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doi: 10.1037/abn0000648 , 10.1101/2020.05.06.20092619 , 10.5281/zenodo.5707441 , 10.5281/zenodo.5707442
pmid: 35901386
pmc: PMC9330670
Abstract Dimensional approaches to psychopathology interrogate the core neurocognitive domains interacting at the individual level to shape diagnostic symptoms. Embedding this approach in prospective longitudinal studies could transform our understanding of the mechanisms underlying neurodevelopmental disorders. Such designs require us to move beyond traditional group comparisons and determine which domain-specific atypicalities apply at the level of the individual, and whether they vary across distinct phenotypic subgroups. As a proof of principle, this study examines how the domain of face processing contributes to a clinical diagnosis of Autism Spectrum Disorder (ASD). We used an event-related potentials (ERPs) task in a cohort of 8-month-old infants with (n=148) and without (n=68) an older sibling with ASD, and combined traditional case-control comparisons with machine-learning techniques like supervised classification for prediction of clinical outcome at 36 months and Bayesian hierarchical clustering for stratification into subgroups. Our findings converge to indicate that a broad profile of alterations in the time-course of neural processing of faces is an early predictor of later ASD diagnosis. Furthermore, we identified two brain response-defined subgroups in ASD that showed distinct alterations in different aspects of face processing compared to siblings without ASD diagnosis, suggesting that individual differences between infants contribute to the diffuse pattern of alterations predictive of ASD in the first year of life. This study shows that moving from group-level comparisons to pattern recognition and stratification can help to understand and reduce heterogeneity in clinical cohorts, and improve our understanding of the mechanisms that lead to later neurodevelopmental outcomes. General Scientific Summary This study suggests that neural processing of faces is diffusely atypical in Autism Spectrum Disorder, and that it represents a strong candidate predictor of outcome at an individual level in the first year of life.
Radboudumc 7: Neurodevelopmental disorders DCMN: Donders Center for Medical Neuroscience, Autism Spectrum Disorder, 150, 610, 220 Statistical Imaging Neuroscience, Infant, Bayes Theorem, psyc, Special Section: Integrating Development Into the Research Domain Criteria (RDoC), 130 000 Cognitive Neurology & Memory, Medical Neuroscience - Radboud University Medical Center, Humans, Prospective Studies, Autistic Disorder, Facial Recognition
Radboudumc 7: Neurodevelopmental disorders DCMN: Donders Center for Medical Neuroscience, Autism Spectrum Disorder, 150, 610, 220 Statistical Imaging Neuroscience, Infant, Bayes Theorem, psyc, Special Section: Integrating Development Into the Research Domain Criteria (RDoC), 130 000 Cognitive Neurology & Memory, Medical Neuroscience - Radboud University Medical Center, Humans, Prospective Studies, Autistic Disorder, Facial Recognition
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