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Autonomic and Brain Responses Associated with
Empathy Deficits in Autism Spectrum Disorder
Xiaosi Gu,
1,2
*Tehila Eilam-Stock,
3,4,5
Thomas Zhou,
3
Evdokia Anagnostou,
6
Alexander Kolevzon,
5,7
Latha Soorya,
8
Patrick R. Hof,
7,9,10
Karl J. Friston,
1
and Jin Fan
3,4,5,7,9,10
*
1
Wellcome Trust Centre for Neuroimaging, University College London, London, United
Kingdom
2
Virignia Tech Carilion Research Institute, Roanoke, Virignia
3
Department of Psychology, Queens College, The City University of New York, Flushing,
New York
4
The Graduate Center, The City University of New York, New York, New York
5
Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, New York
6
Bloorview Research Institute, University of Toronto, Toronto, Canada
7
Seaver Autism Center for Research and Treatment, Icahn School of Medicine at Mount Sinai,
New York, New York
8
Rush University, Chicago
9
Fishberg Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York,
New York
10
Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, New York
r r
Abstract: Accumulating evidence suggests that autonomic signals and their cortical representations are
closely linked to emotional processes, and that related abnormalities could lead to social deficits.
Although socio-emotional impairments are a defining feature of autism spectrum disorder (ASD),
empirical evidence directly supporting the link between autonomic, cortical, and socio-emotional
abnormalities in ASD is still lacking. In this study, we examined autonomic arousal indexed by skin
conductance responses (SCR), concurrent cortical responses measured by functional magnetic reso-
nance imaging, and effective brain connectivity estimated by dynamic causal modeling in seventeen
unmedicated high-functioning adults with ASD and seventeen matched controls while they performed
an empathy-for-pain task. Compared to controls, adults with ASD showed enhanced SCR related to
empathetic pain, along with increased neural activity in the anterior insular cortex, although their
behavioral empathetic pain discriminability was reduced and overall SCR was decreased. ASD individ-
uals also showed enhanced correlation between SCR and neural activities in the anterior insular cortex.
Additional Supporting Information may be found in the online
version of this article.
Contract grant sponsor: National Institute of Health [NIH]; Con-
tract grant number: R21 MH083164; Contract grant sponsor: Icahn
School of Medicine at Mount Sinai (to J.F.); Contract grant spon-
sor: James S. McDonnell Foundation Grant; Contract grant num-
ber: 22002078 (to P.R.H.); Contract grant sponsor: Wellcome Trust
(to K.J.F.); Contract grant sponsor: Wellcome Trust Principal
Award (Dr. Read Montague) (to X.G.)
Correction added on 05 October 2015, after first online
publication.
*Correspondence to: Xiaosi Gu, PhD; Wellcome Trust Centre for
Neuroimaging, University College London, London WC1N 3BG,
UK. E-mail: xiaosi.gu@ucl.ac.uk and Jin Fan, PhD; Department of
Psychology, Queens College, The City University of New York,
Flushing, New York 11367. E-mail: jin.fan@qc.cuny.edu
Received for publication 8 January 2015; Revised 8 April 2015;
Accepted 3 May 2015.
DOI: 10.1002/hbm.22840
Published online 21 May 2015 in Wiley Online Library
(wileyonlinelibrary.com).
rHuman Brain Mapping 36:3323–3338 (2015) r
V
C2015 The Authors Human Brain Mapping Published by Wiley Periodicals, Inc.
This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and
reproduction in any medium, provided the original work is properly cited.
Importantly, significant group differences in effective brain connectivity were limited to greater reduc-
tion in the negative intrinsic connectivity of the anterior insular cortex in the ASD group, indicating a
failure in attenuating anterior insular responses to empathetic pain. These results suggest that aberrant
interoceptive precision, as indexed by abnormalities in autonomic activity and its central representa-
tions, may underlie empathy deficits in ASD. Hum Brain Mapp 36:3323–3338, 2015.V
C2015 The Authors.
Human Brain Mapping Published by Wiley Periodicals, Inc.
Key words: autism spectrum disorder; autonomic nervous system; functional magnetic resonance
imaging; brain connectivity; empathy; dynamic causal modeling; interoceptive inference
r r
INTRODUCTION
Autism spectrum disorder (ASD) is a family of neurode-
velopmental disorders with a wide range of sensory and
socio-emotional deficits [Chiu et al., 2008; Dinstein et al.,
2012; Happe et al., 2006]. Empathy, the ability to share
vicariously the feelings of others, is an important social-
emotional faculty [Gu et al., 2010; Moriguchi et al., 2007]
and is compromised in individuals with ASD [Baron-
Cohen and Wheelwright, 2004]. Empathy is considered a
multifaceted construct, including at least emotional conta-
gion and arousal and cognitive perspective-taking [de
Waal, 2008]. Previous studies have demonstrated abnor-
malities in various aspects of empathy in individuals with
ASD, including difficulties in mentalizing and perspective-
taking [Fan et al., 2014; Hadjikhani et al., 2014; Minio-
Paluello et al., 2009], as well as heightened affective
arousal to emotional stimuli [Fan et al., 2014; Smith, 2009].
However, there has not been a mechanistic account for
these socio-emotional deficits in ASD.
It has been proposed that a core component of empathy
is the mechanism through which the observer gains access
to the subjective state of another person via the observer’s
own neural and bodily representations [Decety and Jack-
son, 2004; Preston and de Waal, 2002; Singer et al., 2009].
The anterior insular cortex (AIC) and its associated auto-
nomic processing are considered to be crucial in support-
ing this embodied or interoceptive “theory of mind”
[Corradi-Dell’Acqua et al., 2011; Craig, 2014; Gu et al.,
2010, 2012; Singer et al., 2009; Wicker et al., 2003]. The AIC
is a critical cortical center in the interoceptive system
which processes information from the body and exerts
autonomic control [Craig, 2009; Craig, 2011; Critchley and
Harrison, 2013; Gu et al., 2013a]. For instance, a direct cor-
relation has been found between autonomic activity
indexed by skin conductance response (SCR) and neural
activity in the AIC measured by functional magnetic reso-
nance imaging (fMRI) during resting state in neurotypical
individuals [Eilam-Stock et al., 2014; Fan et al., 2012].
Using fMRI [Gu and Han, 2007; Gu et al., 2010, 2013b],
activation likelihood estimate meta-analysis and neuropsy-
chological approaches [Gu et al., 2012], we previously
demonstrated that the AIC is specifically activated during,
and is necessary for, empathetic pain processing. Impor-
tantly, we showed that even when the participant’s atten-
tion was directed away from the painful aspect of images
depicting another person’s pain, the AIC was still more
activated for painful compared to neutral stimuli, while
the anterior cingulate cortex showed comparable activa-
tions for painful and neutral stimuli [Gu et al., 2010].
Moreover, accumulating evidence suggests that autonomic
signals and their higher-order rerepresentations are crucial
for emotional feelings [Craig, 2002; Critchley and Harrison,
2013; Ekman et al., 1983; Gray and Critchley, 2007; Harri-
son et al., 2010; Rainville et al., 2006].
