instead refer to correlation and regression results as,
simply, relationships with interoceptive ability.
Influences on Emotional Empathy in ASD
The ASD group showed increased anxiety as compared to the TD group. However, the
inclusion of anxiety in partial correlations only made a significant impact on one relationship that
was observed, and anxiety was not a significant predictor of any of the ROI’s activation in
regression analysis. Consistent with the findings of Lassalle et al. (2018), in the ASD group,
when including anxiety in the model, an emotional empathy outcome was no longer positively
associated with right AI activation at a significant level during facial expression observation. This
result suggests that, in ASD, anxiety may influence the relationship between trait empathy and
activation of the right AI, a neural region commonly associated with emotional empathy
processing. Further evidence is needed to substantiate a direct link between anxiety and the
neural and behavioral outcomes of emotional empathy in ASD, and future studies should include
alexithymia in emotional empathy investigations.
Limitations and Future Directions
There are many limitations to consider when interpreting the current study’s results.
First, individuals diagnosed with ASD consist of a very heterogeneous population; the relatively
small sample size and homogeneity of the participants in this sample (all right handed, IQ > 80,
etc.), make generalization to the whole spectrum of ASD limited. Some of the main variables of
interest in the study can only be measured in individuals who are verbal, future studies should
further investigate the influence of VCI on the relationships reported here. Additionally, the AQC
was used as a gold standard tool for measuring alexithymia in youth, however, like the TAS-20
(Bagby et al., 1994), this questionnaire captures more cognitive aspects of alexithymia including
identifying emotions and communicating emotions, but does not measure additional
emotionalizing aspects, which should also be explored in ASD.
Secondly, the measures used to capture interoception, alexithymia, and empathy ability,
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are self-report measures. Accurate self-report can be particularly challenging for individuals with
ASD who may have difficulty with metacognition and self-referential insight. For this reason,
there was an experimenter-led interview which also measured some of the main variables in the
self-report data. Future studies may consider examining these questions in a larger and more
heterogeneous sample of ASD participants, employing more observational and standardized
measures when possible, including the multidimensional emotional aspects of alexithymia, such
as emotionalizing and fantasizing.
Lastly, the imaging task was a passive task, future studies may consider comparing
passive facial expression observation to more active tasks with specific instructions to
encourage empathizing. Additionally, in some cases the fMRI data was analyzed by combining
two facial expression conditions; one that displayed overt emotions (e.g., happy, sad, angry)
and another condition that consisted of more neutral facial expressions (e.g., puff cheeks, raise
eyebrows). Future studies should include more stimuli in order to have the power to separate
stimuli presentations and explore differential impacts of expressions presented.
Conclusions
Here we show that support for the alexithymia hypothesis behaviorally in personal
distress, and neurally in the amygdala. In particular, higher alexithymia was associated with
increased personal distress, and was the strongest predictor of amygdala activation, though this
pattern differed in the left and right amygdala. Additionally, controlling for alexithymia reduced
the correlation between AI and emotional empathy to a trend level (p = .052). Thus alexithymia,
more than ASD diagnosis, may represent abnormal neural functioning in the amygdala, and
thus impact aspects of emotional empathy processing.
However, inconsistent with our hypotheses, the results do not support the notion that
alexithymia is a strong predictor of AI activity, although it may modulate the relationship
between emotional empathy and AI activity. Instead, emotional empathy and interoceptive
measures (not alexithymia scores) were the strongest predictors of AI activity, consistent with
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the notion that the AI receives sensory and visceral signals and is involved in translating these
to feeling states, including empathic concern (Damasio & Carvalho, 2013). In the IFGop, where
significant group differences have been found during observation and imitation of facial
expressions (Dapretto et al., 2006; Greimel et al., 2010; Kilroy et al., 2020; Klapwijk et al., 2016;
Schulte-Rüther et al., 2011), no relationship with alexithymia was observed either. Instead,
IFGop activity was most predicted by empathic concern and ADHD impairment levels. In
addition to action simulation, imitation, and motor processing, the right IFG in particular has
previously been associated with motor response inhibition (Aron et al., 2014). Individuals with
ADHD may have to make greater efforts to stay still, and to inhibit pantomiming motor actions
during a passive action observation task, resulting in greater IFG activation. Therefore, the
IFGop seems to be involved with the inter-relation between empathic concern and motor
inhibitory processing, which may be important for translating empathic emotions into appropriate
motor action programs in ASD, consistent with the IFGop involvement in social and motor
processing via sensorimotor simulation (de Waal & Preston, 2017).
Thus, our data indicate a dynamic interplay between: 1) the amygdala, which is strongly
involved in emotional empathy processes and affected by alexithymia presence, differentially in
right and left hemisphere, above and beyond ASD diagnosis; 2) the AI, which is strongly
involved in visceral and interoceptive sensory processing, and empathic concern processing;
and 3) the IFGop, which is involved in empathic concern, attentional/inhibitory, and social and
motor processing, and shows differences when comparing groups based on ASD diagnosis.
These results help clarify previous inconsistencies in the literature – namely activity in some
regions commonly found to be hypoactive in ASD (IFGop, insula) are related to empathic
concern, whereas activity in other regions (amygdala) are more related to presence of
alexithymia than an ASD diagnosis and are involved in processing related to emotional empathy
– personal distress.
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Chapter 5. Alexithymia, Interoception, Emotional Empathy and Task-Based Functional
Neural Connectivity Autism Spectrum Disorder
Abstract
Background: The purpose of this study was to answer the question: how do emotional
empathy, alexithymia, and interoception interact with neural connectivity of mechanisms of
emotion processing during facial expression observation in TD and ASD youth. Methods: Study
participants included youth ages 8 to 17 (mean 12.04 ± 2.20) who were typically developing
(TD; n = 37, 11 female) or had a diagnosis of ASD (n = 28, 6 female). Participants completed
Interpersonal Reactivity Index (IRI: Davis, 1983), Empathy Questionnaire for Children and
Adolescents (EmQue-CA; Rieffe, Ketelaar & Wiefferink, 2010), Alexithymia Questionnaire for
Children (AQC; Rieffe et al., 2006), and Child and Adolescent Symptom Inventory (CASI-Anx;
Sprafkin et al., 2002), self-report questionnaires. A measure of self-reported interoceptive
sensibility (Body Perception Questionnaire, BPQ-VSF; 25 TD, 18 ASD) and an interview
measure of bodily sensation during emotion experience (BSE; 25 TD, 23 ASD) were also
collected in a subset of individuals. fMRI data was collected inside a 3-T Siemens MAGNETOM
Prisma scanner, where participants observed video clips of facial expressions presented in a
block design. Functionally defined neural regions of interest (ROIs) spheres were created from a
neurosynth empathy meta-analysis, and included the left ACC, left ventral AI, right dorsal AI,
right amygdala and right IFGop. Psycho-physiological interaction (PPI) analysis was performed
to determine task-based functional connectivity between left ventral AI seed region, and the rest
of the brain. Parameter estimates of predetermined ROIs were extracted, and pairwise group
comparisons were conducted using two-tailed independent samples t-tests (TD vs ASD).
Pearson partial correlation coefficients were computed to assess relationships between
behavioral variables and ROI connectivity parameter estimates (all, TD, ASD). To better
understand which features were the strongest predictors of neural connectivity in the ASD group
in particular, stepwise multiple linear regression analysis was used to estimate model fit of
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predictors on ROI connectivity during the fMRI task. To further test the alexithymia hypothesis,
hierarchical linear regression models were run across all participants to assess whether
alexithymia explained variance in emotional empathy outcomes and connectivity of ROIs during
emotional facial expression observation above and beyond the influence of diagnostic group.
Results: Behaviorally, the ASD group had increased alexithymia severity (AQC COMM) and
physiological hyperarousal (PH-C), but no significant differences from TD were found in the
ASD group for self-reported interoception (BPQ-VSF, BSE, BSE-neg) or emotional empathy
skills (IRI PD & IRI EC). Across all participants, alexithymia scores were positively correlated
with personal distress, and negatively correlated with empathic concern. A hierarchical linear
regression model including: age, sex, WASI-II: VCI, and AQC total behaviorally predicted higher
personal distress above and beyond the influence of group status (ASD). For bodily awareness
variables we observed: across all participants, physiological hyperarousal was positively
correlated with alexithymia, and the BPQ-VSF was negatively correlated with empathic concern;
but in the ASD group sensation felt during emotion was negatively correlated with personal
distress, alexithymia, and anxiety. Neurally, in the whole brain analysis, the ASD group had
higher connectivity between the left ventral AI and the right lateral prefrontal cortex than the TD
group during non-emotional facial expression observation. Across all participants, and in
individual groups, there was a negative correlation between alexithymia severity total score and
left AI-left precuneus connectivity during all facial expressions. Across all participants, and in
individual groups (TD, ASD) there was a negative correlation between interoceptive sensibility
and connectivity between the left AI and the left dorsal premotor cortex during non-emotional
facial expressions. In our neural ROI’s, in the ASD group, right dorsal AI-left ventral AI
connectivity was negatively correlated with alexithymia during all facial expressions. Across all
participants and within the ASD group BSE-neg was positively correlated with right IFG-left
ventral AI connectivity during emotional facial expression observation. In the ASD group bodily
sensation awareness during emotion significantly predicted 24% of the variance in left ACC-AI
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connectivity. Post-hoc analysis, in the ASD group only, demonstrated correlations between
connectivity of left ventral AI and right amygdala for the ADIR language and communication
impairments subscale. Conclusions: Behaviorally we show that interoception and emotional
empathy ability are intact in ASD, and that alexithymia severity is higher in ASD. We find inverse
patterns of relationships with emotional empathy and alexithymia, where alexithymia is
positively correlated with personal distress and negatively correlated with empathic concern. We
show support for the alexithymia hypothesis only in the personal distress domain, as alexithymia
predicted higher personal distress above and beyond the influence of group status (ASD).
Neurally, alexithymia is associated with reduced left AI-left precuneus connectivity, and reduced
right dorsal AI-left ventral AI connectivity during facial expression observation. For bodily
awareness variables we observed that BPQ-VSF may be indexing maladaptive empathy
outcomes, (reduced emotional empathy) and may measure something more similar to
physiological hyperarousal, which was positively correlated with alexithymia. However, for our
interview variable reports of sensation felt during emotion were negatively correlated with
personal distress, alexithymia, and anxiety – suggesting this method of collecting interoceptive
information may be more similar to what is found in interoceptive accuracy studies. This is
echoed in the neural data, where here was a negative correlation between interoceptive
sensibility and connectivity between the left AI and the left dorsal premotor cortex, but a positive
relationship between BSE and right IFG-left ventral AI connectivity, left ACC-AI connectivity
during facial expression observation. Lastly, in the ASD group, we see higher connectivity
between left ventral AI-right lateral prefrontal cortex during facial expression observation,
perhaps reflecting increased inhibitory control over emotional action responses, increased
modulation of automatic emotion reactions, and potentially affect labeling processes as a form
of emotion regulation. Additionally, autism severity as indexed by communication deficits, was
linked with decreased left ventral AI-right amygdala connectivity, regions known to be highly
involved with socio-emotion and salience processing.
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Introduction
Empathy, the ability to understand and experience the feelings of others, is one
important social skill that may be compromised in ASD (Dvash & Shamay-Tsoory, 2014).
Cognitive empathy involves mentally imagining how another person is thinking or feeling (de
Waal & Preston, 2017), while emotional empathy involves sharing the emotions others are
experiencing (Davis et al., 1994). There is a large body of research that suggests that
individuals with ASD have difficulties with cognitive empathy (for review see Frith & Happé,
2005). However, evidence for emotional empathy difficulties are mixed. Some studies find that
individuals with ASD display reduced emotional empathy (Bos & Stokes, 2018; Kasari et
al.,1990; Lombardo et al., 2007; Mathersul et al., 2013; Minio-Paluello et al., 2009; Peterson et
al., 2014; Schulte-Rüther et al., 2011; Shamay-Tsoory et al., 2002; Sigman et al., 1992;
Sucksmith et al., 2013; Yirmiya et al., 1992) while others have not found support for any
emotional empathy differences (Bellebaum et al., 2014; Deschamps et al., 2014; Dziobek et al.,
2008; Hadjikhani et al., 2014; Jones et al., 2010; Markram et al., 2007; Pouw et al., 2013;
Rogers et al., 2007; Rueda et al., 2015; Schwenck et al., 2012; Smith, 2009). Some evidence
suggests deficits in empathy in ASD may be attributed to co-occurring alexithymia rather than
ASD diagnosis (Bird & Cook, 2013), while others suggest interoception impairments underlie
potential reductions in emotional empathy in ASD (Quattrocki & Friston, 2014). Further, many
hypothesize that underlying ASD symptomatology could be due to global abnormalities in neural
connectivity (e.g. Baharmand & Doesburg, 2019; Nair et al., 2020; O’Reilly et al., 2017; Valenti
et al., 2019). Neural functional connectivity, a measure of the synchronization of activation
among brain regions, is a proxy for coordination of processing between brain regions. In this
study, I will focus on how interoceptive sensibility and alexithymia may differentially impact the
neural connectivity of regions associated with emotional empathy during a facial expression
observation task in youth with ASD and their typically developing (TD) peers.
Neural Mechanisms of Emotional Empathy
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The network of neural mechanisms involved in emotional empathy include the inferior
frontal gyrus pars opercularis (IFGop), anterior cingulate cortex (ACC), amygdala, and the
anterior insula (AI, Dvash & Shamay-Tsoory, 2014). These regions (AI, ACC, amygdala, IFGop)
are commonly active when experiencing emotion and when observing, or even hearing, another
person having an emotional experience (e.g., Adolphs, 2010; Fan et al., 2011; Hein & Singer,
2008; Jabbi et al., 2007; Sander et al., 2007; Seara-Cardoso et al., 2016). If these regions are
damaged, patients may demonstrate deficits in emotional empathy and emotion recognition
(Shamay-Tsoory et al., 2009). Further, individual differences in trait empathy have been
correlated with increased activity in the IFGop (e.g., Aziz-Zadeh et al., 2010; Gazzola et al.,
2006; Jackson et al., 2005; Kaplan & Iacoboni, 2006; Saarela et al., 2007), AI (e.g., Seara-
Cardoso et al., 2016; Singer et al., 2006), amygdala (e.g., Seara-Cardoso et al., 2016), and the
ACC (Masten et al., 2011). Other studies have focused on connectivity between emotional
empathy brain regions during empathy or emotional face processing tasks. Jabbi and Keysers
(2008) demonstrated functional connectivity between IFG and insula when observing others
experiencing disgust. During emotional face processing some have found significant
connectivity between left and right amygdala (Skelly & Decety, 2012), and between amygdala
and premotor cortices (Diano et al., 2017). In empathy for pain studies, viewing people in painful
scenarios resulted in increased functional connectivity between right IFG and ACC (Naor et al.,
2020) as well as left amygdala and ACC, supplementary motor area, medial orbitofrontal cortex
and AI (Akitsuki & Decety, 2009). In a task where participants read vignettes, stories that
described emotional pain showed connectivity between amygdala and regions of the
mentalizing/theory of mind network (temporal parietal junction, superior temporal sulcus, and
precuneus), and connectivity in pain matrix brain regions (ACC, insula) in stories describing
physical pain (Bruneau et al., 2015).
