Tseng et al., 2015

Differences in Neural Activity When Processing
Emotional Arousal and Valence in Autism
Spectrum Disorders
Angela Tseng,1
Zhishun Wang,1
Yuankai Huo,1
Suzanne Goh,1
James A. Russell,2
and Bradley S. Peterson1,3
*
1
Department of Psychiatry, Columbia University College of Physicians and Surgeons and
New York State Psychiatric Institute, New York, NY, USA
2
Department of Psychology, Boston College, Chestnut Hill, MA, USA
3
Children’s Hospital Los Angeles and the Keck School of Medicine at the University of
Southern California, Institute for the Developing Mind, Children’s Hospital Los Angeles, Keck
School of Medicine at the University of Southern California, Los Angeles, CA, USA
r r
Abstract: Individuals with autism spectrum disorders (ASD) often have difficulty recognizing and
interpreting facial expressions of emotion, which may impair their ability to navigate and communicate
successfully in their social, interpersonal environments. Characterizing specific differences between
individuals with ASD and their typically developing (TD) counterparts in the neural activity subserv-
ing their experience of emotional faces may provide distinct targets for ASD interventions. Thus we
used functional magnetic resonance imaging (fMRI) and a parametric experimental design to identify
brain regions in which neural activity correlated with ratings of arousal and valence for a broad range
of emotional faces. Participants (51 ASD, 84 TD) were group-matched by age, sex, IQ, race, and socioe-
conomic status. Using task-related change in blood-oxygen-level-dependent (BOLD) fMRI signal as a
measure, and covarying for age, sex, FSIQ, and ADOS scores, we detected significant differences across
diagnostic groups in the neural activity subserving the dimension of arousal but not valence. BOLD-
signal in TD participants correlated inversely with ratings of arousal in regions associated primarily
with attentional functions, whereas BOLD-signal in ASD participants correlated positively with arousal
ratings in regions commonly associated with impulse control and default-mode activity. Only minor
differences were detected between groups in the BOLD signal correlates of valence ratings. Our find-
ings provide unique insight into the emotional experiences of individuals with ASD. Although behav-
ioral responses to face-stimuli were comparable across diagnostic groups, the corresponding neural
activity for our ASD and TD groups differed dramatically. The near absence of group differences for
valence correlates and the presence of strong group differences for arousal correlates suggest that indi-
viduals with ASD are not atypical in all aspects of emotion-processing. Studying these similarities and
differences may help us to understand the origins of divergent interpersonal emotional experience in
persons with ASD. Hum Brain Mapp 00:000–000, 2015. VC 2015 Wiley Periodicals, Inc.
Additional Supporting Information may be found in the online
version of this article.
Contract grant sponsor: NIMH; Contract grant numbers: R01
MH089582 (BSP), 2 T32 MH16434 (BSP)
*Correspondence to: Bradley S. Peterson, M.D., 4650 Sunset Blvd.
MS# 135, Los Angeles, CA 90027, E-mail: bpeterson@chla.usc.edu
Received for publication 27 May 2015; Revised 21 September
2015; Accepted 19 October 2015.
DOI: 10.1002/hbm.23041
Published online 00 Month 2015 in Wiley Online Library
(wileyonlinelibrary.com).
r Human Brain Mapping 00:00–00 (2015) r
VC 2015 Wiley Periodicals, Inc.

Key words: autism spectrum disorders; arousal; valence; facial emotion; fMRI
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INTRODUCTION
Autism spectrum disorders (ASD) are a set of complex
neurodevelopmental disabilities that cause lifelong impair-
ments in social ability, communication, and behavioral
flexibility [American Psychiatric Association, 2000]. Indi-
viduals with ASD often have difficulty recognizing and
interpreting facial expressions of emotions, which may
impair their ability to understand the intentionality and
minds of others, a capacity needed for successful social
communication [Golan et al., 2006; Grelotti et al., 2002].
Despite having a general consensus that persons with ASD
are atypical in their processing of human faces and emo-
tional expressions [Harms et al., 2010; Sasson, 2006],
researchers do not agree on the underlying brain and
behavioral mechanisms through which individuals with
ASD decode emotional faces. Some prior research suggests
that individuals with ASD rely more on cognitive-
perceptual systems involving explicit cognitive or verbally
mediated processes to interpret facial expressions of emo-
tions, in contrast to neurotypical individuals who process
emotions more automatically [Harms et al., 2010; Pelphrey
et al., 2007].
Although ASD is generally considered to involve deficits
in emotion recognition, prior studies have provided only
inconsistent evidence for those deficits. For example, sev-
eral studies have reported that adults and children with
ASD have more difficulty recognizing, responding to, and
expressing emotions than typically developing (TD) indi-
viduals [Ashwin et al., 2006; Tantam et al., 1989; Uljarevic
and Hamilton, 2013] and more than persons with other
neurodevelopmental disorders [Celani et al., 1999; Riby
et al., 2008]. However, other studies have reported typical
levels of facial emotion recognition in persons with ASD
[Castelli, 2005; Harms et al., 2010; Ozonoff et al., 1990;
Tseng et al., 2014].
Disparities in findings for the recognition and under-
standing of emotions in individuals with ASD may, to
some extent, be due to fundamental differences in the
underlying model of emotion implicitly assumed when
designing those studies. That underlying model has most
often been the theory of basic emotions [Ekman, 1992; Pan-
ksepp, 1992], which posits that each member of a core set
of discrete, or “basic,” emotions (e.g., anger, sadness, or
happiness) are subserved by its own distinct and inde-
pendent neural system [Ekman, 1992; Panksepp, 1992].
Earlier reviews have documented the limitations and
inconsistencies of this theory, including the absence of
one-to-one mappings of individual emotions with specific
facial expressions, motor behaviors, and autonomic
responses, as well as the absence of evidence for a core set
of emotions from which other emotions derive [Hamann,
2012; Posner et al., 2005; Russell, 1980; Vytal and Hamann,
2010]. Moreover, interpreting findings from neuroimaging
studies based on the theory of basic emotions is compli-
cated by the subtraction method employed in most func-
tional imaging designs, in which brain activity is
measured by comparing two tasks or stimuli that are
assumed to differ only in the cognitive process of interest.
Most functional imaging studies based on the theory of
basic emotions have contrasted neural responses to indi-
vidual emotions with neural responses to stimuli intended
to be emotion-neutral. Unfortunately, the use of “neutral”
faces as control stimuli is an inherent confound in emotion
research because of the difficulties involved in creating
truly “neutral” stimuli [Killgore and Yurgelun-Todd, 2004;
Klein et al., 2015; Posner et al., 2005; Thomas et al., 2001].
Additionally, most imaging studies of the basic emo-
tions theory have focused only on a small number of
Abbreviation
ACC anterior cingulate cortex
AMYG amygdala
BOLD blood oxygen level dependent
Broca Broca’s area
Cb cerebellum
CN caudate nucleus
Cu cuneus
DLPFC dorsolateral prefrontal cortex
FG fusiform gyrus
HIPP hippocampus
ILPFC inferolateral prefrontal cortex
INS insula
IPC inferior parietal cortex
IPS intraparietal sulcus
M1 primary motor cortex
MCC middle cingulate cortex
MFG middle frontal gyrus
MOG middle occipital gyrus
MTG middle temporal gyrus
OFC orbitofrontal cortex
PC parietal cortex
PCC posterior cingulate cortex
PCu precuneus
PreMC premotor cortex
PrG precentral gyrus
PUT putamen
S1 primary somatosensory cortex
S2 secondary somatosensory cortex
SFG superior frontal gyrus
SMA supplementary motor area
SPL superior parietal lobule
STS superior temporal sulcus
THAL thalamus
V1 primary visual cortex
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emotions, generally those of high arousal and negative
valence (e.g., fear and anger), low arousal and negative
valence (e.g., sadness), or moderate arousal and positive
valence (e.g., happy). Consequently, researchers have had
trouble disentangling the differing contributions of arousal
and valence to the neural correlates of emotions. For
example, “happy” stimuli are often the only positive
valence emotions included in study designs. Comparing
them to stimuli that are putatively neutral or even nega-
tively valenced confounds the positive arousal component
with the positive valence of the happy stimulus. In effect,
even when comparing happy with putatively neutral faces,
both of these types of stimuli have not only a valence, but
also an arousal component that is never considered as con-
tributing to the reported fMRI activation [Fusar-Poli et al.,
2009; Harms et al., 2010; Murphy et al., 2003].
