Abstract

Autism spectrum disorders are associated with social and emotional deficits, the aetiology of which are not well understood. A growing consensus is that the autonomic nervous system serves a key role in emotional processes, by providing physiological signals essential to subjective states. We hypothesized that altered autonomic processing is related to the socio-emotional deficits in autism spectrum disorders. Here, we investigated the relationship between non-specific skin conductance response, an objective index of sympathetic neural activity, and brain fluctuations during rest in high-functioning adults with autism spectrum disorder relative to neurotypical controls. Compared with control participants, individuals with autism spectrum disorder showed less skin conductance responses overall. They also showed weaker correlations between skin conductance responses and frontal brain regions, including the anterior cingulate and anterior insular cortices. Additionally, skin conductance responses were found to have less contribution to default mode network connectivity in individuals with autism spectrum disorders relative to controls. These results suggest that autonomic processing is altered in autism spectrum disorders, which may be related to the abnormal socio-emotional behaviours that characterize this condition.

Introduction

Autism spectrum disorder (ASD) manifests early in development, and is characterized by deficits in social interaction and communication, as well as stereotyped and repetitive behaviours, and restricted interests in domains of activities (American Psychiatric Association, 2013). Individuals with ASD also exhibit difficulties in emotional processing, particularly in self-awareness of feelings (Hill et al., 2004; Silani et al., 2008) and in the interpretation of feelings of others (Hobson et al., 1988; Baron-Cohen, 1991; Bal et al., 2010). Despite the extensive investigation into the aetiology of ASD, the psychophysiological correlates of the socio-emotional deficits that characterize the disorder are not yet clear. Further exploration of the physiological and neural substrates of social and emotional processes is critical for better understanding, diagnosis, and treatment of ASD.

The autonomic nervous system regulates the physiological events associated with emotional experiences, including changes in heart rate, respiration, pupil dilation and sweating (Craig, 2002; Barrett et al., 2007). James (1884) and Lange (1885) proposed that these physiological changes are essential precursors for the subjective feeling of emotions. This idea has since been supported and extended by studies demonstrating that different emotional stimuli generate distinct autonomic activities, which are interpreted by the brain as different emotional experiences (Ekman, 1983; Rainville et al., 2006; Harrison et al., 2010). False physiological feedback affects emotional attributions (Valins, 1966; Liebhart, 1977), and emotional interpretation of ambiguous stimuli (Truax, 1983; Gray et al., 2007). As social situations are often ambiguous and require rapid interpretation of emotional cues, autonomic nervous system signals play a critical role in socio-emotional processing.

The initial processing of autonomic signals takes place in the reticular formation, brainstem and thalamic nuclei, and the hypothalamus, whereas higher-order processing of these signals takes place mainly in the somatosensory cortex, supplementary motor area, anterior insular cortex, and anterior cingulate cortex (Boucsein, 1992; Damasio et al., 2000; Craig, 2002; Porges, 2003; Critchley, 2005). Specifically, the anterior insular cortex, through bidirectional neural connections with the thalamus, amygdala, nucleus accumbens, anterior cingulate cortex and orbitofrontal cortex, is suggested to have a critical role in the integration of bottom-up interoceptive and exteroceptive signals with top-down predictions and evaluations of emotional states (Craig, 2002, 2003; Gray et al., 2007; Harrison et al., 2010; Critchley et al., 2011; Seth et al., 2011; Gu et al., 2013). The anterior insular cortex and anterior cingulate cortex are involved in socio-emotional processing (Damasio et al., 2000; Adolphs, 2002; Phillips et al., 2003; Frith and Frith, 2007; Gu et al., 2012, 2013) in addition to their role in autonomic nervous system regulation, further supporting the important relationship between socio-emotional processing and autonomic activity. Abnormal autonomic nervous system activity may, therefore, be a potential source of the socio-emotional deficits that characterize ASD.

Physiological studies have demonstrated abnormal autonomic nervous system activity in ASD. Abnormal autonomic activity related to external stimuli has been found in individuals with ASD, particularly when those stimuli were of a social or emotional nature (Hirstein et al., 2001; Kylliainen and Hietanen, 2006; Vaughan Van Hecke et al., 2009). Abnormalities in basal autonomic activity have been observed in ASD as well, including reduced baseline cardiac parasympathetic activity (Ming et al., 2005), lower amplitude of respiratory sinus arrhythmia (Bal et al., 2010), and larger baseline pupil dilation (Anderson and Colombo, 2009; Anderson et al., 2013) when compared with matched neurotypical control subjects. Gastrointestinal symptoms have also been found to be significantly more prevalent in ASD than in neurotypical samples (Horvath et al., 1999; Molloy and Manning-Courtney, 2003; White, 2003). These findings of increased sympathetic and decreased parasympathetic activity suggest an imbalance between these two systems in ASD.

Autonomic activity has also been linked to functional connectivity patterns in the resting brain (Birn et al., 2008; Iacovella and Hasson, 2011; Fan et al., 2012; Chang et al., 2013). In a recent study we showed significant contributions of skin conductance response (SCR) signal, an objective and sensitive marker of sympathetic neural activity (Vetrugno et al., 2003), to the connectivity strength of the default mode network (DMN) in a healthy cohort (Fan et al., 2012). Previous investigations of resting-state functional connectivity in ASD have demonstrated weaker connectivity of the DMN compared with neurotypical control subjects (Kennedy and Courchesne, 2008b; Monk et al., 2009; Assaf et al., 2010; Weng et al., 2010). It is possible that these findings of weaker DMN connectivity in ASD may be explained, at least in part, by differences in autonomic nervous system activity between the groups.

We hypothesized that ASD is associated with abnormal autonomic and correlated brain activities. This may stem from alterations in the central generation of autonomic response, the peripheral conductance of autonomic nervous system signals, and/or the central representations of physiological changes. To test our hypothesis, we recorded skin conductance and simultaneously measured brain activity during rest using functional MRI from high-functioning adults with ASD, as well as from demographically matched neurotypical control subjects. We predicted that the rate of non-specific (not task-evoked) SCR would differ between the groups, and that there would be differences in brain activity and connectivity associated with SCR, such that (i) SCR would be associated with subcortical and cortical brain regions that process and modulate autonomic activity to a lesser extent in ASD compared with neurotypical controls; and (ii) differences in DMN connectivity strength between ASD and neurotypical controls could be explained, at least partially, by differences in autonomic nervous system activity.

Materials and methods

Participants

Seventeen high-functioning adults with autism (n = 12) or Asperger’s syndrome (n = 5) (ASD group) and 15 matched neurotypical control participants were evaluated at the Seaver Autism Centre for Research and Treatment, Icahn School of Medicine at Mount Sinai (Table 1 for demographic data). All participants underwent a diagnostic evaluation consisting of psychiatric, medical, and developmental assessments, as well as IQ measurement using the Wechsler Adult Intelligence Scale (WAIS-III) (Wechsler, 1997). Diagnoses of autism or Asperger’s syndrome were determined by psychiatric interview according to the Diagnostic and Statistical Manual for Mental Disorders, Fourth Edition (DSM-IV-TR), and confirmed by the Autism Diagnostic Observation Schedule-Generic (ADOS-G) (Lord et al., 2000), as well as the Autism Diagnostic Interview-Revised (ADI-R) (Lord et al., 1994). Exclusion criteria included epilepsy, history of schizophrenia, schizoaffective disorder or other Axis I mental disorders, except for obsessive-compulsive disorder (given the phenotypic overlap with ASD), and use of depot neuroleptic medication or other psychoactive drugs within the 5 weeks prior to participation. Participants who had a lifetime history of substance/alcohol dependence and/or abuse within the last year were also excluded. For the healthy control group, participants were excluded based on medical illness or history in first-degree relatives of developmental disorders, learning disabilities, autism, affective disorders, and anxiety disorders. All participants provided written informed consent, approved by the Icahn School of Medicine at Mount Sinai Institutional Review Board.

Table 1

Demographic data (means ± SD) of ASD and neurotypical control groups

Subject characteristics ASD NC t P 
 (n = 17) (n = 15)   
Age (years) 26.1 ± 6.5 27.1 ± 8.2 0.37 0.72 
Handedness score 60.0 ± 53.40 87.3 ± 11.6 2.06 0.06 
Years of educationa 14.90 ± 2.3 16.2 ± 1.8 1.7 0.10 
Parents’ socio-economic status 88.35 ± 18.73 90.73 ± 23.19 0.32 0.75 
Full-scale IQ 110.3 ± 18.6 112.6 ± 12.5 0.42 0.68 
ASD diagnosis (autism/Asperger’s) 12/5    
ADI-Rb     
    Social 18.3 ± 8.0    
    Verbal communication 16.0 ± 4.8    
    Repetitive behaviour 5.9 ± 3.0    
    Development 3.0 ± 1.6    
ADOS-G     
    Communication 3.1 ± 1.5    
    Social 7.3 ± 2.6    
    Imagination 0.7 ± 0.5    
    Stereotyped behaviours 1.4 ± 1.4    
Subject characteristics ASD NC t P 
 (n = 17) (n = 15)   
Age (years) 26.1 ± 6.5 27.1 ± 8.2 0.37 0.72 
Handedness score 60.0 ± 53.40 87.3 ± 11.6 2.06 0.06 
Years of educationa 14.90 ± 2.3 16.2 ± 1.8 1.7 0.10 
Parents’ socio-economic status 88.35 ± 18.73 90.73 ± 23.19 0.32 0.75 
Full-scale IQ 110.3 ± 18.6 112.6 ± 12.5 0.42 0.68 
ASD diagnosis (autism/Asperger’s) 12/5    
ADI-Rb     
    Social 18.3 ± 8.0    
    Verbal communication 16.0 ± 4.8    
    Repetitive behaviour 5.9 ± 3.0    
    Development 3.0 ± 1.6    
ADOS-G     
    Communication 3.1 ± 1.5    
    Social 7.3 ± 2.6    
    Imagination 0.7 ± 0.5    
    Stereotyped behaviours 1.4 ± 1.4    

aYears of education data was not available for five participants, therefore ASD: n = 14, neurotypical controls (NC): n = 13.

bADI-R scores were not available for one participant, therefore n = 16 for this measure.

Data acquisition

Skin conductance response acquisition

SCR was acquired according to the procedure described by Fan et al. (2012). Briefly, SCR was recorded using the GSR100C amplifier (BIOPAC Systems), 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 electrodes that are attached to the skin. This allows for the measurements of both skin conductance level and SCR, which vary with sweat gland activity due to stress, arousal or emotional excitement. Skin conductance (measured in μS) was recorded using a 2000-Hz sampling rate (gain = 2 μS/V, both high-pass filters = DC, low-pass filter = 10 Hz). After cleaning the skin with alcohol swabs, two EL507 disposable EDA (isotonic gel) electrodes were placed on the palmar surface of the distal phalanges of the big and second toes of left foot. The electrode leads were shielded and the signal was low-pass filtered (using the MRI-Compatible MRI CBL/FILTER System MECMRI-TRANS) to reduce radiofrequency interference from the scanner. BIOPAC recording was synchronized to the E-Prime (Psychology Software Tools) program through the parallel port of the computers to enable precise time alignment of skin conductance recording with scan onsets.

Image acquisition

All brain images were obtained using a 3 T Siemens Allegra MRI system at the Icahn School of Medicine at Mount Sinai, and were acquired parallel to the anterior-posterior commissures axis (AC-PC). Foam padding was used to reduce head motion. Whole-brain anatomical T2-weighted images were acquired in high-resolution using a turbo spin-echo plus sequence: 40 axial slices of 4 mm thickness; skip = 0 mm; repetition time = 4050 ms; echo time = 99 ms; flip angle = 170°; field of view = 240 mm; matrix size = 448 × 512; voxel size = 0.47 × 0.47 × 4 mm. After the anatomical image acquisition, one run of T2*-weighted images was obtained during rest, corresponding to the T2-weighted images localization, using a 6 min gradient echoplanar imaging sequence for resting-state functional MRI: 40 axial slices, 4-mm thick; skip = 0 mm; repetition time = 2500 ms; echo time = 27 ms; flip angle = 82°; field of view = 240 mm; matrix size = 64 × 64; in-plane resolution = 3.75 × 3.75 mm. The resting-state functional MRI run started with two dummy volumes before the onset of the fixation to allow for equilibration of T1 saturation effects, followed by 144 image volumes. Refer to the Supplementary material for a detailed procedure of the eyes-open resting-state scan.