Several other brain regions encoding biological informa-
tion are also involved in social and emotional processes
[Saxe, 2006]. The extrastriate body area (EBA) is involved
in the exteroceptive processing of visual features related to
the body during empathetic responses [Lamm and Decety,
2008]. Although much attention has been devoted to gen-
eral visual deficits in ASD [Behrmann et al., 2006; Dakin
and Frith, 2005; Kaiser et al., 2010], little is known about
the involvement of EBA in ASD. The prefrontal cortex
(PFC), especially the lateral PFC (LPFC), has been associ-
ated with executive control and information integration
during socio-emotional processing, and is a domain-
general area [Corbetta and Shulman, 2002; Levy and Wag-
ner, 2011; Romanski, 2007]. Deficits in LPFC responses
have been found in individuals with ASD [Kaiser et al.,
2010; Shafritz et al., 2008; Silk et al., 2006], supporting the
hypothesis that ASD individuals have difficulty in inte-
grating information from different modalities [Happe and
Frith, 2006].
Considering the complex nature of socio-emotional func-
tions and the manifestation of abnormalities at both sen-
sory and socio-emotional levels in ASD, it is important to
review normative accounts of the disorder. Several recent
articles have proposed such models of ASD [Friston et al.,
2013b; Lawson et al., 2014; Pellicano and Burr, 2012; Quat-
trocki and Friston, 2014; Van de Cruys et al., 2014) based
on the notion that the brain uses generative models of the
world to actively infer the causes of sensory input to pre-
dict appropriate (expected) visceral and motor responses
[Friston, 2010; Friston et al., 2013a]. In this setting, the
influence of these prediction errors is nuanced by their
expected precision. Computationally, precision corresponds
to reliability or inverse variability. Psychologically,
rGu et al. r
r3324 r
precision can be regarded as the attention paid to sensory
channels [Feldman and Friston, 2010]. Physiologically, this
precision or attention is thought to be mediated by the
postsynaptic gain or sensitivity of neuronal populations
reporting prediction error [Bastos et al., 2012]. The specific
failure in ASD has been attributed to a relative increase in
the precision of sensory evidence over the precision of
higher (extrasensory) beliefs [Friston et al., 2013b; Lawson
et al., 2014; Pellicano and Burr, 2012; Quattrocki and Fris-
ton, 2014; Van de Cruys et al., 2014].
Crucially, it has been hypothesized that aberrantly high
precision in the interoceptive domain might account for
selective socio-emotional deficits in ASD [Friston et al.,
2014; Van de Cruys et al., 2014], given the intimate rela-
tionship between autonomic activity, their related cortical
responses, and socio-emotional awareness. These pro-
posals provide a useful framework for a quantitative and
mechanistic understanding of socio-emotional deficits in
ASD in terms of failures in Bayesian inference, leading to
false inference about interoceptive and emotional states,
particularly in the context of prosocial and affiliative inter-
actions. Based on these proposals and empirical findings
on interoception, we have recently proposed that the AIC
integrates bottom-up interoceptive signals with top-down
predictions to generate a representation or expectation
about embodied states [Gu et al., 2013a]. This mechanism
enables the AIC to contextualize descending predictions to
visceral systems that provide a point of reference for auto-
nomic reflexes. This process has been termed interoceptive
inference, namely, Bayesian inference about interoceptive
states [Gu and FitzGerald, 2014; Seth, 2013; Seth et al.,
2011]. Empirically, it remained unclear how deficits in
interoceptive inference directly contribute to socio-
emotional deficits in ASD.
We hypothesized that individuals with ASD would
show abnormally high interoceptive precision during
empathetic pain processing, considering previous findings
of increased autonomic activities [Hirstein et al., 2001; Kyl-
liainen and Hietanen, 2006; Van Hecke et al., 2009] and
heightened emotional arousal in ASD [Fan et al., 2014]
during socio-emotional processing. To test this hypothesis,
we used simultaneous SCR and fMRI measures during a
well-validated empathy-for-pain paradigm [Gu et al., 2012;
Gu et al., 2010] in high-functioning male adults with ASD
and matched healthy controls (HC). Importantly, we mod-
eled interoceptive precision in terms of the modulatory effect
exerted by experimental context (i.e. viewing others’ pain)
on the within-area self-connection of AIC using dynamic
causal modeling (DCM) [Friston et al., 2003; Penny et al.,
2004]. The self-connection of a given neural region is
assumed to be negative so that its activity returns to equi-
librium levels; thereby modeling cortical gain control.
Experimentally induced increases of gain are modeled as
an attenuation of self-inhibition—that effectively increases
the excitability of neuronal populations (i.e., disinhibition).
Therefore, changes in self-disinhibition reflect changes in
gain (or precision) following experimental manipulations.
Using DCM, we also modeled the directed interactions
among the LPFC, AIC, and EBA, and estimated how
experimental context modulates directed connections
among these cortical areas [Friston et al., 2003; Penny
et al., 2004; Stephan et al., 2010] to test a competing
hypothesis that decreased precision at the higher level of
LPFC and decreased top-down connectivity from the
LPFC to AIC, rather than increased interoceptive precision,
contributes to empathy deficits in ASD. Our hypothesis
makes a number of specific predictions: individuals with
ASD would show (1) disinhibited (peripheral) autonomic
responses to arousing empathetic pain stimuli; (2) disin-
hibited or increased cortical response to empathetic pain
in brain regions subserving interoceptive and autonomic
processes, such as the AIC; and (3) greater modulation of
self-connectivity within the AIC by empathetic pain.
MATERIALS AND METHODS
Participants
We recruited 17 unmedicated high-functioning adult
males with ASD and 18 matched HC participants through
the Seaver Autism Center for Research and Treatment at
the Icahn School of Medicine at Mount Sinai (ISMMS).
One HC participant was excluded due to chance-level
behavioral performance on the empathy-for-pain para-
digm, resulting in a final sample of 17 participants in each
group (Table I). One additional HC participant had incom-
plete SCR data and was therefore excluded from the SCR
analysis, yielding n517 for ASD and n516 for HC for
TABLE I. Demographic data
ASD (n517) HC (n517) P
Age (years) 26.2 66.4 26.8 67.8 >0.7
Handedness score 73.5 635.3 75.6 640.5 >0.8
Parental SES
a
91.3 616.8 92.0 622.6 >0.9
Subject SES
a
27.9 614.6 32.7 615.4 >0.3
Full Scale IQ 109.5 618.0 113.5 611.9 >0.4
ASD diagnosis
(autism/Asperger)
12/5
ADI-R
b
Social 18.6 68.0
Verbal communication 15.5 64.9
Repetitive behavior 6.2 63.7
Development 3.5 61.5
ADOS-G
c
Communication 3.161.3
Social 6.9 62.1
Imagination 0.7 60.5
Stereotyped behaviors 1.3 61.4
a
SES data was not available for one ASD participant and one HC
participant.