An alternative theoretical framework to the theory of
basic emotions is the “Circumplex Model of Affect,” which
holds that the subjective experience of all emotions arises
from the linear combination of two independent neuro-
physiological systems, valence and arousal. Valence refers
to hedonic tone, or the degree to which an emotion is
pleasant or unpleasant, whereas arousal represents the
degree to which an emotion is associated with high or low
energy. Under this model, a “happy” response to a stimu-
lus arises from relatively intense activation of the neural
system associated with positive valence and moderate acti-
vation of the neural system associated with positive
arousal. Other emotional states thus arise from the same
two underlying neurophysiological systems but differ in
degree of activation of each. Because all emotions can be
represented as a linear combination of the dimensions of
arousal and valence, emotions shade imperceptibly from
one into another along the contour of the two-dimensional
circumplex [Posner et al., 2005]. The subjective experience
of the neurophysiological signals for valence and arousal
is determined by interpretations of the signals in relation
to the experiential context of the stimuli and memories of
prior experiences of similar sensations [Posner et al., 2005;
Russell, 2003]. Thus the labeling of our subjective experi-
ence as one emotion rather than another nearby emotion is
the consequence, in part, of cognitive interpretation of the
neurophysiological experiences of arousal and valence
within the situational context [Russell, 2005].
Several studies have provided evidence for the existence
of distinct neural systems that subserve the experience of
emotional valence and arousal [Colibazzi et al., 2010;
Gerber et al., 2008; Posner et al., 2009]. However, to our
knowledge, no other studies have examined whether neu-
ral activity in circuits that subserve processing of the two
dimensions of facial emotions differ between individuals
with ASD and their TD counterparts. A prior publication
from our laboratory reported that, in the same sample, the
ASD group performed nearly as well as, and in a similar
pattern to, the TD group when participants were asked to
rate emotional faces for arousal and valence [Tseng et al.,
2014]. However, without corresponding data on brain
activity, determining whether the TD and ASD groups
recruited the same neural systems to appraise emotional
stimuli is impossible. Typical-level behavioral performance
on emotion-processing tasks does not exclude the possibil-
ity of atypical neurocognitive processing of emotional
information. Rather, abnormalities in emotion-processing
might be obscured in some individuals because they have
developed compensatory strategies that yield “typical”
levels of behavioral performance. Indeed, higher-
functioning individuals with ASD might capitalize on their
cognitive resources to identify facial expressions. For
example, studies employing emotion-matching paradigms
[Piggot et al., 2004; Rump et al., 2009] are more likely than
studies using emotion-labeling paradigms [Katsyri et al.,
2008; Piggot et al., 2004; Rutherford and Towns, 2008] to
reveal differences in behavioral performance between TD
and higher-functioning ASD groups. For some individuals
with ASD, the use of emotion labels in a task may facili-
tate recognition of facial expressions of emotions, espe-
cially when they are trained to identify emotions as part of
an intervention program [Tanaka et al., 2010].
Although functional imaging studies of emotion-
processing in ASD have yielded inconsistent ﬁndings, sev-
eral have reported hypofunctioning in ASD in regions
associated with socio-emotional processing (e.g., INS,
AMYG) [Di Martino et al., 2009], in extrastriate cortices
[Deeley et al., 2007], ventral PFC [Ashwin et al., 2007;
Hadjikhani et al., 2006], medial-frontal and orbito-frontal
cortices [Bachevalier and Loveland, 2006; Loveland et al.,
2008; Ogai et al., 2003], ACC and FG [Hall et al., 2003],
striatum, and IFG [Dapretto et al., 2006] compared to TD
controls. Conversely, studies have found increased activity
for ASD compared to TD groups in STS, ACC [Ashwin
et al., 2007; Hall et al., 2003; Pelphrey et al., 2007], and
parieto-occipital regions [Dapretto et al., 2006; Hubl et al.,
2003; Wang et al., 2004] when viewing facial emotions.
These increases in neural activity may derive from
increased visual and motor attention [Dapretto et al.,
2006], more effortful processing of speciﬁc facial features
within the given social contexts [Ashwin et al., 2007], and
increased attentional load [Wang et al., 2004], supporting
the possibility that emotional processing is more effortful
and less automatic in individuals with ASD than in their
TD counterparts.
To address whether neural activity in circuits that sub-
serve processing of arousal and valence differ between
individuals with ASD and TD individuals, we applied a
parametric experimental design to identify brain regions
in which neural activity correlated with arousal and
valence ratings for a broad range of facial emotions. The
use of a parametric design allows us to compare emotional
stimuli across multiple levels or through incremental
changes along the affective dimensions of arousal and
valence. For example, parametric manipulation of emo-
tional stimuli that change incrementally in the degree of
r FMRI of Arousal and Valence Dimensions in ASD r
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arousal or valence that they generate can be mapped
against concomitant variations in neural activity. Accord-
ingly, activity in neural structures or pathways that corre-
late with the degree of emotional arousal or valence
induced by the emotional probes can be assessed in indi-
vidual participants [Posner et al., 2005]. In the present
study, we used BOLD-signal intensity to index neural
activity as participants viewed photographs depicting
emotional faces. We identified brain regions in which
BOLD-signal systematically covaried with ratings of
arousal or valence in ASD and TD groups, and we deter-
mined the areas in which these correlations differed statis-
tically across emotional dimensions and diagnostic groups,
indicating the differential associations of these regions
with processing arousal or valence within each group. We
sought to identify similarities and differences in neural
activity when participants with ASD and TD participants
view and rate these experiences of facial emotions [Gerber
et al., 2008; Russell et al., 1989]. Given the socio-emotional
deficits associated with ASD, we hypothesize that the ASD
group will show abnormal patterns of brain activation
when compared to controls, particularly in brain regions
associated with processing of emotional stimuli in persons
with ASD, including AMY, INS, CN, PFC, OFC, and ACC.
MATERIALS AND METHODS
Study procedures were approved by New York State
Psychiatric Institute’s Institutional Review Board. All par-
ticipants provided informed written consent or assent and
received payment for participating (See Supporting Infor-
mation for detailed consent procedures).
Participants
We recruited 51 individuals with ASD (6F, ages: 7–60
years, Mean: 27.5 6 13.1 years) and 84 TD individuals (22F,
ages: 7–60 yrs, Mean: 24.0 6 11.4 years) from the New
York City area. A wide age-range was included in our
sample in order to understand better the developmental
trajectory of emotional processing in this under-studied
group. For example, if the child participants with ASD
performed similarly to our adult participants with ASD,
then we might infer that any emotional deficits found are
likely a static, trait-like disturbance. We also hoped to use
cross-sectional data from this investigation to generate
hypotheses for future longitudinal research.
Participants were group-matched by age, sex, IQ
(Wechsler Abbreviated Scale of Intelligence) [Wechsler,
1999], handedness (Edinburgh Handedness Inventory)
[Oldfield, 1971], race, and socioeconomic status (Hollings-
head Index of Social Status) [Hollingshead, 1975]. Mean
full scale IQ (FSIQ) was 109.2 6 19.4 for the ASD group
and 115.9 6 12.4 for the TD group. Mean verbal IQ (VIQ)
and mean performance IQ (PIQ) for both groups did not
differ significantly so we opted to conduct further analyses
of IQ using only FSIQ as a covariate (Table I).