Data analysis

Skin conductance analysis

Skin conductance level was calculated as the average of all data points on the skin conductance waveform for each of the participants. A t-test was conducted between the skin conductance level values of the groups to examine possible differences in basal skin conductance levels. AcqKnowledge software (version 4.2) was used in order to identify and count SCRs (Supplementary material). The number of valid SCR events was determined for each participant, and a t-test was then conducted to examine possible group differences in the number of SCRs.

Regression analysis

To correlate SCR with brain fluctuations, the skin conductance waveform was down-sampled by averaging the data points in each 2.5-s bin matching the repetition time (2.5 s) of the echoplanar imaging scan of functional image acquisition. Because relaxation causes skin conductance level to decrease slowly in a linear fashion, the SCR waveform was detrended and band-pass filtered with the same frequency range (0.01–0.08 Hz) that is used in a typical resting-state functional MRI analysis. The similarity between the SCR and haemodynamic response function curves (Bach et al., 2010a, b) allows for using SCR as a regressor in the model directly without transformations, using the same filtering band for both SCR and blood oxygenation level-dependent signals (0.01–0.08 Hz) (Fan et al., 2012). SCR was then entered as a regressor in a general linear model (Friston et al., 1995) using statistical parametric mapping package (SPM8; Wellcome Trust Centre for Neuroimaging, London, UK). Numbers of SCR were not equated between the groups to avoid specification error (Supplementary material). The echoplanar imaging scans were realigned to the first volume, timing corrected, coregistered to the T2 image, normalized to a standard template (MNI, Montreal Neurological Institute), resampled to a 2 × 2 × 2 mm voxel size, and spatially smoothed with an 8 mm full-width at half-maximum Gaussian kernel. The general linear model was then conducted with the SCR time series as a predictor of the observed blood oxygen level-dependent signals. Low-frequency drifts in signal were removed using a standard high-pass filter with a 128 s cut-off. Serial correlation was estimated using an autoregressive AR(1) model. To remove non-neural noise from the data, ventricle and white-matter signals were extracted using corresponding masks and were entered as covariates. In addition, the six parameters generated during motion correction were also entered as covariates. Head motions (for all participants) did not exceed 2.5 mm of displacement or 2.5° of rotation in any direction. Finally, mean voxel value was used for global calculation and grand mean scaling, applied with global normalization to further remove non-specific noise (Van Dijk et al., 2010). Detailed justifications for using the global mean correction can be found in the Supplementary material.

The contrast images from the participants in each group were entered into a second-level random effect group analysis. The resultant voxel-wise statistical maps were thresholded for significance using a cluster-size algorithm that protects against an inflation of the false-positive rate of multiple comparisons; an uncorrected P-value of 0.05 for the height (intensity) threshold of each activated voxel and extent threshold of k = 120 was used based on Monte Carlo simulation (Slotnick and Schacter, 2004). Assuming an individual voxel type I error of P < 0.05, a cluster extent of 120 contiguous resampled voxels (2 × 2 × 2 mm) was indicated as necessary to correct for multiple voxel comparisons at P < 0.05. Statistical results were mapped onto the standardized surface of the cerebral cortex.

Functional connectivity analysis of the default mode network

To investigate the relationship between non-specific SCR and brain connectivity during rest, a functional connectivity analysis was conducted using a seed region within the posterior cingulate cortex as in previous studies (Koshino et al., 2005). Specifically, time-series volumes of functional MRI scan images were preprocessed for each participant using the Data Processing Assistant for Resting-State fMRI (DPARSF) toolbox (Yan and Zang, 2010). This included slice timing correction, realignment, coregistration, normalization and spatial smoothing (using an 8 mm full-width at half-maximum Gaussian kernel) as in the regression analysis. In addition, de-trending (to remove the systematic drift) and temporal filtering (band-pass, 0.01–0.08 Hz, to reduce the effect of low-frequency drift and high-frequency physiological signal or noise) were applied. Time course of the posterior cingulate cortex (left and right combined) was then extracted using the automated anatomical labelling template (Tzourio-Mazoyer et al., 2002), and a voxel-wise linear correlation between the mean time course of the posterior cingulate cortex and the time course of each voxel in the whole brain was calculated using the resting-state functional MRI data analysis toolkit (Song et al., 2011). The six head motion parameters, global mean signal, white matter signal, and CSF signal were included as covariates. A two-sample t-test was then conducted to examine between-group connectivity differences.

To test the effects of SCR on brain connectivity, changes in functional connectivity of the posterior cingulate cortex before and after regressing out SCR effects were examined and compared between the groups in an ANOVA model. SCR signals were regressed out as covariates in a second voxel-wise connectivity analysis, followed by transformations of the correlation coefficients using Fisher’s r-to-z’ transformations. Posterior cingulate cortex time course was also extracted after regressing out the SCR signal. In addition, to examine SCR contributions to the posterior cingulate cortex functional connectivity within each group, a paired t-test was conducted for each group before versus after regressing out SCR signals for the posterior cingulate cortex functional connectivity maps. Posterior cingulate cortex connectivity analysis is reported here because it allows comparisons with previous studies investigating DMN connectivity in ASD that used a posterior cingulate cortex seed (Monk et al., 2009; Weng et al., 2010), and because the posterior cingulate cortex has a more focal anatomical definition than alternative seed regions. However, the ventromedial prefrontal cortex, also commonly used as a DMN seed, is potentially more relevant to ASD abnormalities, emotional processing and autonomic pathways. We, therefore, conducted an additional functional connectivity analysis for the time course of the ventromedial prefrontal cortex, using the coordinates (−1, 47, −4) reported by Fox et al. (2005), and group differences were examined. To further examine possible differences in the connectivity of areas that are related to emotional and autonomic signal processing, we also tested the functional connectivity of the anterior insular cortex (Supplementary material). It is important to note that there were no significant differences in head motion between the two groups, as was indicated by the root mean squares of both overall head motion displacement and rotation and the temporal derivatives (Supplementary material).

Functional connectivity analysis of the whole brain

To examine whether SCR contributes to the whole brain connectivity, a voxel-wise whole brain functional connectivity strength analysis was performed both before and after regressing out the SCR signal. Images were preprocessed using DPARSF, similar to the posterior cingulate cortex functional connectivity analysis, with two exceptions. First, the resolution of resultant echoplanar imaging was 3 × 3 × 3 mm after normalization to reduce computational load. Second, to reduce artificial local correlations between voxels introduced by smoothing, a 4 mm full-width at half-maximum Gaussian kernel was used (instead of 8 mm as in the posterior cingulate cortex connectivity analysis). For each voxel, functional connectivity strength was measured as the summed weights of all connections linking this voxel and every other voxel. Pearson correlation coefficients for the time series of every possible pair of voxels were calculated to obtain the whole brain correlation matrix for each participant. The calculation was constrained within a customized mask (n voxels = 48 159) including all voxels with grey matter tissue probability >20% on the averaged grey matter map of all participants. The functional connectivity strength (FCS) was computed for each voxel as follows:  

formula
where rij is the correlation coefficient of voxel i and voxel j, zij is the normalized rij value using Fisher’s r-to-z transformation, and 0.3 is the threshold set to eliminate the potential contributions of weak connections arising from noise. To evaluate the reproducibility of our results, additional functional connectivity strength maps were calculated using both 0.2 and 0.4 correlation thresholds; these computations led to no major changes in our primary results. The connectivity map was then standardized by converting to Z scores so that maps across participants could be averaged and compared. The Z score transformation is:  
formula
where forumla are mean and SD of the functional connectivity strength across all the voxels in the whole-brain map.

Notably, in graph theory, the functional connectivity strength is referred to as ‘degree centrality’ or ‘degree’ of weighted networks, and voxels with high functional connectivity strength usually play important roles in information transformation (Buckner et al., 2009; Cole et al., 2010; Zuo et al., 2012; Liang et al., 2013; Wang et al., 2013). To examine the SCR contribution to whole brain connectivity, functional connectivity strength maps before and after regressing out the SCR signals were compared within each group using paired t-tests. Between-group differences of SCR signal contribution to whole brain functional connectivity (i.e. functional connectivity strength maps before versus after regressing out SCR signals) were examined in an ANOVA model. All statistical maps were corrected for multiple comparisons to a significance level of P < 0.05 by combining the individual voxel P-value < 0.05 with cluster size >67 voxels, based on Monte Carlo simulation (Ledberg et al., 1998).

Relationship between autism symptom severity, skin conductance and imaging data

Correlations between the ADI-R subscale scores of each of the ASD participants and number of SCRs and overall skin conductance level were examined. In addition, correlations between the individual ADI-R subscale scores and SCR-brain association, posterior cingulate cortex connectivity, ventromedial prefrontal cortex connectivity, and whole brain connectivity maps were examined using regression analyses. The ADI-R four subscales include ratings of social interaction (subscale A), communication (subscale B), repetitive and restricted behaviour (subscale C), and early development (subscale D). Higher scores on each scale indicate higher symptom severity in each of the specific domains. Note that these exploratory correlation analyses were not corrected for multiple comparisons.

Modelling central generation and representation of skin conductance response

The central generation and representation (feedback) of SCR were modelled by shifting the SCR vector in relation to the haemodynamic response. Two models were created: a generation model in which brain activation preceded the SCR signal by one repetition time (2.5 s), and a representation model in which SCR preceded brain activation by one repetition time (Critchley et al., 2000; Patterson et al., 2002) (see Supplementary material for details of the modelling).

Results

Electrodermal activity

A t-test revealed no significant difference in skin conductance level between ASD (15.96 ± 1.83 µS) and neurotypical controls [17.27 ± 1.76 µS, t(30) = 0.51, P > 0.05, two-tailed] (Fig. 1A). However, during the scan period, the ASD group had significantly less non-specific SCRs (4.76 ± 1.34) compared with the neurotypical control group [12.2 ± 3.13, t(30) = 2.19, P < 0.05, two-tailed] (Fig. 1B).

Figure 1

(A) Skin conductance level (SCL) and (B) number of skin conductance responses (SCR) during the entire rest session (6 min). NC = neurotypical controls.

Figure 1

(A) Skin conductance level (SCL) and (B) number of skin conductance responses (SCR) during the entire rest session (6 min). NC = neurotypical controls.

Skin conductance response and resting state brain activity

In the neurotypical control group, there were significant positive correlations between SCR and the anterior insular cortex, dorsal anterior cingulate cortex, supplementary motor area, medial prefrontal cortex, thalamus, superior parietal lobule, calcarine cortex, cuneus, and basal ganglia, as well as negative correlations with the precentral gyrus, superior parietal lobule, posterior cingulate cortex, precuneus, and inferior parietal lobule (Table 2 and Fig. 2A). In the ASD group, SCR was positively correlated with the lingual gyrus, calcarine cortex, superior and inferior parietal lobule, precentral and postcentral gyri, middle temporal gyrus, and inferior frontal gyrus, and was negatively correlated with the anterior cingulate cortex, supplementary motor area, medial prefrontal cortex, basal ganglia, inferior parietal and temporal gyri, posterior midcingulate cortex (see Vogt, 2005 for definition), and the thalamus (Table 3 and Fig. 2B). A between-group comparison similarly revealed that medial frontal brain regions were generally more correlated with SCR in the neurotypical control group, whereas posterior and sensory regions were generally more correlated with SCR in the ASD group (Table 4 and Fig. 2C and D).

Figure 2

Positive and negative correlations between non-specific SCR and brain fluctuations during rest in (A) neurotypical control subjects, and (B) adults with ASD. Red indicates voxels with positive correlations, whereas blue indicates voxels with negative correlations. (C) Stronger correlations in neurotypical control subjects compared with ASD (neurotypical controls > ASD), and (D) stronger correlations in ASD compared with neurotypical control subjects (ASD > neurotypical controls). These hemispheric surfaces were visualized using BrainNet Viewer (http://www.nitrc.org/projects/bnv/, Xia et al., 2013).

Figure 2

Positive and negative correlations between non-specific SCR and brain fluctuations during rest in (A) neurotypical control subjects, and (B) adults with ASD. Red indicates voxels with positive correlations, whereas blue indicates voxels with negative correlations. (C) Stronger correlations in neurotypical control subjects compared with ASD (neurotypical controls > ASD), and (D) stronger correlations in ASD compared with neurotypical control subjects (ASD > neurotypical controls). These hemispheric surfaces were visualized using BrainNet Viewer (http://www.nitrc.org/projects/bnv/, Xia et al., 2013).

