Disconnection from others in autism is more than just a feeling: whole-brain neural synchrony in adults during implicit processing of emotional faces
© The Author(s). 2017
Received: 20 July 2016
Accepted: 16 February 2017
Published: 22 February 2017
Socio-emotional difficulties in autism spectrum disorder (ASD) are thought to reflect impaired functional connectivity within the “social brain”. Nonetheless, a whole-brain characterization of the fast responses in functional connectivity during implicit processing of emotional faces in adults with ASD is lacking.
The present study used magnetoencephalography to investigate early responses in functional connectivity, as measured by interregional phase synchronization, during implicit processing of angry, neutral and happy faces. The sample (n = 44) consisted of 22 young adults with ASD and 22 age- and sex-matched typically developed (TD) controls.
Reduced phase-synchrony in the beta band around 300 ms emerged during processing of angry faces in the ASD compared to TD group, involving key areas of the social brain. In the same time window, de-synchronization in the beta band in the amygdala was reduced in the ASD group across conditions.
This is the first demonstration of atypical global and local synchrony patterns in the social brain in adults with ASD during implicit processing of emotional faces. The present results replicate and substantially extend previous findings on adolescents, highlighting that atypical brain synchrony during processing of socio-emotional stimuli is a hallmark of clinical sequelae in autism.
KeywordsAutism Functional connectivity Magnetoencephalography Social brain Emotional faces Young adults
Socio-emotional deficits represent a core symptom in autism spectrum disorder (ASD) and are thought to have modulatory effects over other clinical manifestations . It has been proposed that those with ASD show a lack of motivation toward social stimuli, such as emotional faces, which are considered to be naturally rewarding in typically developed (TD) individuals [1–3]. Other authors related socio-emotional difficulties in ASD to a deficit in theory of mind , or to a pervasive difficulty in learning stimuli’s emotional salience . In the past years, different theoretical approaches have highlighted atypical activation of specific brain regions in individuals with ASD, such as the amygdalae [6, 7], insulae [8, 9], fusiform gyri  or superior temporal sulci . Nonetheless, recently it has been suggested that ASD can be described as a neural systems disorder , characterized by widespread abnormalities throughout the brain .
Specifically, dysfunctional emotional processing and an impaired ability to recognize others’ emotions correctly have been related to disturbances in functional connectivity among brain areas important to assessing stimuli’s salience [13, 14]. In autism, this is thought to reflect atypical maturation of the “social brain”, a network of brain areas which shows co-activation across social tasks [15, 16]. The social brain includes limbic and paralimbic regions which code for different aspects of emotional significance of social stimuli, such as the amygdalae  and anterior hippocampi , the anterior insulae , the medial and ventromedial prefrontal cortices , and the anterior temporal lobes . Other areas in the social brain are involved in representing shape, movement and conceptual information about animate entities (i.e. the fusiform gyri, the posterior cingulate/precuneus and regions within and near the posterior superior temporal sulci) [1, 21], in action understanding (i.e. somatosensory and anterior intraparietal cortices)  and social communication (i.e. the left inferior frontal gyrus)  (for a review see ). Studies using functional magnetic resonance imaging (fMRI) have reported impaired (usually decreased) connectivity in the social brain network in individuals with ASD at rest [24, 25], during processing of faces  and emotional faces [27, 28]. Recent evidence suggested that hypo-connectivity in autism is more evident during implicit compared to explicit emotional processing, and explicit emotional processing may be relatively preserved in high-functioning individuals with ASD due to learning and experience . Since it is well known that implicit processing of emotional stimuli, such as emotional faces, relies on quick and automatized processes in the brain [29–31], a precise characterization of the time course of early brain activations in ASD is very relevant to understanding socio-emotional processing in ASD.