Additional individuals participated (N 5 4 ASD; N 5 1
TD) but were not included in the final sample due to exces-
sive head motion in the scanner.
TD participants were excluded if they met DSM-IV-TR
criteria for a current Axis-I-disorder, or had lifetime his-
tory of developmental delay or other indicators of ASD,
psychosis, substance abuse disorder, head trauma, seizure
disorder, or other neurological illness. None of the TD par-
ticipants were taking prescription or over-the-counter
medications; however, the use of dietary supplements was
not assessed.
Participants with ASD were evaluated by an expert cli-
nician and met Diagnostic and Statistical Manual of Men-
tal Disorders, Fourth Edition, Text Revision (DSM-IV-TR)
[American Psychiatric Association, 2000] criteria for autis-
tic disorder, Asperger syndrome, or pervasive develop-
mental disorder-not otherwise specified (PDD-NOS).
Diagnoses were confirmed with the Autism Diagnostic
Interview-Revised [Lord et al., 1994] and the Autism diag-
nostic observation schedule (ADOS) [Lord et al., 1989]. A
detailed list of current medications was recorded for every
participant (available on request).
At the beginning of each study session, an experienced
member of the study team explained verbally the nature
of the research protocol, including potential risks and
TABLE I. Participant characteristics
ASD TD
Participants (N) 51 84
ASD subtype
PDD-NOS 9 —
Asperger’s syndrome 24 —
Autistic disorder 18 —
Mean age (yrs) 27.5 6 13.1 24.0 6 11.4
Children (<18 yrs) (N (%)) 12 (24%) 31 (37%)
Males (N (%)) 45 (88%) 62 (74%)
Caucasian (N (%)) 40 (78%) 60 (71%)
Mean SESa
50 53
Mean FSIQb
109.2 6 19.4 115.9 6 12.4
Mean VIQ 110.9 6 20.9 115.7 6 13.2
Mean PIQ
Mean ADOS
(social affect 1 restrictive,
repetitive behaviors)c
105.0 6 17.6
10.9 6 3.1
112.9 6 11.8
—
Mean ADOS—calibrated
severity scores
(modules 2 and 3)d
7.5 6 1.8 —
a
SES scores for 7 TD and 14 ASD participants were unavailable.
b
FSIQ scores for 1 TD participant and 1 ASD participant were
unavailable.
c
ADOS scores for 4 ASD participants were unavailable.
d
ADOS CSS scores were calculated for participants tested using
Modules 2 and 3 (N 5 10).
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benefits, to all potential participants and (in the case of a
minor) to their parents. For participants 8 to <18 years
old, assent was discussed and obtained. For adult partici-
pants (>18 years), the study member obtaining consent
explained the protocol and associated risks to the prospec-
tive participant before asking participants to sign the con-
sent form. For all adult participants with ASD, an
independent assessment of capacity to consent was con-
ducted by a clinical monitor who was independent of the
study team. When the clinical monitor determined that the
participant lacked the capacity to consent (i.e., the partici-
pant did not demonstrate an understanding of the proce-
dures, alternatives, and potential risks and benefits of the
study, and that participation was voluntary), an author-
ized legal representative was designated to provide
informed consent.
Emotion Paradigm
We used a parametric experimental design to identify
brain regions in which neural activity correlated with par-
ticipant ratings of arousal or valence (Fig. 1).
Neural activity was indexed using BOLD-signal inten-
sity as participants viewed photographs of emotional
faces. After viewing each face, participants rated arousal
and valence simultaneously by selecting an individual box
on a 9 3 9 two-dimensional grid. Location on the x-axis
indicated the participant’s rating of valence (left 5 negative
valence, right 5 positive valence), and location on the y-
axis indicated the rating of arousal (top 5 high arousal,
bottom 5 low arousal). We recorded the selected box as
two integer scores, each ranging from 24 to 14, represent-
ing valence and arousal.
Each trial consisted of 3 sequential epochs: (1) Visual
presentation of a photograph of a human face for 18 s. The
photographs were copied, with permission, from the 20
distinct stimuli used by Russell and Bullock [1985] for
their studies of the affective circumplex. Thirteen of these
20 images were taken from Ekman and Friesen’s [1976]
“Pictures of Facial Affect” and depicted expressions of a
number of emotions (two pictures each of emotional faces
commonly classified as expressing happiness, surprise,
fear, anger, disgust, or sadness, and one commonly classi-
fied as neutral). Russell and Bullock supplemented this set
to represent better portions of the circumplex that the
Ekman series under-sampled (emotions associated with
low arousal but positive or neutral valence). These
included two photographs each of actors and actresses
expressing boredom, contentment, and sleepiness, as well
as one expressing excitement. (2) Visual presentation of a
two-dimensional grid on which participants indicated their
ratings of arousal and valence for each stimulus by mov-
ing an arrow controlled by an MRI-compatible computer
mouse. This screen remained visible until the participant
clicked the mouse button, up to a maximum of 20 s. (3)
Visual presentation of a fixation point (1) at the center of
the participant’s visual field. The fixation point was dis-
played immediately following the rating of valence and
arousal. The durations of rating and gaze fixation were
each variable, but when summed together always equaled
20 s. One imaging run consisted of 20 trials presented in a
pseudorandom order (but uniform from participant to
participant).
Visual stimuli were presented to each participant via
MRI-compatible LCD goggles (Resonance Technology,
Northridge, CA) using E-Prime software, version 1.138
running on a Dell IBM-compatible computer. Measures of
stimulus durations and reaction times were accurate to 20
ms. Stimuli were presented at the center of the partici-
pant’s visual field, subtending 198 of the vertical and 158
of the horizontal visual field.
Prior to the study session, all participants were given a
practice session with the task so they could familiarize
themselves with task instructions, the types of stimuli they
would be seeing (practice stimuli were not shown during
the study session), the grid on which they would be rating
Figure 1.
Emotion paradigm.
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arousal and valence, and the computer mouse they would
be clicking to indicate their ratings. Researchers were
available to review the practice responses in detail, to
explain the instructions further, or to answer any ques-
tions about the task during this practice round to ensure
full comprehension. Each participant was told, “You will
be shown a face that expresses a certain feeling. You will
be asked to assess the feeling on the chart shown
below. . ..On the chart, the vertical dimension represents
degree of arousal. Arousal has to do with how awake,
alert, or energetic a person is. . .. The right half of the chart
represents pleasant feelings—the farther to the right, the
more pleasant. The left half represents unpleasant feel-
ings—the farther to the left, the more unpleasant. . .. Dur-
ing the experiment, you will first be shown a face. This
will appear on the screen for 18s. Then you will be shown
the grid. When the grid appears, you will click on the area
you think best describes the face. . .Try to think about the
feeling expressed by the face during the 18s shown. It will
not be on the screen when you are shown the grid.” At
the time of instruction and during the experiment itself,
the words “High Pleasure” appeared to the right of the
grid, and “High Energy” above the grid. The shortened
practice version consisted of three faces—one each
expressing sadness, happiness, and anger. To minimize
the possibility of habituation, none of the practice faces
were identical to actual experimental stimuli. During the
scan, researchers monitored on-line behavioral responses
in real-time so that we could ensure attention to the task.