b
ADI-R scores were not available for one ASD participant. ASD:
autism spectrum disorder; HC: healthy control. Data are reported
as means 6standard deviation.
rAutonomic and Brain Responses in ASD r
r3325 r
the SCR results. Two ASD and two HC participants were
excluded from the fMRI analysis due to excessive head
motion, yielding 15 participants in each group for the
fMRI results. One of the HC participants, who was
excluded due to motion, also did not complete the self-
report questionnaires. Individuals in the ASD group met
diagnostic criteria for autism disorder (n512) or Asperger
syndrome (n55) by psychiatric interview according to the
Diagnostic and Statistical Manual-IV (DSM-IV-TR) [Associ-
ation, 2000], confirmed by the Autism Diagnostic
Interview-Revised (ADI-R; [Lord et al., 1994]) and Autism
Diagnostic Observation Schedule-Generic (ADOS-G; [Lord
et al., 2000]), except for one participant for whom ADI-R
scores were unavailable. Participants who met criteria only
for Pervasive Developmental Disorder not Otherwise
Specified (PDD-NOS) by DSM-IV-TR were excluded. Other
exclusion criteria included epilepsy, history of schizophre-
nia, schizoaffective disorder or other Axis I mental disor-
ders except obsessive-compulsive disorder (given the
phenotypic overlap with ASD), and use of depot neurolep-
tic medication or other psychoactive drugs within five
weeks prior to participation. For the HC group, exclusion
criteria were medical illness or history in first-degree rela-
tives of developmental disorders, learning disabilities,
autism, affective disorders, and anxiety disorders. Partici-
pants from both groups with a history of substance or
alcohol dependency or abuse within 1 year prior to partici-
pation were excluded as well. Each group had 16 right-
handed and 1 left-handed participants (measured by the
Edinburgh Inventory Handedness Questionnaire [Oldfield,
1971]. HCs were matched with ASD participants for age,
parental and participants’ socioeconomic status (SES), and
Full-Scale Intelligence Quotient (FSIQ) measured with the
Wechsler Adult Intelligence Scale [WAIS-III; [Wechsler,
1997]], and had an FSIQ in the low average range or
higher (>80). All participants provided written informed
consent, approved by the ISMMS Institutional Review
Board.
Behavioral paradigm
Participants were presented with 256 color photographs
of hands or feet of individuals in painful or nonpainful sit-
uations (Fig. 1), and were asked to judge whether the per-
son shown in the image was suffering from pain or not.
As in our previous studies [Gu et al., 2010, 2012, 2013b],
these photos depicted everyday life scenarios and were
taken from a first-person perspective to avoid mental rota-
tion. Half of the pictures showed painful situations and
the other half showed nonpainful scenarios that were iden-
tical in terms of physical properties such as brightness and
contrast. All images were slightly blurred with a Gaussian
filter to remove gender or age related information.
Sixty-four images were presented in each run, for a total
of four runs. Stimuli were presented in an event-related
fMRI design and presentation of each type of picture (8
types for laterality: left/right, body part: hand/foot, and
pain: painful/nonpainful) were counterbalanced in a Latin
Square (pseudo-randomized) design with all types of pic-
ture proceeded and followed by other types with an equal
probability. There was a 30-s fixation period at the begin-
ning and end of each run to allow skin conductance and
hemodynamic responses to return to baseline. It has been
shown that 30-s is sufficient for both the skin conductance
response [Bach et al., 2010] and haemodynamic response
[Friston et al., 1995] to return to baseline. This reflects the
formal similarity between the impulse response function
for SCR and the hemodynamic response function (HRF)
[Bach et al., 2010]. Each 5.5-s trial consisted of a picture
presentation and two response options (i.e., no pain vs.
pain) for 2.5-s. The participants were asked to make
responses within the 2.5-s time window. This was fol-
lowed by 3-s of fixation. Participants made button-press
responses with their right hand.
Behavioral data analysis
We analyzed behavioral accuracy data using signal
detection theory (SDT) [Snodgrass and Corwin, 1988]. SDT
is a method that discerns signal from noise, and assumes
that the perceiver has a distribution of internal responses
for both signal and noise. Participants’ sensitivity to sig-
nals is calculated as d05(l
s
–l
n
)/sd, where l
s
is the mean
of the signal (pain) distribution, l
n
is the mean of the
noise (no pain) distribution, and sd is the common stand-
ard deviation of both distributions. A larger d0represents
better discrimination accuracy and a smaller d0denotes
poorer discriminability. Decision bias bwas calculated as
b5f
s
(k)/f
n
(k), where f
s
(k) is the height of the signal distri-
bution at a given criterion kand f
n
(k) is the height of the
noise distribution at the same k.
Trait assessments
All participants completed personality assessments of
trait alexithymia and trait empathy. Trait alexithymia was
measured using the 20-item Toronto Alexithymia Scale
(TAS-20) [Bagby et al., 1994]; higher scores indicate greater
difficulty in emotional awareness and greater degree of
Figure 1.
Empathy-for-pain paradigm. Participants viewed images of others
in painful and nonpainful situations and indicated whether the
person in the image was suffering from pain.
rGu et al. r
r3326 r
alexithymia. Trait empathy was measured using the Empa-
thy Quotient (EQ), a 40-item self-report questionnaire
without subscales [Baron-Cohen and Wheelwright, 2004];
higher scores indicate greater trait empathy.
Statistical comparisons
Because group comparisons were based on a priori
hypotheses in small samples, we used the nonparametric
bootstrapping method [Hasson et al., 2003; Mooney, 1993]
for the behavioral, SCR, and DCM connectivity parameters
to assess the probability of observing a difference between
two groups (n
1
participants for the HC group and n
2
par-
ticipants for the ASD group) by chance. The bootstrapping
procedure was conducted with 10,000 iterations as follows:
(1) n
1
participants were selected randomly as the surrogate
HC group, from the whole group of (n
1
1n
2
) participants
including both ASD and HC participants; (2) n
2
partici-
pants were selected randomly as the surrogate ASD group
from the whole group of (n
1
1n
2
) participants; and (3) the
tvalue of the difference between the two surrogate groups
was calculated. After 10,000 iterations, the distribution of
the tvalues was obtained. The observed tvalue (i.e.,
between ASD and HC groups) was then calculated and
compared along the tdistribution. If the probability of
obtaining the observed tvalue along the permutated dis-
tribution of tvalue was less than 5%, we considered the
difference between the ASD and HC groups to be signifi-
cant. For correlations, we calculated Pearson correlation
coefficients and statistical significance was set at P50.05,
two-tailed.