Table 2

Positive and negative correlations between SCR and brain activation in neurotypical controls

Region L/R BA x y z T Z P k 
Positive 
Mid frontal gyrus 36 46 32 4.73 3.59 0.000 12334 
Superior parietal lobule 30 −40 40 4.66 3.56 0.000  
Superior frontal gyrus 22 62 3.97 3.20 0.001  
Thalamus  −12 −4 3.93 3.17 0.001  
Caudate nucleus  12 14 18 3.87 3.14 0.001  
Thalamus  14 −8 12 3.82 3.11 0.001  
Caudate nucleus  −14 12 10 3.77 3.08 0.001  
Lateral globus pallidus  24 −18 −2 3.76 3.08 0.001  
Mid frontal gyrus 10 38 50 24 3.76 3.08 0.001  
Caudate nucleus  −16 18 3.74 3.06 0.001  
Anterior cingulate cortex 32 16 20 36 3.74 3.06 0.001  
Anterior cingulate cortex 32 −16 10 46 3.65 3.01 0.001  
Anterior cingulate cortex 32 26 38 3.61 2.99 0.001  
Anterior insular cortex  −34 26 3.59 2.97 0.001  
Anterior insular cortex  32 18 3.53 2.93 0.002  
Caudate nucleus  14 18 3.49 2.91 0.002  
Mid frontal gyrus 42 32 3.35 2.83 0.002  
Mid frontal gyrus 48 22 26 3.35 2.82 0.002  
Orbitofrontal cortex 11 26 48 −12 3.05 2.63 0.004  
Anterior insular cortex  32 26 2.97 2.57 0.005  
Mid frontal gyrus 10 −24 32 18 2.91 2.53 0.006  
Inferior frontal gyrus 44/45 58 22 2.88 2.51 0.006  
Putamen  26 18 −6 2.87 2.50 0.006  
Superior frontal gyrus −4 24 44 2.81 2.46 0.007  
Precentral gyrus 44 42 2.76 2.42 0.008  
Mid temporal gyrus 21/22 52 −22 −2 2.70 2.39 0.009  
Supplementary motor area 22 50 2.32 2.10 0.018  
Inferior parietal gyrus 40 56 −40 52 4.38 3.42 0.000 253 
Supramarginal gyrus 40 48 −36 42 2.66 2.36 0.009  
Dentate nucleus  −26 −40 −36 3.73 3.06 0.001 509 
Cerebellum crus VI  −24 −64 −24 3.61 2.98 0.001  
Superior parietal lobule −22 −42 42 3.67 3.02 0.001 309 
Calcarine cortex 17 −16 −76 3.13 2.68 0.004 518 
Cuneus 18 −4 −94 10 3.12 2.67 0.004  
Calcarine cortex 17 −12 −82 12 2.77 2.43 0.008  
Cuneus 18 −92 16 2.64 2.34 0.010  
Calcarine cortex 17 −86 10 2.61 2.32 0.010  
Lingual gyrus 19 −16 −74 2.40 2.16 0.015  
Precentral gyrus −24 −16 62 2.34 2.12 0.017  
Negative 
Precentral gyrus −2 −20 72 3.70 3.04 0.001 329 
Precentral gyrus −22 62 2.34 2.12 0.017  
Superior parietal lobule −22 −58 58 2.76 2.42 0.008 616 
Posterior cingulate gyrus 23 −6 −46 24 2.60 2.31 0.010  
Posterior cingulate gyrus 23 −54 22 2.60 2.31 0.011  
Precuneus −8 −58 42 2.25 2.05 0.020  
Posterior central gyrus 31 12 −54 38 2.19 2.00 0.023  
Posterior cingulate gyrus 31 −2 −54 36 2.15 1.97 0.024  
Posterior cingulate gyrus 31 −58 30 2.02 1.86 0.031  
Inferior parietal lobule 40/22 −58 −42 24 2.74 2.41 0.008 133 
Inferior parietal lobule 40 64 −22 32 2.66 2.35 0.009 161 
Region L/R BA x y z T Z P k 
Positive 
Mid frontal gyrus 36 46 32 4.73 3.59 0.000 12334 
Superior parietal lobule 30 −40 40 4.66 3.56 0.000  
Superior frontal gyrus 22 62 3.97 3.20 0.001  
Thalamus  −12 −4 3.93 3.17 0.001  
Caudate nucleus  12 14 18 3.87 3.14 0.001  
Thalamus  14 −8 12 3.82 3.11 0.001  
Caudate nucleus  −14 12 10 3.77 3.08 0.001  
Lateral globus pallidus  24 −18 −2 3.76 3.08 0.001  
Mid frontal gyrus 10 38 50 24 3.76 3.08 0.001  
Caudate nucleus  −16 18 3.74 3.06 0.001  
Anterior cingulate cortex 32 16 20 36 3.74 3.06 0.001  
Anterior cingulate cortex 32 −16 10 46 3.65 3.01 0.001  
Anterior cingulate cortex 32 26 38 3.61 2.99 0.001  
Anterior insular cortex  −34 26 3.59 2.97 0.001  
Anterior insular cortex  32 18 3.53 2.93 0.002  
Caudate nucleus  14 18 3.49 2.91 0.002  
Mid frontal gyrus 42 32 3.35 2.83 0.002  
Mid frontal gyrus 48 22 26 3.35 2.82 0.002  
Orbitofrontal cortex 11 26 48 −12 3.05 2.63 0.004  
Anterior insular cortex  32 26 2.97 2.57 0.005  
Mid frontal gyrus 10 −24 32 18 2.91 2.53 0.006  
Inferior frontal gyrus 44/45 58 22 2.88 2.51 0.006  
Putamen  26 18 −6 2.87 2.50 0.006  
Superior frontal gyrus −4 24 44 2.81 2.46 0.007  
Precentral gyrus 44 42 2.76 2.42 0.008  
Mid temporal gyrus 21/22 52 −22 −2 2.70 2.39 0.009  
Supplementary motor area 22 50 2.32 2.10 0.018  
Inferior parietal gyrus 40 56 −40 52 4.38 3.42 0.000 253 
Supramarginal gyrus 40 48 −36 42 2.66 2.36 0.009  
Dentate nucleus  −26 −40 −36 3.73 3.06 0.001 509 
Cerebellum crus VI  −24 −64 −24 3.61 2.98 0.001  
Superior parietal lobule −22 −42 42 3.67 3.02 0.001 309 
Calcarine cortex 17 −16 −76 3.13 2.68 0.004 518 
Cuneus 18 −4 −94 10 3.12 2.67 0.004  
Calcarine cortex 17 −12 −82 12 2.77 2.43 0.008  
Cuneus 18 −92 16 2.64 2.34 0.010  
Calcarine cortex 17 −86 10 2.61 2.32 0.010  
Lingual gyrus 19 −16 −74 2.40 2.16 0.015  
Precentral gyrus −24 −16 62 2.34 2.12 0.017  
Negative 
Precentral gyrus −2 −20 72 3.70 3.04 0.001 329 
Precentral gyrus −22 62 2.34 2.12 0.017  
Superior parietal lobule −22 −58 58 2.76 2.42 0.008 616 
Posterior cingulate gyrus 23 −6 −46 24 2.60 2.31 0.010  
Posterior cingulate gyrus 23 −54 22 2.60 2.31 0.011  
Precuneus −8 −58 42 2.25 2.05 0.020  
Posterior central gyrus 31 12 −54 38 2.19 2.00 0.023  
Posterior cingulate gyrus 31 −2 −54 36 2.15 1.97 0.024  
Posterior cingulate gyrus 31 −58 30 2.02 1.86 0.031  
Inferior parietal lobule 40/22 −58 −42 24 2.74 2.41 0.008 133 
Inferior parietal lobule 40 64 −22 32 2.66 2.35 0.009 161 

Height threshold: T = 1.76, P < 0.05.

Extent threshold: k = 120.

L = left; R = right; BA = Brodmann area.

Table 3

Positive and negative correlations between SCR and brain activation in ASD

Region L/R BA x y z T Z P k 
Positive 
Lingual gyrus 19 14 −58 −6 3.67 3.08 0.001 4196 
Superior occipital gyrus 19 16 −82 20 3.48 2.96 0.002  
Lingual gyrus 18/19 16 −80 −4 3.44 2.93 0.002  
Calcarine cortex 17 −14 −82 3.28 2.82 0.002  
Inferior parietal lobule 40 −48 −50 48 3.27 2.82 0.002  
Cerebellum crus VI  24 −58 −26 3.21 2.78 0.003  
Superior parietal lobule −30 −64 58 3.17 2.75 0.003  
Fusiform gyrus 19 −22 −62 −10 2.95 2.60 0.005  
Inferior parietal lobule 40 −32 −52 42 2.89 2.55 0.005  
Fusiform gyrus 19 28 −68 −10 2.61 2.35 0.009  
Parahippocampal gyrus 28 −14 −44 2.52 2.28 0.011  
Lingual gyrus 37 28 −48 −6 2.42 2.20 0.014  
Cerebellum crus IV  −50 −16 2.4 2.19 0.014  
Calcarine cortex 17/18 −6 −66 18 2.36 2.15 0.016  
Parahippocampal gyrus 27 12 −38 −4 2.35 2.15 0.016  
Cerebellum crus VI  −2 −74 −10 2.3 2.11 0.018  
Mid temporal gyrus 21 58 −32 −10 3.58 3.03 0.001 222 
Precentral gyrus −36 −2 56 3.29 2.84 0.002 127 
Postcentral gyrus −64 −18 32 3.19 2.76 0.003 352 
Superior temporal gyrus 42 −52 −14 14 2.84 2.52 0.006  
Mid temporal gyrus 21 −52 −46 −2 2.92 2.58 0.005 483 
Mid temporal gyrus 21 −62 −26 2.39 2.18 0.015  
Inferior frontal gyrus 47 −40 40 −8 2.54 2.29 0.011 173 
Inferior parietal lobule 40 38 −58 48 2.3 2.11 0.018 121 
Negative 
Posterior mid cingulate cortex 31 −4 −18 42 3.33 2.86 0.002 850 
Anterior cingulate cortex 24 −4 38 2.6 2.34 0.010  
Supplementary motor area −4 −22 58 2.21 2.04 0.021  
Anterior cingulate cortex 32 28 30 1.95 1.83 0.034  
Superior frontal gyrus (medial) 10 60 3.19 2.76 0.003 2221 
Superior frontal gyrus (medial) 12 60 3.17 2.75 0.003  
Superior frontal gyrus (medial) 62 30 3.03 2.65 0.004  
Superior frontal gyrus 12 28 60 2.88 2.54 0.005  
Anterior cingulate cortex (pregenual) 24 −14 46 −4 2.57 2.32 0.010  
Superior frontal gyrus (medial) 48 44 2.51 2.27 0.012  
Superior temporal gyrus 42 −48 −28 18 3.15 2.74 0.003 279 
Inferior parietal lobule 40 −58 −40 34 2.82 2.50 0.006  
Putamen  16 16 −6 2.63 0.004 294 
Putamen  24 −2 2.08 1.93 0.027  
Red nucleus   −22 2.99 2.62 0.004 208 
Inferior parietal gyrus 40 56 −32 32 2.78 2.48 0.007 203 
Thalamus  −14 14 2.72 2.43 0.008 311 
Caudate nucleus  −10 −4 16 2.68 2.40 0.008  
Inferior temporal gyrus 37 34 −74 2.55 2.30 0.011 136 
Parahippocampal gyrus 36 −30 −30 2.47 2.24 0.013 124 
Putamen  −20 −8 2.08 1.94 0.026  
Cerebellum crus VII  −22 −70 −40 2.39 2.18 0.015 178 
Anterior cingulate cortex (subgenual) 32 10 30 −8 2.37 2.16 0.015 213 
Region L/R BA x y z T Z P k 
Positive 
Lingual gyrus 19 14 −58 −6 3.67 3.08 0.001 4196 
Superior occipital gyrus 19 16 −82 20 3.48 2.96 0.002  
Lingual gyrus 18/19 16 −80 −4 3.44 2.93 0.002  
Calcarine cortex 17 −14 −82 3.28 2.82 0.002  
Inferior parietal lobule 40 −48 −50 48 3.27 2.82 0.002  
Cerebellum crus VI  24 −58 −26 3.21 2.78 0.003  
Superior parietal lobule −30 −64 58 3.17 2.75 0.003  
Fusiform gyrus 19 −22 −62 −10 2.95 2.60 0.005  
Inferior parietal lobule 40 −32 −52 42 2.89 2.55 0.005  
Fusiform gyrus 19 28 −68 −10 2.61 2.35 0.009  
Parahippocampal gyrus 28 −14 −44 2.52 2.28 0.011  
Lingual gyrus 37 28 −48 −6 2.42 2.20 0.014  
Cerebellum crus IV  −50 −16 2.4 2.19 0.014  
Calcarine cortex 17/18 −6 −66 18 2.36 2.15 0.016  
Parahippocampal gyrus 27 12 −38 −4 2.35 2.15 0.016  
Cerebellum crus VI  −2 −74 −10 2.3 2.11 0.018  
Mid temporal gyrus 21 58 −32 −10 3.58 3.03 0.001 222 
Precentral gyrus −36 −2 56 3.29 2.84 0.002 127 
Postcentral gyrus −64 −18 32 3.19 2.76 0.003 352 
Superior temporal gyrus 42 −52 −14 14 2.84 2.52 0.006  
Mid temporal gyrus 21 −52 −46 −2 2.92 2.58 0.005 483 
Mid temporal gyrus 21 −62 −26 2.39 2.18 0.015  
Inferior frontal gyrus 47 −40 40 −8 2.54 2.29 0.011 173 
Inferior parietal lobule 40 38 −58 48 2.3 2.11 0.018 121 
Negative 
Posterior mid cingulate cortex 31 −4 −18 42 3.33 2.86 0.002 850 
Anterior cingulate cortex 24 −4 38 2.6 2.34 0.010  
Supplementary motor area −4 −22 58 2.21 2.04 0.021  
Anterior cingulate cortex 32 28 30 1.95 1.83 0.034  
Superior frontal gyrus (medial) 10 60 3.19 2.76 0.003 2221 
Superior frontal gyrus (medial) 12 60 3.17 2.75 0.003  
Superior frontal gyrus (medial) 62 30 3.03 2.65 0.004  
Superior frontal gyrus 12 28 60 2.88 2.54 0.005  
Anterior cingulate cortex (pregenual) 24 −14 46 −4 2.57 2.32 0.010  
Superior frontal gyrus (medial) 48 44 2.51 2.27 0.012  
Superior temporal gyrus 42 −48 −28 18 3.15 2.74 0.003 279 
Inferior parietal lobule 40 −58 −40 34 2.82 2.50 0.006  
Putamen  16 16 −6 2.63 0.004 294 
Putamen  24 −2 2.08 1.93 0.027  
Red nucleus   −22 2.99 2.62 0.004 208 
Inferior parietal gyrus 40 56 −32 32 2.78 2.48 0.007 203 
Thalamus  −14 14 2.72 2.43 0.008 311 
Caudate nucleus  −10 −4 16 2.68 2.40 0.008  
Inferior temporal gyrus 37 34 −74 2.55 2.30 0.011 136 
Parahippocampal gyrus 36 −30 −30 2.47 2.24 0.013 124 
Putamen  −20 −8 2.08 1.94 0.026  
Cerebellum crus VII  −22 −70 −40 2.39 2.18 0.015 178 
Anterior cingulate cortex (subgenual) 32 10 30 −8 2.37 2.16 0.015 213 