Emotional Empathy in ASD: Neural
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Numerous studies have investigated neural responses of empathy for pain in ASD (Bird
et al., 2010; Fan et al., 2014; Gu et al., 2015; Hadjikhani et al., 2014; Krach et al., 2015;
Lassalle et al., 2018; Li et al., 2018), with mixed results. Activation of emotion processing
regions during empathy for pain paradigms have shown no differences (AI & ACC; Krach et al.,
2015), increased neural responses (eg., ACC; Hadjikhani et al., 2014; AI; Gu et al., 2015) and
decreased neural responses in (ACC & AI; Fan et al., 2014; IFG; Lasalle et al., 2018) in
individuals with ASD. In models that do not include pain stimuli, numerous studies have
demonstrated individuals with ASD show decreased activation in the IFG to facial expressions
(Dapretto et al., 2006; Greimel et al., 2010; Kilroy et al., 2020; Klapwijk et al., 2016; Schulte-
Rüther et al., 2011) and when observing others experiencing disgusting tastes (Bastiaansen et
al., 2011). A meta-analysis of 39 studies also concluded that the AI has also consistently been
shown to have reduced activation in ASD in social vs. non-social processing, including facial
expression observation (Di Martino et al., 2009).
The AI has also been implicated in resting state functional connectivity (rsFC) studies,
with the hypothesis that this area, as well as the rest of the salience network (SN; ventral AI,
dorsal ACC, amygdala, hypothalamus, ventral striatum, thalamus, substantia nigra/ventral
tegmental area) is hypoconnected (Abbott et al., 2016; Ebisch et al., 2011; Marshall et al., 2020;
Nomi et al., 2019; Toyomaki & Murohashi, 2013; Uddin & Menon, 2009) is similar to TD
(Hoffman et al., 2016; Shi et al., 2020) or is hyperconnected (Uddin et al., 2013) in individuals
with ASD. Interestingly, reduced rsFC within the SN has been correlated with the degree of
social deficits in individuals with ASD (Abbott et al., 2016). Additionally, reduced connectivity
between nodes of the SN including AI and amygdala has suggested altered emotional arousal
and impaired simulation of others emotions in ASD (Ebisch et al., 2011). These findings indicate
the role of the SN in impairments in social and emotional functioning in ASD. However, studies
that have found no TD and ASD differences suggest preserved self-other distinction (emotional
egocentricity) during an emotional empathy task in individuals with ASD (Hoffman et al., 2016).
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Although more sparse, there are task-based functional connectivity (tbFC) studies
relevant to emotional empathy processing in ASD as well, many of which focus on the
amygdala. Monk and colleagues (2010) observed that individuals with ASD had stronger tbFC
between right amygdala and ventromedial prefrontal cortex, and weaker connectivity between
right amygdala and temporal lobe compared to TD participants in an attention cueing task with
emotional faces. Similarly, TD participants have demonstrated higher left amygdala-
ventromedial prefrontal cortex connectivity for sad faces, compared with an ASD group (Swartz
et al., 2013). Decreased connectivity between the right amygdala and the left inferior frontal
cortex was also observed in a group of ASD participants viewing emotional facial expressions
(Hoffman et al., 2015). In another study using only fearful faces, relative to the TD group,
children with ASD and co-occurring disruptive behaviors showed reduced connectivity between
the amygdala and middle temporal gyrus, precuneus, and superior parietal cortex (Ibrahim et
al., 2019). These conflicting findings, and the dearth of tbFC studies, suggest that additional
factors could be influencing functional connectivity in ASD, and therefore impacting social-
emotional functioning.
Alexithymia
One explanation for these inconsistent findings is that perhaps emotional empathy
difficulties seen in ASD are accounted for by co-morbid alexithymia (Bird & Cook, 2013; Bird et
al., 2010; Bird et al., 2011; Brewer et al., 2015; Cook et al., 2013; Gaigg et al., 2018; Heaton et
al., 2012; Sivathasan et al., 2020). Alexithymia (trouble recognizing, describing, and
distinguishing emotions in oneself) is common in individuals with high-functioning ASD (50%;
Hill et al., 2004; Kinnaird et al., 2019), while the incidence of alexithymia in the typical population
is estimated between 5-10% (Berthoz et al., 2011; Kinnaird et al., 2019). Both alexithymia
scores and an Interpersonal Reactivity Index (IRI: Davis, 1983) score (perspective taking and
empathic concern combined) have been shown to significantly correlate with activity in the AI
and left amygdala in individuals with ASD during an emotion introspection task (Silani et al.,
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2008). Others have shown that activation of AI to others' limbs in pain correlated with
alexithymia scores, and accounted for TD and ASD differences in empathy scores on the IRI
(Bird et al., 2010). Similarly, Lassalle et al. (2019) found when viewing pictures depicting hands
and feet in painful or disgusting situations, neural differences between TD and ASD groups
disappeared if alexithymia was controlled for (Lassalle et al., 2019).
Some data from connectivity studies support the alexithymia hypothesis. A structural
connectivity study demonstrated that alexithymia (and not an ASD diagnosis) was predictive of
reduced covariance of frontal affective networks from the insula to posterior regions, whereas
ASD was predictive of covariance in theory of mind networks of dorsomedial prefrontal cortex
and temporoparietal junction (Bernhardt et al., 2014). To my knowledge, no evidence to date
indicates how trait alexithymia impacts rsFC or tbFC in ASD. However, trait alexithymia alone
has been studied in rsFC. Studies have demonstrated lower connectivity within the default
mode network including the ACC, and higher connectivity in the precentral gyrus and right IFG
in high alexithymia individuals (Liemburg et al., 2012). However, another group found that
alexithymia was associated with higher intrinsic neural activity in rostral dorsal ACC and lower
connectivity in the amygdala than TD participants (Han et al., 2018). Another study found that
the degree of rsFC between the left amygdala and right dorsolateral prefrontal cortex was
positively associated with alexithymia scores in a group of refugees after controlling for
depression, post-traumatic stress disorder, and traumatic experiences (Kim et al., 2019). Lastly,
recent work reported lower functional and structural connectivity in the alexithymia groups
between pairs of regions: right inferior temporal gyrus – post central gyrus; inferior temporal
gyrus – left insula; right superior frontal gyrus – right ACC; left superior frontal gyrus – left ACC
(Fang et al., 2018). Given the evidence that alexithymia is characterized by neural functional
connectivity disturbances, and the high incidence of alexithymia in ASD, further research is
needed to explore the influence alexithymia has on functional connectivity in individuals with
ASD. Due to the fact that alexithymia was not measured or accounted for in many of the earlier
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cited studies in ASD, a difference in alexithymia severity among participants with ASD could
potentially explain discrepant findings in emotional empathy related neural connectivity.
Interoception
Interoceptive sensibility, one’s aptitude to consciously perceive internal sensory
experiences, has also been linked to empathic ability (Fukushima et al., 2011; Grynberg &
Pollatos, 2015). Individuals with ASD may have alterations in self-reported interoceptive
sensibility, and interoceptive accuracy (e.g., heart rate tracking; for review DuBois et al., 2016),
particularly in childhood and adolescence (Failla et al., 2019; Nicholson et al., 2019). The AI is
cited as a common neural mechanism for both interoception (Critchley et al., 2005), and
emotion processing (Berthoz et al., 2002; Frewen et al., 2008; Karlsson et al., 2008), and the
amygdala is also thought to be a key region in a large network supporting allostasis and
interoceptive processing (Kleckner et al., 2017). In TD individuals, a meta-analysis of
overlapping activation between interoceptive accuracy, emotion, and theory of mind fMRI
studies found that the three task types converged in the AI, amygdala, right IFG, basal ganglia,
and medial anterior temporal lobe (Adolfi et al., 2017). It is suggested that co-activation of these
areas may result in an evaluation of internal bodily state, and in combination with external cues,
lead to higher level social cognition (Adolfi et al., 2017). Insular responses during cardiac
interoception tasks have been associated with self-reported autism social severity scores in
adults (Failla et al., 2019). Brewer et al. (2016) suggest that interoceptive failure leads to
alexithymia, which may then cause primary social impairments. In support of this hypothesis,
Murphy et al. (2018) found that those with high alexithymic traits relied less on interoceptive
cues for modulating respiratory output, muscular exertion, and taste sensitivity, when controlling
for comorbid conditions like ASD or anxiety. Some studies show that alexithymia can explain
interoceptive difficulties in ASD, and that interoceptive deficits should be considered a symptom
of alexithymia, rather than ASD per se (Shah et al., 2016, 2017). However, recent work has not
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provided evidence for interoceptive accuracy impairments in a sample of adults with ASD,
whether or not co-occurring alexithymia was considered (Nicholson et al., 2018).
Importantly, interoceptive accuracy and interoceptive sensibility may be independent
from each other (Garfinkel et al., 2015), and do not always agree in the directionality of their
relationships behaviorally (Pallotos & Georgiou, 2016), particularly in ASD (Garfinkel et al.,
2016). Further, while interoceptive accuracy ability (e.g. heart rate tracking paradigms) is
associated with higher activation and connectivity of emotional empathy regions (Kleint et al.,
2015; Sedeño et al., 2014; Wang et al., 2019) self-reported interoceptive sensibility has, in
some cases, been associated with lower neural activation and connectivity in emotional
empathy regions (Stern et al., 2017; Wang et al., 2020). Self-reported interoceptive sensibility
may be related to impaired multisensory integration and cognitive-affective control, resulting in
increased hyper-vigilance toward bodily signals (Wang et al., 2020). In fact, Stoica and Depue
(2020) found that in TD individuals, emotional empathy (personal distress in particular) and an
interoceptive sensibility subscale were negatively related to each other, and increased resting
state BOLD variability in the right IFGop was associated with increased personal distress and a
reduced interoceptive sensibility. Authors suggest that directing attention towards bodily
sensations may relieve personal distress, but not necessarily allow for perspective taking or
prosocial behavior with the individual experiencing pain without employing additional cognitive
strategies in the mentalizing/theory of mind network (Stoica & Depue, 2020). Additionally, in
some cases research has found that interoceptive sensibility is positively correlated with higher
alexithymia severity (Ernst et al., 2014; Longarzo et al., 2015), although assessing interoceptive
sensitivity (e.g., via the heartbeat detection task) report an inverse correlation with alexithymia
(Herbert et al., 2011). There is a dearth of research examining functional connectivity or
coactivation of regions associated with interoception and empathy in ASD. Only one study has
demonstrated reduced functional connectivity in an ASD group as compared to controls in an
interoceptive accuracy task in the AI, dorsal ACC, anterior prefrontal cortex, intraparietal sulcus,
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middle frontal cortex, and precuneus (Barttfeld et al., 2012). In this study, the AI was the most
predictive region of connectivity differences between an exteroceptive and interoceptive
attentional condition, and had a strong correlation with ADOS scores in the ASD group (Barttfeld
et al., 2012).
Alexithymia, Interoception and Empathy
Mul et al. (2018) compared a TD group with two groups of adults with ASD, one group
with and one without alexithymia. ASD participants with high alexithymia demonstrated lower
emotional empathy and lower cognitive empathy than ASD participants with low alexithymia
(Mul et al., 2018). In the same study, across groups ASD participants showed both reduced
interoceptive sensibility on the Multidimensional Assessment of Interoceptive Awareness (MAIA
Mehling et al., 2012); and heart-rate tracking interoceptive accuracy, and that alexithymia
scores mediated the relationship between interoceptive sensibility and emotional empathy (Mul
et al., 2018). However, given the BPQ-VSF results from Chapter 3 (negative relationship with
ROIs) and recent data (Stern et al., 2017; Wang et al., 2020), it may be that measures like the
BPQ-VSF will show a negative correlation with functional connectivity due to its potential to
index maladaptive symptoms of self-focused interoception more consistent with anxiety than
with interoceptive accuracy (see Trevisan et al., 2020 for review).
The current paper will address some of the gaps in this literature in the following ways.
First, unlike Mul and colleagues (2018), the current study will separate two major components of
emotional empathy (personal distress and empathic concern). Secondly, to my knowledge
although there has been some connectivity studies in this area (e.g. Barttfeld et al., 2012;
Bernhardt et al., 2014; Fang et al., 2018), there is no study to date that has explored task-based
functional connectivity relationships between alexithymia and interoception, with emotional
empathy in ASD.
Given the findings in chapters 2 and 3, it was hypothesized that: 1) overall, participants
with ASD compared to TD controls would have higher alexithymia, and intact interoceptive
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sensibility, and emotional empathy ability; 2) that interoceptive sensibility and alexithymia
severity would be negatively associated with emotional empathy ability; 3) participants with
ASD compared to TD controls would have lower neural connectivity in emotion-related brain
regions of interest (ROIs: insula, amygdala, IFG, and ACC) during facial expression
observation; 4) across participants and within each group (ASD, TD), emotional empathy would
be positively associated, and alexithymia severity and interoceptive sensibility would be
negatively associated with neural connectivity in ROIs; 5) within the ASD group anxiety may
contribute to relationships observed between emotional empathy, interoceptive sensibility, and
alexithymia with ROI connectivity during facial expression observation; 6) across participants,
higher alexithymia will predict higher personal distress and lower connectivity between neural
ROIs during facial expression observation.
Methods
Participants
Study participants included youth ages 8 to 17 (mean 12.04 ± 2.20) who were TD (n =
37, 11 female) or had a diagnosis of ASD (n = 28, 6 female). A measure of interoceptive
sensibility (25 TD, 18 ASD) and an interview measure of bodily sensation during emotion
experience (25 TD, 23 ASD) were collected in a subset of individuals from the total group due to
being added later in the study. Participants were recruited through advertisements and contacts
in community centers, social media sites, parent group website postings, healthcare clinics, and
schools. Inclusion criteria for all groups included: IQ > 80 on at least one composite score
(Verbal Comprehension Index, Perceptual Reasoning Index, Full Scale IQ–2, or the Full Scale
IQ–4) by a trained staff member on the Wechsler Abbreviated Scale of Intelligence 2nd edition
(WASI-II; Wechsler, 2011); and English fluency of child and parent. As this was part of a larger
imaging study, all participants were additionally screened for MRI compatibility, had no history of
loss of consciousness greater than five minutes, were right-handed, and were born after 36
weeks of gestation. Each family was informed about study procedures in accordance with the
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protocol approved by the University of Southern California’s Institutional Review Board.