Skin conductance acquisition and analysis
Skin conductance was acquired during fMRI scanning
as described in our previous study [Fan et al., 2012]. In
brief, skin conductance was recorded using the GSR100C
amplifier (BIOPAC Systems, Goleta, CA), together with
the base module MP150 and the AcqKnowledge software
(version 3.9.1.6). The GSR100C measures skin conductance
by applying a constant voltage of 0.5 V between two elec-
trodes that are attached to the palmar skin. Skin conduct-
ance (measured in lS) was recorded using a 2000-Hz
sampling rate (gain 52lS/V, both high pass filters 5DC,
low pass filter 510 Hz). After cleaning the skin with alco-
hol preps, two EL507 disposable EDA (isotonic gel) elec-
trodes were placed on the palmar surface of the distal
phalanges of the big and second toes of left foot. The sig-
nal was low-pass filtered (using the MRI-Compatible MRI
CBL/FILTER System MECMRI-TRANS) to reduce radio
frequency interference from the scanner. BIOPAC record-
ing was synchronized to the E-Prime program via the par-
allel port of the computers. Event markers were recorded
to enable precise time alignment of skin conductance
recording with scan onsets and task trials.
To analyze the SCR data, we applied general linear
modeling (GLM) using SCRalyze v.b2.1.7 [Bach et al.,
2010]. SCR data were epoched into individual runs, and
the range of trimming was determined by the beginning
of the first marker and the end of the last marker of each
block with 30-s before and after the first and last marker
for baseline. Consistent with fMRI analysis (see fMRI
methods below), the two vectors for onsets of the events
(“all images” and “painful images”) in seconds were
extracted based on the corresponding markers recorded.
The regressors were then generated by convolving the vec-
tors with the canonical response function of the SCR [Bach
et al., 2010]. GLM was then performed with a band-pass
filter (first-order Butterworth filter) with a band-pass filter
of 0.01-0.12 Hz, similar to our previous study on SCR [Fan
et al., 2012]. The data were then normalized to control for
between-subject differences in skin conductance response
amplitude. The bvalues (nondimensional) corresponding
to the two regressors were obtained from the GLM of each
participant for between-group statistical testing.
We also extracted single trial SCRs associated with the
presentation of each stimulus by modeling each trial as a
separate regressor and “all other images” and “all other
painful images” as two further regressors, iterated over 64
trials and over 4 sessions. These trial-to-trial SCR parame-
ter estimates were later used as a parametric modulatory
regressor for an fMRI analysis to test for trial-by-trial cor-
relations between SCR and fMRI responses (see fMRI
methods).
fMRI data acquisition and preprocessing
All MRI acquisitions were obtained on a 3-T Siemens
Allegra MRI system at ISMMS. All participants underwent
only one session with all scanning sequences. The whole
scan session lasted about 1.5-h. Foam padding was used to
keep participants’ heads still. All images were acquired
along axial planes parallel to the anterior commissure-
posterior commissure (AC-PC) line. A high-resolution
TABLE II. Behavioral results (mean 6SD).
Hit (choose pain when
the image is painful)
False alarm (choose pain
when the image is
nonpainful)
Miss (choose no pain
when the image is painful)
Correct rejection (choose
no pain when the image
is nonpainful)
ASD 0.85 60.13 0.11 60.10 0.15 60.13 0.89 60.10
HC 0.94 60.04 0.07 60.07 0.06 60.04 0.93 60.07
rAutonomic and Brain Responses in ASD r
r3327 r
T2-weighted anatomical volume of the whole brain were
acquired on an axial plane parallel to the AC-PC line, with
a turbo spin-echo (TSE) pulse sequence with the following
parameters: 40 axial 4 mm-thick slices, skip 50 mm, repe-
tition time (TR) 54050 ms, echo time (TE) 599 ms, flip
angle 51708, field of view (FOV) 5240 mm, matrix
size 5448 3512, voxel size 50.47 30.47 34 mm. T2*-
weighted images were acquired for fMRI. Slices were
obtained corresponding to the T2-weighted images. The
fMRI imaging was performed using a gradient echoplanar
imaging (GE-EPI) sequence: 40 axial slices, 4 mm-thick,
and skip 50 mm, TR 52500 ms, TE 527 ms, flip
angle 5828, FOV 5240 mm, and matrix size 564 364.
Each run started with two dummy volumes before the
onset of the task to allow for equilibration of T1 saturation
effects. A total of four EPI runs with 165 image volumes
per run were acquired for each participant. Event-related
analyses of the fMRI data from the task were conducted
using statistical parametric mapping (SPM8; Wellcome
Department of Imaging Neuroscience, London, UK). The
functional images were adjusted for slice timing, realigned
to the first volume, coregistered to the T2 image, normal-
ized to a standard template (MNI, Montreal Neurological
Institute), resampled to a 2 3232 mm voxel size, and
spatially smoothed with an 8 3838 mm full-width-at-
half-maximum (FWHM) Gaussian kernel.
General linear modeling of fMRI data
For the main fMRI analysis presented in Fig. 4A, we
included two task events of interest; onset of “all images”
and “painful images”, in first-level GLM [Friston et al.,
1995]. In this model specification, parameter estimates of
“painful images” represent brain activations related to
empathetic pain that is over and above the activation
related to “all images”. This can be considered as equiva-
lent to the response to the “non-painful” images. We used
this model specification to specify unique driving and
modulatory inputs for DCM (see below). The responses to
“painful images” in this setting are equivalent to the acti-
vations related to “painful” minus “non-painful” from the
mathematically equivalent model where “painful images”
and “non-painful images” are modeled separately (see
Supporting Information Fig. S1). For the analysis on SCR-
fMRI correlation presented in Fig. 4B, we constructed an
additional GLM where trial-by-trial parameter estimates of
SCR were entered as parametric modulators at the onset
of stimulus presentation.
GLM was conducted for the functional scans from each
participant by modeling the observed event-related BOLD
signals and regressors to identify the relationship between
the task events and the hemodynamic response. Regres-
sors were created by convolving a train of delta functions
representing the sequence of individual events with the
default SPM basis function [Friston et al., 1998]. Six param-
eters generated during motion correction were entered as
covariates. First-level SPMs from all participants were
entered into a second-level between-group analysis. We
used the Monte Carlo method to determine the statistical
threshold [Slotnick et al., 2003]. The general idea is to
model the whole brain volume of 64 364 340 original
voxels, assume type I error of an individual voxel at
P<0.05, smooth the volume with a 3-dimensional 8-mm
FWHM Gaussian kernel, and then count the size of each
contiguous cluster of voxels. After 1000 iterations, a proba-
bility associated with each cluster extent is calculated
across all iterations, and a cluster extent can be chosen to
achieve the desired correction for multiple comparisons.
Assuming an individual voxel type I error of P<0.05, a
cluster extent of 120 contiguous resampled voxels (2 32
32mm
3
) was indicated as necessary to correct for multi-
ple comparisons at P<0.05 (see Supporting Information
Fig. S2). This same threshold was applied to all contrasts.
For the analysis of brain–behavior relationship, we
quantified BOLD responses to viewing painful images in
our main regions of interest (ROIs), namely, AIC, EBA,
and LPFC, based on the peak activations averaged over
both groups as listed in Table III (5-mm spherical ROI cen-
tered at [238, 20, 2] for AIC, [246, 270, 24] for EBA, and
Figure 2.
