Stratifying the autistic phenotype using electrophysiological indices of social perception

Luke Mason, Carolin Moessnang, Christopher Chatham, Lindsay Ham, Julian Tillmann, Guillaume Dumas, Claire Ellis, Claire S. Leblond, Freddy Cliquet, Thomas Bourgeron, Christian Beckmann, Tony Charman, Beth Oakley, Tobias Banaschewski, Andreas Meyer-Lindenberg, Simon Baron-Cohen, Sven Bölte, Jan K. Buitelaar, Sarah Durston, Eva LothBob Oranje, Antonio Persico, Flavio Dell'Acqua, Christine Ecker, Mark H. Johnson, Declan Murphy, Emily J.H. Jones

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)

Abstract

Autism spectrum disorder (ASD) is a neurodevelopmental condition characterized by difficulties in social communication, but also great heterogeneity. To offer individualized medicine approaches, we need to better target interventions by stratifying autistic people into subgroups with different biological profiles and/or prognoses. We sought to validate neural responses to faces as a potential stratification factor in ASD by measuring neural (electroencephalography) responses to faces (critical in social interaction) in N = 436 children and adults with and without ASD. The speed of early-stage face processing (N170 latency) was on average slower in ASD than in age-matched controls. In addition, N170 latency was associated with responses to faces in the fusiform gyrus, measured with functional magnetic resonance imaging, and polygenic scores for ASD. Within the ASD group, N170 latency predicted change in adaptive socialization skills over an 18-month follow-up period; data-driven clustering identified a subgroup with slower brain responses and poor social prognosis. Use of a distributional data-driven cutoff was associated with predicted improvements of power in simulated clinical trials targeting social functioning. Together, the data provide converging evidence for the utility of the N170 as a stratification factor to identify biologically and prognostically defined subgroups in ASD.

Original languageEnglish
Article numbereabf8987
Pages (from-to)eabf8987
JournalScience translational medicine
Volume14
Issue number658
DOIs
Publication statusPublished - 17 Aug 2022

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