Height threshold: T = 1.74, P < 0.05.

Extent threshold: k = 120.

L = left; R = right; BA = Brodmann area.

Table 4

Two-sample t-test between neurotypical control subjects and ASD for SCR correlations with brain activation

Region L/R BA x y z T Z P k 
Controls > ASD 
Superior temporal gyrus 42 −48 −28 18 3.89 3.48 0.000 202 
Supramarginal gyrus 40 −58 −42 26 2.21 2.12 0.017  
Superior frontal gyrus (medial) 10 60 10 3.86 3.45 0.000 12511 
Supplementary motor area 14 58 3.79 3.40 0.000  
Thalamus  10 −10 12 3.63 3.28 0.001  
Red nucleus  −2 −22 3.60 3.25 0.001  
Anterior cingulate cortex 24 −4 −18 42 3.46 3.15 0.001  
Anterior cingulate gyrus (pregenual) 32 −14 46 −4 3.42 3.11 0.001  
Caudate nucleus  −10 −4 16 3.08 2.85 0.002  
Anterior cingulate gyrus (subgenual) 32 10 30 −8 3.03 2.81 0.002  
Superior frontal gyrus (medial) 46 42 3.02 2.80 0.003  
Putamen  22 2.91 2.71 0.003  
Anterior cingulate cortex  24 32 2.85 2.66 0.004  
Anterior cingulate gyrus (pregenual) 24 38 10 2.82 2.63 0.004  
Caudate nucleus  −4 2.80 2.62 0.004  
Mid frontal gyrus 10 28 60 20 2.79 2.61 0.005  
Putamen  28 −8 2.71 2.55 0.005  
Anterior cingulate cortex 24 26 30 2.64 2.48 0.007  
Anterior insular cortex  28 18 −10 2.56 2.42 0.008  
Posterior cingulate cortex 31 −8 −38 46 2.55 2.41 0.008  
Inferior frontal gyrus 45 52 24 2.53 2.39 0.008  
Putamen  −20 −8 2.47 2.34 0.010  
Supplementary motor area 14 68 2.46 2.33 0.010  
Posterior cingulate cortex 31 16 −32 42 2.32 2.21 0.013  
Superior frontal gyrus 26 32 50 2.30 2.19 0.014  
Supplementary motor area −10 72 2.29 2.18 0.015  
Mid frontal gyrus 46 42 28 42 2.27 2.16 0.015  
Mid frontal gyrus 36 38 3.00 2.79 0.003 679 
Precentral gyrus 44 −6 50 2.57 2.42 0.008  
Cerebellum crus VIII  −20 −68 −38 2.98 2.77 0.003 410 
Mid frontal gyrus 46 −24 46 26 2.90 2.70 0.003 351 
Inferior occipital gyrus 19 42 −78 2.78 2.61 0.005 193 
Supramarginal gyrus 40 56 −32 30 2.71 2.54 0.006 570 
Anterior insular cortex  −38 24 2.45 2.33 0.010 172 
Cerebellum crus IX  −4 −46 −46 2.34 2.23 0.013 202 
Pons  −10 −20 −44 2.28 2.17 0.015  
ASD > Controls 
Cuneus 19 16 −82 20 3.96 3.52 0.000 5113 
Lingual gyrus 18 14 −70 −2 3.56 3.23 0.001  
Superior parietal lobule −30 −64 58 3.41 3.11 0.001  
Inferior parietal lobule 40 −46 −54 48 3.23 2.97 0.001  
Cerebellum crus VI  24 −56 −24 3.19 2.93 0.002  
Mid occipital gyrus 19 −44 −76 14 2.91 2.71 0.003  
Cerebellum crus IV  10 −50 −10 2.82 2.63 0.004  
Lingual gyrus 18 −8 −84 −6 2.80 2.62 0.004  
Calcarine sulcus 17 −4 −68 20 2.54 2.40 0.008  
Fusiform gyrus 19 −24 −62 −14 2.22 2.12 0.017  
Lingual gyrus 37 26 −44 −8 2.19 2.09 0.018  
Calcarine cortex 17 18 −64 14 2.12 2.03 0.021  
Cerebellum crus VI  −12 −72 −20 2.07 1.99 0.023  
Cerebellum crus IV  −8 −42 −4 2.03 1.96 0.025  
Postcentral gyrus −64 −18 32 3.94 3.51 0.000 456 
Precentral gyrus −34 56 2.89 2.69 0.004 233 
Mid temporal gyrus 21 −60 −34 2.88 2.69 0.004 487 
Inferior frontal gyrus 45 −42 42 2.87 2.67 0.004 234 
Mid temporal gyrus 21 64 −40 −4 2.80 2.62 0.004 233 
Inferior parietal lobule 39 42 −64 44 2.35 2.24 0.013 139 
Region L/R BA x y z T Z P k 
Controls > ASD 
Superior temporal gyrus 42 −48 −28 18 3.89 3.48 0.000 202 
Supramarginal gyrus 40 −58 −42 26 2.21 2.12 0.017  
Superior frontal gyrus (medial) 10 60 10 3.86 3.45 0.000 12511 
Supplementary motor area 14 58 3.79 3.40 0.000  
Thalamus  10 −10 12 3.63 3.28 0.001  
Red nucleus  −2 −22 3.60 3.25 0.001  
Anterior cingulate cortex 24 −4 −18 42 3.46 3.15 0.001  
Anterior cingulate gyrus (pregenual) 32 −14 46 −4 3.42 3.11 0.001  
Caudate nucleus  −10 −4 16 3.08 2.85 0.002  
Anterior cingulate gyrus (subgenual) 32 10 30 −8 3.03 2.81 0.002  
Superior frontal gyrus (medial) 46 42 3.02 2.80 0.003  
Putamen  22 2.91 2.71 0.003  
Anterior cingulate cortex  24 32 2.85 2.66 0.004  
Anterior cingulate gyrus (pregenual) 24 38 10 2.82 2.63 0.004  
Caudate nucleus  −4 2.80 2.62 0.004  
Mid frontal gyrus 10 28 60 20 2.79 2.61 0.005  
Putamen  28 −8 2.71 2.55 0.005  
Anterior cingulate cortex 24 26 30 2.64 2.48 0.007  
Anterior insular cortex  28 18 −10 2.56 2.42 0.008  
Posterior cingulate cortex 31 −8 −38 46 2.55 2.41 0.008  
Inferior frontal gyrus 45 52 24 2.53 2.39 0.008  
Putamen  −20 −8 2.47 2.34 0.010  
Supplementary motor area 14 68 2.46 2.33 0.010  
Posterior cingulate cortex 31 16 −32 42 2.32 2.21 0.013  
Superior frontal gyrus 26 32 50 2.30 2.19 0.014  
Supplementary motor area −10 72 2.29 2.18 0.015  
Mid frontal gyrus 46 42 28 42 2.27 2.16 0.015  
Mid frontal gyrus 36 38 3.00 2.79 0.003 679 
Precentral gyrus 44 −6 50 2.57 2.42 0.008  
Cerebellum crus VIII  −20 −68 −38 2.98 2.77 0.003 410 
Mid frontal gyrus 46 −24 46 26 2.90 2.70 0.003 351 
Inferior occipital gyrus 19 42 −78 2.78 2.61 0.005 193 
Supramarginal gyrus 40 56 −32 30 2.71 2.54 0.006 570 
Anterior insular cortex  −38 24 2.45 2.33 0.010 172 
Cerebellum crus IX  −4 −46 −46 2.34 2.23 0.013 202 
Pons  −10 −20 −44 2.28 2.17 0.015  
ASD > Controls 
Cuneus 19 16 −82 20 3.96 3.52 0.000 5113 
Lingual gyrus 18 14 −70 −2 3.56 3.23 0.001  
Superior parietal lobule −30 −64 58 3.41 3.11 0.001  
Inferior parietal lobule 40 −46 −54 48 3.23 2.97 0.001  
Cerebellum crus VI  24 −56 −24 3.19 2.93 0.002  
Mid occipital gyrus 19 −44 −76 14 2.91 2.71 0.003  
Cerebellum crus IV  10 −50 −10 2.82 2.63 0.004  
Lingual gyrus 18 −8 −84 −6 2.80 2.62 0.004  
Calcarine sulcus 17 −4 −68 20 2.54 2.40 0.008  
Fusiform gyrus 19 −24 −62 −14 2.22 2.12 0.017  
Lingual gyrus 37 26 −44 −8 2.19 2.09 0.018  
Calcarine cortex 17 18 −64 14 2.12 2.03 0.021  
Cerebellum crus VI  −12 −72 −20 2.07 1.99 0.023  
Cerebellum crus IV  −8 −42 −4 2.03 1.96 0.025  
Postcentral gyrus −64 −18 32 3.94 3.51 0.000 456 
Precentral gyrus −34 56 2.89 2.69 0.004 233 
Mid temporal gyrus 21 −60 −34 2.88 2.69 0.004 487 
Inferior frontal gyrus 45 −42 42 2.87 2.67 0.004 234 
Mid temporal gyrus 21 64 −40 −4 2.80 2.62 0.004 233 
Inferior parietal lobule 39 42 −64 44 2.35 2.24 0.013 139 

Height threshold: T = 1.7, P < 0.05.

Extent threshold: k = 120.

L = left; R = right; BA = Brodmann area.

