Background:
Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder characterized by primary difficulties in social function. Individuals with ASD display slowed neural processing of faces, ...as indexed by the latency of the N170, a face-sensitive event-related potential. Currently, there are no objective biomarkers of ASD useful in clinical care or research. Efficacy of behavioral treatment is currently evaluated through subjective clinical impressions. To explore whether the N170 might have utility as an objective index of treatment response, we examined N170 before and after receipt of an empirically validated behavioral treatment in children with ASD.
Method:
Electroencephalography (EEG) data were obtained on a preliminary cohort of preschool-aged children with ASD before and after a 16-week course of PRT and in a subset of participants in waitlist control (16-weeks before the start of PRT) and follow-up (16-weeks after the end of PRT). EEG was recorded while participants viewed computer-generated faces with neutral and fearful affect.
Results:
Significant reductions in N170 latency to faces were observed following 16 weeks of PRT intervention. Change in N170 latency was not observed in the waitlist-control condition.
Conclusions:
This exploratory study offers suggestive evidence that N170 latency may index response to behavioral treatment. Future, more rigorous, studies in larger samples are indicated to evaluate whether the N170 may be useful as a biomarker of treatment response.
Humans are innately social creatures and the social environment strongly influences brain development. As such, the human brain is primed for and sensitive to social information even in the absence ...of explicit task or instruction. In this study, we examined the influence of different levels of interpersonal proximity on resting state brain activity and its association with social cognition. We measured EEG in pairs of 13 typically developing (TD) adults seated in separate rooms, in the same room back-to-back, and in the same room facing each other. Interpersonal proximity modulated broadband EEG power from 4-55 Hz and individual differences in self-reported social cognition modulated these effects in the beta and gamma frequency bands. These findings provide novel insight into the influence of social environment on brain activity and its association with social cognition through dual-brain EEG recording and demonstrate the importance of using interactive methods to study the human brain.
Atypical neural response to faces is thought to contribute to social deficits in autism spectrum disorder (ASD). Compared to typically developing (TD) controls, individuals with ASD exhibit delayed ...brain responses to upright faces at a face‐sensitive event‐related potential (ERP), the N170. Given observed differences in patterns of visual attention to faces, it is not known whether slowed neural processing may simply reflect atypical looking to faces. The present study manipulated visual attention to facial features to examine whether directed attention to the eyes normalizes N170 latency in ASD. ERPs were recorded in 30 children and adolescents with ASD as well as 26 TD children and adolescents. Results replicated prior findings of shorter N170 latency to the eye region of the face in TD individuals. In contrast, those with ASD did not demonstrate modulation of N170 latency by point of regard to the face. Group differences in latency were most pronounced when attention was directed to the eyes. Results suggest that well‐replicated findings of N170 delays in ASD do not simply reflect atypical patterns of visual engagement with experimental stimuli. These findings add to a body of evidence indicating that N170 delays are a promising marker of atypical neural response to social information in ASD.
Lay Summary
This study looks at how children's and adolescents' brains respond when looking at different parts of a face. Typically developing children and adolescents processed eyes faster than other parts of the face, whereas this pattern was not seen in ASD. Children and adolescents with ASD processed eyes more slowly than typically developing children. These findings suggest that observed inefficiencies in face processing in ASD are not simply reflective of failure to attend to the eyes.
Individuals with autism spectrum disorder (ASD) demonstrate difficulty with social interactions and relationships, but the neural mechanisms underlying these difficulties remain largely unknown. ...While social difficulties in ASD are most apparent in the context of interactions with other people, most neuroscience research investigating ASD have provided limited insight into the complex dynamics of these interactions. The development of novel, innovative "interactive social neuroscience" methods to study the brain in contexts with two interacting humans is a necessary advance for ASD research. Studies applying an interactive neuroscience approach to study two brains engaging with one another have revealed significant differences in neural processes during interaction compared to observation in brain regions that are implicated in the neuropathology of ASD. Interactive social neuroscience methods are crucial in clarifying the mechanisms underlying the social and communication deficits that characterize ASD.
Social neuroscience research investigating autism spectrum disorder (ASD) has yielded inconsistent findings, despite ASD being well-characterized by difficulties in social interaction and ...communication through behavioral observation. In particular, specific etiologies and functional and structural assays of the brain in autism have not been consistently identified. To date, most social neuroscience research has focused on a single person viewing static images. Research utilizing interactive social neuroscience featuring dual-brain recording offers great promise for the study of neurodevelopmental disabilities. Reward processing has been implicated in the pathology of ASD, yet mixed findings have brought uncertainty about the role reward processing deficits may play in ASD. The current study employed dual-brain EEG recording to examine reward processing during live interaction and its relation to autistic traits. Sixteen typically developing (TD) adults played a competitive treasure-hunt game against a computer and against a human partner. EEG results revealed enhanced neural sensitivity to reward outcome during live interaction with a human competitor. Further, individuals with higher levels of autistic traits demonstrated reduced sensitivity to reward outcome during live interaction. These findings provide novel insight into reward processing mechanisms associated with autistic traits, as well as support the necessary utility of interactive social neuroscience techniques to study developmental disorders.
Predictive modeling using neuroimaging data has the potential to improve our understanding of the neurobiology underlying psychiatric disorders and putatively information interventions. Accordingly, ...there is a plethora of literature reviewing published studies, the mathematics underlying machine learning, and the best practices for using these approaches. As our knowledge of mental health and machine learning continue to evolve, we instead aim to look forward and "predict" topics that we believe will be important in current and future studies. Some of the most discussed topics in machine learning, such as bias and fairness, the handling of dirty data, and interpretable models, may be less familiar to the broader community using neuroimaging-based predictive modeling in psychiatry. In a similar vein, transdiagnostic research and targeting brain-based features for psychiatric intervention are modern topics in psychiatry that predictive models are well-suited to tackle. In this work, we target an audience who is a researcher familiar with the fundamental procedures of machine learning and who wishes to increase their knowledge of ongoing topics in the field. We aim to accelerate the utility and applications of neuroimaging-based predictive models for psychiatric research by highlighting and considering these topics. Furthermore, though not a focus, these ideas generalize to neuroimaging-based predictive modeling in other clinical neurosciences and predictive modeling with different data types (e.g., digital health data).
Predictive models in neuroimaging are increasingly designed with the intent to improve risk stratification and support interventional efforts in psychiatry. Many of these models have been developed ...in samples of children school-aged or older. Nevertheless, despite growing evidence that altered brain maturation during the fetal, infant, and toddler (FIT) period modulates risk for poor mental health outcomes in childhood, these models are rarely implemented in FIT samples. Applications of predictive modeling in children of these ages provide an opportunity to develop powerful tools for improved characterization of the neural mechanisms underlying development. To facilitate the broader use of predictive models in FIT neuroimaging, we present a brief primer and systematic review on the methods used in current predictive modeling FIT studies. Reflecting on current practices in more than 100 studies conducted over the past decade, we provide an overview of topics, modalities, and methods commonly used in the field and under-researched areas. We then outline ethical and future considerations for neuroimaging researchers interested in predicting health outcomes in early life, including researchers who may be relatively new to either advanced machine learning methods or using FIT data. Altogether, the last decade of FIT research in machine learning has provided a foundation for accelerating the prediction of early-life trajectories across the full spectrum of illness and health.
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