Compared to fMRI, magnetoencephalography (MEG) provides complementary information about the timing of the neural response, while still maintaining a good spatial resolution [32, 33]. Moreover, MEG allows the investigation of connectivity patterns at different frequency scales, which are differently modulated by emotional dimensions. In particular, emotional valence (pleasant/unpleasant) is usually represented by stimulus-related changes in higher frequencies (beta/gamma) in early time windows, whereas arousal is reflected by lower frequencies [34–42]. MEG and EEG studies consistently report beta band modulation during both explicit and implicit emotion recognition and understanding of social-emotional stimuli [43, 44]. During implicit emotional processing of dynamic facial expressions, synchronization in the beta band between fronto-limbic (i.e. pulvinar, caudate, cingulate, and amygdala) and posterior regions of the social brain (i.e. superior temporal sulcus) was observed when the facial expressions become emotionally salient . This finding, along with the observed spatially concomitant fMRI BOLD response, led to the interpretation that the beta rhythm subserves feed-forward/feed-back mechanisms between fronto-limbic and posterior areas during emotional processing of socially relevant stimuli. Moreover, several studies of implicit emotional processing of faces using both intracranial recordings  and MEG [46, 47] suggest a role for fast gamma synchronization in subcortical limbic regions (e.g. thalamus and amygdala) in response to negatively valenced faces, followed by synchronization in both frontal and posterior areas. Accordingly, gamma synchrony has been successfully employed in assessing thalamo-cortico-limbic circuitry during implicit emotional processing of facial expressions .
Thus, the study of oscillatory responses in the beta and gamma ranges reliably characterizes fast spatiotemporal coupling in the social brain in response to emotional stimuli. Importantly, consistent results also come from several studies investigating implicit emotional processing of facial expressions. This is particularly relevant for the study of autism, since, as mentioned above, fMRI studies have indicated that hypo-connectivity in autism is more evident during implicit compared to explicit emotional processing . Two MEG studies have examined functional connectivity during emotional face processing in ASD, and both employed an implicit emotional paradigm. Khan et al.  investigated MEG functional connectivity involving the fusiform gyri, in adolescents and young adults with ASD. During processing of fearful and angry faces, reduced connectivity within the right fusiform gyrus and between the fusiform and the left precuneus, left inferior frontal gyrus and left anterior cingulate was reported in ASD, within 320 ms. In accordance with these results, decreased connectivity in beta band was also seen in adolescents with ASD during implicit processing of angry faces, in the first 400 ms after the stimuli . In the latter study, reduced beta connectivity in ASD was anchored in connections between visual (e.g. the fusiform) and limbic (e.g. the insula) areas involved in face-processing, consistent with the role of beta band in coupling limbic and posterior visual activation .
The present study
Forty-four young adults were included in the study, 22 with ASD (7 females, age = 26.4 ± 4.1) and 22 TD controls (8 females, age = 26.0 ± 3.9), matched for age and sex. All subjects had no history of neurological or neurodevelopmental disorder (other than autism), no standard contraindication for MEG and MRI, IQ ≥70, language skills adequate for task comprehension and normal or corrected to normal visual acuity. As is typical in clinical samples, the majority of ASD participants were taking standard psychotropic medications at the time of the study (e.g. Ritalin, antidepressants and anxiolytics), while controls were free from medication. All participants read and signed an informed written consent, after receiving a complete description of the study. Testing and recordings were completed in the MEG Lab at the Hospital for Sick Children, and the Research Ethics Board of the hospital gave institutional approval for the study.
The diagnosis of ASD was confirmed by expert clinicians, through clinical evaluation, medical reports and the Autism Diagnostic Observation Schedule-2 (ADOS-2) . ASD group had a mean ADOS severity score of 6.2 ± 2.43 out of 10 [52–54]. Full-scale IQ was measured using the two-subtest form of the Wechsler Abbreviated Scale of Intelligence (WASI) , except for 1 ASD and 2 control participants. The two groups were matched for IQ (ASD mean = 114.0 ± 18, controls mean = 114.3 ± 10.1).