Skin conductance response and resting state functional connectivity

Posterior cingulate cortex seed-based analysis results

Using the posterior cingulate cortex as a seed region, the neurotypical control group had stronger connectivity between the seed and areas of the DMN, compared to the ASD group, mainly with the ventromedial prefrontal cortex and the left inferior parietal lobule (Table 5 and Fig. 3A). Stronger connectivity was found in ASD between the posterior cingulate cortex and the superior and inferior occipital gyri, temporal pole, lingual gyrus, fusiform gyrus, amygdala, hippocampus, posterior insular cortex, supplementary motor area, and precentral and postcentral gyri (Table 5 and Fig. 3B). The general linear model revealed a significant interaction between group (ASD versus neurotypical control subjects) and SCR (before versus after regressing out SCR), wherein SCR had greater positive effects on the connectivity between posterior cingulate cortex and medial prefrontal and orbitofrontal cortices, precentral and postcentral gyri, supramarginal gyrus, and superior and inferior parietal gyri in neurotypical control subjects than in ASD (Table 6 and Fig. 3C). In contrast, SCR had a greater positive impact on the connectivity between the posterior cingulate cortex and the posterior insular cortex, lingual gyrus, amygdala, hippocampus, parahippocampal and fusiform gyri, superior and middle occipital gyri, and superior and inferior temporal gyri in ASD than in neurotypical control subjects (Table 6 and Fig. 3D). For paired t-test results within each group (before > after regressing out SCR signal), see Supplementary Table 1 and Supplementary Fig. 1. For ventromedial prefrontal cortex and anterior insular cortex functional connectivity results, see Supplementary Tables 2 and 3, and Supplementary Figs 2 and 3.

Figure 3

Functional connectivity of the posterior cingulate cortex, and an interaction between group (neurotypical controls versus ASD) and SCR (before versus after regressing out SCR signal) on posterior cingulate cortex connectivity. (A) Stronger connectivity in neurotypical controls compared with ASD (neurotypical controls > ASD). (B) Stronger connectivity in ASD, compared to neurotypical controls (ASD > neurotypical controls). (C) Stronger effects of SCR on posterior cingulate cortex connectivity in neurotypical control subjects compared to ASD [neurotypical controls (with-without SCR) > ASD (with-without SCR)]. (D) Stronger effects of SCR on posterior cingulate cortex connectivity in ASD compared with neurotypical control subjects [ASD (with-without SCR) > neurotypical controls (with-without SCR)].

Figure 3

Functional connectivity of the posterior cingulate cortex, and an interaction between group (neurotypical controls versus ASD) and SCR (before versus after regressing out SCR signal) on posterior cingulate cortex connectivity. (A) Stronger connectivity in neurotypical controls compared with ASD (neurotypical controls > ASD). (B) Stronger connectivity in ASD, compared to neurotypical controls (ASD > neurotypical controls). (C) Stronger effects of SCR on posterior cingulate cortex connectivity in neurotypical control subjects compared to ASD [neurotypical controls (with-without SCR) > ASD (with-without SCR)]. (D) Stronger effects of SCR on posterior cingulate cortex connectivity in ASD compared with neurotypical control subjects [ASD (with-without SCR) > neurotypical controls (with-without SCR)].

Table 5

Two sample t-test between neurotypical control subjects and ASD for the functional connectivity of the posterior cingulate cortex

Region L/R BA x y z T Z P k 
Controls > ASD 
Mid frontal gyrus 10 −24 44 22 5.35 4.45 0.000 7314 
Superior frontal gyrus 10 16 52 20 4.34 3.79 0.000  
Anterior cingulate cortex 24 20 10 30 3.99 3.54 0.000  
Superior frontal gyrus −14 16 60 3.29 3.01 0.001  
Anterior cingulate cortex (subgenual) 32 28 −4 3.27 3.00 0.001  
Superior frontal gyrus −6 28 58 3.21 2.96 0.002  
Anterior cingulate cortex 32 14 28 30 3.18 2.92 0.002  
Mid frontal gyrus −38 58 3.11 2.87 0.002  
Superior frontal gyrus 10 32 58 3.06 2.83 0.002  
Superior frontal gyrus (medial)  32 44 28 2.84 2.65 0.004  
Caudate nucleus  −6 12 2.58 2.43 0.007  
Caudate nucleus  14 2.49 2.36 0.009  
Orbitofrontal cortex 11 −6 38 −18 2.26 2.15 0.016  
Anterior cingulate cortex (subgenual) 32 −6 30 −4 2.17 2.07 0.019  
Caudate nucleus  −12 16 2.12 2.03 0.021  
Cerebral peduncle  14 −20 −32 3.56 3.23 0.001  
Cerebral peduncle  −18 −20 −30 3.34 3.06 0.001  
Posterior cingulate cortex 31 −10 −46 36 3.97 3.53 0.000 1852 
Posterior cingulate cortex 31 −44 28 3.37 3.08 0.001  
Precuneus −72 30 2.89 2.69 0.004  
Precuneus −8 −62 36 2.49 2.36 0.009  
Temporal parietal junction 22 −52 −56 24 3.48 3.16 0.001 1193 
Cerebellum crus II  32 −78 −46 3.42 3.12 0.001 682 
Mid temporal gyrus 21 −64 −34 −10 3.37 3.08 0.001 783 
Inferior temporal gyrus 21 −58 −22 −20 3.01 2.79 0.003  
Superior frontal gyrus 22 74 3.36 3.07 0.001 128 
Mid temporal gyrus 21 −52 −34 3.02 2.80 0.003 147 
Superior parietal lobule 14 −58 74 2.63 2.47 0.007 131 
Inferior frontal gyrus 47 −50 34 −2 2.42 2.29 0.011 196 
ASD > Controls 
Cuneus 18 −18 −92 14 4.55 3.94 0.000 16754 
Superior occipital gyrus 19 −24 −80 14 4.47 3.88 0.000  
Inferior occipital gyrus 18 28 −86 −8 4.31 3.77 0.000  
Superior parietal lobule 26 −70 38 4.07 3.60 0.000  
Superior occipital gyrus 19 24 −70 24 3.94 3.51 0.000  
Fusiform gyrus 19 42 −70 −14 3.90 3.48 0.000  
Cerebellum crus IV  14 −40 −18 3.79 3.40 0.000  
Inferior longitudinal fasciculus 41 40 −30 3.71 3.34 0.000  
Medial occipital gyrus 18 28 −80 10 3.70 3.33 0.000  
Fusiform gyrus 19 32 −76 3.69 3.32 0.000  
Inferior temporal gyrus 37 52 −58 −10 3.64 3.29 0.001  
Cerebellum crus IV  −50 −2 3.22 2.96 0.002  
Superior occipital gyrus 19 16 −82 30 3.21 2.95 0.002  
Inferior occipital gyrus 19 40 −78 3.17 2.92 0.002  
Parahippocampal gyrus 36 28 −28 3.08 2.85 0.002  
Lingual gyrus 19 16 −76 −10 3.05 2.82 0.002  
Pons  10 −24 −26 2.91 2.71 0.003  
Hippocampus 34 −30 −20 −8 2.88 2.68 0.004  
Inferior occipital gyrus 19 −40 −80 2.87 2.67 0.004  
Pons  −2 −30 −14 2.86 2.67 0.004  
Fusiform gyrus 19 −28 −66 −14 2.78 2.60 0.005  
Superior parietal lobule 19 −22 −64 30 2.78 2.60 0.005  
Lingual gyrus 18 −8 −64 −4 2.77 2.59 0.005  
Amygdala  22 −18 2.66 2.50 0.006  
Temporal pole 38 32 −40 2.44 2.31 0.010  
Cerebellum crus VII 4/43 −8 −72 −36 3.53 3.21 0.001 450 
Rolandic operculum  −40 −8 18 3.37 3.08 0.001 757 
Superior temporal gyrus 22 −64 −6 2.78 2.60 0.005  
Posterior insular cortex  −44 2.56 2.42 0.008  
Inferior frontal gyrus 45 48 20 3.15 2.90 0.002 945 
Rolandic operculum 4/43 42 14 2.71 2.54 0.006  
Posterior insular cortex  44 2.35 2.24 0.013  
Temporal pole 38 −28 18 −34 3.14 2.90 0.002 227 
Precentral gyrus 44 −10 34 2.91 2.71 0.003 412 
Precentral gyrus 50 −4 46 2.72 2.55 0.005  
Postcentral gyrus −42 −22 48 2.90 2.70 0.003 263 
Supplementary motor area −6 −4 60 2.88 2.68 0.004 424 
Supplementary motor area 58 2.67 2.51 0.006  
Inferior parietal lobule 40 42 −38 60 2.69 2.53 0.006 309 
Region L/R BA x y z T Z P k 
Controls > ASD 
Mid frontal gyrus 10 −24 44 22 5.35 4.45 0.000 7314 
Superior frontal gyrus 10 16 52 20 4.34 3.79 0.000  
Anterior cingulate cortex 24 20 10 30 3.99 3.54 0.000  
Superior frontal gyrus −14 16 60 3.29 3.01 0.001  
Anterior cingulate cortex (subgenual) 32 28 −4 3.27 3.00 0.001  
Superior frontal gyrus −6 28 58 3.21 2.96 0.002  
Anterior cingulate cortex 32 14 28 30 3.18 2.92 0.002  
Mid frontal gyrus −38 58 3.11 2.87 0.002  
Superior frontal gyrus 10 32 58 3.06 2.83 0.002  
Superior frontal gyrus (medial)  32 44 28 2.84 2.65 0.004  
Caudate nucleus  −6 12 2.58 2.43 0.007  
Caudate nucleus  14 2.49 2.36 0.009  
Orbitofrontal cortex 11 −6 38 −18 2.26 2.15 0.016  
Anterior cingulate cortex (subgenual) 32 −6 30 −4 2.17 2.07 0.019  
Caudate nucleus  −12 16 2.12 2.03 0.021  
Cerebral peduncle  14 −20 −32 3.56 3.23 0.001  
Cerebral peduncle  −18 −20 −30 3.34 3.06 0.001  
Posterior cingulate cortex 31 −10 −46 36 3.97 3.53 0.000 1852 
Posterior cingulate cortex 31 −44 28 3.37 3.08 0.001  
Precuneus −72 30 2.89 2.69 0.004  
Precuneus −8 −62 36 2.49 2.36 0.009  
Temporal parietal junction 22 −52 −56 24 3.48 3.16 0.001 1193 
Cerebellum crus II  32 −78 −46 3.42 3.12 0.001 682 
Mid temporal gyrus 21 −64 −34 −10 3.37 3.08 0.001 783 
Inferior temporal gyrus 21 −58 −22 −20 3.01 2.79 0.003  
Superior frontal gyrus 22 74 3.36 3.07 0.001 128 
Mid temporal gyrus 21 −52 −34 3.02 2.80 0.003 147 
Superior parietal lobule 14 −58 74 2.63 2.47 0.007 131 
Inferior frontal gyrus 47 −50 34 −2 2.42 2.29 0.011 196 
ASD > Controls 
Cuneus 18 −18 −92 14 4.55 3.94 0.000 16754 
Superior occipital gyrus 19 −24 −80 14 4.47 3.88 0.000  
Inferior occipital gyrus 18 28 −86 −8 4.31 3.77 0.000  
Superior parietal lobule 26 −70 38 4.07 3.60 0.000  
Superior occipital gyrus 19 24 −70 24 3.94 3.51 0.000  
Fusiform gyrus 19 42 −70 −14 3.90 3.48 0.000  
Cerebellum crus IV  14 −40 −18 3.79 3.40 0.000  
Inferior longitudinal fasciculus 41 40 −30 3.71 3.34 0.000  
Medial occipital gyrus 18 28 −80 10 3.70 3.33 0.000  
Fusiform gyrus 19 32 −76 3.69 3.32 0.000  
Inferior temporal gyrus 37 52 −58 −10 3.64 3.29 0.001  
Cerebellum crus IV  −50 −2 3.22 2.96 0.002  
Superior occipital gyrus 19 16 −82 30 3.21 2.95 0.002  
Inferior occipital gyrus 19 40 −78 3.17 2.92 0.002  
Parahippocampal gyrus 36 28 −28 3.08 2.85 0.002  
Lingual gyrus 19 16 −76 −10 3.05 2.82 0.002  
Pons  10 −24 −26 2.91 2.71 0.003  
Hippocampus 34 −30 −20 −8 2.88 2.68 0.004  
Inferior occipital gyrus 19 −40 −80 2.87 2.67 0.004  
Pons  −2 −30 −14 2.86 2.67 0.004  
Fusiform gyrus 19 −28 −66 −14 2.78 2.60 0.005  
Superior parietal lobule 19 −22 −64 30 2.78 2.60 0.005  
Lingual gyrus 18 −8 −64 −4 2.77 2.59 0.005  
Amygdala  22 −18 2.66 2.50 0.006  
Temporal pole 38 32 −40 2.44 2.31 0.010  
Cerebellum crus VII 4/43 −8 −72 −36 3.53 3.21 0.001 450 
Rolandic operculum  −40 −8 18 3.37 3.08 0.001 757 
Superior temporal gyrus 22 −64 −6 2.78 2.60 0.005  
Posterior insular cortex  −44 2.56 2.42 0.008  
Inferior frontal gyrus 45 48 20 3.15 2.90 0.002 945 
Rolandic operculum 4/43 42 14 2.71 2.54 0.006  
Posterior insular cortex  44 2.35 2.24 0.013  
Temporal pole 38 −28 18 −34 3.14 2.90 0.002 227 
Precentral gyrus 44 −10 34 2.91 2.71 0.003 412 
Precentral gyrus 50 −4 46 2.72 2.55 0.005  
Postcentral gyrus −42 −22 48 2.90 2.70 0.003 263 
Supplementary motor area −6 −4 60 2.88 2.68 0.004 424 
Supplementary motor area 58 2.67 2.51 0.006  
Inferior parietal lobule 40 42 −38 60 2.69 2.53 0.006 309 

Height threshold: T = 1.7, P < 0.05.