Participants and parents then provided their written child assent and parental consent.
TDs were excluded if they had any psychological or neurological disorder, including
ADHD and generalized anxiety disorder. They were also excluded if they had a first degree
relative with an ASD diagnosis, or scored above a t-score of 60 on the Social Responsiveness
Scale, Second Edition (SRS-2; Constantino & Gruber, 2012); indicating a risk for ASD
symptoms including reduced social awareness, reciprocal social interaction, or presence of
restricted or repetitive behaviors. Eligible ASD participants had a previous diagnosis through
clinical diagnostic interview or diagnostic assessment, and met criteria on the Autism Diagnostic
Observation Schedule, Second Edition (ADOS-2; Lord et al., 2000), the Autism Diagnostic
Interview-Revised (ADI-R; Lord et al., 1994), or both administered by a trained researcher.
Individuals in the ASD group were excluded if they had another diagnosis of neurological or
psychological disorder with the exception of Developmental Coordination Disorder, attention
deficit hyperactivity disorder (ADHD), or generalized anxiety disorder, due to their high
prevalence in ASD (Mazzone et al., 2012). Eleven ASD participants had established
prescriptions for psychotropic medication at the time of the study (for ADHD and anxiety), and
no TD participants reported any prescription medication use.
Assessments
Anxiety and ADHD Measures
In addition to the inclusion criteria measures, the 20-item version of the parent-report
Childhood Adolescent Symptom Inventory (CASI-Anx; Sprafkin et al., 2002), was used to
assess symptoms of the six DSM-IV defined anxiety disorders. The 10-item Conners third
edition (Conners-3AI; Conners, 2008), a parent measure for children ages 6-18 years, was used
to characterize levels of ADHD symptoms. Both the CASI-Anx and the Conners-3AI are rated
on a four-point Likert scale from “never” to “very often.”
Empathy Measures
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Empathy ability was assessed using the self-report Interpersonal Reactivity Index (IRI;
Davis, 1983). Questions are rated on a five-point point Likert scale from “does not describe me
well” to “describes me very well”. The IRI is a 28-item self-report measure consisting of four 7-
item subscales: 1) Perspective Taking (IRI PT) a tendency to adopt and understand the
viewpoint of others, 2) Empathic Concern (IRI EC) feelings of sympathy and concern for others,
3) Personal Distress (IRI PD) feelings of personal unease in tense interpersonal settings, and 4)
Fantasy (IRI FS) a tendency to transpose oneself into the feelings of fictional characters. All IRI
subscales are reported in initial group comparisons, and subsequent analyses focus on the two
IRI aspects of emotional empathy (IRI EC and IRI PD).
The Empathy Questionnaire for Children and Adolescents (EmQue-CA; Rieffe et al.,
2010) is a 20-item self-report measure, represents three facets of empathy observable in
children: (1) affective empathy (closely related to empathic concern in this study) (2) cognitive
empathy and (3) intention to comfort. Questions are rated on a three-point Likert scale from “not
true” to “often true”. According to Overgaauw et al. (2017) cronbach’s alphas indicated good
internal consistencies with affective empathy, cognitive empathy, and intention to comfort; and
the affective and cognitive empathy scales were positively correlated with the corresponding IRI
scales. Cognitive and emotional empathy EmQue-CA subscales are reported in initial group
comparisons, and subsequent analyses focus on the affective empathy subscale (EmQue-CA
AE).
Additionally, trained research staff administered two subtests which are part of the
Developmental Neuropsychological Assessment (NEPSY-II; Korkman et al., 2007) Social
Perception Domain. Subtests used captured (1) Theory of Mind (ToM) that assesses, or visual
and verbal ability to comprehend the perspectives, experiences, and beliefs of others, and (2)
Affect Recognition (AR) that assesses the, or ability to identify and discriminate between facial
affects. The NEPSY-II ToM subscale is an additional measure of CE ability beyond the self-
report IRI.
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Alexithymia Measure
Alexithymia was measured using the 20-item self-report Alexithymia Questionnaire for
Children (AQC; Rieffe et al., 2006). Items were rated on a three-point Likert scale from “not true”
to “often true”, and consisted of questions like “I am often confused about the way I am feeling
inside.” Two subscales are used to assess alexithymia: difficulty identifying feelings (AQC ID),
and difficulty describing and communicating feelings (AQC COMM). The third subscale,
externally oriented thinking (AQC EOT) was not used in this study due to an unacceptably low
Cronbach’s alpha in the original psychometric paper, and because alexithymia can be reliably
assessed in youth without the eight items rating the AQC EOT dimension (Loas et al., 2017).
Considering this, the 2 factor total was calculated instead and used as the AQC total in this
study.
Embodiment Measures: Interoception, Physiological Arousal & Bodily Sensation of
Emotion
For interoceptive sensibility, the 12 item self-report Body Perception Questionnaire-Body
Awareness Very Short Form (BPQ-VSF; Cabrera et al., 2018; Porges, 1993) was used to
assess the subjective awareness of the function and reactivity of target organs and structures
that are innervated by the autonomic nervous system. The BPQ-VSF only probes aspects of the
body awareness subscale of the original BPQ, with items like, “During most situations, I am
aware of how fast I am breathing.” Questions are rated on a five-point Likert scale from “never”
to “always.”
Another measure used to capture bodily experience, the Physiological Hyperarousal
Scale for Children (PH-C; Laurent et al., 2004) is a brief self-report measure of frequency of
physical experiences associated with physiological hyperarousal. This 18-item scale asks
questions such as, “How often have you felt or experienced being unable to catch your breath in
the last two weeks?”. Questions are rated on a five-point Likert scale from “never” to “all of the
time.” It is important to note that while the BPQ-VSF and the PH-C capture similar bodily
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information, the BPQ-VSF is more focused on general awareness of bodily sensations i.e. “how
fast my heart is beating,” and the PH-C is probing the frequency of physiological experiences
associated with hyperarousal i.e. “heart-pounding.”
As an additional measure of interoceptive and emotional awareness, a semi-structured
interview using the emBODY tool (Nummenmaa et al., 2014) was administered. This interview
assesses participants' ability to identify somatotopic patterns of physical feeling associated with
each of the six basic emotions (i.e., anger, fear, disgust, happiness, sadness, and surprise).
Participants were asked to draw on a picture of human bodies where they felt increasing or
decreasing activation while experiencing different emotions. After the drawing task was
completed, a qualitative interview was carried out by a trained staff member probing what types
of situations elicit the given emotion, where participants feel that emotion in their body, and what
type of sensation occurs within physical localizations. The interview consists of questions such
as: “What kinds of things make you feel afraid?; Can you tell me how you feel when you're
afraid?; Can you show me where in your body you feel fear, and describe that sensation?”
Interviews were transcribed and coded by two raters, and a third tie-break rater for use of
physical descriptions of felt emotion. All instances describing the body were coded for use of
embodied language mentioning a physical sensation (BSE; e.g., “I know I am angry because I
feel my head getting hot and chest-pounding”) and collapsed across all emotions and across the
negative emotions (BSE-neg).
Behavioral Data Analysis
Group Comparisons: Testing Hypothesis 1
To test hypothesis 1, that overall, participants with ASD compared to TD controls would
have lower interoceptive sensibility, higher alexithymia, and lower emotional empathy ability,
pairwise group comparisons between TD and ASD groups were conducted using two-tailed
independent samples t-tests on all behavioral measure outcome variables.
Partial Correlations: Testing Hypothesis 2
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To test hypothesis 2, that emotional empathy would be positively correlated with
interoceptive sensibility and would be negatively correlated with alexithymia severity, Pearson
partial correlation coefficients were computed to assess relationships between behavioral
variables in all participants, the ASD group alone, and the TD group alone, while controlling for
age, gender, and VCI . For all behavioral analyses, extreme outlier data was removed from
participants whose scores on any given measure were more than 3 times the interquartile range
from the first and third quartile relative to the entire group. Scatter plots were visually analyzed
for the presence of outliers to confirm validity of correlations.
fMRI Analysis
fMRI Procedure, Acquisition & Preprocessing
fMRI procedure, task stimuli, fMRI acquisition, and data preprocessing were done
following the protocol previously published in Kilroy et al. (2020; see supplementary data for
details). Prior to scanning, all participants completed a practice session in a mock scanner to
prepare for the scanning environment and to ensure minimal head motion. Participants
completed an action observation task in the scanner (Kilroy et al., 2020). There were no
significant differences in head motion between the two groups.
Functional Connectivity Analysis: Whole Brain –Testing Hypothesis 3
To investigate group differences in connectivity of emotional empathy regions during
facial expression observation trials, psycho-physiological interaction (PPI; Friston et al., 1997)
analyses were performed. The PPI analysis was used to compare the functional correlation of a
core region for interoceptive and emotion processing (left ventral AI; see details in ROI selection
below) to the rest of the brain during the task. PPI design matrix contained five columns of
variables: (1) emotional faces vs rest task contrast vector; (2) non-emotional faces vs rest task
contrast vector; (3) a time-series of left ventral AI seed region; (4) an interaction variable that
represents the interaction of emotional faces contrast vector and left ventral AI seed time course
(5) an interaction variable that represents the interaction of non-emotional faces contrast vector
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and left ventral AI seed time course. Data for the seed region was extracted from individual time
series from a 6mm spherical region, centered on subject-specific activations in the left ventral
AI. Correlation in activity between the seed region and the other regions in the brain that are
significantly different between task and rest yielded the PPI effect.
Experimental stimulus conditions were each modeled with a separate regressor derived
from a convolution of the task design and a double gamma function to represent the
hemodynamic response, and the temporal derivative of each task regressor was also included
as an additional regressor. Subject-specific motion correction parameters were entered as
nuisance regressors. Each group was entered into multivariate linear regression models for the
task for exploring main effects and between-group comparisons of connectivity. In all whole-
brain analyses, mean-centered age, sex and VCI were entered as covariates in the model.
Individual participants' statistical images were entered into the higher level mixed-effects
analyses using FSL’s FLAME Stage 1 algorithm. Resulting group level images for all models
were thresholded using FSL’s cluster probability algorithm, with a cluster-forming threshold of Z
> 3.1, a cluster size probability threshold of p < .001. To identify PPI during facial observation
compared to rest, two stimulus conditions (emotional facial expressions, non-emotional facial
expressions) were collapsed into one facial expression condition to determine the connectivity
of all stimuli compared to resting baseline. Similar analyses were performed for each individual
stimulus condition (emotional facial expressions, non-emotional facial expressions).
Functional Connectivity Analysis: Regions of Interest
Regions of interest (ROIs) were chosen using the Neurosynth database, which performs
automated large-scale meta-analyses of fMRI data. Depending on anatomy, regions consisted
of either 4mm (amygdala) or 6mm (all others) spheres drawn around peak activation of the AI,
ACC, IFGop, and amygdala in a meta-analysis of 187 empathy studies. ROI’s were only used if
they overlapped with activation in the Neurosynth meta-analysis, which was not always present
in both hemispheres. The left ventral AI was chosen as the seed for the PPI analysis (over the
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right dorsal AI) due to the overlap observed in that region and the main effect of our facial
expression observation task. Percent signal change was extracted from these functionally
defined ROIs (Figure 1) using the featquery function in FSL. For all ROI analyses, extreme
outlier data was removed from participants whose mean percent signal change in the queried
ROI were more than 3 times the interquartile range from the first and third quartile relative to the
entire group. For the ROI analyses, as my primary focus was on understanding the relationship
between neural connectivity in emotional empathy regions and social/emotional measures
during an emotion processing task, I focused on the trials where participants viewed emotional
facial expression. Additionally, given the nature of PPI analyses, to maximize statistical power, I
also conducted analyses using the “all faces” condition as it had the highest number of trials.
ROI Connectivity Group Comparisons: Testing Hypothesis 3
To further test hypothesis 3 in the specified ROI’s, group differences in connectivity
between TD and ASD groups were assessed using two-tailed independent samples t-tests.
ROI Partial Correlations: Testing Hypothesis 4-6
To test hypothesis 4, that emotional empathy would be positively correlated and
alexithymia severity and interoceptive sensibility would be negatively correlated with neural
connectivity in ROIs, two methodologies were used.
1. Pearson partial correlation coefficients were computed to assess relationships
between behavioral variables with parameter estimates of ROI connectivity in all participants,
the ASD group alone, and the TD group alone, while controlling for age, gender, and the VCI.
Scatter plots were visually analyzed for the presence of outliers to confirm validity of
correlations.
2. In the ASD group only, our main clinical group of interest, stepwise multiple linear
regression analysis was used to estimate model fit of strongest predictors on ROI connectivity in
the combined facial expression condition. Data was centered for each group and the following
behavioral variables were entered into a stepwise multiple linear regression model: gender,
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WASI-II: VCI, age, CASI-Anx, AQC ID, AQC COMM, Conners-3AI, SRS-2, PH-C, BPQ-VSF,
BSE, IRI EC, IRI PD, EmQue-CA AE. Due to the reduced sample size of participants with
available BPQ-VSF scores, if that variable was not a significant predictor in the initial model, the
regression was run a second time in the larger sample of participants with the BPQ-VSF
removed as a model predictor. The criterion for entering a variable into the model was set at
F = 0.05, with a removal probability of F = 0.10. Homoscedasticity and normality of residuals
were assessed, and the variance inflation (VIF) and tolerance statistics were used to assess
independent variables for multicollinearity.
Figure 1
Neural Regions of Interest From Neurosynth Empathy Meta-Analysis Map
Note. A. Left inferior frontal gyrus pars opercularis (X = 17, Y = 71, Z = 43) B. Left anterior
cingulate cortex (X = 49, Y = 75, Z = 50) C. Right dorsal anterior insula (X = 24, Y = 65, Z = 34),
D. Left ventral anterior insula (X = 64, Y = 69, Z = 31) E. Right amygdala (X = 31, Y = 61, Z =
23).
Due to the high incidence of comorbidity with anxiety (Simonoff et al., 2008) in ASD, and
the association between anxiety and increased interoceptive sensibility (Ehlers & Breuer, 1992),
anxiety symptoms also were controlled for in secondary analyses in the ASD group. To test
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hypothesis 5, that within the ASD group anxiety may contribute to relationships observed
between ROI connectivity and emotional empathy and interoceptive sensibility, Pearson partial
correlation coefficients were computed to assess relationships between interoception and
emotional empathy with ROI parameter estimates while controlling for age, gender, VCI and
anxiety in each model. The relationships were compared to previous partial correlations
observed when anxiety was not added as a covariate in the model.