Emotional face task in MEG
The task used in the present study has been previously described . Briefly, during each trial, a face (angry, happy or neutral) was shown on either side of a central fixation cross, for 80 ms, to avoid time for any saccades; on the other side, a scrambled pattern of the face, matched for luminosity, contrast and colour, was presented simultaneously. A varying inter-trial interval (1300–1500 ms) preceded the next stimulus presentation. Participants were instructed to press left or right buttons as quickly as possible to indicate the side of the scrambled pattern, ignoring the faces. Thus, the processing of the emotional content remained implicit. The stimuli consisted of 75 colour photographs of 25 actors, posing for each of the three emotional expressions, taken from the NimStim Set of Facial expression . Development of the MacBrain Face Stimulus Set was overseen by Nim Tottenham and supported by the John D. and Catherine T. MacArthur Foundation Research Network on Early Experience and Brain Development. Please contact Nim Tottenham at email@example.com for more information concerning the stimulus set. The emotional faces and their scrambled counterparts were presented in random order, twice on each side, resulting in a total of 300 trials. Response latencies were recorded. Images were back-projected onto a screen at 79 cm from the eyes, with a visual angle of 6.9°, thus in the parafoveal region.
MEG signal was recorded inside a magnetically shielded room, with a 151-channel CTF system (CTF-MISL, Coquitlam, BC, Canada), at a 600-Hz sampling rate. An online low-pass filter at 150 Hz was applied to the data, along with a third-order gradient cancellation. Fiducial coils on the left and right pre-auricular points and on the nasion of each subject allowed head position to be recorded continuously during the task. After the MEG, a T1-weighted MRI sequence was acquired (3D SAG MRPAGE, GRAPPA = 2, TR = 2300 ms, TE = 2.96 ms, FA = 9°, 256 × 256 matrix, 192 slices, thickness = 1 mm, isotropic voxels) with a 12-channel head coil in a 3 T MRI scanner (MAGNETOM Tim Trio, Siemens AG, Erlangen, Germany). The same fiducial points used in MEG were replaced with radio-opaque markers for the MRI, to allow for precise MEG/MRI co-registration. In both the MEG and MRI, the participants lay supine.
Data processing and statistics
Response latencies were entered in a mixed analysis of variance (ANOVA) with group (ASD/TD) as a between factor, and emotion (angry/happy/neutral) as a within factor. STATISTICA 7.0 software (StatSoft Inc, Tulsa, OK) was used for statistical analysis.
MEG data: preprocessing
MEG data were band-pass filtered offline (1–150 Hz), and a notch filter at 60 Hz and harmonics was applied. Epochs were created in the broad −1500- to 3000-ms interval around stimulus presentation, in order to prevent boundary effects, and were excluded when intra-trial head motion exceeded 5 mm. The median head position from the remaining epochs was used for source reconstruction. A single shell head model was calculated from each individual’s MRI and each individual’s brain space was normalized to a template brain (ICBM 152) . The time-series of brain activation for the 90 seeds of the Automated Anatomical Labeling (AAL) atlas  were estimated through a linearly constrained minimum variance (LCMV) beamformer . Beamformers act as spatial filters, which reconstruct the signal from the desired region of interest (ROI) while suppressing signals from all other unwanted sources . Due to the minimization of the contribution of the sources outside the ROI, beamformers are effective at suppressing ocular and non-ocular artefacts, therefore not requiring trial-by-trial artefact rejection based on visual inspection . Moreover, as detailed below, the connectivity measure which was employed in the present study (the phase lag index; PLI) protects from potentially residual noise from common sources.
