Daniel Y.-J. Yang

An integrative neural model of social perception, action observation, and theory of mind.

In the field of social neuroscience, major branches of research have been instrumental in describing independent components of typical and aberrant social information processing, but the field as a whole lacks a comprehensive model that integrates different branches. We review existing research related to the neural basis of three key neural systems underlying social information processing: social perception, action observation, and theory of mind. We propose an integrative model that unites these three processes and highlights the posterior superior temporal sulcus (pSTS), which plays a central role in all three systems. Furthermore, we integrate these neural systems with the dual system account of implicit and explicit social information processing. Large-scale meta-analyses based on Neurosynth confirmed that the pSTS is at the intersection of the three neural systems. Resting-state functional connectivity analysis with 1000 subjects confirmed that the pSTS is connected to all other regions in these systems. The findings presented in this review are specifically relevant for psychiatric research especially disorders characterized by social deficits such as autism spectrum disorder.

Brain Mechanisms for Processing Affective (and Nonaffective) Touch Are Atypical in Autism

C-tactile (CT) afferents encode caress-like touch that supports social-emotional development, and stimulation of the CT system engages the insula and cortical circuitry involved in social-emotional processing. Very few neuroimaging studies have investigated the neural mechanisms of touch processing in people with autism spectrum disorder (ASD), who often exhibit atypical responses to touch. Using functional magnetic resonance imaging, we evaluated the hypothesis that children and adolescents with ASD would exhibit atypical brain responses to CT-targeted touch. Children and adolescents with ASD, relative to typically developing (TD) participants, exhibited reduced activity in response to CT-targeted (arm) versus non-CT-targeted (palm) touch in a network of brain regions known to be involved in social-emotional information processing including bilateral insula and insular operculum, the right posterior superior temporal sulcus, bilateral temporoparietal junction extending into the inferior parietal lobule, right fusiform gyrus, right amygdala, and bilateral ventrolateral prefrontal cortex including the inferior frontal and precentral gyri, suggesting atypical social brain hypoactivation. Individuals with ASD (vs. TD) showed an enhanced response to non-CT-targeted versus CT-targeted touch in the primary somatosensory cortex, suggesting atypical sensory cortical hyper-reactivity.

Building a Social Neuroscience of Autism Spectrum Disorder

Autism spectrum disorder (ASD) is an early onset neurodevelopmental disorder marked by impairments in reciprocal social interaction, communication, and the presence of repetitive or restricted interests and behaviors. Despite great phenotypic heterogeneity and etiologic diversity in ASD, social dysfunction is the unifying feature of ASD. This chapter focuses on understanding the neural systems involved in the processing of social information and its disruption in ASD by reviewing the conceptual background and highlighting some recent advances. In addition, work investigating an alternative interpretation of autistic dysfunction, problems with interconnectivity, and consequent difficulties with complex information processing are addressed.

Brain responses to biological motion predict treatment outcome in young children with autism

Autism spectrum disorders (ASDs) are common yet complex neurodevelopmental disorders, characterized by social, communication and behavioral deficits. Behavioral interventions have shown favorable results—however, the promise of precision medicine in ASD is hampered by a lack of sensitive, objective neurobiological markers (neurobiomarkers) to identify subgroups of young children likely to respond to specific treatments. Such neurobiomarkers are essential because early childhood provides a sensitive window of opportunity for intervention, while unsuccessful intervention is costly to children, families and society. In young children with ASD, we show that functional magnetic resonance imaging-based stratification neurobiomarkers accurately predict responses to an evidence-based behavioral treatment—pivotal response treatment. Neural predictors were identified in the pretreatment levels of activity in response to biological vs scrambled motion in the neural circuits that support social information processing (superior temporal sulcus, fusiform gyrus, amygdala, inferior parietal cortex and superior parietal lobule) and social motivation/reward (orbitofrontal cortex, insula, putamen, pallidum and ventral striatum). The predictive value of our findings for individual children with ASD was supported by a multivariate pattern analysis with cross validation. Predicting who will respond to a particular treatment for ASD, we believe the current findings mark the very first evidence of prediction/stratification biomarkers in young children with ASD. The implications of the findings are far reaching and should greatly accelerate progress toward more precise and effective treatments for core deficits in ASD.

An unbiased Bayesian approach to functional connectomics implicates social-communication networks in autism.