To probe hypothesis 5 an additional method for exploring the relative influence of
alexithymia, anxiety and ASD symptomatology on emotional empathy was carried out using
partial correlations. AQC total, CASI-II, or SRS-II, scores were added into partial correlation
models of significant relationships and impact on effect size and significance of relationships
was observed.
To specifically test the alexithymia hypothesis – that differences between TD and ASD in
emotional empathy are actually due to differences in trait alexithymia (Hypothesis 6; Bird &
Cook, 2013; Bird et al., 2010; Bird et al., 2011; Brewer et al., 2015; Cook et al., 2013; Gaigg et
al., 2018; Heaton et al., 2012; Mul et al., 2018; Shah et al., 2016, Shah et al., 2017; Sivathasan
et al., 2020) a hierarchical multiple linear regression model was used across all participants. In
order to probe the impact of alexithymia on ROI activation during emotional face processing
(ACC, IFGop, amygdala, AI), nuisance regressors of age, VCI, and sex were entered into the
model before alexithymia and group. Data was centered and behavioral variables were entered
into a hierarchical multiple linear regression model in the following order: age, sex, WASI-II:
VCI, AQC total, group. Significance of alexithymia as a predictor was assessed before and after
the addition of group to the model. Homoscedasticity and normality of residuals were assessed,
and the VIF and tolerance statistics were used to assess independent variables for
multicollinearity.
Results
Behavioral Group Differences
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This sample is identical to the sample used in Chapter 3; please refer to that chapter for
statistical details and tables of behavioral data. The findings are summarized here for
convenience. The ASD group had increased social impairment (SRS-2), alexithymia severity
(AQC COMM), ADHD symptomatology (Conners-3AI), physiological hyperarousal (PH-C), and
anxiety (CASI-II), and a nearly significant decrease in ToM skills (p = .052; NEPSY-II ToM). No
significant differences from TD were found in the ASD group for self-reported interoception
(BPQ-VSF, BSE, BSE-neg) or emotional empathy skills (IRI PD & IRI EC).
Behavioral Partial Correlations
Alexithymia
Across all participants, alexithymia scores were positively correlated with an emotional
empathy subscale, IRI PD (AQC ID: r = .395, p = .002; AQC COMM: r = .390, p = .002; AQC
total: r = .443, p < .001). This same positive relationship with IRI PD was seen in the ASD
group, with all the AQC variables (AQC ID: r = .527, p = .007; AQC COMM: r = .556, p = .004;
AQC total: r = .610, p = .001; Table 2) and in the TD group at a non-significant level (AQC total:
r = .322, p = .063; Table 2). An opposite pattern emerges in the IRI EC subscale, across all
participants, AQC COMM was negatively correlated with IRI EC (r = -.298, p = .019). This
relationship was not observed in the individual groups, although the ASD group alone did show
a non-significant trend (r = -.353, p = .083; Table 2). Across all alexithymia is positively
correlated with anxiety (AQC COMM: r = .362, p = .004; AQC total: r = .322, p = .011), and in
the ASD group at a non-significant level (AQC total: r = .370, p = .069).
Interoception, Physiological Hyperarousal, & emBODY Interview
Across all participants, the BPQ-VSF was negatively correlated with the EmQue-CA AE
(r = -.317, p = .046). There were no other significant relationships with BPQ-VSF across all or in
individual groups (p’s > .05). Across all participants, PH-C was positively correlated with
alexithymia (AQC COMM: r = .304, p = .020; AQC total: r = .200, p = .022). In the TD group, the
PH-C was also positively correlated with alexithymia (AQC ID: r =.374, p = .032; AQC total: r =
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.388, p = .026; Table 2) and IRI PD (r = .381, p = .029). In the ASD group there were no
significant relationships with PH-C and alexithymia, anxiety, or emotional empathy variables (p’s
> .05).
In the ASD group, sensation felt during emotion was negatively correlated with IRI PD
(BSE: r = -.472. p = .027; BSE-neg: r = -.469 p = .043), the AQC total (BSE: r = -.422, p = .050;
BSE-neg: r = -.500, p = .029), and anxiety (BSE-neg: r = -.472, p = .041). There were no
significant correlations within the TD group or across all participants.
Connectivity Group Differences & Correlations: Whole Brain Analysis
Between Group Differences
The ASD group had higher connectivity between the left ventral AI and the right lateral
prefrontal cortex than the TD group during non-emotional facial expression observation. There
were no significant TD > ASD differences in left ventral AI connectivity with the whole brain (Z =
3.1).
Correlations
Across all participants, there was a negative correlation between alexithymia severity
total score and connectivity between the left AI and the left precuneus during all facial
expressions (Figure 2 & 3). This relationship was consistently negative and well distributed in
parameter estimates of individual groups (TD: r = -.700, p < .001; ASD: r = -.444, p = .026).
Across all participants, there was a negative correlation between interoceptive sensibility and
connectivity between the left AI and the dorsal premotor cortex during non-emotional facial
expressions (Figure 2 & 3). This relationship was also significant in parameter estimates of
individual groups (TD: r = -.652, p = .001; ASD: r = -.583, p = .023).
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Figure 2
Whole Brain PPI with Left Ventral Anterior Insula
Note. A. Non-emotional facial expressions whole brain contrast ASD > TD in right lateral
prefrontal cortex B. All facial expressions negative whole brain correlation with Alexithymia
Questionnaire for Children total score and left precuneus C. Non-emotional facial expressions
negative whole brain correlation with Body Perception Questionnaire Very Short Form total
score in the left dorsal premotor cortex.
CONNECTIVITY: INTEROCEPTION, ALEXITHYMIA, EMOTIONAL EMPATHY
Figure 3
Scatterplots of Significant Correlations: Behavior and Left AI Connectivity
Alexithymia
Interoceptive
A. B.
C. D.
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Note: A. Partial correlation between left AI and right IFGop connectivity with AQC total in ASD;
B. Partial correlation between left AI and right IFGop connectivity with BSE-neg in ASD; C.
Partial correlation between left AI and left precuneus connectivity with AQC total across all; D.
Partial correlation between left AI and left dorsal premotor cortex connectivity with BPQ-VSF
across all; AI = anterior insula, IFGop = inferior frontal gyrus pars opercularis; AQC =
Alexithymia Questionnaire for Children; BSE-neg = bodily sensation experienced during
negative emotions; BPQ-VSF = Body Perception Questionnaire-Very Short Form; TD = typically
developing; ASD = autism spectrum
Connectivity Group Differences & Correlations: ROI Analysis
Between Group Differences
There were no group differences between TD & ASD connectivity between left ventral AI
and any of the predetermined ROI’s (ACC, AI, IFG, amygdala).
Correlations
Alexithymia Correlations. In the ASD group, connectivity between right dorsal AI and
left ventral AI was negatively correlated with AQC COMM (r = -.510, p = .011), and AQC Total (r
= -.499, p = .013) during all facial expression observation. Both of the observed relationships
reported remained significant (p’s < .02) if anxiety was added to the models.
Interoception Correlations. Across all participants and within the ASD group BSE-neg
was positively correlated with connectivity between right IFG and left ventral AI (All: r = .362, p =
.019; ASD: r = .506, p = .027; Figure 3) during emotional facial expression observation. Both of
the observed relationships reported remained significant (p’s < .03) if anxiety was added to the
models.
Emotional Empathy Correlations. There were no significant correlations between
connectivity of left ventral AI and any of the predetermined ROI’s (ACC, AI, IFG, amygdala) with
any emotional empathy variables (IRI PD, IRI EC, EmQue-CA AE) across all, or in individual
groups.
Stepwise Regressions. A significant regression equation for the left ACC was observed
(F(1, 19) = 6.033, p = .024), with an R
2
of 0.241. ASD participants predicted left ACC-AI
connectivity was equal to -0.167 + .181 (BSE). Results indicated that in the ASD group bodily
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sensation awareness during emotion predicted 24% of the variance in left ACC-AI connectivity.
Specifically, higher BSE scores predicted higher left ACC-AI connectivity during facial
expression observation. In the ASD group, none of the included variables significantly predicted
the variance in IFGop, right AI, or right amygdala connectivity with left AI.
Hierarchical Regressions – Hypothesis 5
Hierarchical linear regression models were run across all participants to assess whether
alexithymia explained variance in emotional empathy outcomes and connectivity of ROIs during
emotional facial expression observation above and beyond the influence of diagnostic group. As
seen in Chapter 3 (identical data) across groups variance in IRI EC and EmQue-CA AE were
not significantly explained by the models, but a model including: age, sex, WASI-II: VCI, and
AQC total behaviorally predicted higher personal distress above and beyond the influence of
group status (ASD). Connectivity between left ventral AI and other ROIs of interest was not
significantly predicted by the model including: age, sex, WASI-II: VCI, and AQC total, or the
model including age, sex, WASI-II: VCI, AQC total, and group in emotional facial expression, or
the all facial expression conditions.
Discussion
This study found that hypothesis 1 was supported; participants with ASD demonstrated
higher alexithymia (in difficulty communicating emotions), however, interoceptive sensibility and
emotional empathy scores were not different between ASD and TD groups. Hypothesis 2, that
emotional empathy would be negatively correlated with interoceptive sensibility and alexithymia
severity, was also partially supported. We found that across all participants, alexithymia was
negatively correlated with IRI EC, and BPQ-VSF was negatively correlated with EmQue-CA AE.
However, alexithymia was positively correlated with IRI PD, and the BSE was negatively
correlated with IRI PD in the ASD group. Hypothesis 3, testing between group neural
connectivity differences in regions commonly involved in emotional empathy, was not
supported; the only group difference was higher connectivity in the ASD group compared to the
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TD group between the left ventral AI and the right lateral prefrontal cortex. Hypothesis 4 was
supported in the ASD group, with less severe alexithymia scores predicting geater left ventral AI
connectivity with the precuneus and right dorsal AI. However, contrary to hypothesis 4, a
positive relationship was seen between BSE-neg and IFGop-AI connectivity, and no
relationships between connectivity and emotional empathy were observed. Hypothesis 5 was
not supported; adding anxiety to any brain-behavior relationships observed in the ASD group
did not impact their significance. Hypothesis 6 was partially supported, alexithymia, more so
than group membership (ASD, TD), predicted greater personal distress. However, contrary to
the hypothesis, alexithymia did not predict neural connectivity between any of the ROI’s during
facial expression observation. The implications of these findings are discussed in depth below.
Connectivity Group Differences
Here we find the only significant between group comparison was that the ASD group
showed greater functional connectivity than the TD group between left ventral AI and right
lateral prefrontal cortex (lPFC) when observing non-emotional facial expressions. The lPFC is
associated with attention, task-switching, higher-order relational processing, and control of
action tendencies elicited by emotional faces (Bramson et al., 2020; Hartogsveld et al., 2018;
Koechlin, 2011; Rushworth et al., 2011). Bramson and colleagues (2020) demonstrated that
inhibitory control over approach or avoidance based emotional actions (pull towards self, or
push away from self, in response to a happy or angry face, respectively) is associated with lPFC
activation in incongruent trials (e.g. push away from body, or avoid, in response to happy face).
Also in incongruent trials, authors observed greater activation in a bilateral insula/IFG cluster.
The findings suggest that stronger activation of lPFC and emotional empathy regions result in
improved control of emotional actions, and potentially a greater ability to regulate automatic
emotional action responses (Bramson et al., 2020). Furthermore, functional connectivity studies
indicate the right lPFC is highly connected with emotion-related brain regions during affect
labeling in response to viewing emotional facial expressions (Lieberman et al., 2007). Taken
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together, the current results of increased connectivity between the right prefrontal and the AI
may indicate that the ASD group utilizes increased modulation of automatic emotion reactions
and potentially affect labeling processes when viewing facial expressions. Increased modulation
of automatic responses to emotional face actions may also be related to the high incidence of
ADHD symptomology in our ASD group, which would potentially disrupt inhibitory control and
attention throughout the task. Future analyses may want to explore the relationship between
connectivity in these regions and severity of ADHD symptoms in a similar paradigm to Bramson
and colleagues (2020).
Emotional Empathy
While we found behavioral relationships between alexithymia, interoceptive sensibility,
and emotional empathy, there were no relationships between emotional empathy variables and
functional connectivity between ROI’s during our task. Behaviorally, personal distress was
related to increased alexithymia (all, ASD), increased physiological hyperarousal (TD) and
reduced bodily sensations experienced during emotions (ASD). Empathic concern was
negatively correlated with alexithymia difficulty communicating emotions (all) and interoceptive
sensibility (BPQ-VSF, all). We see that personal distress is associated with negative emotional
outcomes (alexithymia) and maladaptive bodily awareness, while empathic concern is related to
positive emotional outcomes (ability to communicate emotions) and lower maladaptive bodily
awareness (BPQ-VSF). Further, the alexithymia hypothesis is supported in the behavioral data,
demonstrating that alexithymia, more so than group membership (ASD, TD), predicted
maladaptive emotional empathy functioning (greater personal distress).
Interoception
There were no behavioral group differences in self-reported interoceptive sensibility, or
in interview data for BSE. In the neural data, across all participants, there was a negative
correlation between interoceptive sensibility and connectivity between the left ventral AI and the
dorsal premotor during non-emotional facial expressions, and this correlation was significant in
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the individual groups (r = -.652, p = .001; ASD: r = -.583). The dorsal premotor cortex has been
implicated in previous studies to be more active during interoceptive accuracy tasks of heart
rate monitoring, or heart rate detection (Klabunde et al., 2019; Kleint et al., 2015; Pollatos et al.,
2007) compared to rest or exteroceptive focus. Given recent findings that interoceptive accuracy
and interoceptive sensibility may be independent from each other (Garfinkel et al., 2015), and
inversely related behaviorally and neurally (Garfinkel et al., 2016; Kleint et al., 2015; Pollatos &
Georgiou, 2016; Sedeño et al., 2014; Stern et al., 2017; Wang et al., 2019; Wang et al., 2020),
the results of negative directionality of connectivity between left AI and dorsal premotor cortex
with self-reported interoceptive sensibility may be expected. If higher interoceptive sensibility
scores on measures like the BPQ-VSF are actually predicting lower interoceptive accuracy and
overestimation of bodily signals, the observed relationship with neural connectivity in key
regions for interoceptive processing should follow. This finding supports the idea that during an
action observation paradigm, interoceptive information is being processed by areas responsible
for motor action planning.
However, interestingly, in the ROI analyses, in the ASD group, BSE and BSE-neg were
positively related to connectivity between right IFG-left ventral AI and left ACC-left ventral AI.