MEG data: functional connectivity
The time-series from each ROI were filtered into canonical bands (theta 4–7 Hz; alpha 8–14; beta 15–30; gamma 30–80), and a Hilbert transform was applied to the filtered data to extract the instantaneous phase at each time sample for each frequency. The PLI was calculated to estimate the cross-trial degree of phase synchronization between all pairwise combinations of the seeds for every time point, resulting in a 90 × 90 adjacency matrix for each subject, at each sample. The PLI quantifies the phase synchrony by calculating the asymmetry of the distribution of the instantaneous phase differences between two time series. It ranges from 0 (completely symmetric, or random, phase distribution) to 1 (completely asymmetric phase distribution); since field/spread volume conduction or leakage of common sources would determine a zero (or 180°) phase lag between the two sources, this measure is unaffected by spurious coupling that could be generated by artefacts instantaneously projecting to each of the ROIs.
The threshold for the t-statistic for the initial univariate testing was adapted to the data, as suggested by Zalesky and colleagues . Therefore, for within contrasts, thresholding values were set to 7.5 and 5 for beta and gamma bands, respectively. For between contrasts, a value of 3.1 was used for both bands. The importance of each node was assessed using the node strength measure (i.e. the sum of weights of links connected to the node), computed using the Brain Connectivity Toolbox . Results were considered to be statistically significant at p < 0.05, corrected. Both the significant components and the node strength data were visualized using BrainNet Viewer .
MEG data: time-frequency analyses
To complement the whole-brain connectivity findings, event-related synchronization/de-synchronization (ERS/D) were explored in specific seeds of interest: seeds of interest were selected among network nodes involved in the significant component that emerged from between-groups statistical contrasts with NBS. In particular, seed selection was based on the literature of brain regions involved in processing of emotional faces in both TD and ASD individuals. A Morlet wavelet transformation, as implemented in Fieldtrip , on each selected seed’s estimated time-series was applied; the proportion of change relative to the baseline (−200 to 0 ms) in each frequency bin at each 10-ms time point was computed.
Neither the main effects for group and emotion, nor the group × emotion interaction were significant for the response latency (all p > 0.21), indicating that both groups performed equally well on the task.
Connectivity: within groups
Control and ASD participants also displayed a significant increase in gamma connectivity in the active window compared to baseline, for each condition (Fig. 2, right; Additional file 1: Table S1). For the gamma band, the calcarine sulcus and the cuneus were highly connected, as well as the nodes of the core, extended and, in particular, emotion systems for face processing. Of note, in contrast to the beta band results, the fusiform gyri, and in particular the right fusiform, were almost always present among the nodes involved in increased gamma connectivity across groups and conditions (Additional file 3: Table S3).
Connectivity: between groups
Beta hypo-connectivity in ASD compared to TD individuals: involved areas, number of connected seeds and group difference in node strength (TD-ASD)
Number of connections
∆ Node strength (TD-ASD)
Superior orbitofrontal cortex
Anterior cingulate cortex
Frontal inferior operulum
Inferior temporal gyrus
Superior temporal gyrus
Middle temporal gyrus
Superior orbitofrontal cortex
Medial orbitofrontal cortex
Superior temporal pole
Medial orbitofrontal cortex
None of the contrasts reached statistical significance.
Since a pattern of reduced connectivity emerged in the ASD group in the beta band, we investigated the time-frequency responses in the beta band in specific seeds of interest, as defined in the “Methods” section. Based on the large literature on TD and ASD individuals and the importance of amygdalae and insulae in processing of emotional faces , we focused on these seeds. Moreover, the majority of the connections involved the lingual gyrus, which, due to its importance in face processing , was also included for further investigation. All the selected sites were left-lateralized, in line with the between-group connectivity results.