Resting-state functional magnetic resonance imaging (rsfMRI) studies reveal a complex pattern of hyper- and hypo-connectivity in children with autism spectrum disorder (ASD). Whereas rsfMRI findings tend to implicate the default mode network and subcortical areas in ASD, task fMRI and behavioral experiments point to social dysfunction as a unifying impairment of the disorder. Here, we leverage a novel Bayesian framework for whole-brain functional connectomics that aggregates population differences in connectivity to localize a subset of foci that are most affected by ASD. Our approach is entirely data-driven and does not impose spatial constraints on the region foci or dictate the trajectory of altered functional pathways. We apply our method to data from the openly shared Autism Brain Imaging Data Exchange (ABIDE) and pinpoint two intrinsic functional networks that distinguish ASD patients from typically developing controls. One network involves foci in the right temporal pole, left posterior cingulate cortex, left supramarginal gyrus, and left middle temporal gyrus. Automated decoding of this network by the Neurosynth meta-analytic database suggests high-level concepts of “language” and “comprehension” as the likely functional correlates. The second network consists of the left banks of the superior temporal sulcus, right posterior superior temporal sulcus extending into temporo-parietal junction, and right middle temporal gyrus. Associated functionality of these regions includes “social” and “person”. The abnormal pathways emanating from the above foci indicate that ASD patients simultaneously exhibit reduced long-range or inter-hemispheric connectivity and increased short-range or intra-hemispheric connectivity. Our findings reveal new insights into ASD and highlight possible neural mechanisms of the disorder.

Pivotal response treatment prompts a functional rewiring of the brain among individuals with autism spectrum disorder.

Behavioral interventions for autism have gained prominence in recent years; however, the neural-systems-level targets of these interventions remain poorly understood. We use a novel Bayesian framework to extract network-based differences before and after a 16-week pivotal response treatment (PRT) regimen. Our results suggest that the functional changes induced by PRT localize to the posterior cingulate and are marked by a shift in connectivity from the orbitofrontal cortex to the occipital–temporal cortex. Our results illuminate a potential PRT-induced learning mechanism, whereby the neural circuits involved during social perception shift from sensory and attentional systems to higher-level object and face processing areas.

Bayesian Community Detection in the Space of Group-Level Functional Differences

We propose a unified Bayesian framework to detect both hyper- and hypo-active communities within whole-brain fMRI data. Specifically, our model identifies dense subgraphs that exhibit population-level differences in functional synchrony between a control and clinical group. We derive a variational EM algorithm to solve for the latent posterior distributions and parameter estimates, which subsequently inform us about the afflicted network topology. We demonstrate that our method provides valuable insights into the neural mechanisms underlying social dysfunction in autism, as verified by the Neurosynth meta-analytic database. In contrast, both univariate testing and community detection via recursive edge elimination fail to identify stable functional communities associated with the disorder.

Neurogenetic analysis of childhood disintegrative disorder

BackgroundChildhood disintegrative disorder (CDD) is a rare form of autism spectrum disorder (ASD) of unknown etiology. It is characterized by late-onset regression leading to significant intellectual disability (ID) and severe autism. Although there are phenotypic differences between CDD and other forms of ASD, it is unclear if there are neurobiological differences.MethodsWe pursued a multidisciplinary study of CDD (n = 17) and three comparison groups: low-functioning ASD (n = 12), high-functioning ASD (n = 50), and typically developing (n = 26) individuals. We performed whole-exome sequencing (WES), copy number variant (CNV), and gene expression analyses of CDD and, on subsets of each cohort, non-sedated functional magnetic resonance imaging (fMRI) while viewing socioemotional (faces) and non-socioemotional (houses) stimuli and eye tracking while viewing emotional faces.ResultsWe observed potential differences between CDD and other forms of ASD. WES and CNV analyses identified one or more rare de novo, homozygous, and/or hemizygous (mother-to-son transmission on chrX) variants for most probands that were not shared by unaffected sibling controls. There were no clearly deleterious variants or highly recurrent candidate genes. Candidate genes that were found to be most conserved at variant position and most intolerant of variation, such as TRRAP, ZNF236, and KIAA2018, play a role or may be involved in transcription. Using the human BrainSpan transcriptome dataset, CDD candidate genes were found to be more highly expressed in non-neocortical regions than neocortical regions. This expression profile was similar to that of an independent cohort of ASD probands with regression. The non-neocortical regions overlapped with those identified by fMRI as abnormally hyperactive in response to viewing faces, such as the thalamus, cerebellum, caudate, and hippocampus. Eye-tracking analysis showed that, among individuals with ASD, subjects with CDD focused on eyes the most when shown pictures of faces.ConclusionsGiven that cohort sizes were limited by the rarity of CDD, and the challenges of conducting non-sedated fMRI and eye tracking in subjects with ASD and significant ID, this is an exploratory study designed to investigate the neurobiological features of CDD. In addition to reporting the first multimodal analysis of CDD, a combination of fMRI and eye-tracking analyses are being presented for the first time for low-functioning individuals with ASD. Our results suggest differences between CDD and other forms of ASD on the neurobiological as well as clinical level.