Behavioral Data Analyses
Behavioral data gathered from the present study were
analyzed and reported in detail in a prior publication
from our laboratory [Tseng et al., 2014]. The participants
were identical across the two studies, with the exception
that several participants were eliminated from fMRI analy-
sis because of excessive head motion in the scanner. In
addition, we collected data for an additional 3 TD adults,
1 TD child, and 4 adults with ASD after the publication of
our prior study. We did not find any significant differen-
ces between our findings with or without the additional
eight participants, so we included them in our fMRI sam-
ple. As described in our previous report, we divided par-
ticipants into four groups by diagnosis and age to
compare behavioral performances across groups: Adult
ASD (N 5 39, 5F, ages: 18–61 years, Mean: 31.9 6 11.8
years), Adult TD (N 5 53, 8F, ages: 18–60 years, Mean:
30.1 6 10.2 years), Child ASD (N 5 12, 1F, ages: 7–17 years,
mean: 13.2 6 3.1 years), and Child TD (N 5 31,14F, ages: 7–
17 years, Mean: 13.7 6 2.7 years). Mean FSIQ scores in
these groups were: Adult ASD (110.2 6 18.4), Adult TD
(116.42 6 1.9), Child ASD (105.6 6 22.2), and Child TD
(115.0 6 13.4). We also divided participants by diagnosis
alone to compare the entire ASD and TD groups.
Multivariate ANCOVAs were conducted with arousal
and valence ratings as dependent variables, group as the
independent variable, and age and gender as covariates to
assess emotion-specific differences between groups. We
used hierarchical multiple regressions for ASD and TD
groups (controlling for age and sex) with arousal and
valence ratings as dependent variables and FSIQ scores as
the independent variable to assess whether IQ was signifi-
cantly correlated with how participants rated each
emotion-type. Similar analyses were conducted with total
ADOS scores (Social Affect (SA) 1 Restrictive, Repetitive
Behaviors (RRB), Mean 5 10.9 6 3.1 [Gotham et al., 2007].
CSS conversion algorithms are not available for partici-
pants over the age of 16 or who were assessed with mod-
ule 4 of the ADOS.
To assess whether severity of diagnosis significantly
correlated with how participants rated each emotion-type,
we used hierarchical multiple regressions for analyses in
the ASD group (controlling for age and sex) in which
arousal or valence ratings were entered separately as the
dependent variable and total ADOS score was the inde-
pendent variable. These regressions were applied sepa-
rately to each facial stimulus. We also conducted these
analyses with only the social affect scores from the ADOS
as the independent variable, because we expected the
social affect measure alone might correlate more strongly
with how participants with ASD rated these affective
stimuli.
Finally, we conducted multivariate ANCOVAs with
arousal or valence ratings entered separately as the
dependent variable, ASD subtype (PDD-NOS, Asperger’s
Syndrome, Autistic Disorder) entered as the independent
variable, and age and gender entered as covariates to
assess whether participant responses varied according to
specific ASD subtype.
Task Performance
So that we could be as confident as possible that all par-
ticipants were performing the task as instructed and to
ensure the face validity of their responses, we first visually
compared each individual’s arousal and valence ratings
qualitatively against the canonical circumplex to ensure
that the responses seemed reasonable. Then, assuming that
the responses of the healthy adults represent the end prod-
uct of development, we used the arousal and valence
scores from typically-developing adults reported by Rus-
sell and Bullock [1985] as reference ratings for “correct”
performance by assessing quantitatively the correlations of
each individual participant’s data with the reference rat-
ings. Our rationale was that an individual responding at
random to the stimuli or who was not understanding or
following instructions would be unlikely to produce a sim-
ilar response pattern to the reference ratings. Then, as a
subset analysis, we removed participants (N 5 13: 4 Child
ASD, 4 Adult ASD, 5 Child TD) whose correlations
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between arousal or valence ratings with the reference val-
ues were significant at P > 0.2 (corresponding to a Pear-
son’s r > 0.42). Similar to findings from our original
analysis with the entire sample (N 5 135), we detected
with this smaller sample (N 5 122) a main effect of diagno-
sis (P < 0.05). Thus, although we were unable to measure
task comprehension directly during the scan, the use of
prescan practice trials and the similarity of results in our
subset analysis with those of the original analysis show
that the vast majority of our participants were able to
understand and perform the task as instructed.
Image Acquisition
Imaging was performed on a GE Signa 3T whole body
scanner (Milwaukee, WI) using a GE single channel quad-
rature head-coil. A 3D spoiled gradient recall (SPGR)
image was acquired for coregistration with axial functional
images and for coregistration with a standard reference
image (Montreal Neurological Institute (MNI)). Functional
images were acquired using a single shot gradient echo
planar (EPI) pulse sequence in groups of 43 axial slices per
volume and 273 volumes per run (preceded by six
“dummy” volumes to ensure scanner stability). Parameters
for the EPI images were: repetition time 5 2,800 ms, echo
time 5 25 ms, flip angle 5 908, acquisition matrix 5 64 3
64, field of view 5 24 cm 3 24 cm, slice thickness 5 3 mm,
skip 5 0.5 mm, receiver bandwidth 5 62.5 kHz, in-plane
resolution 5 3.75 mm 3 3.75 mm. Each run lasted 13 min
1 s, for a total EPI scan time of 39 min 3s.
Image Preprocessing
Prior to statistical analyses, we used SPM8 (http://www.
fil.ion.ucl.ac.uk/spm/, run under MATLAB2009b) to pre-
process the fMRI data. Slice timing was corrected using the
middle slice (22 of 43) as the timing reference. Slice timing
corrected functional images were then realigned to the mid-
dle image of the middle run for motion correction for three
translational directions and rotations. Images with motion
greater than one voxel were excluded from all subsequent
analyses. Motion corrected images of each participant were
coregistered to the corresponding T1-weighted high-resolu-
tion anatomical image, which in turn was spatially normal-
ized to the standard MNI template with voxel dimensions of
3 mm3
. These participant-specific normalization parameters
were then used to warp the functional images into the same
MNI template. A spatial smoother with a Gaussian kernel of
8-mm Full Width at Half Maximum was applied to the func-
tional images, which were then temporally filtered using a
Discrete Cosine Transform high-pass filter with a cutoff fre-
quency of 1/128 Hz to remove low frequency noise such as
scanner drift.
We then assessed data quality by plotting motion param-
eters and mean intensity values for raw, normalized, and
smoothed images for each run in each participant. Visual
inspection allowed us to identify scans for which average
intensity values across voxels were significantly outside the
mean and which occurred at the same moment as a large
head movement. We also used histogram plots for each
contrast image in each participant to help identify outliers
for mean intensity that might have been missed by the
batch preprocessing procedure. We used the ArtRepair
algorithm (http://cibsr.stanford.edu/tools/human-brain-
project/artrepair-software.html) to detect and repair those
image volumes that were contaminated by spiking motion
artifacts and outliers [Mazaika et al., 2009]. Volumes with
motion larger than 1mm were repaired. Participants
for whom motion affected more than 15% of their data
(>41/273 volumes per run) were excluded from further
analyses; based on this criterion, we eliminated from our
final analysis 1 TD and 4 ASD participants (from the origi-
nal 140 participants).
Statistical Analyses
We analyzed fMRI data at the individual (first) level
using a general linear model (GLM) to detect BOLD-
signal correlates of arousal or valence within each indi-
vidual participant and at the group (second) level using
Bayesian posterior inference [Neumann and Lohmann,
2003] at a posterior probability threshold of 98.75%, to
detect random effects of arousal or valence correlates
within and between diagnostic groups. We covaried for
age and sex of the participants. We also conducted addi-
tional analyses covarying for FSIQ in all participants and
for ADOS scores in the ASD group. We assessed the main
effects of arousal and valence ratings on BOLD-signal for
each diagnostic group (TD, ASD). We also assessed
BOLD-signal correlates with quadratics of arousal and
valence ratings, allowing us to assess at each voxel
whether the correlation of ratings with BOLD-signal had
a significant curvilinear component. We included simulta-
neously in our model the main effects and quadratic val-
ues of arousal and valence ratings (including them
separately yielded identical findings). Finally, we assessed
whether the within-group valence and arousal correlates
differed significantly across ASD and TD participants by
assessing the interactions of the correlations with diagnos-
tic group. We included simultaneously in our model the
main effects and their interactions with diagnostic group
to ensure that the models were hierarchically well formu-
lated. We plotted the scatters for the linear and quadratic
associations of BOLD-signal with ratings of arousal and
valence to assess the distribution of data around the
regressions and to determine the group contributions to
significant interactions.