To the best of our knowledge, this is the first study investigating whole-brain MEG connectivity in adults with ASD compared to TD controls during implicit processing of emotional faces. The ASD group showed reduced connectivity in the beta band compared to TD subjects during implicit processing of angry faces. In particular, reduced connectivity in ASD involved several areas of the social brain. Specifically, both low-level visual areas, such as the calcarine cortex, and face-sensitive regions, such as the lingual gyrus and the inferior temporal gyrus , were significantly hypo-connected in ASD compared to TD individuals. Other regions pertaining to the extended system for face processing , such as the superior temporal gyrus, the temporal pole and the anterior cingulate cortex, were also under-connected in ASD. Importantly, the network included the amygdala, the insula and the striatum, which are central in the emotional processing of faces. Of note, these structures are deep and/or subcortical regions of the brain; therefore, the present results align with the large body of MEG literature which supports the use of beamforming algorithms for MEG source reconstruction of subcortical neural activity [41, 46, 67–75]. Based on beamforming solutions, functional connectivity within thalamo-cortico-limbic circuits has been studied using MEG during processing of emotional faces . Moreover, our results largely converge with recent fMRI findings which reported reduced connectivity between limbic (e.g. amygdala) and cortical regions of the social brain in ASD compared to TD individuals, during both explicit  and implicit  processing of emotional faces. The present study adds to the literature by providing a precise characterization in the time domain of reduced functional connectivity in ASD individuals.
The majority of the seeds involved were lateralized to the left hemisphere. A model has been proposed that anterior regions of the left cerebral hemisphere subtend approach motivation, while the anterior regions of the right hemisphere are responsible for withdrawal motivation [76–82]. It has been shown that, although a negatively valenced emotion, anger elicits approach motivation and behaviours, associated with greater left than right brain activation [83–87]. Thus, hypo-connectivity in the left hemisphere during processing of angry faces supports the theory of poor motivation toward social stimuli in ASD compared to TD individuals [1–3]. Intriguingly, our findings largely overlap with results from Leung and colleagues who used the same task on adolescents with autism (range 12–15 years) . Nonetheless, the latter study did not find the strong left lateralization for angry faces. This suggests that hemispheric specialization in emotional face processing evolves during the development and ASD individuals may show an atypical developmental trajectory. Supporting this interpretation, subjects with ASD show an opposite pattern of MEG lateralization compared to TD individuals for other cognitive functions, such as language. While TD individuals showed a shift toward left-lateralization for language as age increased, those with ASD showed the opposite pattern, that is a right-sided lateralization with age . This is in line with structural studies which highlight a shift toward right lateralization in cortical volume in ASD . Interpretations of laterality effects, however, ought to be taken with caution, since no statistical tests on connectivity strength at left and right sites were run. Beamformers are not ideal for estimation of stimulus-driven oscillatory activity of bilateral sources, which can be highly synchronized , and therefore for testing laterality effects. The supposition that differences exist in the developmental trajectory of emotional processing should be investigated using other source localization algorithms as well, and by research comparing connectivity asymmetries at different ages, or longitudinally.
Consistent with other research, under-connectivity in social brain areas in ASD emerged for angry, but not happy faces [41, 49]. Behavioural studies have shown that autism is associated with specific difficulties in processing of negative emotional faces , and in particular angry faces . In a previous study on adolescents with ASD , hypo-connectivity for angry faces had been related to the observation that the ability to identify negative emotions, such as anger and fear, matures at later ages, and therefore negative emotions may require more complex processing compared to positive emotions [93–96]. This development-related explanation may not hold for our results with adults with ASD. Another possible interpretation relates to angry faces being negatively valenced social stimuli, which convey information about potential danger or harm for the individual. Thus, compared to positively valenced stimuli, they are thought to be processed very quickly in the brain, allowing the organism to prepare a rapid behavioural response [42, 78, 97]. Therefore, it is plausible that atypical functional connectivity in ASD emerged due to deficits in the high-demanding quick processing requirements of negative content, especially in the context of our very rapidly presented implicit processing task.