Extent threshold: k = 120.

L = left; R = right; BA = Brodmann area.

Table 6

Interaction of group (neurotypical controls versus ASD) and SCR (with SCR versus without SCR) for the functional connectivity of the posterior cingulate cortex

Region L/R BA x y z T Z P k 
Controls (with-without) > ASD (with-without) 
Posterior cingulate cortex 31 −48 36 4.85 4.13 0.000 3566 
Posterior cingulate cortex 31 −12 −40 40 4.63 3.99 0.000  
Superior parietal lobule −34 −36 66 4.17 3.67 0.000  
Supramarginal gyrus 40 −62 −42 30 4.04 3.58 0.000  
Inferior parietal lobule 40 −42 −36 56 3.86 3.45 0.000  
Posterior cingulate cortex 23 −28 40 3.64 3.29 0.001  
Precuneus −46 46 3.58 3.24 0.001  
Postcentral gyrus −20 −38 54 3.22 2.96 0.002  
Postcentral gyrus −42 −20 56 3.16 2.91 0.002  
Precentral gyrus −36 −22 66 3.05 2.82 0.002  
Postcentral gyrus 62 −8 38 2.69 2.52 0.006  
Precentral gyrus −54 44 4.07 3.60 0.000 266 
Mid frontal gyrus 10 −8 54 20 3.98 3.54 0.000 443 
Mid frontal gyrus  10 58 22 3.66 3.30 0.000  
Mid frontal gyrus 46 24 2.70 2.53 0.006  
Superior frontal gyrus −14 −6 72 3.92 3.49 0.000 256 
Gyrus rectus 11 −10 20 −16 3.11 2.87 0.002  
Cerebellum crus VIIIB  16 −58 −58 3.67 3.31 0.000 128 
Posterior cingulate cortex 26 −6 −42 3.60 3.25 0.001 124 
Cerebellum crus VI  14 −70 −24 3.57 3.23 0.001 264 
Cerebellum crus I  36 −68 −30 2.91 2.71 0.003  
Angular gyrus 39 −46 −70 32 3.57 3.23 0.001 215 
Dentate nucleus  12 −48 −26 3.48 3.16 0.001 178 
Cerebellum crus I  −28 −88 −30 3.32 3.04 0.001 146 
Superior parietal lobule 34 −48 58 3.28 3.01 0.001 895 
Postcentral gyrus 16 −40 58 3.06 2.83 0.002  
Superior parietal lobule 20 −62 60 2.69 2.52 0.006  
Mid temporal gyrus 21 60 −52 3.05 2.82 0.002  
Inferior temporal gyrus 37 52 −62 2.58 2.43 0.008  
Precentral gyrus 26 −14 66 2.92 2.72 0.003 203 
Mid frontal gyrus 30 −8 60 2.89 2.69 0.004  
Inferior frontal gyrus 47 −50 32 −14 2.88 2.69 0.004 126 
Inferior frontal gyrus 47 −34 24 −16 2.80 2.62 0.004  
Supplementary motor area −8 58 2.86 2.67 0.004 194 
ASD (with-without) > Controls (with-without) 
Posterior insular cortex  42 −10 −12 4.98 4.21 0.000 1464 
Putamen  26 −10 4.73 4.06 0.000  
Superior temporal gyrus 42 46 −28 3.30 3.02 0.001  
Posterior insular cortex  46 3.24 2.97 0.001  
Hippocampus 34 32 −10 −20 2.94 2.73 0.003  
Parahippocampal gyrus 28 30 −10 −32 2.62 2.47 0.007  
Amygdala  32 −2 −28 2.45 2.32 0.010  
Posterior insular cortex  −44 4.72 4.05 0.000 5420 
Transverse temporal gyrus 41 −40 −26 4.66 4.00 0.000  
Fusiform gyrus 37 22 −52 −10 4.39 3.82 0.000  
Parahippocampal gyrus 28 −28 −22 −30 4.12 3.64 0.000  
Mid temporal gyrus 21 −50 −14 −4 3.84 3.44 0.000  
Lingual gyrus 19 14 −56 −4 3.81 3.41 0.000  
Lingual gyrus 19 −12 −70 3.80 3.40 0.000  
Hippocampus 34 −16 −32 −2 3.56 3.23 0.001  
Fusiform gyrus 20 −36 −32 −10 3.36 3.07 0.001  
Superior temporal gyrus 42 −56 −28 3.23 2.97 0.001  
Precuneus 31 −66 26 2.99 2.78 0.003  
Superior parietal lobule 19 −22 −80 46 2.94 2.74 0.003  
Calcarine cortex 17/18 −24 −64 2.91 2.71 0.003  
Precuneus −2 −78 42 2.55 2.41 0.008  
Anterior cingulate cortex 24 −12 38 4.36 3.80 0.000 512 
Anterior cingulate cortex 24 42 3.10 2.86 0.002  
Superior occipital gyrus 19 22 −92 24 4.22 3.71 0.000 846 
Superior occipital gyrus 18 −16 −96 20 3.96 3.52 0.000  
Mid occipital gyrus 18 30 −86 18 3.68 3.32 0.000  
Mid frontal gyrus 11 −34 62 −12 4.08 3.61 0.000 295 
Fusiform gyrus 37 34 −38 −20 3.90 3.48 0.000 826 
Inferior temporal gyrus 37 54 −62 −10 3.57 3.23 0.001  
Cerebellum crus I  50 −68 −28 3.33 3.05 0.001  
Superior temporal gyrus 22 66 −22 3.72 3.34 0.000 344 
Mid frontal gyrus 10 36 52 −6 3.19 2.94 0.002 180 
Paracentral lobule −8 −28 68 2.59 2.44 0.007 170 
Caudate nucleus  14 20 2.50 2.36 0.009 197 
Region L/R BA x y z T Z P k 
Controls (with-without) > ASD (with-without) 
Posterior cingulate cortex 31 −48 36 4.85 4.13 0.000 3566 
Posterior cingulate cortex 31 −12 −40 40 4.63 3.99 0.000  
Superior parietal lobule −34 −36 66 4.17 3.67 0.000  
Supramarginal gyrus 40 −62 −42 30 4.04 3.58 0.000  
Inferior parietal lobule 40 −42 −36 56 3.86 3.45 0.000  
Posterior cingulate cortex 23 −28 40 3.64 3.29 0.001  
Precuneus −46 46 3.58 3.24 0.001  
Postcentral gyrus −20 −38 54 3.22 2.96 0.002  
Postcentral gyrus −42 −20 56 3.16 2.91 0.002  
Precentral gyrus −36 −22 66 3.05 2.82 0.002  
Postcentral gyrus 62 −8 38 2.69 2.52 0.006  
Precentral gyrus −54 44 4.07 3.60 0.000 266 
Mid frontal gyrus 10 −8 54 20 3.98 3.54 0.000 443 
Mid frontal gyrus  10 58 22 3.66 3.30 0.000  
Mid frontal gyrus 46 24 2.70 2.53 0.006  
Superior frontal gyrus −14 −6 72 3.92 3.49 0.000 256 
Gyrus rectus 11 −10 20 −16 3.11 2.87 0.002  
Cerebellum crus VIIIB  16 −58 −58 3.67 3.31 0.000 128 
Posterior cingulate cortex 26 −6 −42 3.60 3.25 0.001 124 
Cerebellum crus VI  14 −70 −24 3.57 3.23 0.001 264 
Cerebellum crus I  36 −68 −30 2.91 2.71 0.003  
Angular gyrus 39 −46 −70 32 3.57 3.23 0.001 215 
Dentate nucleus  12 −48 −26 3.48 3.16 0.001 178 
Cerebellum crus I  −28 −88 −30 3.32 3.04 0.001 146 
Superior parietal lobule 34 −48 58 3.28 3.01 0.001 895 
Postcentral gyrus 16 −40 58 3.06 2.83 0.002  
Superior parietal lobule 20 −62 60 2.69 2.52 0.006  
Mid temporal gyrus 21 60 −52 3.05 2.82 0.002  
Inferior temporal gyrus 37 52 −62 2.58 2.43 0.008  
Precentral gyrus 26 −14 66 2.92 2.72 0.003 203 
Mid frontal gyrus 30 −8 60 2.89 2.69 0.004  
Inferior frontal gyrus 47 −50 32 −14 2.88 2.69 0.004 126 
Inferior frontal gyrus 47 −34 24 −16 2.80 2.62 0.004  
Supplementary motor area −8 58 2.86 2.67 0.004 194 
ASD (with-without) > Controls (with-without) 
Posterior insular cortex  42 −10 −12 4.98 4.21 0.000 1464 
Putamen  26 −10 4.73 4.06 0.000  
Superior temporal gyrus 42 46 −28 3.30 3.02 0.001  
Posterior insular cortex  46 3.24 2.97 0.001  
Hippocampus 34 32 −10 −20 2.94 2.73 0.003  
Parahippocampal gyrus 28 30 −10 −32 2.62 2.47 0.007  
Amygdala  32 −2 −28 2.45 2.32 0.010  
Posterior insular cortex  −44 4.72 4.05 0.000 5420 
Transverse temporal gyrus 41 −40 −26 4.66 4.00 0.000  
Fusiform gyrus 37 22 −52 −10 4.39 3.82 0.000  
Parahippocampal gyrus 28 −28 −22 −30 4.12 3.64 0.000  
Mid temporal gyrus 21 −50 −14 −4 3.84 3.44 0.000  
Lingual gyrus 19 14 −56 −4 3.81 3.41 0.000  
Lingual gyrus 19 −12 −70 3.80 3.40 0.000  
Hippocampus 34 −16 −32 −2 3.56 3.23 0.001  
Fusiform gyrus 20 −36 −32 −10 3.36 3.07 0.001  
Superior temporal gyrus 42 −56 −28 3.23 2.97 0.001  
Precuneus 31 −66 26 2.99 2.78 0.003  
Superior parietal lobule 19 −22 −80 46 2.94 2.74 0.003  
Calcarine cortex 17/18 −24 −64 2.91 2.71 0.003  
Precuneus −2 −78 42 2.55 2.41 0.008  
Anterior cingulate cortex 24 −12 38 4.36 3.80 0.000 512 
Anterior cingulate cortex 24 42 3.10 2.86 0.002  
Superior occipital gyrus 19 22 −92 24 4.22 3.71 0.000 846 
Superior occipital gyrus 18 −16 −96 20 3.96 3.52 0.000  
Mid occipital gyrus 18 30 −86 18 3.68 3.32 0.000  
Mid frontal gyrus 11 −34 62 −12 4.08 3.61 0.000 295 
Fusiform gyrus 37 34 −38 −20 3.90 3.48 0.000 826 
Inferior temporal gyrus 37 54 −62 −10 3.57 3.23 0.001  
Cerebellum crus I  50 −68 −28 3.33 3.05 0.001  
Superior temporal gyrus 22 66 −22 3.72 3.34 0.000 344 
Mid frontal gyrus 10 36 52 −6 3.19 2.94 0.002 180 
Paracentral lobule −8 −28 68 2.59 2.44 0.007 170 
Caudate nucleus  14 20 2.50 2.36 0.009 197 

Height threshold: T = 1.7, P < 0.05.

