Fractionating Autism Based on Neuroanatomical Normative Modeling

Overview

Journal Transl Psychiatry

Specialties Psychiatry
Public Health

Date 2020 Nov 7

PMID 33159037

Citations 24

Authors

Mariam Zabihi

Dorothea L Floris

Seyed Mostafa Kia

Thomas Wolfers

Julian Tillmann

Alberto Llera Arenas

Carolin Moessnang

Tobias Banaschewski

Rosemary Holt

Simon Baron-Cohen

Eva Loth

Tony Charman

Thomas Bourgeron

Declan Murphy

Christine Ecker

Jan K Buitelaar

Christian F Beckmann

Andre Marquand

Affiliations

Soon will be listed here.

Abstract

Autism is a complex neurodevelopmental condition with substantial phenotypic, biological, and etiologic heterogeneity. It remains a challenge to identify biomarkers to stratify autism into replicable cognitive or biological subtypes. Here, we aim to introduce a novel methodological framework for parsing neuroanatomical subtypes within a large cohort of individuals with autism. We used cortical thickness (CT) in a large and well-characterized sample of 316 participants with autism (88 female, age mean: 17.2 ± 5.7) and 206 with neurotypical development (79 female, age mean: 17.5 ± 6.1) aged 6-31 years across six sites from the EU-AIMS multi-center Longitudinal European Autism Project. Five biologically based putative subtypes were derived using normative modeling of CT and spectral clustering. Three of these clusters showed relatively widespread decreased CT and two showed relatively increased CT. These subtypes showed morphometric differences from one another, providing a potential explanation for inconsistent case-control findings in autism, and loaded differentially and more strongly onto symptoms and polygenic risk, indicating a dilution of clinical effects across heterogeneous cohorts. Our results provide an important step towards parsing the heterogeneous neurobiology of autism.

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