The present results also extend previous EEG/MEG studies showing the importance of beta band in processing of emotional valence of social and non-social emotional stimuli [35, 42, 98]. In particular, we replicated the increase in signal coherence within the beta band reported during processing of visual emotional stimuli [39, 41]. Moreover, by combining the connectivity analysis with a time-frequency analysis on seeds of interests, it emerged that the increase in beta connectivity overlapped in time with a decrease in beta power in the 220–340-ms window. Our results align with studies showing that event-related beta power correlates negatively with the hemodynamic response in task-relevant brain areas [99, 100]. Beta de-synchronization has been reported during processing of salient external events , both in EEG [43, 102] and MEG studies , and it has been interpreted as an adaptation process in response to the perceived emotional information . In the present study, beta ERD in the left amygdala was reduced in ASD compared to TD individuals irrespective of stimulus condition, suggesting that participants with ASD had a less pronounced response to stimulus salience. Overall, we propose that the general difficulty in extracting emotional information from faces in ASD, as indicated by amygdala hypo-responsivity, is associated with impaired long-range communication between areas of the social brain, which instead is specific for highly salient angry faces.
It was surprising that the involvement of the fusiform gyri did not emerge in the connectivity differences between ASD and TD individuals. This may be attributed to the timing of the beta peak in connectivity, which occurred after the time window where the fusiform activation is usually maximal (i.e. 130–200 ms) . As a confirmation of this hypothesis, the within group results showed that the fusiform was more involved in the gamma response to the task, which peaked between 142 and 192 ms. Previous research demonstrated that, during processing of human faces, gamma band is associated with “structural encoding”, which represents the integration of the configural metrics and face-feature characteristics into a detailed representation . Other MEG studies also found that face-induced gamma oscillations, albeit not representing the same neurophysiological process as the M170, were generated by common sources, including the right fusiform gyrus . Although connectivity in gamma band in autism during processing of emotional faces has been rarely investigated, one study reported differences in gamma power during emotional face processing in ASD compared to TD individuals . Thus, while the group effect in the gamma band connectivity was not significant in the present study, future studies should further clarify the role of gamma band in emotional face processing in ASD.
The present study provides the first demonstration of hypo-connectivity in adults with ASD compared to TD individuals during emotional face processing, using a whole-brain MEG approach. Under-connectivity in ASD was evident in the beta band during processing of angry faces and involved key areas of the social brain largely in the left hemisphere, such as the amygdala, the insula and the lingual gyrus. Complementary time-frequency analyses highlighted hypo-activation of the amygdala which, together with the pattern of under-connectivity in the social brain, could contribute to the socio-emotional difficulties in ASD. These findings shed light on important neural underpinnings of the disorder, providing potential for the development of new techniques to aid in the diagnosis and assessment of the efficacy of treatments.
Automated Anatomical Labeling
Autism Diagnostic Observation Schedule-2
Analysis of variance
Autism spectrum disorder
Functional magnetic resonance imaging
Full-width at half maximum
Linearly constrained minimum variance
Phase lag index
Region of interest
Wechsler Abbreviated Scale of Intelligence
The authors would like to thank Marc Lalancette for help in data collection, as well as Anne Keller and Simeon Wong for support in the data analysis. The authors acknowledge Klara Kovarski for reference suggestions.
This work was funded by the Canadian Institutes of Health Research (CIHR) (Grants: MOP-119541 and MOP-142379) to MJT. The funder had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; and preparation, review, or approval of the manuscript.
Availability of data and materials
The datasets during and/or analysed during the current study available from the corresponding author on reasonable request.
RM participated in the data analysis, result interpretation, and drafting of the manuscript. RCL participated in the design of the study, acquisition of data and editing of the manuscript. MJT participated in the conception and design of the study, guiding data analysis, result interpretation and editing of the manuscript. BTD participated in the creation of the data analysis pipeline, guiding data analyses, result interpretation, and editing of the manuscript. All authors read and approved the final manuscript.
The authors declare that they have no competing interests.
Consent for publication
Ethics approval and consent to participate
All participants read and signed an informed written consent, after receiving a complete description of the study. Testing and recordings were completed in the MEG Lab at the Hospital for Sick Children, and the Research Ethics Board of the hospital gave institutional approval for the study.
Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
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