This result is found only for measurements of interoception during the self experience of
emotions (BSE and BSE-neg). These relationships go in the expected direction of interoceptive
accuracy measures, and they remain significant if anxiety is added to the models. This could
mean that individuals with low interoceptive ability are possibly any of the following (which are
not mutually exclusive): 1) more accurate at reporting interoceptive sensibility in an interview
style when asked to recall specific situations that elicited the feelings; 2) more accurate at
reporting interoceptive sensibility in the context of emotionally salient negative experiences
rather than global body awareness; 3) The BPQ-VSF is indexing maladaptive symptoms of self-
focused interoception more consistent with anxiety than with interoceptive accuracy (Trevisan et
al., 2020) while the BSE and BSE-neg variables are indexing interoceptive sensibility that
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promotes positive emotional outcomes of attention to bodily signals as has been indexed in
other approaches to measuring interoceptive sensibility, like the Multidimensional Assessment
of Interoceptive Awareness (MAIA; Mehling et al., 2012, Flasinski et al., 2020). Option 3 is
supported by the behavioral data in the ASD group, where we see BSE is negatively correlated
with anxiety; but across all participants, BPQ-VSF is negatively correlated with a measure of
emotional empathy similar to empathic concern(EmQue-CA AE). In the TD group, we see that
physiological hyperarousal was also positively correlated with alexithymia and personal distress.
These results further support that the BPQ-VSF may be indexing negative aspects of bodily
awareness, more similar to those related to physiological hyperarousal. Further research should
be conducted to compare reporting of interoceptive experiences via questionnaires versus
experimenter-led interviews, alongside direct tasks of interoceptive accuracy in order to assess
how these outcomes differentially relate to interoceptive ability, alexithymia, and emotional
empathy.
Alexithymia
We found alexithymia was related to functional connectivity in two separate analyses.
First, across all participants and in individual groups (TD, ASD), alexithymia scores were related
to decreased connectivity between left ventral AI and the left precuneus. This indicates that
decreased connectivity between this emotion-processing region and the precuneus, a region
involved in exteroceptive and interoceptive bodily sensations, (Araujo et al., 2015) is related to
alexithymia deficits. Second, we find that connectivity between the left ventral AI and right dorsal
AI is related to alexithymia in the ASD group during observation of emotional facial expressions.
The left AI-right AI relationship was particularly driven by the communicating emotions subscale
of the AQC. These results suggest that degree of alexithymia is impacted by interhemispheric
cross-talk between the left and right hemisphere of the AI. Indeed, previous work has posited
that alexithymia may be a result of disconnection between right hemisphere emotion processing
and left hemisphere linguistic processing (MIller, 1986), and supporting this notion, reduced
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white matter diffusivity (mainly functional anisotropy) has previously been found in the corpus
callosum in ASD compared to TD (Andrews et al., 2019; Barnea-Goraly et al., 2010; Cheon et
al., 2011; Jou et al., 2011; Kumar et al., 2010; Noriuchi et al., 2010; Shukla et al., 2011). To our
knowledge, this is the first study probing task-based connectivity with alexithymia in ASD, as
prior studies focused on resting state connectivity, and within it, mainly the default-mode
network. These findings allow for a better understanding of how alexithymia interacts with ASD,
for those individuals with an ASD diagnosis and high trait alexithymia.
Autism Severity
As an additional post-hoc analysis, in the ASD group only, correlations between
connectivity of emotion related ROIs and ASD symptom severity measures were assessed. A
significant negative correlation was found between the left ventral AI and right amygdala for the
ADIR language and communication impairments subscale (r = -.459, p = .032). The language
and communication subsection of the ADIR measures language impairment particular to autism
(stereotyped utterances, pronoun reversal, and social usage of language). Thus, autism severity
as indexed by communication deficits, is linked with decreased connectivity between the left
ventral AI and right amygdala, regions known to be highly involved with socio-emotion and
salience processing.
Anterior Insula & IFGop
Positive interoceptive markers (greater BSE-neg) are associated with AI-IFGop
connectivity and reduced personal distress in individuals with ASD. Taken together, these
associations may indicate that strong connectivity between the AI and IFGop is related to higher
interoception ability and better emotion regulation. This is consistent with previous studies
indicating that better emotion regulation is related to connectivity between the IFGop and
emotion-related brain regions (Torre & Lieberman, 2018).
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Limitations and Future Directions
PPI
One major limitation of the PPI analysis is the lack of statistical power inherent in the
approach (O’Reilly et al., 2012). This may be why here we did not observe many group
differences in connectivity between TD and ASD participants in the predetermined ROI’s, and
perhaps why emotional empathy behavioral data was not related to ROI connectivity. The PPI is
created using task and seed activity which are both included in the model, therefore it only has
power to demonstrate effects that cannot be explained by the task or the seed time course. This
makes it both very difficult to detect effects and may make the PPI susceptible to more false
negatives, which is why a conservative Z threshold of 3.1 needed to be used here. Additionally,
this method is designed specifically to probe context related changes in connectivity. There is a
chance that the imaging task used here may have been too subtle to elicit task-based
connectivity effects due to the passive observation of stimuli. Future studies may consider
comparing passive facial expression observation to more active tasks with specific instructions
to encourage empathizing with individuals depicted in the stimuli. Additionally, PPI has a
general weakness of only allowing for analysis of one seed region of interest in each model,
unlike other functional network connectivity models which allow for more flexibility in resulting
network information, and potentially unexpected overlapping networks (Xu et al., 2013). While
the seed region used in the PPI analysis (left AI) was hypothesis driven, it is still a limiting factor
for understanding the comprehensive group differences and functional connectivity relationships
that may exist outside this specific node investigated for connectivity. Indeed, data from chapter
3 indicate that probing an amygdala seed may also shed light on our research questions related
to alexithymia, which may be explored in future studies. Lastly, PPI can only expose task-
dependent changes in ROI connectivity and cannot determine causal relationships between
regions like other methods (i.e. dynamic causal modeling; Friston et al., 2003) so we cannot
determine directionality of which regions are predominantly influencing others in the network.
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General limitations
There are many limitations to consider when interpreting the current study’s results.
First, as previously stated, individuals diagnosed with ASD consist of a very heterogeneous
population; the relatively small sample size and homogeneity of the participants in this sample
(all right-handed, IQ > 80, etc.), make generalization to the whole spectrum of ASD limited.
Some of the main variables of interest in the study can only be measured in individuals who are
verbal, future studies should further investigate the influence of VCI on the relationships
reported here. Additionally, the AQC was used as a gold standard tool for measuring
alexithymia in youth, however, like the TAS-20 (Bagby et al., 1994), this questionnaire captures
more cognitive aspects of alexithymia including identifying emotions and communicating
emotions but does not measure additional emotionalizing aspects which should also be
explored in ASD.
Secondly, the measures used to capture interoception, alexithymia, and empathy ability,
are self-report measures. Accurate self-report can be particularly challenging for individuals with
ASD who may have difficulty with metacognition and self-referential insight. For this reason,
there was an experimenter-led interview which also measured some of the main variables in the
self-report data. Future studies may consider examining these questions in a larger and more
heterogeneous sample of ASD participants, employing more observational and standardized
measures when possible. Additionally, future studies should include the more multidimensional
aspects of emotionalizing and fantasy in alexithymia; and attention regulation, body listening,
and body trusting in interoceptive sensibility, which were not included here due to the measures
used in this study.
Conclusions
Whole brain analysis indicates that the ASD group compared to the TD group may be
employing more inhibitory control during facial expression observation, as they had higher
connectivity between left ventral AI and the right lateral prefrontal cortex, perhaps also marking
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alterations in emotion regulation and affect labelling in ASD. This work also compliments
previous studies suggesting that BPQ-VSF may be indexing maladaptive features of
interoceptive sensibility which would predict lower interoceptive accuracy and overestimation of
bodily signals (see Trevisan et al., 2020 for review), by demonstrating a negative relationship
between BPQ-VSF scores and neural connectivity between key interoception-related ROIs.
However, this study did not support the notion that differences in neural processing of
interoception can be accounted for by alexithymia severity.
Data from the current study supports previous notions that the AI is one of the core hubs
underlying alexithymia disruption in ASD (e.g., Bird et al., 2010; Goerlich-Dobre et al., 2015;
Silani et al., 2008). Connectivity between the right dosal AI and left ventral AI, and the left
precuneus and left ventral AI, were significantly related to alexithymia. The former relationship
may mark the importance of cross hemispheric processing for alexithymia, while the latter may
mark the importance of connectivity between left hemisphere regions involved in emotion-
processing and interoceptive/exteroceptive sensory processing in alexithymia. Further, IFGop-
AI connectivity was also related to higher BSE-neg in individuals with ASD. The IFGop and AI
have previously been shown to have reduced activation in ASD during facial expression
observation (Dapretto et al., 2006; Di Martino et al., 2009; Greimel et al., 2010; Kilroy et al.,
2020; Klapwijk et al., 2016; Schulte-Rüther et al., 2011) and are associated with empathic
concern ability (Shamay-Tsoory et al., 2009; Kaplan & Iacoboni, 2006; Singer et al., 2004).
Thus, increased IFG-AI connectivity indexes: higher adaptive awareness of bodily sensations to
negative emotions and reduced alexithymia. However, the hierarchical regression analysis
suggested that neither alexithymia nor group membership are primary predictors of connectivity
between any of our ROIs. Nevertheless, these latter results should be interpreted with caution,
as they may be due to reduced power in PPI analyses, which were discussed previously.
Thus, here we found individuals with ASD have stronger connectivity between the insula
and lateral prefrontal cortex, and that connectivity between the insula and other regions involved
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with emotional and empathic processing are related to individual differences in interoceptive
sensibility and alexithymia. This data may contribute to the development of target therapies for
individuals with ASD and altered empathic ability, interoceptive sensibility, and/or alexithymia.
Although more research is needed, targeted therapies for such individuals might include
contemplative mindfulness based interventions (present-moment focused breath and body
scanning, compassion and loving kindness meditations, and perspective-taking exercises;
Singer & Engert, 2019) which have recently been associated with improvement on a number of
psychological processes that may be relevant to ASD including: interoceptive functioning,
empathic responding, emotion regulation, emotional self-referential processing, theory of mind,
and functional neuroplastic changes (ReSOURCE Project; for review, see Singer & Engert,
2019). Further research is needed to better understand the impact of these long-term in
individuals with ASD and empathy difficulties, interoceptive differences, and/or alexithymia.
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Chapter 6. General Discussion: Neural and Behavioral Relationships of Alexithymia,
Interoception, and Emotional Empathy in Autism Spectrum Disorder
Introduction
Empathy, the ability to understand and experience the feelings of others, is one
important social skill that may be compromised in ASD (Dvash & Shamay-Tsoory, 2014).
Cognitive empathy involves mentally imagining how another person is thinking or feeling (de
Waal & Preston, 2017), while emotional empathy involves sharing the emotions others are
experiencing (Davis et al., 1994). There is a large body of research that suggests that
individuals with ASD have difficulties with cognitive empathy (for review see Frith and Happé,
2005). However, evidence for emotional empathy difficulties are mixed. Some studies find that
individuals with ASD display reduced emotional empathy (Bos & Stokes, 2018; Kasari et
al.,1990; Lombardo et al., 2007; Mathersul et al., 2013; Minio-Paluello et al., 2009; Peterson et
al., 2014; Schulte-Rüther et al., 2011; Shamay-Tsoory et al., 2002; Sigman et al., 1992;
Sucksmith et al., 2013; Yirmiya et al., 1992) while others have not found support for any
emotional empathy differences (Bellebaum et al., 2014; Deschamps et al., 2014; Dziobek et al.,
2008; Hadjikhani et al., 2014; Jones et al., 2010; Markram et al., 2007; Pouw et al., 2013;
Rogers et al., 2007; Rueda et al., 2015; Schwenck et al., 2012; Smith, 2009).
Behavioral and neural evidence suggests deficits in empathy in ASD may be attributed
to co-occurring alexithymia rather than ASD symptomatology alone (Bird & Cook, 2013; Bird et
al., 2010; Bird et al., 2011; Brewer et al., 2015; Cook et al., 2013; Gaigg et al., 2018; Heaton et
al., 2012; Sivathasan et al., 2020). Others suggest interoception impairments are responsible for
inconsistent reductions seen in emotional empathy in ASD (Fukushima et al., 2011; Grynberg &
Pollatos, 2015; Quattrocki & Friston, 2014). In this dissertation, I have focused on how
interoceptive sensibility and alexithymia differentially impact the behavioral and neural variables
associated with emotional empathy in youth with ASD and their typically developing (TD) peers.
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Summary of Findings
Study 1: Behavioral
In Study 1, results demonstrated ASD youth displayed: intact emotional empathy ability
and interoceptive sensibility, increased alexithymia severity, and reduced cognitive
empathy/theory of mind ability. Major findings include: 1) In ASD and TD, alexithymia in ASD is
associated with lower empathic concern, and higher personal distress; 2) In the TD group (with
low incidence of anxiety), those with higher interoceptive sensibility tended to have lower
personal distress; and 3) in the ASD group, greater alexithymia severity, and higher personal
distress to others’ emotions is associated with reduced reporting of bodily sensations during
negative emotions (BSE-neg).
In this study, no significant differences between TD and ASD in either emotional
empathy subscore were observed. While correlation analyses in this paper support previous
work that alexithymia is related to reduced emotional empathy in ASD in the empathic concern
domain, a novel finding is that the opposite pattern is true in the personal distress domain. The
divergent patterns in IRI PD and IRI EC could account for previous discrepant findings regarding
emotional empathy ability in ASD. Some studies that refer to emotional empathy in ASD are
only referring to the empathic concern domain (e.g. Dziobek et al., 2008; Trimmer et al., 2017),
while other studies consider characteristics of both personal distress and empathic concern
together as a measure of emotional empathy ability on measures like the Multifaceted Empathy
Test (e.g. Mazza et al., 2014). Personal distress is an index of emotional empathy that refers to
the tendency to feel personal pain when exposed to the tension, pain, or suffering of others.
While this is an aspect of greater emotional empathy, it is also associated with maladaptive
outcomes such as ruminative coping, neuroticism, depression, self-criticism, and negative self-
concept (Kim & Han, 2018). Personal distress may actually block empathic interaction and
prosocial behavior by encouraging avoidance of overwhelm from others’ suffering (Kim & Han ,
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2018). The current results suggest that alexithymia is marked by reduced ability to engage in
other-oriented empathic concern, and an increased tendency towards self-oriented personal
distress when exposed to another’s suffering.
In this study, no significant differences in interoceptive sensibility were observed
between TD and ASD youth. These results are inconsistent with others who find interoceptive
sensibility differences in adults with ASD (reduced: Elwin et al., 2012; Fiene & Brownlow, 2015;
increased: Garfinkel et al., 2016). Importantly, for TD participants, a group with significantly
lower alexithymia scores and anxiety scores than the ASD group, we see a relationship
between higher interoceptive sensibility and lower personal distress, and higher physiological
hyperarousal with higher personal distress. In emBODY interviews, greater alexithymia severity
and higher personal distress to others’ emotions were associated with lower BSE-neg. Thus,
although there may not be reductions in interoceptive sensibility in ASD, there is evidence that
reduced awareness of interoceptive information, during actual emotion experience (rather than
a questionnaire [BPQ-VSF]), was related to both alexithymia severity and increased personal
distress in our ASD sample.