Extent threshold: k = 120.

L = left; R = right; BA = Brodmann area.

Voxel-wise whole brain connectivity

A between-group comparison revealed that the neurotypical control group had higher functional connectivity strength in the posterior cingulate cortex, precuneus, medial prefrontal cortex, anterior cingulate cortex, gyrus rectus, posterior insular cortex, superior temporal gyrus, and inferior temporal, parietal, and occipital gyri (Table 7 and Fig. 4A). The ASD group had higher functional connectivity strength in the hippocampus, inferior temporal gyrus, temporal pole, lingual gyrus, calcarine cortex and amygdala, all on the left, as well as the cerebellum bilaterally (Table 7 and Fig. 4B). A significant interaction (Group × SCR) was also found; SCR signal contributed significantly to the connectivity of the posterior cingulate cortex, medial prefrontal cortex, anterior cingulate cortex, inferior parietal lobule, and the right anterior and posterior insular cortex in neurotypical control subjects, compared with ASD (Table 8 and Fig. 4C). In ASD there was significant contribution of SCR signal to the connectivity of the hippocampus, amygdala, nucleus accumbens, calcarine cortex, and the fusiform, lingual, and inferior occipital gyri, compared with neurotypical control subjects (Table 8 and Fig. 4D). For within-group paired t-test results (before > after SCR regression), see Supplementary Table 4 and Supplementary Fig. 4.

Figure 4

Voxel-wise whole brain functional connectivity, and an interaction between group (neurotypical controls versus ASD) and SCR (before versus after regressing out the SCR signal) in voxel-wise whole-brain connectivity. (A) Stronger connectivity in neurotypical controls compared with ASD (neurotypical controls > ASD). (B) Stronger connectivity in ASD, compared to neurotypical control subjects (ASD > neurotypical controls). (C) Stronger effects of SCR on whole-brain connectivity in neurotypical control subjects compared to ASD [neurotypical controls (with-without SCR) > ASD (with-without SCR)]. (D) Stronger effects of SCR on whole-brain connectivity in ASD compared to neurotypical control subjects [ASD (with-without SCR) > neurotypical controls (with-without SCR)].

Figure 4

Voxel-wise whole brain functional connectivity, and an interaction between group (neurotypical controls versus ASD) and SCR (before versus after regressing out the SCR signal) in voxel-wise whole-brain connectivity. (A) Stronger connectivity in neurotypical controls compared with ASD (neurotypical controls > ASD). (B) Stronger connectivity in ASD, compared to neurotypical control subjects (ASD > neurotypical controls). (C) Stronger effects of SCR on whole-brain connectivity in neurotypical control subjects compared to ASD [neurotypical controls (with-without SCR) > ASD (with-without SCR)]. (D) Stronger effects of SCR on whole-brain connectivity in ASD compared to neurotypical control subjects [ASD (with-without SCR) > neurotypical controls (with-without SCR)].

Table 7

Two sample t-test between neurotypical controls and ASD for whole-brain functional connectivity

Region L/R BA x y z T Z P k 
Controls > ASD 
Superior temporal gyrus 38 42 −15 4.88 4.15 0.000 104 
Posterior insular cortex  45 −12 3.03 2.81 0.002  
Inferior temporal gyrus 37 42 −63 −9 3.77 3.38 0.000 119 
Inferior occipital gyrus 19 48 −75 3.31 3.03 0.001  
Gyrus rectus 11 −3 36 −18 3.75 3.37 0.000 361 
Superior frontal gyrus (medial) 33 39 3.60 3.26 0.001  
Anterior cingulate cortex 32 −9 42 3.52 3.19 0.001  
Anterior cingulate cortex (subgenual) 32 30 −6 3.42 3.12 0.001  
Superior frontal gyrus (medial) 51 36 3.17 2.92 0.002  
Anterior cingulate cortex 24 12 36 3.08 2.85 0.002  
Superior frontal gyrus (medial) −3 45 24 3.07 2.84 0.002  
Inferior parietal lobule 39 45 −54 36 3.45 3.14 0.001 101 
Posterior cingulate cortex 23 −3 −36 39 3.34 3.05 0.001 376 
Precuneus 31 −9 −57 36 2.74 2.57 0.005  
Precuneus 19 −54 30 2.56 2.42 0.008  
ASD > Controls 
Hippocampus  −30 −21 −12 4.61 3.98 0.000 120 
Inferior temporal gyrus 20 −45 −27 −21 2.65 2.49 0.006  
Amygdala  −27 −18 2.29 2.18 0.015  
Calcarine cortex 17 −3 −63 4.02 3.57 0.000 79 
Lingual gyrus 19 −12 −48 3.63 3.28 0.001  
Cerebellum crus VIII  −36 −63 −54 4.02 3.57 0.000 119 
Cerebellum crus II  30 −78 −48 3.99 3.55 0.000 112 
Temporal pole 38 −39 15 −30 2.99 2.77 0.003  
Cerebellum crus VII  −30 −39 −39 3.60 3.25 0.001 130 
Cerebellum crus I  39 −57 −33 3.41 3.11 0.001 81 
Region L/R BA x y z T Z P k 
Controls > ASD 
Superior temporal gyrus 38 42 −15 4.88 4.15 0.000 104 
Posterior insular cortex  45 −12 3.03 2.81 0.002  
Inferior temporal gyrus 37 42 −63 −9 3.77 3.38 0.000 119 
Inferior occipital gyrus 19 48 −75 3.31 3.03 0.001  
Gyrus rectus 11 −3 36 −18 3.75 3.37 0.000 361 
Superior frontal gyrus (medial) 33 39 3.60 3.26 0.001  
Anterior cingulate cortex 32 −9 42 3.52 3.19 0.001  
Anterior cingulate cortex (subgenual) 32 30 −6 3.42 3.12 0.001  
Superior frontal gyrus (medial) 51 36 3.17 2.92 0.002  
Anterior cingulate cortex 24 12 36 3.08 2.85 0.002  
Superior frontal gyrus (medial) −3 45 24 3.07 2.84 0.002  
Inferior parietal lobule 39 45 −54 36 3.45 3.14 0.001 101 
Posterior cingulate cortex 23 −3 −36 39 3.34 3.05 0.001 376 
Precuneus 31 −9 −57 36 2.74 2.57 0.005  
Precuneus 19 −54 30 2.56 2.42 0.008  
ASD > Controls 
Hippocampus  −30 −21 −12 4.61 3.98 0.000 120 
Inferior temporal gyrus 20 −45 −27 −21 2.65 2.49 0.006  
Amygdala  −27 −18 2.29 2.18 0.015  
Calcarine cortex 17 −3 −63 4.02 3.57 0.000 79 
Lingual gyrus 19 −12 −48 3.63 3.28 0.001  
Cerebellum crus VIII  −36 −63 −54 4.02 3.57 0.000 119 
Cerebellum crus II  30 −78 −48 3.99 3.55 0.000 112 
Temporal pole 38 −39 15 −30 2.99 2.77 0.003  
Cerebellum crus VII  −30 −39 −39 3.60 3.25 0.001 130 
Cerebellum crus I  39 −57 −33 3.41 3.11 0.001 81 

Height threshold: T = 1.7, P < 0.05.

Extent threshold: k = 67.

L = left, R = right; BA = Brodmann area.

Table 8

Interaction of group (neurotypical controls versus ASD) and SCR (with SCR versus without SCR) for the functional connectivity of the whole brain

Region L/R BA x y z T Z P k 
Controls (with-without) > ASD (with-without) 
Anterior cingulate cortex 32 42 18 5.08 4.28 0.000 606 
Superior frontal gyrus (medial) −3 30 60 4.13 3.65 0.000  
Superior frontal gyrus (medial) 33 42 4.03 3.57 0.000  
Anterior cingulate cortex (pregenual) 24 −6 33 3.95 3.51 0.000  
Anterior cingulate cortex (subgenual) 25 24 −6 2.96 2.75 0.003  
Inferior frontal gyrus 45 48 21 4.17 3.67 0.000 157 
Anterior insular cortex  33 21 −9 2.76 2.59 0.005  
Posterior insular cortex  42 −15 −6 4.13 3.65 0.000 78 
Cerebellum crus II  −9 −84 −24 3.26 2.99 0.001 87 
Mid frontal gyrus 33 15 54 3.20 2.95 0.002 197 
Mid frontal gyrus 42 24 42 3.08 2.85 0.002  
Posterior cingulate cortex  23 −12 30 3.20 2.94 0.002 230 
Posterior cingulate cortex 26 −42 27 2.97 2.76 0.003  
Posterior cingulate cortex 31 −3 −27 36 2.89 2.69 0.004  
Precuneus 31 −12 −57 36 2.81 2.62 0.004  
Inferior parietal lobule 40 −51 −51 39 3.12 2.88 0.002 99 
Angular gyrus 39 −57 −60 27 2.58 2.43 0.007  
Inferior parietal lobule 40 36 −51 42 2.96 2.75 0.003 92 
Vermis IV  −54 −21 2.64 2.48 0.007 78 
ASD (with-without) > Controls (with-without) 
Hippocampus  −30 −21 −12 4.43 3.86 0.000 74 
Amygdala  −21 −3 −15 2.16 2.07 0.019  
Cerebellum crus VII  39 −69 −54 2.76 2.58 0.005 80 
Nucleus accumbens  −21 −15 3.67 3.31 0.000 92 
Caudate nucleus  −6 12 −3 3.08 2.85 0.002  
Calcarine cortex 18 −90 12 3.53 3.20 0.001 203 
Calcarine cortex 17 −9 −66 3.34 3.05 0.001  
Cuneus 19 −81 36 2.51 2.37 0.009  
Cerebellum crus VI  −12 −72 −18 3.19 2.93 0.002 92 
Inferior occipital gyrus 18 −18 −90 −6 2.33 2.22 0.013  
Fusiform gyrus 37 42 −60 −15 3.11 2.87 0.002 145 
Lingual gyrus 18 15 −54 2.93 2.73 0.003  
Inferior occipital gyrus 18 33 −93 −12 3.06 2.83 0.002 109 
Region L/R BA x y z T Z P k 
Controls (with-without) > ASD (with-without) 
Anterior cingulate cortex 32 42 18 5.08 4.28 0.000 606 
Superior frontal gyrus (medial) −3 30 60 4.13 3.65 0.000  
Superior frontal gyrus (medial) 33 42 4.03 3.57 0.000  
Anterior cingulate cortex (pregenual) 24 −6 33 3.95 3.51 0.000  
Anterior cingulate cortex (subgenual) 25 24 −6 2.96 2.75 0.003  
Inferior frontal gyrus 45 48 21 4.17 3.67 0.000 157 
Anterior insular cortex  33 21 −9 2.76 2.59 0.005  
Posterior insular cortex  42 −15 −6 4.13 3.65 0.000 78 
Cerebellum crus II  −9 −84 −24 3.26 2.99 0.001 87 
Mid frontal gyrus 33 15 54 3.20 2.95 0.002 197 
Mid frontal gyrus 42 24 42 3.08 2.85 0.002  
Posterior cingulate cortex  23 −12 30 3.20 2.94 0.002 230 
Posterior cingulate cortex 26 −42 27 2.97 2.76 0.003  
Posterior cingulate cortex 31 −3 −27 36 2.89 2.69 0.004  
Precuneus 31 −12 −57 36 2.81 2.62 0.004  
Inferior parietal lobule 40 −51 −51 39 3.12 2.88 0.002 99 
Angular gyrus 39 −57 −60 27 2.58 2.43 0.007  
Inferior parietal lobule 40 36 −51 42 2.96 2.75 0.003 92 
Vermis IV  −54 −21 2.64 2.48 0.007 78 
ASD (with-without) > Controls (with-without) 
Hippocampus  −30 −21 −12 4.43 3.86 0.000 74 
Amygdala  −21 −3 −15 2.16 2.07 0.019  
Cerebellum crus VII  39 −69 −54 2.76 2.58 0.005 80 
Nucleus accumbens  −21 −15 3.67 3.31 0.000 92 
Caudate nucleus  −6 12 −3 3.08 2.85 0.002  
Calcarine cortex 18 −90 12 3.53 3.20 0.001 203 
Calcarine cortex 17 −9 −66 3.34 3.05 0.001  
Cuneus 19 −81 36 2.51 2.37 0.009  
Cerebellum crus VI  −12 −72 −18 3.19 2.93 0.002 92 
Inferior occipital gyrus 18 −18 −90 −6 2.33 2.22 0.013  
Fusiform gyrus 37 42 −60 −15 3.11 2.87 0.002 145 
Lingual gyrus 18 15 −54 2.93 2.73 0.003  
Inferior occipital gyrus 18 33 −93 −12 3.06 2.83 0.002 109 

Height threshold: T = 1.7, P < 0.05.