Behavioral and trait measurements. (A) ASD group showed
impaired empathetic pain discriminability d0.(B) There was no
significant group difference in decision bias b.(C) ASD group
showed greater trait alexithymia, that is, greater difficulty in
emotional awareness, measured by the 20-item Toronto Alexi-
thymia Scale (TAS-20). (D) The ASD group also showed
impaired trait empathy, measured by the empathy quotient.
ASD, autism spectrum disorder; HC, healthy control. **P<0.01,
***P<0.001, n.s., not significant. Error bars indicate standard
errors of the mean.
rGu et al. r
r3328 r
[58,14,22] for LPFC]). These parameter estimates were then
correlated with behavioral measures, such as the behav-
ioral sensitivity index d’ and trait measures of EQ and
TAS-20.
Dynamic causal modeling
DCM uses a deterministic model of neural dynamics in
a network of interacting brain areas to provide Bayesian
estimates of the effective strength of synaptic connections
among neuronal populations and modulatory effect of
experimental manipulations as well as model evidence
[Friston et al., 2003; Penny et al., 2004]. Specifically, DCM
uses a differential equation _
x¼A1uBðÞx1Cu where xis a
vector of the neuronal states summarizing the activity of
regions of interests (ROIs) and u is a vector representing
external or experimental input. Please see Supporting
Information Fig S3 for a simplified illustration of the gen-
eral idea behind this model. The A matrix includes the
strength of fixed connections between ROIs; that is, con-
nections that are not modulated by experimental input.
The B matrix represents the degree of the modulation of
these connections induced by the experimental input; that
is, painful stimuli or “modulatory input.” Matrix C is the
direct influence on the ROI usually attributed to sensory
input (i.e., EBA directly receives visual body information;
i.e., all stimuli or “driving input”). These results, therefore,
are generative models of the brain that provide Bayesian
posterior estimates of the effective strength of synaptic
connections among neuronal populations and modulatory
or contextual effect of experimental manipulations [Friston
et al., 2003; Penny et al., 2004]. DCM also allows one to
define models with different network properties, and then
select the best model or the best family of models using
Bayesian model comparison [Stephan et al., 2009, 2010].
Model specification
DCM was implemented using SPM (SPM12; Wellcome
Department of Imaging Neuroscience, London, UK). Our
three-region DCM was motivated by the main results of
the fMRI GLM analysis (see Fig. 4A and Table III) show-
ing group differences in the AIC, EBA, and LPFC. How-
ever, the selection of ROI coordinates was based on the
group average, rather than group difference, related to
empathetic pain. The AIC is our key ROI and is important
for interoceptive processing of emotional stimuli [Gu et al.,
2013a]. The EBA is involved in (exteroceptive) processing
of visual features of body parts, as reviewed in the Intro-
duction [Lamm and Decety, 2008]. The EBA was included
to contrast with the interoceptive AIC pathway but also
needed as the node for visual input (i.e., “driving input”).
The LPFC is involved in general cognitive control and
executive functions [Corbetta and Shulman, 2002; Levy
and Wagner, 2011]. Although they are rather minimal,
these models have the requisite hierarchical structure: two
cortical levels (high: LPFC; low: AIC and EBA) and two
pathways (interoceptive: LPFC-AIC; exteroceptive: LPFC-
EBA). Only unilateral ROIs were included because—for
both AIC and LPFC—we only detected unilateral activa-
tion related to painful images across both groups; and for
all three ROIs, group differences in pain-related activations
were limited to one hemisphere. We assumed reciprocal
(extrinsic) connections among all three regions as well as
recurrent self (intrinsic) connections within all three
regions. Image viewing served as a driving input to EBA
in all models. Viewing painful images served as modula-
tory input changing either no intrinsic connections (models
1–4), or intrinsic connections in AIC, EBA, and LPFC
(models 5–8). Additionally, painful images could modulate
either no connection between AIC and EBA, and LPFC
(models 1 and 5), forward connections from AIC and EBA
to LPFC (models 2 and 6), backward connections from
LPFC to AIC and EBA (models 3 and 7), or reciprocal con-
nections between AIC and EBA, and LPFC (model 4 and
8; see Fig. 5 for the 8 models).
Time series extraction
We created volumes of interest (VOIs) of 8-mm radius
based on each participant’s local maximum of empathetic
pain-related activation closest to group mean: AIC, cen-
tered at [238 20 2]); EBA, centered at [246 270 24]); and
LPFC, centered at [58 14 22] (see Table III). We then
extracted fMRI time series from individual VOIs using
their principal eigenvariates. One HC participant failed to
display activation in these three VOIs, even at P<0.1
uncorrected and was therefore excluded from the analysis.
Bayesian model selection
DCMs were estimated at the within-subject or individ-
ual level first. Each participant had one DCM per model
Figure 3.
Skin conductance response (SCR). (A) ASD group showed
increased SCR to painful images compared to HC group. (B)
SCR related to viewing all images was significantly lower in the
ASD group compared to HC group. * P<0.05. ASD, autism
spectrum disorder; HC, healthy controls. Error bars indicate
standard errors of the mean.
rAutonomic and Brain Responses in ASD r
r3329 r
per session. For the same model, four DCMs for the four
sessions were averaged within-subject using Bayesian
model averaging [Penny et al., 2010] in a parameter-
specific fashion, to allow for later statistical compari-
sons. We then conducted Bayesian model selection
(BMS) among all models at both the model level and the
family level (with or without changes in intrinsic con-
nections). Model inference was made at the group level
using random effects. The model/family with the high-
est excedance probability was selected as the optimal
model/family. Group differences in the parameters of
the optimal model were tested with the bootstrapping
method as described before. The Bonferroni procedure
was used to correct for multiple comparisons [Dunn,
1961].
RESULTS
Behavioral and trait measurements
The matrix of hit, false alarm, miss, and correct
rejectionratesareshowninTableII.TheASDgroup
showed significantly lower d0than the HC group
(bootstrapping P<0.01;Fig.2A),suggestingthatASD
individuals had decreased empathetic pain discrimina-
bility. Group difference in decision bias bdid not
reach significance (bootstrapping P>0.4; Fig. 2B). The
ASD group also showed greater alexithymia (boot-
strapping P<0.001; Fig. 2C) and lower trait empathy
(bootstrapping P<0.001; Fig. 2D) than the HC group.
We also explored the correlation between d0and trait
measures. There was a significant negative correlation
between d0and trait alexithymia measured by TAS-20
(r520.38, P50.02), suggesting that lower sensitivity
to others’ pain was related to higher trait alexithymia
across both groups. The correlation between d0and the
EQ was not significant for both groups combined or
either group separately (r50.2, P>0.1). These results
are consistent with previous findings on impaired
empathy and emotional awareness in ASD individuals
[Baron-Cohen and Wheelwright, 2004] and also suggest
that our visual empathy paradigm was effective in
probing the behavioral characteristics related to empa-
thy in ASD.
Figure 4.
(A) Whole brain activations related to empathetic pain. Both
ASD and HC groups showed activation in anterior insular cortex
(AIC), extrastriate body area (EBA), and lateral prefrontal cortex
(LPFC). Adults with ASD showed greater activation in AIC and
EBA, and less activation in LPFC compared to HC (see Table III).