First-level analysis
We used GLM in SPM8 for the analyses of data at the
individual level. We modeled preprocessed BOLD time
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series data at each voxel, using 8 independent functions
(Fn) or regressors that consisted of:
Fn(1): the canonical hemodynamic response function
(HRF) convolved with a box car function (BCF)
derived from the onsets and durations of the pre-
sentation of facial stimuli
Fn(2): Fn(1) modulated by the linear arousal rating for
each stimulus
Fn(3): Fn(1) modulated by the linear valence rating for
each stimulus
Fn(4): Fn(1) modulated by the quadratic arousal rating
for each stimulus
Fn(5): Fn(1) modulated by the quadratic valence rating
for each stimulus
Fn(6): the canonical HRF convolved with a BCF index-
ing the manual responses of each participant to
the task stimuli
Fn(7): the canonical HRF convolved with a BCF index-
ing the presentation of a fixation cross
Fn(8): a constant
Our model, which included the main effects and quad-
ratic values of arousal and valence ratings on a 24 to 4
scale for each participant, was estimated using the
Restricted Maximum Likelihood (ReML) algorithm. Task-
related T-contrast images were generated using SPM8 con-
trast manager. We ran our models for valence and arousal
separately (i.e., with functions 1, 2, 4, 6, 7, and 8 for
arousal and with functions 1, 3, 5, 7, and 8 for valence)
and both with and without the quadratic terms (functions
4 and 5) to ensure that the model was not over-specified;
the findings for the linear arousal and valence terms in
these reduced models were unchanged from findings for
the model that included all eight functions. We thus
elected to present findings for the full model so that we
could account for every event that occurs during the task,
allowing us to control for signal variability in each trial.
Also, by including both linear and quadratic components
we were able to assess whether the response is truly linear
across the range of ratings or whether it is curvilinear
[Acton and Friston, 1998; Buchel et al., 1996, 1998; Fracko-
wiak, 2004]. We also ran our model using both the SPM
default that orthogonalizes parametric variables, as well as
without orthogonalization, because we were concerned
that if our regressors were inter-correlated and we did
orthogonalize our modulators, then the explained variance
in BOLD-signal would not be assigned to any of the
regressors and our power would be reduced for statistical
testing. We also wanted to ensure that our findings were
robust with respect to orthogonalization. Our findings
were nearly identical with or without orthogonalization,
so we elected to present our findings using the orthogonal-
ized analyses. Finally, we also ran the GLMs with motion
parameters as regressors and found that they had no sig-
nificant effect on our findings, so we elected to present
our findings without motion regressors in the model.
Second-level analysis
We used Bayesian inference to detect random effects by
assessing the posterior probability of detecting within or
between group difference, b, given the activation map that
we attained in a particular contrast. We used a posterior
probability of greater than 98.75% as the threshold for sta-
tistical significance in each of the contrast maps and, in
addition, required a spatial extent of at least eight contigu-
ous voxels to further strengthen the biological validity of
our findings. Unlike a more conventional second-level
analysis that uses classical parametric inference to detect a
group effect in a statistical parametric map by disproving
the null hypothesis (b 5 0) at each voxel of the image, a
group effect using the Bayesian method infers the poste-
rior probability of detecting the observed group effects
(b 6¼ 0), given the data in a posterior probability map [Neu-
mann and Lohmann, 2003]. Whereas the voxelwise tests in
a statistical parametric map require correction for the
number of statistical comparisons performed, the Bayesian
method, because it infers posterior probability, by defini-
tion, does not generate false positives and does not require
adjustment of its P values based on stringent P value
thresholding (a feature of these analyses that has been con-
firmed in numerous simulations and empirical studies)
[Friston and Penny, 2003; Friston et al., 2002].
Post-Hoc Analyses
Several additional analyses ensured that possible con-
founding effects did not unduly influence our findings. We
conducted post-hoc analyses while covarying for FSIQ in all
participants and ADOS scores in analyses involving only
participants with ASD. Results did not differ significantly
when we covaried for age, sex, FSIQ, or ADOS scores in par-
ticipants with ASD. Additionally, we assessed the age-by-
diagnosis interaction but found none; restricting our ASD
sample to participants who were older than 18-years did not
change our findings from those for our overall sample
(ASD: 24% of group (12/51) and TD: 37% of group (31/84)).
Additionally, we analyzed our dataset with only male par-
ticipants (45 ASD, 62 TD) and found the patterns of activa-
tion to be similar to those for our main model (Supporting
Information Fig. S4). We also assessed age correlations
within each group and detected none that were significant
for valence or arousal. Finally, restricting analyses to partici-
pants who were medication-na€ıve (ASD: 68% (34/51)
yielded the same results as for our overall sample.
RESULTS
Behavioral Data
On the whole, our behavioral findings suggest that
while participants in the ASD group rated arousal and
valence for a wide range of emotions similar to individu-
als in the TD group, emotion ratings for the ASD groups
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along both valence and arousal dimensions were some-
what constricted in their ranges relative to those of the TD
groups. These findings did not change when we covaried
for overall intelligence. Also, for the ASD group, correla-
tions of ADOS scores with arousal and valence yielded
only one marginally significant finding: ADOS scores cor-
related with valence ratings for surprise faces (b 5 0.32,
t42 5 2.1, P 5 0.05). Results did not vary by ASD subtype.
Emotion-specific Analyses
Given that emotional processing in typically developing
adults is presumably the desired outcome of emotional
processing in typical and atypical development, we used
our average Adult TD data as a point of reference for visu-
ally comparing data from the other three groups, even
though our primary analyses of the behavioral data treated
age as a continuous variable. We report subtle but signifi-
cant differences between groups for specific emotions,
although the overall assignment of valence and arousal
scores across the all emotion-types were similar for the
ASD and TD groups (Fig. 2).
Ratings for both valence and arousal dimensions of emo-
tions in the child ASD group were somewhat constricted in
their ranges relative to those of the Adult TD group: the child
ASD group reported significantly lower arousal ratings for
high arousal emotions such as Excited (F3,63 5 3.53, P5 0.0008)
and surprised (F3,63 5 3.38, P5 0.0013), and higher arousal rat-
ings for Sleepy, a low arousal emotion, (F3,63 5 2.02, P5 0.048).
They also reported significantly less negative valence ratings
for negatively valenced emotions, including Disgusted
(F3,63 5 2.01, P5 0.049) and Sad (F3,63 5 2.83, P5 0.006), and a
trend for less positive valence ratings than the adult TD group
for the positively valenced excited (F3,63 5 1.96, P5 0.055) and
happy (F3,63 5 1.90, P5 0.061) (Fig. 2A).
Emotions for the adult ASD group relative to the adult
TD group also showed a trend for constriction in their
ranges; they reported significantly less negative valence
ratings for the negatively valenced sad faces (F3,90 5 2.33,
P 5 0.022) (Fig. 2B).
No significant age differences were detected within the
ASD groups. However, adult TD participants did report
higher arousal ratings than Child TD participants for the
negatively valenced angry (F3,82 5 2.64, P 5 0.01), disgusted
(F3,82 5 2.46, P 5 0.016), sad (F3,82 5 2.82, P 5 0.006), and
scared faces (F3,82 5 2.14, P 5 0.036) and less positive valence
ratings for excited faces (F3,47 5 2.818, P 5 0.045) (Fig. 2C).
fMRI Data
Main effects
Linear and quadratic correlates of arousal. These analy-
ses revealed significant inverse linear and quadratic correla-
tions of BOLD-signal with arousal ratings for our TD
participants in ILPFC and DLPFC, dorsal ACC, inferoposte-
rior PC, dorsal PC, CN, and PUT. For ASD participants, we
detected significant positive linear associations of BOLD-
signal with arousal ratings in the posterior temporal/infe-
rior PC, mesial wall (pregenual and dorsal portions of SFG
and ACC), premotor and supplementary motor regions, Cu
and PCu, subcortical regions (all basal ganglia nuclei,
THAL), and dorsal Cb (Fig. 3, Tables II and IV).