Based on this study and consistent with previous work, atypical emotional empathy may
actually be a feature of alexithymia and not ASD (e.g. Bird & Cook, 2013; Brewer et al., 2015;
Oakley et al., 2016). Additionally, the results of the current study suggest that even for those
with alexithymia, there is not necessarily a lack of empathic feeling, but perhaps an actual
increase in personal distress to others' pain rather than an other-oriented empathic concern that
is prosocially expressed. Thus, ASD youth with concomitant alexithymia do not lack empathic
feelings, but rather experience an increase in personal distress to others' pain; this may result in
avoidance rather than prosocial action, but should not be characterized as an absence of a
capacity for empathy. The findings of this study are summarized in Figure 1 below.
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Figure 1
Emotional Empathy Results Summary Across all Behavioral Analyses in Study 1
Note. Figure 1 summarizes the major findings for both groups in the two emotional empathy
subscales of the Interpersonal Reactivity Index (personal distress and empathic concern) across
the three analysis methods used in the study. TD = typically developing; ASD = autism
spectrum disorder.
Study 2: Neural Activity Correlates
Study 2 explored how relationships between interoception, alexithymia, and emotional
empathy were related to neural activation of emotion processing regions during observation of
facial expressions. Major findings include: 1) support for the alexithymia hypothesis in personal
distress and the right amygdala 2) reduced left IFGop activation in individuals with ASD
compared to TD individuals; 3) empathic concern as the strongest predictor of left IFGop
activation and ADHD symptom severity as the strongest predictor of right IFGop activation in
ASD; 4) opposite relationships in left and right amygdala with alexithymia 5) opposite patterns of
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relationships between interview vs self-report questionnaires of interoceptive information with
ROI’s.
We find support for the alexithymia hypothesis behaviorally in personal distress, and
neurally in the amygdala. In particular, higher alexithymia was associated with increased
personal distress, and increased activation in the right amygdala, above and beyond ASD
diagnosis.
Consistent with prior studies, when comparing between groups, we found reduced
IFGop activity in individuals with ASD (Dapretto et al., 2006; Greimel et al., 2010; Kilroy et al.,
2020; Klapwijk et al., 2016; Schulte-Rüther et al., 2011) during facial expression observation.
Additionally, the strongest predictor of left IFGop activation in the ASD group was empathic
concern. Thus in individuals with ASD, reduced IFGop activation may relate to difficulties with
empathic concern. Although we find no behavioral differences in empathic concern in the ASD
group, these results indicate that some of the neural regions important for empathic processing
may be compromised in ASD and alternate neural routes may be utilized instead, regardless of
alexithymia comorbidity. Right IFGop was predicted by ADHD impairment levels; individuals
with ADHD may have to make greater efforts to inhibit movement, or pantomiming motor actions
during the action observation task, resulting in greater IFG activation. The IFGop seems to be
involved in between empathic concern and motor processing, which may be important for
translating empathic emotions into appropriate motor action programs in ASD.
For activity in the amygdala, we find a relationship with alexithymia, though there are
laterality differences. In the left hemisphere, in the ASD group we find that scores on the
alexithymia communicating emotions subscale are the strongest predictor of reduced left
amygdala activity. However, in the right hemisphere we find the opposite pattern: across
groups, alexithymia total score predicts increased activity in the right amygdala, above and
beyond the influence of group. Taken together with the findings linking alexithymia and personal
distress; in the non-speaking right hemisphere, increased emotion processing of others in the
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amygdala may lead to overwhelming emotions, and be related to higher alexithymia. In the
language-capable left hemisphere, in individuals with ASD, ability to speak about one’s
emotions (lower alexithymia for communicating emotions) is instead linked with higher emotion
resonance with others (higher activity in left amygdala during processing of other’s facial
expressions). Further, while the right amygdala seems to be involved in emotion detection and
is automatically activated by emotion stimuli and may provide autonomic activation, the left
amygdala may decode the arousal signaled in more fine-grained and sustained emotional
stimulus evaluation (Glascher & Adolphs, 2003; Wright et al., 2001) particularly for negative
affect (Baas et al., 2004).These results may have implications for therapies in individuals with
ASD and alexithymia designed to impact empathic responses and down-regulate negative
emotions (Payer et al., 2012) -- perhaps by utilizing affect labeling interventions (Kircanski et al.,
2012).
An unexpected finding was the negative relationships between BPQ-VSF and activity in
the amygdala, AI, and IFGop. Similarly, a negative correlation was found with the PH-C and the
amygdala in the TD group. Generally poor interoceptive ability may result in hypervigilance to
bodily signals and have an inverse relationship with interoceptive accuracy in ASD (Garfinkel et
al., 2016). Further, while heart rate tracking paradigms are associated with higher activation and
connectivity of emotional empathy regions (e.g. Kleint et al., 2015) self-reported interoceptive
sensibility has, in some cases, been associated with lower neural activation and connectivity in
emotional empathy regions (e.g. Wang et al., 2020). Interestingly, we see an opposite pattern in
the interview assessment which uses experimenter-led questioning about frequency, location,
and intensity of physical sensation experienced during multiple emotional states. There was a
positive association with AI activation (the expected direction) in the ASD group. Thus, although
children with ASD who have higher AI activation to facial expressions may report a lower
general sense of interoception, these same people do report higher instances of experiencing
bodily sensations during emotional experiences.
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Figure 2
ROI Group Differences
Note. TD and ASD group percent signal change in left inferior frontal gyrus pars opercularis
during non-emotional facial expression condition.
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Figure 3
Study 2 Scatter Plots of Significant Correlations in the ASD Group
Note. A. Empathy Questionnaire for Children and Adolescents Affective Empathy subscore with
right anterior insula percent signal change during facial expression observation (partial correlation
significance with age, VCI, and gender: r = .413, p = .047) B. Body Perception Questionnaire-Very
Short Form with left amygdala percent signal change during facial expression observation (partial
correlation significance with age, VCI, and gender: r = -.627, p = .012).
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Study 3: Neural Functional Connectivity
Study 3 explored how relationships between interoception, alexithymia, and emotional
empathy were related to neural connectivity of emotion processing regions during facial
expression observation. Major findings included 1) alexithymia is associated with reduced left
AI-left precuneus connectivity, and reduced right dorsal AI-left ventral AI connectivity during
facial expression observation; 2) Similar to study 2, we find inverse relationships between BPQ-
VSF and interoceptive interview variables (BSE, BSE-neg) with behavioral and neural outcomes
of alexithymia and emotional empathy; 3) in the ASD group, we see higher connectivity between
left ventral AI-right lateral prefrontal cortex during facial expression observation. These are
discussed in more detail below.
Neurally, alexithymia is associated with decreased connectivity between the left AI and
the precuneus, a region involved in a region involved in exteroceptive and interoceptive bodily
sensations. Second, we find that connectivity between the left ventral AI and right dorsal AI is
related to alexithymia in the ASD group during observation of emotional facial expressions. The
left AI-right AI relationship was particularly driven by the communicating emotions subscale of
the AQC. These results suggest that degree of alexithymia is impacted by interhemispheric
cross-talk between the left and right hemisphere of the AI. Indeed, previous work has posited
that alexithymia may be a result of disconnection between right hemisphere emotion processing
and left hemisphere linguistic processing (Miller, 1986) and supporting this notion, reduced
white matter diffusivity (mainly functional anisotropy) has previously been found in the corpus
callosum in ASD compared to TD (Andrews et al., 2019; Barnea-Goraly et al., 2010; Cheon et
al., 2011; Jou et al., 2011; Kumar et al., 2010; Noriuchi et al., 2010; Shukla et al., 2011).
Similar to study 2, we observed that BPQ-VSF may be indexing maladaptive empathy
outcomes, (reduced emotional empathy) and may measure something more similar to
physiological hyperarousal, which was positively correlated with alexithymia. However, for our
interview variable reports of sensation felt during emotion were negatively correlated with
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personal distress, alexithymia, and anxiety-- suggesting this method of collecting interoceptive
information may be more similar to what is found in interoceptive accuracy studies. This is
echoed in the neural data, where there was a negative correlation between interoceptive
sensibility and connectivity between the left AI and the middle/superior frontal gyrus, but a
positive relationship between BSE and right IFG-left ventral AI connectivity and left ACC-AI
connectivity during facial expression observation. However, interestingly, in the ROI analyses, in
the ASD group, BSE and BSE-neg were positively related to connectivity between right IFG-left
ventral AI and left ACC-left ventral AI. These relationships go in the expected direction of
interoceptive accuracy measures, and they remain significant if anxiety is added to the models.
Taken together, these results could suggest that the BPQ-VSF is indexing maladaptive
symptoms of self-focused interoception more consistent with anxiety than with interoceptive
accuracy (Trevisan et al., 2020) while the BSE and BSE-neg variables are indexing
interoceptive sensibility that promotes positive emotional outcomes of attention to bodily signals
as has been indexed in other approaches to measuring interoceptive sensibility, like the
Multidimensional Assessment of Interoceptive Awareness (MAIA; Mehling et al., 2012, Flasinski
et al., 2020).
The only significant finding from the between group comparison was that the ASD group
showed greater functional connectivity than the TD group between left ventral AI and right
lateral prefrontal cortex (lPFC) when observing non-emotional facial expressions. The lPFC is
associated with attention, higher-order relational processing, control of action tendencies elicited
by emotional faces, and emotion regulation or reappraisal (Berkman & Lieberman, 2009;
Bramson et al., 2020; Hartogsveld et al., 2018; Hooker & Knight, 2006; Koechlin, 2011;
Rushworth et al., 2011). Bramson and colleagues (2020) demonstrated that inhibitory control
over approach or avoidance based emotional actions is associated with both bilateral insula/IFG
and lPFC activation in incongruent trials (e.g. push away from body, or avoid, in response to
happy face), suggesting that stronger activation of lPFC may indicate a greater ability to
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regulate automatic emotional action responses (Bramson et al., 2020). Furthermore, studies
indicate the lPFC is most active during reappraisal in response to viewing negative emotional
stimuli (Oschner et al., 2002). Taken together, these results may indicate that the ASD group
utilizes increased modulation of automatic emotion responses when viewing facial expressions.
Figure 4
Whole Brain PPI with Left Ventral Anterior Insula
Note. A. Non-emotional facial expressions whole brain contrast ASD > TD in right lateral
prefrontal cortex B. All facial expressions negative whole brain correlation with Alexithymia
Questionnaire for Children total score and left precuneus C. Non-emotional facial expressions
negative whole brain correlation with Body Perception Questionnaire Very Short Form total
score in the left dorsal premotor cortex.
Conclusions From Studies 1-3
Alexithymia
Bird & Cook’s (2013) alexithymia hypothesis is only partially supported by this work.
Here we show that support for the alexithymia hypothesis behaviorally in personal distress, and
neurally in the right amygdala. In particular, higher alexithymia was associated with increased
personal distress, and increased activation in the right amygdala. As regressions do not test
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causality, it may also be the case that the more strongly an individual feels personal distress by
the experiences of others, the more overwhelmed by emotional experiences they are, and this
may impact their ability to identify and communicate their emotions (alexithymia). Thus
alexithymia, above and beyond ASD diagnosis, may index higher personal distress and
abnormal neural functioning in the amygdala. Thus we find support for the alexithymia
hypothesis only with PD and within activity of the right amygdala. That is to say, while in Study 1
alexithymia does seem related to all aspects of emotional empathy behaviorally (PD & EC),
hierarchical regressions demonstrate that alexithymia cannot explain previous deficits in the
empathic concern domain of emotional empathy, or in the AI, ACC or IFGop, above and beyond
ASD diagnosis.
In study 2, our findings in the amygdala show different patterns in the left and right
hemisphere. In the left hemisphere, in the ASD group we find that scores on the alexithymia
communicating emotions subscale are the strongest predictor of reduced left amygdala activity.
However, in the right hemisphere we find the opposite pattern: across groups, alexithymia total
score predicts increased activity in the right amygdala, above and beyond the influence of
group. While Study 2 demonstrates the importance of amygdala function in alexithymia, Study 3
supports previous findings that AI is also involved in alexithymia (e.g. Bird et al., 2010;
Hogeveen et al., 2016; Silani et al., 2008). We find that connectivity between the left ventral AI
and right dorsal AI is related to alexithymia in the ASD group during observation of emotional
facial expressions. Further, left AI-right AI connectivity was particularly driven by the
communicating emotions subscale of the AQC. These results suggest that degree of
alexithymia is impacted by interhemispheric cross-talk between the left and right hemisphere of
the AI. Indeed, previous work has posited that alexithymia may be a result of disconnection
between right hemisphere emotion processing and left hemisphere linguistic processing (MIller,
1986). Additionally, while in study 2 we observed that individuals with poor ability to speak about
their emotions (high alexithymia difficulty communicating emotions) have more activity in the
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right amygdala, and less activity in left amygdala; in study 3 we found that individuals with ASD
with difficulties in communication in general (ADIR language and communication subscale) also
have less connectivity with right amygdala and left (speaking hemisphere) insula. Thus, future
studies may explore the benefits of use of emotion communication and affect labeling therapies
in social and empathic processing in individuals with ASD and comorbid alexithymia.
Interoception and Embodied Measures
In this sample, there is no behavioral evidence that interoceptive sensibility is reduced in
individuals with ASD in either a self-report measure of interoceptive sensibility (BPQ-VSF), or in
an interview measure of bodily sensations experienced during emotion (BSE). However,
physiological hyperarousal is higher in individuals with ASD. In the TD group, physiological
hyperarousal is positively correlated with personal distress and alexithymia. This relationship is
in opposition to the BPQ-VSF in the TD group, where personal distress is negatively associated
with interoceptive sensibility. Thus, for TD individuals, the BPQ-VSF may not index maladaptive
emotional outcomes, but physiological hyperarousal does. However when we look across all,
the BPQ-VSF was negatively correlated with the EmQue-CA AE, indicating that perhaps BPQ-
VSF is behaving differently when a sample with higher anxiety and higher alexithymia are added
(ASD group). These findings are echoed in the neural data. In study 2, physiological
hyperarousal is negatively correlated with activation in the right amygdala during facial
expression observation in the TD group, such that the lower your physiological hyperarousal,
the higher your amygdala activation. A similar pattern is seen in the ASD group (a group with
higher alexithymia and anxiety) for the BPQ-VSF which is negatively associated with AI, IFGop,
and amygdala activation during facial expression observation. Additionally, in study 3, across all
participants, there was a negative correlation between BPQ-VSF and connectivity between the
left AI and the middle/superior frontal gyrus during non-emotional facial expressions. This
relationship was also significant in parameter estimates of individual groups (TD, ASD). These
results suggest that in the ASD group, BPQ-VSF may be indexing more negative aspects of
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attention to bodily signals, much like physiological hyperarousal in TD participants. Directing
attention towards bodily sensations may relieve personal distress, but not necessarily allow for
perspective taking or prosocial behavior with the individual experiencing pain without employing
additional cognitive strategies in the mentalizing/theory of mind network (Stoica & Depue, 2020)
skills which in this sample, and in other studies are reduced in ASD (for review see Frith &
Happé, 2005).