(B) Whole brain activation related to SCR. Compared to HC,
ASD individuals showed greater activation in the AIC and several
other brain regions (see Table IV). ASD, autism spectrum disor-
der; HC, healthy control (P<0.05 uncorrected and k>120,
equivalent to P<0.05 corrected for multiple comparisons).
rGu et al. r
r3330 r
Skin conductance response
The ASD group showed greater empathetic pain-related
SCR (beta coefficient of regressor “painful images”) com-
pared to the HC group (Fig. 3A; bootstrapping P50.05).
The ASD group’s average pain-related SCRs were greater
than zero (one-sample t-test t
(16)
52.79, P<0.05), but the
HC group’s average pain-related SCRs were not different
TABLE III. Brain activations related to empathetic pain.
Region L/R BA XyZZk
Both groups
Inferior occipital gyrus
a
L19246 270 24 5.88 1859
Mid occipital gyrus L 18 232 290 6 5.23
Inferior temporal gyrus L 20 246 246 214 2.97
Supramarginal gyrus L 2 262 228 38 5.63 2199
Inferior parietal gyrus L 40 234 242 48 3.91
Superior parietal gyrus L 40 238 246 58 2.99
Inferior temporal gyrus R 37 52 262 28 5.2 1624
Mid occipital gyrus R 18 30 288 2 4.5
Mid occipital gyrus R 19 36 286 10 4.44
Rolandic operculum L 43 238 24 14 4.74 3013
Precentral gyrus L 6 250 6 22 4.51
Mid insular cortex L 238 0 2 4.09
Anterior insular cortex* L 238 20 2 3.35
Supramarginal gyrus R 2 62 224 40 3.89 596
Supramarginal gyrus R 40 54 232 48 2.89
Supramarginal gyrus R 40 40 234 42 2.1
Inferior frontal gyrus R 45 54 36 0 3.76 222
Mid/inferior frontal gyrus
a
R 44 58 14 22 2.92 267
Precentral gyrus R 6 54 10 40 1.89
ASD >HC
Calcarine cortex R 18 20 284 12 4.38 474
Hippocampus L 34 224 226 24 3.45 1537
Midbrain L 26228 220 3.23
Midbrain R 10 226 218 3.13
Lingual gyrus R 17 14 254 10 3.43 650
Retrosplenial cortex R 29 12 242 18 2.72
Retrosplenial cortex L 30 26250 6 3.31 1669
Calcarine cortex L 17 212 278 10 3.13
Mid occipital gyrus L 18 224 292 12 3
Hippocampus R 34 32 236 24 3 217
Hippocampus R 34 30 222 210 2.46
Fusiform gyrus R 37 34 236 212 2.02
Inferior frontal gyrus R 45 42 28 8 2.83 251
Anterior insular cortex R 34 28 14 2.46
Anterior insular cortex R 30 26 22 2.16
Frontal operculum L 44 234 22 214 2.82 277
Anterior insular cortex L 238 12 212 2.65
Mid temporal gyrus L 37 242 254 2 2.54 183
Mid temporal gyrus L 21 248 250 6 2.32
Mid temporal gyrus L 37 246 266 12 2.01
ASD <HC
Cerebellum L 216 258 240 2.99 120
Cerebellum L 226 248 234 1.89
Superior temporal gyrus R 22 52 230 8 2.58 251
Superior temporal gyrus R 41 40 238 8 1.93
Mid/inferior frontal gyrus R 44 60 20 14 2.51 231
Mid/inferior frontal gyrus R 44 54 18 30 2.43
P<0.05 uncorrected and k>120 (equivalent to P<0.05 corrected for multiple comparisons).
a
Coordinates used for volume of interest definition. BA, Brodmann’s areas. L/R, left/right. ASD: autism spectrum disorder; HC: healthy
control.
rAutonomic and Brain Responses in ASD r
r3331 r
from zero (one-sample t-test t
(15)
520.24, P>0.8). These
results suggest increased autonomic responses when view-
ing painful stimuli in the ASD group, but not in the HC
group. However, event-evoked SCRs related to viewing all
images (beta coefficient of regressor “all images”) were
significantly lower in the ASD group compared to the HC
group (Fig. 3B; bootstrapping P<0.05): the ASD partici-
pants’ overall SCRs were not different from zero (one-sam-
ple t-test t
(16)
50.57, P>0.5), and the HC group’s SCRs
were significantly greater than zero (one-sample t-test
t
(15)
52.85, P<0.05). These results are consistent with pre-
vious finding of reduced resting state nonspecific SCRs in
ASD individuals [Eilam-Stock et al., 2014].
fMRI general linear modeling results
The main fMRI GLM analysis revealed that both ASD
and HC groups showed empathetic pain-related activa-
tions in left AIC, left EBA, and right LPFC (Fig. 4A and
Table III). These results are consistent with our previous
study using the same stimuli [Gu et al., 2010]. Whole brain
group comparisons showed that relative to the HC group,
the ASD group had greater responses in the AIC and EBA,
yet decreased activation of right LPFC (Fig. 4A and Table
III). Additional analyses of brain-behavior correlation,
based on parameter estimates related to “painful images”
within the three ROIs, showed that there was no signifi-
cant correlation between any of the ROIs responses and d0,
EQ, or TAS-20 (all r<0.3, P>0.1). Overall activations
related to viewing all images are listed in Supporting
Information Table S1.
We also examined whole brain activations correlated
with SCRs on a trial-by-trial basis (Fig. 4B and Table IV).
Compared to HC participants, ASD individuals showed
greater correlation with SCR in the right AIC, supramargi-
nal gyrus, anterior cingulate cortex, paracentral lobule,
and precuneus. These results suggest that ASD
TABLE IV. Brain activations related to SCR.
Region L/R BA xyzZk
ASD >HC
Supramarginal gyrus R 2 54 236 34 2.54 226
Insula R 40 6 28 2.45 388
Inferior frontal gyrus R 47 38 22 212 2.28
Anterior cingulate L 32 28 16 42 2.42 123
Anterior cingulate L 32 216 10 42 2.35
Paracentral lobule L 4 26230 56 2 142
Precuneus L 4 210 240 68 1.82
Precuneus L 5 210 248 64 1.81
ASD <HC
Caudate L 226 10 16 2.98 700
Precuneus L 29 212 246 12 3.46 1344
Caudate L 24 28 2 3.29 518
Caudate R 22 24 14 3.13
Caudate R 14 22 12 3.03
Cerebellum R 26 256 244 3 259
Cerebellum R 32 260 240 2.56
Cerebellum R 14 264 240 2.04
Inferior temporal gyrus L 20 254 216 226 2.94 182
Mid temporal gyrus L 20 252 12 224 2.57
Inferior temporal gyrus L 20 244 2 230 2.55
Fusiform R 20 34 24230 2.67 211
Superior temporal pole R 38 40 14 226 2.47
Hippocampus R 20 34 26222 2.43
Mid temporal gyrus L 37 254 264 14 2.6 205
Angular gyrus L 39 252 268 28 2.26
Mid occipital gyrus L 19 244 278 14 2.04
Cerebellum L 24246 250 2.57 245
Cerebellum L 0 250 244 2.56
Fusiform L 37 242 250 216 2.05 130
Inferior occipital gyrus L 37 252 262 216 1.99
Fusiform L 19 242 270 216 1.97
P<0.05 uncorrected and k>120 (equivalent to P<0.05 corrected for multiple comparisons). BA, Brodmann’s areas. L/R, left/right.