Conjunction maps of the linear and quadratic effects of
arousal in each group show regions where both linear and
quadratic effects were detected (e.g., Cb, Broca’s, CN,
DLPFC, PCu), and scatterplots show the combined effect of
the linear and curvilinear components of the correlation
(Fig. 4; Supporting Information Fig. S-2, Tables S-1A, S-1C).
Linear and quadratic correlates of valence. BOLD-signal
correlated with ratings of linear valence and quadratic
valence similarly for both diagnostic groups. In both TD
and ASD participants, valence ratings correlated positively
with BOLD-signal in ACC, FG, and, and inversely with
BOLD-signal in posterior PC, S1, M1 and SMA. We did
find regions where the correlation of valence ratings with
BOLD-signal differed significantly across the ASD and TD
groups but they were very small in spatial extent and of
Figure 2.
Emotion-specific group comparisons of behavioral findings.
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questionable biological significance (Fig. 5, Tables III
and V; Supporting Information Tables S-1B, S-1D, S-3).
Interactions
Diagnosis-by-arousal. Our analyses revealed significant
differences in the correlation of arousal ratings with
BOLD-signal across ASD and TD participants (ASD > TD)
in AMYG, HIPP, CN, ACC, and SFG (Fig. 3, Table VI).
Diagnosis-by-valence. We only detected minor differen-
ces across groups in the correlation of valence ratings with
BOLD-signal, suggesting that brain activity in individuals
with ASD is typical for some components of emotion-
processing (Fig. 5).
DISCUSSION
Our goal was to assess whether activity in neural systems
underlying the processing of arousal and valence of facial
emotions in individuals with ASD differ from their TD
counterparts. Although participants from both diagnostic
groups performed with a comparable level of accuracy on
Figure 3.
Arousal correlates (A) Regions of significant correlations of
BOLD-signal with ratings of arousal for TD participants. (Posi-
tive correlations are coded in red to yellow, and inverse correla-
tions are coded in green to blue.). (B) Scatterplots of
correlations for BOLD-signal with ratings of arousal for TD par-
ticipants (green) or participants with ASD (purple). (C) Regions
of significant correlations of BOLD-signal with ratings of arousal
for participants with ASD. (D) The regions where the correla-
tion of arousal ratings with BOLD-signal for participants with
ASD differs significantly from the correlation of arousal ratings
with BOLD-signal for TD participants. (ASD > TD coded in red
to yellow, TD >ASD coded in blue to green). (E) Scatterplots
of correlations for BOLD-signal with ratings of arousal for TD
participants (green) and participants with ASD (purple) in
regions where the correlation of arousal ratings with BOLD for
participants with ASD differs significantly from the correlation of
arousal ratings with BOLD for TD participants.
r Tseng et al. r
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TABLE II. Regions of significant linear correlations of BOLD signal with ratings of AROUSAL for participants with ASD
Anatomical regionsa
Location MNI coordinates
ZSide BA x y z Correlation
Linear arousal
Premotor cortex R 6, 1 39 228 67 8.21 Positive
Dorsolateral prefrontal cortex L 8 26 47 25 8.13 Positive
Cerebellum L 26 261 220 8.04 Positive
Dorsolateral prefrontal cortex L 9 221 20 58 7.97 Positive
Dorsolateral prefrontal cortex R 21 41 49 7.66 Positive
Broca’s area L 251 20 13 4.23 Positive
a
Regions were signiﬁcant at P < 0.001 or posterior probability > 99.0%. (All of the other tables in this document show signiﬁcance at
P < 0.0125 or posterior probability > 98.75%).
TABLE III. Regions of significant linear correlations of BOLD signal with ratings of VALENCE for participants with ASD
Anatomical regions Side BA x Y z Z Correlation
Linear Valence
Cerebellum L 23 252 238 4.94 Positive
Cerebellum R 9 255 238 4.69 Positive
Primary visual cortex R 17/18 30 258 4 4.58 Positive
Lingual gyrus R 18 12 288 25 4.28 Positive
Dorsolateral prefrontal cortex L 218 62 10 4.01 Positive
Hippocampus R 24 216 214 3.79 Positive
Fusiform gyrus R 39 222 223 3.75 Positive
Primary visual cortex L 18 215 255 10 3.74 Positive
Anterior cingulate cortex L 29 35 25 3.55 Positive
Hippocampus R 27 240 1 3.54 Positive
Inferotemporal visual area L 239 231 214 3.29 Positive
Caudate nucleus L 23 20 22 3.2 Positive
Orbitofrontal cortex R 27 32 211 3.04 Positive
Broca’s area L 44 254 20 13 6.93 Negative
Primary somatosensory cortex L 3b 260 24 19 6 Negative
Primary motor cortex L 254 5 28 5.93 Negative
Secondary somatosensory cortex L 260 27 13 5.7 Negative
Insula L 233 23 10 5.33 Negative
Posterior parietal cortex R 24 270 49 5.28 Negative
Parietal-temporal cortex L 248 240 43 4.85 Negative
Primary somatosensory cortex R 1 63 213 31 4.85 Negative
Dorsolateral prefrontal cortex R 27 5 55 4.58 Negative
Dorsolateral prefrontal cortex L 236 53 13 4.56 Negative
Visual association cortex R 36 282 34 4.52 Negative
Supplementary motor area R 6 12 11 46 4.48 Negative
Parietal-temporal cortex R 45 240 40 4.45 Negative
Dorsolateral prefrontal cortex R 44 39 5 43 4.39 Negative
Broca’s area R 44 45 14 34 4.23 Negative
Inferotemporal visual area R 54 267 28 4.14 Negative
Primary visual cortex L 18 29 288 28 3.2 Negative
Broca’s area R 45 54 20 1 3.09 Negative
r 11 r

the emotion-rating task [Tseng et al., 2014], we detected dra-
matic differences across diagnostic groups in the neural activ-
ity subserving the dimensions of emotion, particularly arousal.
BOLD-signal correlated linearly with ratings of emotional
arousal, but in opposite directions and in differing locations
for the two groups. BOLD-signal in TD participants correlated
inversely with ratings of arousal in regions associated primar-
ily with attentional functions, whereas BOLD-signal in ASD
participants correlated positively with arousal ratings in
regions most commonly associated with impulse control and
with default-mode activity. In contrast, we found that BOLD-
signal correlated with ratings of valence similarly across the
TD and ASD groups, positively in regions associated previ-
ously with processing of emotional faces, and inversely in sen-
sorimotor regions. Only minor group differences were
detected in the BOLD-signal correlates with valence ratings.
Arousal-related Activity in TD Participants
Brain regions that correlated inversely with ratings of
arousal in our TD group included the ILPFC, DLPFC, dor-
sal ACC, and inferoposterior and dorsal PC, as well as CN
and PUT, regions thought to support attentional functions
[Petersen and Posner, 2012]. Attention is the means by
which brains optimize the ﬂexible use of limited cognitive
resources to process prioritized stimuli (i.e., to select task-
relevant and ignore task-irrelevant information) [Mansouri
et al., 2009], plan for future contingencies, inhibit compet-
ing responses, and pursue long-term goals [Pennington
and Ozonoff, 1996]. The neural substrates of attention are
generally thought to include ACC, inferolateral and pre-
frontal cortices, and basal ganglia [Fan et al., 2005; Raz
and Buhle, 2006], regions that correlated inversely with
arousal ratings in the TD group.