The opposite seems to be true in the BSE & BSE-neg interview variables. In the ASD
group, sensation felt during emotion was negatively correlated with IRI PD, the AQC total, and
anxiety. In study 2, there was a positive association with AI activation in the ASD group. In study
3, across all and within the ASD group BSE-neg was positively correlated with connectivity
between right IFG and left ventral AI. So although children with ASD who have higher AI
activation and higher AI-IFG connectivity to facial expressions report higher instances of
experiencing bodily sensations during emotional experiences. The BPQ-VSF may be indexing
maladaptive symptoms of self-focused interoception more consistent with anxiety than with
interoceptive accuracy (Trevisan et al., 2020), particularly in ASD, while the BSE and BSE-neg
variables are indexing interoceptive sensibility that promotes positive outcomes of attention to
bodily signals as has been indexed in other approaches to measuring interoceptive sensibility
like the Multidimensional Assessment of Interoceptive Awareness (MAIA; Mehling et al., 2012,
Flasinski et al., 2020). Positive interoceptive markers (greater BSE-neg) are associated with AI-
IFGop connectivity and reduced personal distress in individuals with ASD. Taken together,
these associations may indicate that strong connectivity between the AI and IFGop is related to
higher interoception ability and better emotion regulation. This is consistent with previous
studies indicating that better emotion regulation is related to connectivity between the IFGop
and emotion-related brain regions (Torre & Lieberman, 2018).
Emotional Empathy in ASD
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Overall, this series of studies suggest that emotional empathy is not reduced in ASD.
The divergent patterns observed in IRI PD and IRI EC with variables of alexithymia and bodily
awareness could account for previous discrepant findings regarding emotional empathy ability in
ASD. The results point to a measurement and operational definition issue when combining the
two subscales as part of one construct.
However, individuals with ASD did show a reduction in IFGop activation during facial
expression observation compared to TD participants, and IFGop activation was best predicted
by empathic concern in the ASD group. Thus, there are unique contributions of underlying
neural processes of empathic concern disruption in ASD, however given no behavioral
differences in EC between groups, there are likely other neural areas compensating for
hypoactivity in the IFGop, as we do not see it in the behavioral data. There were no connectivity
reductions observed in the ASD group.
Figure 5
Summary of Neural and Behavioral Relationships in Chapters 3 & 4
Note. ACC= anterior cingulate cortex; ADHD = attention deficit hyperactivity disorder symptom
severity; ADIR C = Autism Diagnostic Interview-Revised qualitative abnormalities in
communication subscale; AI = anterior insula; AQC = Alexithymia Questionnaire for Children
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total score; AQC C = Alexithymia Questionnaire for Children difficulty communicating emotions
subscale; ASD = autism spectrum disorder; BPQ-VSF = Body Perception Questionnaire-Very
Short Form; BSE = Bodily sensations experienced during emotion; BSE-neg = Bodily
sensations experienced during negative emotions; EC = Interpersonal Reactivity Index
empathic concern subscale; IFGop = inferior frontal gyrus pars opercularis; L = left; lPFC =
lateral prefrontal cortex; PH-C = Physical Arousal Questionnaire for Children; R = right.
Proposed Model of Emotion Responding in ASD
Across the dissertation, in behavior, neural activity, and neural connectivity certain traits
consistently clustered together to reflect associations between patterns of embodied
responding, emotion understanding, and emotional empathy style. Here, I attempt to
characterize these two styles observed in ASD as a “reactive” and a “responsive” style to bodily
cues, emotions, and emotional empathy.
The reactive style consists of characteristics that suggest a sense of general overwhelm
from bodily signals and emotions of the self and others. A reactive style in this ASD sample
behaviorally consists of higher physiological hyperarousal and bodily awareness, alexithymia,
anxiety, and personal distress to others suffering. Previous studies indicate that personal
distress may block empathic interaction and prosocial behavior by encouraging avoidance of
overwhelming feelings when viewing others’ suffering (Kim & Han, 2018). Neural regions that
were associated with these traits were increased right amygdala activity and the increased
connectivity between left AI and right lPFC in ASD during facial expression observation. This is
consistent with some previous studies, that suggest that the right amygdala is involved in
increased reactive/impulsive aggression in individuals with alexithymia (Farah et al., 2018), and
with increased responses to displays of anger in individuals with alexithymia (Hadjikhani et al.,
2017). The reactive style would likely require strategies of emotion regulation and inhibition to
dampen overwhelming feelings. The only significant between group comparison in Study 3 was
that the ASD group (a group with significantly higher alexithymia, anxiety, and physiological
hyperarousal) showed greater functional connectivity than the TD group between left ventral AI
and right lateral prefrontal cortex (lPFC) when observing non-emotional facial expressions. The
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lPFC is associated with control of action tendencies elicited by emotional faces, and with
emotion regulation strategies like reappraisal (Bramson et al., 2020; Ochsner et al., 2002).
Stronger activation of lPFC creates a greater ability to regulate automatic emotional action
responses (Bramson et al., 2020). Furthermore, functional connectivity studies indicate the right
lPFC is highly connected with emotion-related brain regions (IFGop, AI) during affect labeling in
response to viewing emotional facial expressions (Lieberman et al., 2007), as a strategy to
down-regulate negative emotions (Payer et al., 2012). Taken together, the current results of
increased connectivity between the right lPFC and the AI may indicate the tendency of the
reactive style utilizing increased modulation of automatic emotion reactions when viewing facial
expressions.
A receptive style consists of characteristics that suggest positive awareness of bodily
signals and emotions of the self and others. A receptive style behaviorally consists of greater
bodily sensations experienced during emotion, low alexithymia severity, low anxiety, and greater
empathic concern towards others suffering. Neural regions that were associated with these
traits were increased left amygdala, right AI, and left IFGop activity during facial expression
observation. Left amygdala activation and gray matter volume has been linked to reduced
alexithymia severity (Goerlich-Dobre et al., 2015; Ihme et al., 2013), particularly in ASD (Silani
et al., 2008). While the right amygdala seems to be involved in emotion detection and is
automatically activated by emotion stimuli and may provide autonomic activation, the left
amygdala may decode the arousal signaled in more fine-grained and sustained emotional
stimulus evaluation (Glascher & Adolphs, 2003; Wright et al., 2001) particularly for negative
affect (Baas et al., 2004). The AI is cited as a common neural mechanism for both interoception
(Critchley et al., 2005), and emotion processing (Berthoz et al., 2002; Frewen et al., 2008;
Karlsson et al., 2008) and is necessary for empathy with others’ pain (Gu et al., 2012). IFGop is
involved in social and motor processing via sensorimotor simulation (de Waal & Preston, 2017)
and may be important for translating empathic emotions into appropriate motor action programs
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in ASD. Further, individual differences in trait empathy have been correlated with increased
activity in the IFGop (e.g. Aziz-Zadeh et al., 2010; Gazzola et al., 2006; Jackson et al., 2005;
Kaplan & Iacoboni, 2006; Saarela et al., 2007), AI (e.g. Seara-Cardoso et al., 2016; Singer et
al., 2006), amygdala (e.g. Seara-Cardoso et al., 2016), and the ACC (Masten et al., 2011) in TD
individuals. Additionally, connectivity between left AI with the right AI, the left precuneus, the left
ACC, and the right IFGop were all associated with characteristics of the receptive style.
Connectivity between this emotion-processing region (left AI) and a region involved in core-self
exteroceptive and interoceptive bodily sensations, (superior precuneus; Araujo et al., 2015), a
region involved in mediating cognitive influences on emotion (left ACC; Stevens et al., 2011),
and a region involved in social and motor processing via sensorimotor simulation (right IFGop;
de Waal & Preston, 2017) may be important for translating bodily sensations, internal emotions,
and empathic concern response patterns in ASD.
Clarifying these clustered patterns of behavior help to better guide therapeutic targets in
sub-groups of individuals with ASD. Literature on potential interventions to target characteristics
associated with the reactive style are reviewed below.
Figure 6
Proposed Reactive-Responsive Profile Model in ASD
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Note. ACC = anterior cingulate cortex; AI = anterior insula; BPQ-VSF = Body Perception
Questionnaire-Very Short Form; IFGop = inferior frontal gyrus pars opercularis; lPFC = lateral
prefrontal cortex.
ADHD
A characteristic that did not fall into either cluster of reactive or responsive styles was
ADHD symptom severity. The ASD group had a higher incidence of ADHD, but no behavioral
relationships were observed in the ASD group between Conners-3AI and other behavioral
variables (e.g. alexithymia, interoceptive functioning, empathy). In study 2, ADHD symptom
severity was the strongest predictor of right IFGop activation in ASD. In addition to action
simulation, imitation, and motor processing, the right IFGop in particular has previously been
associated with motor response inhibition (Aron et al., 2014). In the context of an action
observation task, individuals with ADHD may have to make greater efforts to stay still, and to
inhibit pantomiming motor actions during a passive task, resulting in greater IFG activation.
Future research may consider trying to understand how ADHD symptoms in ASD could fit into
the relationships observed in the reactive-responsive model, given that there is evidence for
emotional empathy disruption in ADHD (Abdel-Hamid et al., 2019; Ay & Kilic, 2019; Groen et al.,
2018).
Discussion, Limitations, & Future Directions
Defining Emotional Empathy in ASD Research
A major takeaway from this dissertation is that a significant effort should be made to
separate the social attentional, emotion processing and normative social behavioral processes
that surround the phenomenon of empathy. As reviewed in study 1, conflation of these terms
can result in the conclusion that reduced capacity for the felt experience of emotional empathy
is a central feature of autism. Based on this work and previous studies, atypical emotional
empathy may be largely due to alexithymia and not ASD (e.g. Bird & Cook, 2013; Brewer et al.,
2015; Oakley et al., 2016). Additionally, even for those with alexithymia, there is not necessarily
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a lack of empathic feeling, but an actual increase in personal distress to others' pain rather than
an other-oriented empathic concern that is prosocially expressed.
Fletcher-Watson and Bird (2019) review how ingroup-outgroup status of individuals with
ASD impacts the way empathy ability in ASD is conceptualized. What has been described as
the “double empathy problem” (Milton, 2012) is supported by evidence that TD individuals less
accurately judge emotional expressions of individuals with ASD (Edey et al., 2016; Sheppard et
al., 2016) and that interactions of two individuals with ASD are rated higher in terms of rapport
than interactions with ASD/TD pairs, by both the participants and diagnosis-blind observers
(Crompton et al., 2020). Normative behavioral responses to emotional signaling of others are by
definition dictated by societal expectations defined by the non-ASD majority. In situations of
others’ emotional display, individuals with ASD may appear to lack feelings of empathy, when in
reality they are indeed experiencing felt empathy, but not following the same rules of behavioral
responding as a TD person (Fletcher-Watson & Bird, 2019). In fact, writings by individuals on
the spectrum describe intense empathic hyperarousal (Elcheson et al., 2018; Williams, 1998),
and other work indicates that object personification in ASD could cause empathic responses to
a wider distribution of targets than empathy in TD individuals (White & Remington, 2019). The
consequences of these misattributions can cause misunderstanding and have harmful effects
on the ASD community. The belief of lack of empathy has facilitated work associating autism
with extremist terrorism (Palermo, 2013), and with experiences of dehumanization in individuals
with ASD (Yergeau, 2013). The findings of this dissertation are supported by many of the issues
raised by Fletcher-Watson and Bird (2019) about emotional empathy in ASD, and implicate a
need for clear definitions, greater use of qualitative and experimental measures that probe
individual experiences of empathy and require reflection on the part of the researchers
responsible for disseminating this work.
Therapeutic Implications
144
In recent years, there has been a surge of interest in mindfulness-based interventions.
For example, a recent large study in TD populations exploring the efficacy of a 9 month
intervention including contemplative mindfulness based interventions found improvement on a
number of psychological processes that may be relevant to behavioral and neural
characteristics reviewed in this dissertation, and in ASD (interoceptive functioning, empathic
responding, emotion regulation, emotional self-referential processing, theory of mind, and
functional neuroplastic changes; ReSOURCE Project; for review, see Singer & Engert, 2019).
Intervention modules included: present-moment focused breath and body scanning,
compassion and loving kindness meditations, and perspective-taking exercises (for review, see
Singer & Engert, 2019). Interoceptive changes included: improved heart rate tracking accuracy
and reduced alexithymia symptom severity (Bornemann & Singer, 2016), increased
interoceptive sensibility (Bornemann et al., 2015), and increased breath controlled changes in
high frequency heart rate variability (Bornemann et al., 2019). Affective outcomes observed in
the intervention included: increased compassion towards individuals in videos experiencing
emotions (Trautwein et al., 2020), increases in emotional self-concept (Lumma et al., 2018), and
increased coping and maintenance of personal positive affect in response to witnessing others
in distress (Klimecki et al., 2013; Klimecki et al., 2014). The training also yielded increased
theory of mind accuracy (Trautwein et al., 2020), and use of adaptive cognitive strategies such
as reappraisal and perspective taking, and decreases in avoidant strategies such as distraction
and refocusing (Hildebrandt et al., 2019).
After the contemplative training, or individual modules of the training, neural changes
associated with behavioral results were also observed. Emotional self-concept following
compassion and loving kindness meditation training positively correlated with cortical thickness
change in mPFC and dlPFC. Additionally, increased self-related emotional word use following
compassion and loving kindness training was positively associated with cortical thickness
change in left pars orbitalis and bilateral dlPFC (Lumma et al., 2018). Interestingly, when
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compared to a control group, the compassion training group had greater activity during viewing
others in distress in medial orbitofrontal cortex, putamen, pallidum, and ventral tegmental area.
In other words, in addition to reduced behavioral experience of negative affect, brain regions
traditionally associated with positive affect were more active viewing others in distress after
compassion training, suggesting neural and behavioral changes associated with coping
strategies to reduce individual personal distress and avoidant behavior in response to difficult
interpersonal interactions (Klimecki et al., 2013; Klimecki et al., 2014).