Extent threshold: k = 67.

L = left; R = right; BA = Brodmann area.

Correlations with clinical symptoms

No significant correlations were found between the number of SCRs, or overall skin conductance level, and any of the four ADI-R subscales, all P’s > 0.05. However, ADI-R subscales for social interaction and communication were negatively correlated with posterior cingulate cortex connectivity with the medial and lateral prefrontal cortices, and with the posterior cingulate cortex and precuneus, respectively. Positive correlations with posterior cingulate cortex connectivity were found in sensory and temporal regions, as well as within the thalamus (Supplementary Table 5 and Supplementary Fig. 5) with all four subscales. Symptom severity on the social interaction and communication subscales was also correlated with decreased ventromedial prefrontal cortex connectivity with the posterior cingulate cortex (i.e. decreased DMN connectivity), as well as increased connectivity with the anterior insular cortex and anterior cingulate cortex (i.e. reduced anticorrelations with the task-positive network) (Supplementary Table 6 and Supplementary Fig. 6A and B). ADI-R correlations with whole brain connectivity revealed negative correlations with the posterior cingulate cortex/precuneus and supplementary motor area (social interaction and communication subscales), and positive correlations with the anterior cingulate cortex and medial prefrontal cortex (restricted behaviour and early development subscales) (Supplementary Table 7 and Supplementary Fig. 7). For ADI-R subscale correlation with brain activity that is associated with SCR, see Supplementary Table 8 and Supplementary Fig. 8.

Central generation and representation of skin conductance responses

One-sample t-tests of both generation and representation models revealed comparable brain activation associated with SCR to the original data in both groups (i.e. anterior brain regions in neurotypical controls, and posterior brain activation in ASD). When the two models were compared within each group, no significant differences were found between representation and generation (representation > generation) in neurotypical control subjects, or between the two models in ASD. There was, however, significant activation in the right thalamus and the left cerebellum in the generation model, as compared with the representation model (generation > representation), in neurotypical control subjects (Supplementary Table 9 and Supplementary Fig. 9).

Discussion

Skin conductance response and its correlations with brain activity

The results demonstrate a reduced number of spontaneous SCRs, as well as abnormal correlations of SCR with brain activation during rest in ASD. This could result from either abnormal peripheral conductance of autonomic nervous system signals, or from alterations in the central generation and/or representation of visceral autonomic arousal states. Previous studies have found abnormal basal autonomic nervous system activity in ASD (Horvath et al., 1999; Molloy and Manning-Courtney, 2003; White, 2003; Ming et al., 2005; Anderson and Colombo, 2009; Bal et al., 2010; Anderson et al., 2013). However, all of these findings indicate increased sympathetic/parasympathetic balance. Our finding of decreased number of SCRs in ASD, therefore, is unlikely to be a result of hypoactivation of the sympathetic autonomic nervous system. Moreover, skin conductance level did not differ between groups, consistent with previous studies (Zahn et al., 1987; Schoen et al., 2008; Mathersul et al., 2013), further suggesting that the electrodermal abnormality in ASD is not of peripheral origin. Thus, the results are likely to reflect alterations in the central generation and/or representation of SCR signals.

SCR is a sensitive index of sympathetic neural activity and can detect even subtle changes in autonomic arousal due to mental activity and thought processes (Nikula, 1991). Our results may, therefore, reflect differences in the central generation of SCR, stemming from different mental activity. More specifically, in neurotypical controls, SCR was positively correlated with the anterior insular cortex, a brain region involved in interoceptive awareness (Craig, 2002, 2009; Critchley et al., 2002; Pollatos et al., 2007; Gu et al., 2013), and with the medial prefrontal cortex, which is implicated in self-referential processing (Craik et al., 1999; Kelley et al., 2002; Macrae et al., 2004; Northoff et al., 2006; Jenkins and Mitchell, 2011). In ASD, however, SCR was highly correlated with visual and auditory cortices. Thus, it is possible that when neurotypical control participants lay in the scanner at rest, they were aware of their inner bodily sensations and were engaged in self-referential thoughts, which drove their SCRs. On the other hand, when participants with ASD were resting in the scanner, they may have concentrated on the noises and the visual information inside the scanner and that may have been the driving force of their autonomic responses.

The results may also reflect alterations in the central representation of autonomic signals. The thalamus, supplementary motor area, anterior insular cortex, and anterior cingulate cortex were positively correlated with SCR in neurotypical controls, but were either not correlated, or negatively correlated with SCR in individuals with ASD. These brain regions were previously implicated in autonomic nervous system signal processing (Boucsein, 1992; Damasio et al., 2000; Craig, 2002; Porges, 2003; Critchley, 2005; Gu et al., 2013). The anterior insular cortex, specifically, was indicated to have a key role in the representation of autonomic signals. Anterior insular cortex was not associated with SCR in the ASD group, further supporting abnormal central autonomic nervous system representation in ASD.

Results from our additional analyses did not reveal significant differences in cortical regions that were uniquely associated with the generation or representation of SCR in both groups, suggesting the involvement of similar brain regions in both the generation and the representation of SCR. These regions corresponded with brain areas that were positively correlated with SCR in each of the groups in our original regression analysis, suggesting that the differences that were originally found between the groups are present both in the generation phase and the representation phase of SCR signals. The higher activation in the right thalamus and left cerebellum that was found during the generation of SCRs in neurotypical controls may suggest that there is an increased autonomic arousal during the generation phase, compared with the representation phase. These results differ from previous findings of brain regions that are uniquely activated in the generation or representation of SCR in a healthy cohort (Critchley et al., 2000). However, differences in methods may account for the discrepancy between the results of these studies. For a more detailed discussion of the afferent and efferent SCR pathways see the online Supplementary material.

Skin conductance response contribution to default mode network and voxel-wise connectivity

Our functional connectivity findings replicated recent work showing reduced connectivity of the DMN in individuals with ASD compared with neurotypical controls (Kennedy and Courchesne, 2008a, b; Monk et al., 2009; Assaf et al., 2010; Weng et al., 2010), and added a critical component: the difference in DMN connectivity between the groups was significantly associated with SCR. Thus, by examining the interaction between SCR signal and group difference in posterior cingulate cortex connectivity, our data indicated significantly higher contributions of the SCR signal to the DMN connectivity in the neurotypical control group, compared to the ASD group. Decreased DMN connectivity in ASD was also found using the ventromedial prefrontal cortex as a seed; however, SCR did not have significant contributions to this network in this analysis. Because the ventromedial prefrontal cortex is a relatively large brain region, these negative results may be associated with the specific seed region of the ventromedial prefrontal cortex that was used in this analysis [based on the coordinates reported by Fox et al. (2005)]. Nevertheless, differences in autonomic-related brain activity may account, at least in part, for the previously observed under-connectivity of the DMN in ASD.

Voxel-wise whole-brain connectivity analysis results were strikingly similar to those obtained for the posterior cingulate cortex connectivity analysis, providing converging evidence for a decreased connectivity in DMN-related brain regions, and increased connectivity along the medial temporal lobe in ASD. Moreover, symptom severity on the social interaction and communication subscales of the ADI-R were negatively correlated with DMN connectivity strength in the posterior cingulate cortex, ventromedial prefrontal cortex and whole brain connectivity analyses. The analysis of the interaction between whole-brain connectivity and SCR revealed significant contributions of SCR signals to the connectivity patterns in both groups, suggesting that differences in autonomic-related brain activity between the groups may account, at least in part, for the differences in brain connectivity.

Possible differences in thought content, as previously discussed, may also account for the differences in DMN connectivity. Previous studies have linked the DMN to the generation of spontaneous thoughts (Mason et al., 2007; Andrews-Hanna et al., 2010) and self-referential mental activity (Whitfield-Gabrieli et al., 2011). Thus, the weaker connectivity of the DMN and higher connectivity of visual and auditory cortices in ASD may indicate an inability to shift attention from exteroceptive to interoceptive signals during rest in those individuals. This interpretation supports a recent model in which ASD is associated with abnormalities in the salience network (Uddin and Menon, 2009; Menon and Uddin, 2010). The salience network is comprised of the anterior insular cortex and anterior cingulate cortex, and is postulated to play a key role in switching between the externally oriented executive control network and the internally oriented DMN (Uddin and Menon, 2009; Menon and Uddin, 2010). Indeed, several studies have found neuropathological and functional abnormalities of the anterior insular cortex in ASD (Di Martino et al., 2009; Ebisch et al., 2011; Santos et al., 2011), and altered functional connectivity patterns of the anterior insular cortex in the ASD group were found in our data set too. It is possible, therefore, that the focus of the ASD participants on visual and auditory information during the scan may have not only driven their SCR, but in fact influenced whole brain connectivity patterns, including connectivity of the DMN. This model, together with the significant contributions of SCR to the connectivity patterns, supports the importance of autonomic activity in modulating resting-state functional connectivity.

This work adds an important element to an emerging line of research showing significant contributions of autonomic nervous system signals to resting state network connectivity in the normal population (Birn et al., 2008; Iacovella and Hasson, 2011; Fan et al., 2012). These studies suggest that incorporating measurements of autonomic signals in resting-state functional MRI connectivity analysis, rather than regressing them out as noise, may provide better insight into the driving forces of brain fluctuations, and the representation of brain activity and connectivity during ‘rest’. The current findings further suggest that examining autonomic nervous system contributions to brain connectivity during rest may significantly improve our understanding of alterations in resting state functional connectivity patterns that were previously observed in a variety of patient populations, such as in individuals with depression (Greicius et al., 2007; Bluhm et al., 2009), schizophrenia (Garrity et al., 2007; Whitfield-Gabrieli et al., 2009; Öngür et al., 2010), bipolar disorder (Ongur et al., 2010) and attention-deficit hyperactivity disorder (Uddin et al., 2008; Cao et al., 2009), in addition to ASD (Kennedy and Courchesne, 2008b; Monk et al., 2009; Assaf et al., 2010; Weng et al., 2010).

Clinical implications

Our data may have important clinical implications for the diagnosis and treatment of ASD. Autonomic measures are relatively easy and inexpensive to obtain, even with individuals of a very young age. Detecting autonomic abnormalities may aid in an early diagnosis of ASD, a process that could be critical to the effectiveness of existing behavioural treatments (Eaves and Ho, 2004; Granpeesheh et al., 2009). In addition, treatments targeting autonomic abnormalities may also reduce autistic symptoms (Murphy et al., 2000; Binstock, 2001; Porges, 2003; Field and Diego, 2008). For example, Murphy et al. (2000) observed that autistic symptoms were significantly reduced after vagal nerve stimulation in four patients with severe autistic behaviours. More research is needed, however, to determine the benefits of such treatments for ASD.

Conclusion

Our data provide evidence for abnormal associations between spontaneous SCR and brain activity and connectivity during rest in ASD. The observed autonomic abnormalities may contribute to the socio-emotional deficits in ASD because autonomic signals are essential to emotional processing. Taking autonomic measurements during investigation of resting state brain activity and connectivity may be critical for between-group comparisons, especially if one of the groups exhibits emotional abnormality, as is the case in many forms of psychopathology.

Funding

This research was supported by the National Institute of Health (NIH) Grant R21 MH083164 and two Research Enhancement Awards from Queens College, City University of New York, to J.F., along with the National Center for Research Resources (NCRR) Grant UL1 RR029887, and a James S. McDonnell Foundation grant (22002078, to P.R.H.). Two additional grants, from the National Natural Science Foundation (Grant No. 81030028) and the National Science Fund for Distinguished Young Scholars (Grant No. 81225012) of China to Y.H., helped in supporting this study. The contents are the sole responsibility of the authors and do not necessarily represent the official views of the aforementioned funding agencies. We would like to acknowledge the Beatrice and Samuel A. Seaver Foundation for their support. The authors report no biomedical financial interests or potential conflicts of interest.

Supplementary material

Supplementary material is available at Brain online.

Abbreviations

    Abbreviations
  • ADI-R

    Autism Diagnostic Interview-Revised

  • ASD

    autism spectrum disorders

  • DMN

    default mode network

  • SCR

    skin conductance response

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