The inverse linear correlations of arousal ratings with
BOLD-signal in attentional networks suggests that TD partici-
pants may have had to allocate progressively more attentional
resources to processing incrementally less-arousing face-stim-
uli. Numerous studies have reported that TD participants
view low-arousal faces as ambiguous—i.e., as stimuli that
may be construed in more than one way, or whose content
requires greater examination of contextual cues to decipher
[Kryklywy et al., 2013; Rosen and Donley, 2006]. Although
earlier studies of ambiguity in emotional faces focused on
fear-related, high-arousal stimuli (such as a fearful face signal-
ing potential threat in the environment) [Adolphs, 2010;
Whalen, 1998; Whalen et al., 2001], more recent studies have
highlighted the inherent ambiguity in many low-arousal faces
(e.g., faces with neutral, bored, or calm expressions) [Thomas
et al., 2001; Tottenham et al., 2013]. These low-arousal faces
provide neither clear safety nor danger signals about the
immediate vicinity and so individuals must allocate more
attentional resources to gauge the potential danger of the faces
[Adolphs, 2010]. Thus, we posit that TD participants enlisted
more attentional resources when viewing low-arousal faces
because they were attempting to assess and classify low-
arousal, ambiguous stimuli [Petersen and Posner, 2012; Posner
and Petersen, 1990; Reiman et al., 1997]. Consistent with this
interpretation, prior studies have reported that attentional net-
works activate more strongly when processing ambiguous
stimuli [Mushtaq et al., 2011; Raz et al., 2007; Volz et al., 2004].
Stimulus Specificity in Activating Attention
Networks during Emotional Tasks
Our current ﬁndings and their interpretations, when
combined with those in our prior studies using differing
TABLE IV. Regions of significant linear correlations of BOLD signal with ratings of AROUSAL for TD participants
Anatomical regions
ZSide BA x y z Correlation
Linear arousal
Inferolateral prefrontal cortex R 27 62 19 7.29 Negative
Broca’s area R 51 41 16 6.96 Negative
Primary motor cortex R 57 11 34 6.74 Negative
Intraparietal sulcus L 244 246 48 6.73 Negative
Parietal-occipital-temporal cortex L 242 255 61 6.73 Negative
Precuneus R 12 270 64 6.34 Negative
Cuneus R 21 285 46 6.31 Negative
Broca’s area R 48 20 34 5.69 Negative
Insula L 239 213 10 5.04 Negative
Insula R 36 26 22 4.53 Negative
Caudate nucleus R 15 8 16 4.3 Negative
Visual association cortex L 23 288 25 4.21 Negative
Visual association cortex L 260 210 217 4.09 Negative
Hippocampus R 18 234 10 4.09 Negative
Putamen L 221 2 7 4.03 Negative
r Tseng et al. r
r 12 r

types of emotional stimuli, suggest that the allocation of
attentional resources may depend critically upon the
nature of the emotional stimulus [Colibazzi et al., 2010;
Gerber et al., 2008; Landa et al., 2013; Posner et al., 2009].
In the present study, for example, we found that neural
activity within attention networks of the TD group corre-
lated inversely with ratings of arousal in emotional faces.
Yet in our prior study using a mood-induction task, neural
activity in attention networks of a different TD group cor-
related inversely with ratings of emotional valence, not
arousal [Colibazzi et al., 2010; Landa et al., 2013] (i.e., neu-
ral activity during mood-induction increased in attention
networks as the valence of mood-inducing stimuli became
more unpleasant and aversive). Moreover, these correla-
tions during mood-induction were significantly stronger
for interpersonal than for non-interpersonal stimuli [Landa
et al., 2013]. The inherently interpersonal nature of faces
may account for the similar neural responses of potentially
threatening faces with aversive interpersonal, mood-
inducing situations.
The differential activation of attention circuits in
response to differing stimuli (emotional faces or induced
Figure 4.
Linear and quadratic correlations of arousal with BOLD-signal in
ASD (A) Regions of significant linear correlations of BOLD-
signal with ratings of arousal for participants with ASD. (Positive
correlations are coded in red to yellow, and inverse correlations
are coded in green to blue.). (B) Scatterplots of linear correla-
tions for BOLD-signal with ratings of arousal for participants
with ASD (orange). (C) Regions of significant linear correlations
of BOLD-signal with ratings of arousal combined with regions of
significant quadratic correlations of arousal ratings for partici-
pants with ASD. Positive linear correlations with arousal are
shown alone (orange), quadratic correlations with arousal alone
(fuschia), and linear1quadratic correlations with arousal (red).
(D) Scatterplots of correlations between BOLD-signal change
and ratings of linear1quadratic correlations with arousal for par-
ticipants with ASD (red) in regions where BOLD correlated
positively for both linear and quadratic correlations with arousal
ratings.
r 13 r

mood) suggests that we cannot easily distinguish or disen-
tangle the allocation of attentional resources from the two
major dimensions of emotion, valence and arousal. It also
demonstrates that instead of identifying neural activity that
subserves or produces the arousal and valence components
of emotional experience, our fMRI paradigm may be detect-
ing varying activity in attention circuits associated with vary-
ing individual experiences of emotion. Our findings show
that attention-related activation can dominate overall neural
activity during emotional tasks, overwhelming the variance
in neural activity that produces arousal and valence.
Arousal in Participants with ASD
Regions where BOLD-signal correlated positively with
arousal ratings in the ASD group included posterior tem-
poral and inferior PC, the mesial wall (pregenual and dor-
sal portions of the superior frontal and anterior cingulate
gyri), PreMC and supplementary motor cortices, Cu and
PCu, all basal ganglia nuclei, THAL, and dorsal Cb. Much
of the pattern of neural activity associated with arousal in
participants with ASD was similar to the pattern of
regional activation in tasks that require suppression of an
automatic response [Peterson et al., 2002; Viviani, 2013]
and in preparing for action. The function of arousal is to
prepare an organism for action, and the restraint of that
motor preparedness from execution during the fMRI
experiment would require activation of an inhibitory
response. Alternatively, participants with ASD may find
more-arousing faces to be aversive, thereby requiring acti-
vation of control networks to suppress their motoric urge
to withdraw from the putatively noxious stimulus [Peter-
son, 2003; Tabu et al., 2012]. Consistent with both
Figure 5.
Valence correlates (A) Regions of significant correlations of
BOLD-signal with ratings of valence for TD participants. (Posi-
tive correlations are coded in red to yellow, and inverse correla-
tions are coded in green to blue.). (B) Scatterplots of
correlations for BOLD-signal with ratings of valence for TD par-
ticipants (green) or participants with ASD (purple). (C) Regions
of significant correlations of BOLD-signal with ratings of valence
for participants with ASD. (D) The regions where the correla-
tion of valence ratings with BOLD for participants with ASD dif-
fers significantly from the correlation of valence ratings with
BOLD for TD participants. (ASD > TD coded in red to yellow,
TD >ASD coded in blue to green). (E) Scatterplots of correla-
tions for BOLD-signal with ratings of valence for TD partici-
pants (green) and participants with ASD (purple) in regions
where the correlation of valence ratings with BOLD for partici-
pants with ASD differs significantly from the correlation of
valence ratings with BOLD for TD participants.
r Tseng et al. r
r 14 r

interpretations, children with ASD reportedly have a
stronger skin conductance response, and therefore are
more aroused, when viewing faces with a direct compared
with averted gaze, and compared to TD children in either
condition [Kylliainen et al., 2012]. Also consistent with
both interpretations are the maps of curvilinear (quadratic)
responses of neural activity related to arousal ratings,
which showed disproportionately stronger BOLD
responses to more arousing stimuli in regions that support
inhibitory responses, including dorsal frontal, ACC, and
basal ganglia regions (Fig. 4).