ASD: autism spectrum disorder; HC: healthy control.
rGu et al. r
r3332 r
participants show an enhanced coupling between SCR and
brain activations in the AIC, when viewing other peoples’
body parts. This provides direct evidence supporting
abnormally high autonomic and brain responses in ASD.
fMRI dynamic causal modeling results
We specified eight 3-region DCMs comprising the AIC,
EBA, and LPFC (Fig. 5A). The model with full reciprocal
connectivity among these three areas (Model 8) showed
the greatest exceedance probability based on model evi-
dence and, therefore, was the best model among all candi-
date models (Fig. 5B). We also compared models with
(Models 5–8) or without (Models 1–4) empathetic pain-
dependent changes in intrinsic connectivity of all three
regions at the family level, and the family with intrinsic
modulation supervened (Fig. 5C). This family comparison
suggests that models where painful images not only mod-
ulate inter-areal connection, but also modulate the self-
connection of each region, were superior to models where
there was no modulation of self-connections. Therefore,
the model with full reciprocal connectivity and intrinsic
connectivity was the winning model in our defined model
space.
Figure 6 shows changes in effective connectivity induced
by viewing others’ pain in the winning model, modeled
by the log scale parameters in the leading diagonal of the
Figure 5.
Dynamic causal modeling model specification and comparison.
(A) Model specification; viewing painful images modulated either
no intrinsic connections (Models 1–4), or intrinsic connections
of AIC, EBA, and LPFC (Models 5-8). Additionally, viewing pain-
ful images could modulate either no extrinsic connections (Mod-
els 1 and 5), forward connections from AIC/EBA to LPFC
(Models 2 and 6), backward connections from LPFC to AIC/EBA
(Models 3 and 7), or reciprocal connections between AIC/EBA
and LPFC (Models 4 and 8). (B) Random effect Bayesian model
selection (BMS) indicates that Model 8 emerges as the winning
model for both groups. (C) Random effect BMS at the family
level shows that the family of models with modulation of self-
connection by painful images (Models 5-8) is better than the
family of models without such modulation of self-connection
(Models 1-4). LPFC, lateral prefrontal cortex; AIC, anterior insu-
lar cortex; EBA, extrastriate body area; ASD, autism spectrum
disorder; HC, healthy control.
rAutonomic and Brain Responses in ASD r
r3333 r
B connectivity matrix in DCM. In this model, painful
images induced disinhibition (i.e., reduced inhibitory self-
connections) in all three regions, as indexed by the nega-
tive log-scale parameters in the B matrix (Fig. 6A). Cru-
cially, we found that significant group differences were
limited to the modulation of the self-connection within
AIC (Fig. 6B); no significant group differences were
detected for pain related changes of other connections (all
Ps>0.05). As predicted, disinhibition or reduction in the
inhibitory self-connection by pain in the AIC was signifi-
cantly greater in the ASD group relative to the HC group
(Fig. 6B; P<0.05 Bonferroni-corrected for multiple com-
parisons). These results suggest that—when viewing
another’s pain—both ASD and HC groups showed a
reduction in AIC self-inhibition (which explains the
increased activation in AIC in the main GLM); however,
the ASD group had a significantly greater disinhibition of
AIC self-connection than the HC group. These results pro-
vide important evidence for larger empathetic pain-related
AIC disinhibition in adults with ASD, and explain the
observed increased AIC activation in ASD participants in
this group, when viewing others’ pain.
Finally, we explored the relationship between the level
of AIC self-disinhibition and behavioral empathy measure-
ments across participants to confirm the impact of disinhi-
bition, following our hypothesis (Fig. 6C). Across groups,
greater disinhibition in the AIC correlated with lower lev-
els of trait empathy measured by the EQ (r50.54,
P<0.001), although the correlation only reached signifi-
cance in the ASD group (r50.45, P<0.05) and not in the
HC group (r50.21, P>0.2). The group difference in the
correlation coefficients between AIC disinhibition and trait
empathy was not significant (z50.65, P>0.5). These
results suggest that disinhibition in the AIC might be a
contributing factor for impaired trait empathy.
DISCUSSION
Our study has three main neurophysiological findings.
First, ASD participants showed enhanced autonomic sig-
nals indexed by SCR when observing others’ pain, albeit
decreased SCR related to viewing all images of body parts.
Second, enhanced SCR was accompanied by increased
AIC activation related to empathetic pain in ASD individ-
uals. Third, DCM analysis revealed greater reduction in
the negative intrinsic connectivity of the AIC in individual
with ASD. Consistent with previous findings [Frith, 1996,
2012; Lord et al., 1994], we also found evidence for impair-
ments in both state and trait empathy in ASD. Specifically,
we found reduced empathetic pain discriminability during
the empathetic pain task, as well as greater difficulty in
emotional awareness and lower empathy levels in self-
report measures in the ASD group. Taken together, these
results suggest that abnormally high interoceptive preci-
sion, indexed by increased responses at both autonomic
and cortical levels, relate to empathy deficits in high-
functioning adults with ASD.
Our finding of abnormal autonomic activity, as indexed
by SCR, in the ASD group complements and extends pre-
vious findings of sympathetic/parasympathetic imbalance
in this disorder [Eilam-Stock et al., 2014; Hirstein et al.,
2001; Kylliainen and Hietanen, 2006]. Specifically, we
observed increased pain-related SCR, yet lower levels of
Figure 6.
Dynamic causal modeling results. (A) The winning model. Num-
bers represent empathetic pain-related modulation of connectiv-
ity (log-scale parameters in the leading diagonal of the B
connectivity matrix [Friston al., 2003; Penny et al., 2004]. (B)
ASD group showed greater disinhibition in the AIC modulated
by empathetic pain, compared to HC group. (C) Greater AIC
disinhibition was correlated with less trait empathy measured by
the empathy quotient [Baron-Cohen and Wheelwright, 2004]. *
P<0.05 Bonferroni-corrected for multiple comparisons. n.s.,
not significant; LPFC, lateral prefrontal cortex; AIC, anterior
insular cortex; EBA, extrastriate body area; ASD, autism spec-
trum disorder; HC, healthy control. Error bars indicate standard
errors of the mean.
rGu et al. r
r3334 r
SCR related to watching all images of body parts in partic-
ipants with ASD. In contrast to earlier studies [Hirstein
et al., 2001; Kylliainen and Hietanen, 2006], we used GLM
to analyze event-related SCR in the present study. This is
a more sophisticated model than previously used, and is
highly suitable for event-related designs, as it allows the
deconvolution of the slow SCR function with the onset of
the events [Bach et al., 2010]. Using this model, our find-
ings indicate increased autonomic arousal indexed by SCR
when observing others’ pain and decreased overall SCR in
ASD participants. Therefore, our results suggest a more
complex autonomic profile than previously postulated
[Eilam-Stock et al., 2014; Hirstein et al., 2001; Kylliainen
and Hietanen, 2006], in which sympathetic activity is
decreased at baseline in ASD, with dysregulated, height-
ened sympathetic responses and arousal when viewing
others’ pain. These results are consistent with our previous
finding of reduced number of nonspecific (nontask-
evoked) SCRs during rest in participants with ASD
[Eilam-Stock et al., 2014].