This work suggests that contemplative interventions show great promise for impacting
neural and behavioral outcomes that are particularly relevant to symptom profiles in individuals
with ASD. However, the current available research on mindfulness interventions in youth with
ASD suffer from methodological limitations of small sample sizes, and a lack of fidelity
measures and control groups. Taking into consideration these limitations, two reviews of studies
of yoga and mindfulness training in children and adolescents with ASD found that the
interventions were feasible in this population and may improve anxiety, imitative behaviors, self-
control, quality of life, social responsiveness, social communication, social cognition, social
motivation, and reduced aggression (Cachia et al, 2016; Semple, 2019). The current study, the
promising work of the ReSOURCE Project, and the two reviews in ASD, warrant future rigorous
testing and validating of similar interventions in youths with ASD, and in particular individual
symptom domains in the reactive style delineated here. As reviewed in Hardison & Roll (2016),
mindfulness-based interventions are a natural fit for integration in occupational therapy practice,
where wholistic mind-body unity is emphasized. Mindfulness practice is an occupation itself
when used as a regular meditative practice, and further can be used as a tool to increase
engagement in other occupations by increasing present-moment focus while doing and being in
daily activities (Elliot, 2011; Reid, 2011). Existing occupational therapy strategies in ASD may
be enhanced by more regularly including mindfulness training in practice.
Limitations
146
There are many limitations to consider when interpreting the current study’s results.
First, as previously stated, individuals diagnosed with ASD consist of a very heterogeneous
population; the relatively small sample size and homogeneity of the participants in this sample
(all right handed, IQ > 80, etc.), make generalizations to the whole spectrum of ASD limited.
Further, due to the demands of the MRI environment, we are likely only capturing a narrow
range of youth with ASD, who can tolerate staying still, the loud noise, and being confined in a
small space. It should be noted that all types of imaging and neurophysiology methods should
be considered for use in future research studies in order to represent a large portion of the
population of individuals with ASD. Some of the main variables of interest in the study can only
be measured in individuals who are verbal, future studies should further investigate the
influence of VCI on the relationships reported here.
Secondly, the measures used to capture interoception, alexithymia, and empathy ability,
are self-report measures. Accurate self-report can be particularly challenging for individuals with
ASD who may have difficulty with metacognition and self-referential insight. For this reason,
there was an experimenter-led interview which also measured some of the main variables in the
self-report data. Future studies may consider examining these questions in a larger and more
heterogeneous sample of ASD participants, employing more observational and standardized
measures when possible. Additionally, future studies should include the more multidimensional
aspects of emotionalizing and fantasy in alexithymia; and attention regulation, body listening,
and body trusting in interoceptive sensibility, which were not included here due to the measures
used in this study.
Lastly, empathy is a multifaceted skill which requires a dynamic interaction of cognitive
and emotional functions. Although cognitive and emotional empathy have been separated to
clarify specific processes' contributions to empathy, it is important to acknowledge the
bidirectional communication that occurs between these networks in empathy functioning.
Deficits in cognitive empathy seem to be consistent in the ASD literature, and deficits in
147
emotional empathy seem to be more variable, therefore it is important that future research
consider the holistic interactions of behavioral and neural functions in both the cognitive and
emotional empathy systems, and how they impact symptomatology in ASD. Additionally, the
AQC was used as a gold standard tool for measuring alexithymia in youth, however, like the
TAS-20 (Bagby et al., 1994), this questionnaire captures more cognitive aspects of alexithymia
including identifying emotions and communicating emotions, but does not measure additional
emotionalizing aspects which should also be explored in ASD.
The imaging task was a passive task, future studies may consider comparing passive
facial expression observation to more active tasks with specific instructions to encourage
empathizing. One major limitation of the connectivity (PPI) analysis is the lack of statistical
power inherent in the approach (O’Reilly et al., 2012). This may be why here we did not observe
many group differences in connectivity between TD and ASD participants in the predetermined
ROI’s, and perhaps why emotional empathy behavioral data was not related to ROI connectivity.
The PPI is created using task and seed activity which are both included in the model, therefore
it only has power to demonstrate effects that cannot be explained by the task or the seed time
course. This makes it both very difficult to detect effects, and may make the PPI susceptible to
more false negatives, which is why a conservative Z threshold of 3.1 needed to be used here.
Additionally, this method is designed specifically to probe context related changes in
connectivity. There is a chance that the imaging task used here may have been too subtle to
elicit task-based connectivity effects due to the passive observation of stimuli. Future studies
may consider comparing passive facial expression observation to more active tasks with
specific instructions to encourage empathizing with individuals depicted in the stimuli.
Additionally, PPI has a general weakness of only allowing for analysis of one seed ROI in each
model, unlike other functional network connectivity models which allow for more flexibility in
resulting network information, and potentially unexpected overlapping networks (Xu et al.,
2013). While the seed region used in the PPI analysis (left AI) was hypothesis driven, it is still a
148
limiting factor for understanding the comprehensive group differences and functional
connectivity relationships that may exist outside this specific node investigated for connectivity.
Indeed data from chapter 3 indicate that probing an amygdala seed may also shed light on our
research questions related to alexithymia, which may be explored in future studies. Lastly, PPI
can only expose task-dependent changes in ROI connectivity and cannot determine causal
relationships between regions like other methods (i.e. dynamic causal modeling; Friston et al.,
2003) so we cannot determine directionality of which regions are predominantly influencing
others in the network.
149
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Supplementary Data
fMRI procedure, acquisition, preprocessing (from Kilroy et al., 2020)
fMRI task used video stimuli from three categories: 1) emotional facial expression
actions (e.g., smiling); 2) non-emotional facial expression actions (e.g., tongue to upper lip); 3)
bimanual hand actions (e.g., hands playing xylophone). The hand actions were not analyzed for
the purposes of this study. Stimuli were presented for 3.75 sec in a block design consisting of
three stimuli per block with a 1.25-sec black screen as a transition between each video/still
followed by a 15-sec rest block (Figure 1). During the rest blocks, participants were shown a
black crosshair in the middle of a white screen. Five blocks of each stimulus condition were
alternated with rest in a pseudo-random sequence. Stimuli were presented using MATLAB with
the Psychophysics Toolbox (Brainard, 1997). Participants were asked to passively observe the
videos.
MRI data was acquired on a 3 Tesla MAGNETOM Prisma (Siemens, Erlangen,
Germany) with a 20-channel head coil. A 5-minute structural T1-weighted MPRAGE was
acquired for each participant (TR = 1950 ms, TE = 3.09 ms, flip angle = 10˚, 256 x 256 matrix,
176 sagittal slices, 1mm isotropic resolution). Each functional scan consisted of an echo-planar
imaging (EPI; 150 whole-brain volumes) with the following parameters: TR = 2s, TE = 30 ms,
flip angle = 90˚, 64x64 matrix, in-plane resolution 2.5x2.5mm, and 41 transverse slices, each
2.5mm thick, covering the whole-brain with a multiband factor of three. Spin Echo EPI field
mapping data was acquired in AP and PA directions with identical geometry to the EPI data for
EPI off-resonance distortion correction (TR = 1020 ms, TE1 = 10 ms, TE2 = 12.46 ms, flip
angle = 90°, FOV = 224 × 224 × 191 mm
3
, voxel size = 2.5mm isotropic).
Subject-level functional imaging analyses were completed using FSL 6.0 (Jenkinson et
al., 2012). The following preprocessing steps were taken: (a) brain extraction for non-brain
removal; (b) spatial smoothing using a Gaussian kernel of FWHM 5mm; (c) B0 unwarping in the
y-direction; (d) ICA-AROMA (Pruim et al., 2015), to remove motion and physiology-related
197
noise; (e) a high pass filter with a cutoff period of 90 sec; (f) realignment of functional volumes
using MCFLIRT. Functional images were registered to the high-resolution anatomical image
using a 7-degrees of freedom linear transformation. Anatomical images were registered to the
MNI-152 atlas using a 12-degree of freedom affine transformation, and then this transformation
was further refined using FNIRT for nonlinear registration (Jenkinson et al., 2002; Jenkinson &
Smith, 2001).
Figure 1
Stimulus and task design
Note. (a) All stimuli were presented in color. For the first three columns, still images taken from
EmStim
©
stimuli videos. Each video played for 3.75 s. The last column illustrates still photos
used to cue action for the execution task (dead plant cued participants to make a sad
expression; the whipped cream location on the actor's face cued the participant where to lick;
the xylophone cued the participant to pantomime playing the instrument). 1. Emotional face
stimuli (left to right: disgust, happy, angry, dead plant). 2. Nonemotional face stimuli (left to right:
puffed cheeks, closed eye, tongue to side, whip cream dollop). 3. Hand actions (left to right:
drumming, hammering, grating, xylophone). (b) Illustration of the task design. Stimulus blocks
included three videos and or stills, presented for 3.75 s followed by a 1.25-s black screen
between each stimuli. Each resting block consisted of a 15-s white screen with a black
crosshair. Image size: 800 × 600. The stimuli were presented via the Resonance Technology
digital goggle system.
Abstract (if available)
Abstract
Background: Social deficits in Autism Spectrum Disorder (ASD) commonly involve abnormalities in intention understanding and empathy. There is substantial evidence to suggest that individuals with ASD have difficulties with cognitive empathy (for review see Frith and Happé, 2005); however, emotional empathy deficits are unclear. The presence of co-occurring alexithymic tendencies in ASD (Bird, et al., 2010) and alterations in interoceptive processing (Fukushima, Terasawa, & Umeda, 2011; Grynberg & Pollatos, 2015) are associated with reductions in empathic ability. The purpose of this study was to answer the question: how do emotional empathy, alexithymia, and interoception interact with 1) each other, 2) neural activity, and 3) neural connectivity of mechanisms of emotion processing during facial expression observation in TD and ASD youth. ❧ Methods: Self-report and interview data were collected to explore relationships between interoceptive sensibility, alexithymia, and emotional empathy in 35 high-functioning youth with ASD and 40 typically developing (TD) controls (ages 8-17). A sub-sample also completed an imaging study (TD; n = 37, ASD n = 28). fMRI data was collected inside a 3-T Siemens MAGNETOM Prisma scanner, where participants observed video clips of facial expressions presented in a block design. Both groups were entered into multivariate linear regression models for the task for exploring main effects and between-group comparisons. Anatomically and functionally defined neural regions of interest (ROIs) included the ACC, AI, amygdala and IFGop. Psycho-physiological interaction (PPI) analysis was performed to determine task-based functional connectivity between left ventral AI seed region, and the rest of the brain. Parameter estimates of predetermined ROIs were extracted. All data were analyzed using two-tailed independent sample t-tests, Pearson partial correlation, stepwise multiple linear regression, and hierarchical linear regression. ❧ Results: Major behavioral findings include: 1) The ASD sample had increased alexithymia and physiological hyperarousal, but no significant differences on any measures of interoception or emotional empathy skill in comparison to TD participants; 2) In ASD and TD, alexithymia in ASD is associated with lower empathic concern, and higher personal distress; 3) In the TD group (with low incidence of anxiety), those with higher interoceptive sensibility tended to have lower personal distress; and 4) in the ASD group, greater alexithymia severity, and higher personal distress to others’ emotions is associated with reduced reporting of bodily sensations during negative emotions (BSE-neg); 5) support for the alexithymia hypothesis in personal distress. Major neural activity findings include: 1) support for the alexithymia hypothesis in the right amygdala; 2) reduced left IFGop activation in individuals with ASD compared to TD individuals; 3) empathic concern as the strongest predictor of left IFGop activation and ADHD symptom severity as the strongest predictor of right IFGop activation in ASD; 4) opposite relationships in left and right amygdala with alexithymia 5) opposite patterns of relationships between interview vs self-report questionnaires of interoceptive information with ROI’s. ❧ Major connectivity findings included 1) alexithymia is associated with reduced left AI-left precuneus connectivity, and reduced right dorsal AI-left ventral AI connectivity during facial expression observation; 2) inverse relationships between BPQ-VSF and interoceptive interview variables (BSE, BSE-neg) with behavioral and neural outcomes of alexithymia and emotional empathy; 3) in the ASD group, higher connectivity between left ventral AI-right lateral prefrontal cortex during facial expression observation. ❧ Conclusions: Behaviorally we show that interoception and emotional empathy ability are intact in ASD, and that alexithymia severity is higher in ASD. We find inverse patterns of relationships with emotional empathy and alexithymia, where alexithymia is positively correlated with personal distress and negatively correlated with empathic concern. We show support for the alexithymia hypothesis only in the personal distress domain, as alexithymia predicted higher personal distress above and beyond the influence of group status (ASD). For bodily awareness variables we observed that BPQ-VSF may be indexing maladaptive empathy outcomes, (reduced emotional empathy) and may measure something more similar to physiological hyperarousal, which was positively correlated with alexithymia. ❧ Our data indicate a dynamic interplay between: 1) the amygdala, which is strongly involved in emotional empathy processes and affected by alexithymia presence, differentially in right and left hemisphere, above and beyond ASD diagnosis; 2) the anterior insula, which is strongly involved in visceral and interoceptive sensory processing, empathic concern processing, and alexithymia; 3) the IFGop, which is involved in empathic concern, attentional/inhibitory, and social and motor processing, interoceptive sensibility, and shows differences when comparing groups based on ASD diagnosis. Across the dissertation, in behavior, neural activity, and neural connectivity certain traits consistently clustered together to reflect associations between patterns of embodied responding, emotion understanding, and emotional empathy style. Here, I attempt to characterize these two styles observed in ASD as a “reactive” and a “responsive” style to bodily cues, emotions, and emotional empathy. Therapeutic implications and future directions are discussed.
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Asset Metadata
Creator
Butera, Christiana Dodd
(author)
Core Title
Behavioral and neural influences of interoception and alexithymia on emotional empathy in autism spectrum disorder
School
School of Dentistry
Degree
Doctor of Philosophy
Degree Program
Occupational Science
Degree Conferral Date
2021-08
Publication Date
07/30/2021
Defense Date
06/07/2021
Publisher
University of Southern California
(original),
University of Southern California. Libraries
(digital)
Tag
alexithymia,autism,Empathy,fMRI,interoception,neural connectivity,OAI-PMH Harvest,personal distress,PPI,psycho-physiological interaction
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application/pdf
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English
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(provenance)
Advisor
Aziz-Zadeh, Lisa (
committee chair
), Baranek, Grace (
committee member
), Cermak, Sharon (
committee member
), Kaplan, Jonas (
committee member
), Williams, Marian (
committee member
)
Creator Email
cbutera@usc.edu
Permanent Link (DOI)
https://doi.org/10.25549/usctheses-oUC15670110
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UC15670110
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etd-ButeraChri-9947
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Butera, Christiana Dodd
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University of Southern California Dissertations and Theses
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Tags
alexithymia
autism
fMRI
interoception
neural connectivity
personal distress
PPI
psycho-physiological interaction