Several regions that correlated positively with arousal
ratings in the ASD group, including medial prefrontal,
anterior cingulate, posterior cingulate, and precuneus cor-
tices, are also components of the default mode network
(DMN). Activity in the DMN is greatest at rest and
declines during cognitively demanding tasks [Raichle
et al., 2001]. In TD individuals, the DMN activates during
performance of various social, emotional, and introspective
tasks [Gusnard et al., 2001; Raichle et al., 2001]. Prior stud-
ies suggest that persons with ASD may not deactivate the
DMN sufﬁciently when performing cognitively demanding
tasks [Kennedy and Courchesne, 2008]. In participants
with ASD, however, we found proportionately greater
neural activity in DMN regions, presumably representing
a more strongly felt social and emotional experience with
progressively more-arousing faces.
Group Similarities and Differences in Arousal
Correlates
We were surprised to ﬁnd the strikingly different pat-
terns of activation to arousal ratings across the TD and
ASD groups. Participants in our TD group increased neu-
ral activity in their attention networks when rating less-
TABLE V. Regions of significant linear correlations of BOLD signal with ratings of VALENCE for TD participants
ZAnatomical regions Side BA x y z Correlation
Linear valence
Primary visual cortex R 17 12 297 4 7.87 Positive
Cuneus R 18 21 2100 10 6.44 Positive
Orbitofrontal cortex L 0 56 28 4.65 Positive
Precuneus L 0 258 19 3.42 Positive
Visual association cortex L 18 215 2103 1 3.23 Positive
Inferotemporal visual area R 45 273 25 3.15 Positive
Anterior cingulate cortex L 23 26 211 3.05 Positive
Visual association cortex L 242 273 25 2.91 Positive
Fusiform gyrus L 230 231 226 2.89 Positive
Supplementary motor area L 0 20 58 4.16 Negative
Precuneus R 9 261 49 4.11 Negative
Parietal-occipital-temporal cortex L 230 252 37 3.97 Negative
Rolandic operculum R 39 219 22 3.95 Negative
Precuneus L 26 279 43 3.84 Negative
Middle cingulate cortex L 29 11 40 3.72 Negative
Primary motor cortex R 6 51 210 52 3.68 Negative
Primary somatosensory cortex R 6 30 231 67 3.67 Negative
Lingual gyrus L 215 285 28 3.57 Negative
Cuneus L 26 285 34 3.54 Negative
Primary motor cortex L 239 5 37 3.51 Negative
Broca’s area L 248 41 1 3.33 Negative
Auditory association cortex R 54 240 13 3.23 Negative
Middle cingulate cortex L 26 219 34 2.93 Negative
Anterior cingulate cortex L 29 26 28 2.72 Negative
Middle cingulate cortex R 12 222 34 2.6 Negative
r 15 r

arousing faces, a pattern absent in the ASD group. These
strong inverse correlations of BOLD signal with arousal rat-
ings in attentional networks of the TD group is counter to
the more intuitive expectation that highly arousing or emo-
tionally engaging stimuli would be more attention-grabbing
than less arousing and less emotionally engaging stimuli.
In contrast, individuals in the ASD group demonstrated
strong positive correlations with arousal ratings in regula-
tory systems (IPC, SFG, THAL, CN, Cu) and the DMN
(ACC, PCC), a pattern similar to that shown in TD controls
in a prior mood-induction study from our laboratory [Coli-
bazzi et al., 2010]. This similarity suggests that participants
with ASD, more than the TD controls, may have been
increasingly immersed in the emotions expressed by the
more arousing face stimuli of the present study. In other
words, activation of the regulatory systems and DMN may
require the absorbing, vitalizing experience of interpersonal
stimuli, whether they are more highly arousing faces for
persons with ASD in the present study or more arousing
interpersonal moods for TD individuals in our prior study.
Thus TD participants in the present study may have
instead approached the rating of facial emotions (as
opposed to the mood induction task of our prior study) as
more of a cognitive than a mood-inducing task, with the rat-
ing of progressively less arousing, more ambiguous, and
potentially threatening faces requiring a progressively
greater allocation of attentional resources. Individuals with
ASD, in contrast, seem either not to have experienced low-
arousal faces as threatening, or they found those faces less
salient and less attention-grabbing than did the TD controls.
Valence
Only minor differences were detected between diagnos-
tic groups in the BOLD signal correlates of valence ratings.
In ASD and TD groups, BOLD-signal correlated positively
with valence ratings in ACC, FG, and OFC, regions impli-
cated in facial emotion processing. Studies of adults sug-
gest, for example, that FG supports the perceptual
identification of faces [Haxby et al., 2000] and more specif-
ically the coding of fearful (high-arousal, high-valence)
faces [Pessoa et al., 2002; Vuilleumier et al., 2001]. The
near absence of significant group differences suggests that
individuals with ASD do not activate the brain atypically
for all components of emotion-processing.
Limitations
One prominent limitation is the absence of eye-tracking
data during participants’ viewing of emotional faces. Prior
studies have shown that individuals with ASD do not
spontaneously attend to, and they may even avoid, the
eyes of face-stimuli, even though the eyes are a rich source
of information about emotional states [Klin et al., 2002].
Although less attention to the eyes of our face-stimuli
could have impaired the ability of participants with ASD
to recognize and rate accurately both valence and arousal
[Kliemann et al., 2010], if participants with ASD had not
attended to task-stimuli, their valence and arousal ratings
would have differed from the ratings of TD participants
[Tseng et al., 2014]. Nevertheless, we cannot exclude the
possibility that subtle group differences in attention to spe-
cific facial features influenced our findings.
Additionally, as is often the case in research on ASD,
we struggled when designing our experiment with the
trade-offs between task difficulty, selection of a task that
can provide scientifically important information, and the
generalizability of the study and its findings to the entire
autism spectrum. We considered multiple issues simulta-
neously. In particular, we needed to include in our study
individuals who would and did understand a task that
addressed meaningfully our fundamental research ques-
tions. Nonverbal individuals, for example, would be
unlikely to understand or perform our task adequately.
TABLE VI. Centers of activation in regions where the linear correlation of BOLD signal intensity with ratings of
AROUSAL for participants with ASD differs significantly from the linear correlation of BOLD with ratings of
AROUSAL for TD participants
Anatomical regionsa
ZSide BA x Y z Correlation
Linear arousal
Broca’s area L 45 248 41 25 5.68 ASD>TD
Dorsolateral prefrontal cortex L 9 224 38 46 5.12 ASD>TD
Dorsolateral prefrontal cortex R 8 12 50 49 5.09 ASD>TD
Broca’s area R 45 57 26 16 4.95 ASD>TD
Caudate nucleus R 18 14 13 4.68 ASD>TD
Hippocampus R 21 228 25 4.56 ASD>TD
Amygdala R 27 21 217 4.41 ASD>TD
Anterior cingulate cortex R 32 12 44 16 4.19 ASD>TD
a
Regions were significant at P < 0.001 or posterior probability > 99.0%. (All of the other tables in this document show significance at
P < 0.0125 or posterior probability > 98.75%).
r Tseng et al. r
r 16 r

Also, if we had included lower functioning persons with
ASD (i.e., those with lower IQs), we would have had to
include control participants with comparable levels of
intelligence, which in turn would introduce a host of con-
founding variables and sample heterogeneity that would
make interpretation of findings difficult. We were careful
to also covary for full-scale IQ, as well as for age and sex,
and detected no significant differences in our findings for
diagnosis. Additionally, the individuals with ASD in our
sample ranged in ASD diagnosis from PDD-NOS to
Asperger’s to Autism (mean ADOS Score 5 10.9 6 3.1),
suggesting that we can extrapolate our findings to individ-
uals with moderate to high-functioning ASD.
CONCLUSIONS
Our findings provide unique insight into the emotional
experiences of individuals with ASD. Although behavioral
responses to face-stimuli were similar across diagnostic
groups, the corresponding neural activity of the behavioral
responses for arousal differed prominently across groups.
TD individuals and persons with ASD seem to find differ-
ing aspects of emotional stimuli to be salient and relevant.
Studying these differences may help us to understand the
origins of atypical interpersonal emotional experiences in
individuals with ASD.
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