It is proposed that autonomic responses of bodily states
are mapped in the brain in a hierarchical fashion from
brainstem and thalamic nuclei, to higher-order representa-
tions in the AIC, anterior cingulate cortex and orbitofrontal
cortex, and that these central maps generate the subjective
experience of emotions [Craig, 2011; Critchley and Harri-
son, 2013; Gu et al., 2013a]. It is therefore not surprising
that heightened SCR to others’ pain were accompanied by
increased AIC activation in our ASD participants. Previ-
ous findings regarding AIC involvement in empathic proc-
essing in individuals with ASD and/or alexithymia (a
condition characterized by deficits in emotional awareness
that is highly comorbid with ASD) are scarce and mixed,
with some demonstrating AIC hypoactivation [Fan et al.,
2014], while others demonstrate hyperactivation [Bird
et al., 2010; Moriguchi et al., 2007]. We speculate that the
differences in findings are due to methodological (e.g.,
task manipulation) and patient heterogeneity issues. The
complex autonomic profile that was observed in our ASD
group may also account for the inconsistency in these
findings.
Our current finding of significant AIC activation related
to empathetic pain is consistent with previous findings on
empathy (Corradi-Dell’Acqua et al., 2011; Gu et al., 2012,
2010; Singer et al., 2009; Wicker et al., 2003] and further
suggests that the AIC encodes shared neural representa-
tions of subjective (interoceptive) states of self and others.
However, it is important to note that abnormally high AIC
activation, and accompanied enhanced autonomic signals,
could interfere with one’s correct behavioral responses to
others’ pain, as observed in the ASD group. Considering
the high level of alexithymia in our ASD participants, it is
possible that they had difficulty interpreting their own
autonomic response correctly. Previous findings have sug-
gested that the cognitive component of empathy is
impaired in ASD [Fan et al., 2014; Hadjikhani et al., 2014;
Minio-Paluello et al., 2009], while emotional contagion
may be preserved [Hadjikhani et al., 2014]. Affective
arousal during empathic processing, however, as meas-
ured by N2 amplitude, was found to be heightened in
ASD participants [Fan et al., 2014]. Together with impaired
behavioral performance on the empathy-for-pain task and
reduced trait empathy and emotional awareness, our
results provide further support for previous findings and
demonstrate both impaired cognitive ability in identifying
other’s pain (i.e., attenuated empathetic pain discriminabil-
ity) and heightened affective arousal during empathic
processing in ASD. Moreover, because the AIC is impor-
tant for integrating the emotional and cognitive compo-
nents of empathy [Gu et al., 2013b], it is also possible that
the implicit interoceptive inference is deficient in ASD,
and therefore, the AIC is more sensitive to interoceptive
cues (prediction errors) due to compensative mechanisms
in these individuals.
A handful of studies have directly examined functional
connectivity of the AIC in ASD [Ebisch et al., 2011; Eilam-
Stock et al., 2014; Price et al., 2008]. Recently, we demon-
strated that SCR was positively correlated with AIC activa-
tion in the HC group during rest, while no such
correlation was found in the ASD group [Eilam-Stock
et al., 2014]. In addition, AIC functional connectivity was
abnormal in the ASD participants [Eilam-Stock et al.,
2014], which is in agreement with findings from other
studies demonstrating abnormal activity and connectivity
of the AIC in individuals with ASD [Uddin and Menon,
2009; Ebisch et al. 2011]. Functional connectivity during
task states, especially effective connectivity during socio-
emotional tasks, is under-investigated [Uddin and Menon,
2009]. Using DCM, one previous study has examined
whether effective brain connectivity was modulated by
attention to social and nonsocial stimuli in ASD individu-
als [Bird et al., 2006]. There was a failure of attention to
social stimuli to modulate connectivity between V1 and
extrastriate areas. Our DCM result extends these findings
on connectivity abnormalities in ASD by suggesting
greater reduction in the negative intrinsic connectivity of
AIC modulated by empathetic pain, and a direct correla-
tion between such intrinsic disinhibition and trait empathy
in ASD.
Importantly, the current findings provide direct support
for recent proposals suggesting that failures in Bayesian
inference, and particularly aberrant precision (i.e., inverse
variance) of the information encoded at various levels of
sensorimotor hierarchies, may contribute to socio-
emotional deficits in ASD [Friston et al., 2013b; Lawson
et al., 2014; Pellicano and Burr, 2012]. Specifically, abnor-
mally high interoceptive precision (i.e., over reliance on
ascending interoceptive information), in the context of
interoceptive inference, would result in hypersensitivity of
principal AIC neurons that provide downstream predic-
tions of interoceptive signals [Friston, 2010; Seth et al.,
2011]. It is this hypersensitivity or increased disinhibition
rAutonomic and Brain Responses in ASD r
r3335 r
we appeal to explaining the empathetic pain-related
increased AIC neural responses and SCR observed in our
study. We suppose that AIC produces interoceptive pre-
dictions and updates these predictions based on ascending
prediction errors about the physiological states of the body
[Craig, 2009; Seth et al., 2011]. AIC also integrates ascend-
ing sensory information with the descending top-down
predictions based on multimodal cues that may constitute
a sense of a sentient self, subjective awareness, and appro-
priate bodily responses [Craig, 2009; Gu et al., 2013a]. Cru-
cially, healthy adults are able to attenuate the precision or
weight of autonomic concomitants of arousing or salient
exteroceptive cues showing others’ pain. In adults with
ASD, however, there may be a failure to attenuate the
influence of autonomic predictions as evidenced by their
heightened SCR as well as increased activity and reduced
self-inhibition in the AIC. Thus, abnormally high emo-
tional arousal would result in difficulty in understanding
the source of these heightened bodily signals and therefore
difficulty in making correct behavioral judgments about
emotional stimuli.
Taken together, our results suggest that autonomic and
cortical representations of bodily states contribute to high-
level socio-emotional processes, and that abnormal auto-
nomic and brain activity underlie empathy deficits in
ASD. Our results also support the proposal of altered
Bayesian inference of bodily and emotional states in ASD.
ACKNOWLEDGMENTS
The authors thank Drs. Will Penny and Guillaume Flandin
for technical help. The authors declare no competing
financial interests.
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