Toward understanding autism heterogeneity: Identifying clinical subgroups and neuroanatomical deviations.

Abstract

Autism spectrum disorder ("autism") is a neurodevelopmental condition characterized by substantial behavioral and neuroanatomical heterogeneity. This poses significant challenges to understanding its neurobiological mechanisms and developing effective interventions. Identifying phenotypically more homogeneous subgroups and shifting the focus from average group differences to individuals is a promising approach to addressing heterogeneity. In the present study, we aimed to parse clinical and neuroanatomical heterogeneity in autism by combining clustering of clinical features with normative modeling based on neuroanatomical measures (cortical thickness [CT] and subcortical volume) within the Autism Brain Imaging Data Exchange data sets (N autism = 861, N nonautistic individuals [N NAI] = 886, age range = 5-64). First, model-based clustering was applied to autistic symptoms as measured by the Autism Diagnostic Observation Schedule to identify clinical subgroups of autism (N autism = 499). Next, we ran normative modeling on CT and subcortical parcellations (N autism = 690, N NAI = 744) and examined whether clinical subgrouping resulted in increased neurobiological homogeneity within the subgroups compared to the entire autism group by comparing their spatial overlap of neuroanatomical deviations. We further investigated whether the identified subgroups improved the accuracy of autism classification based on the neuroanatomical deviations using supervised machine learning with cross-validation. Results yielded two clinical subgroups primarily differing in restrictive and repetitive behaviors (RRBs). Both subgroups showed increased homogeneity in localized deviations with the high-RRB subgroup showing increased volume deviations in the cerebellum and the low-RRB subgroup showing decreased CT deviations predominantly in the postcentral gyrus and fusiform cortex. Nevertheless, substantial within-group heterogeneity remained highlighted by the lack of improvement of the classifier's performance when distinguishing between the subgroups and NAI. Future research should aim to further reduce heterogeneity incorporating additional neuroanatomical clustering in even larger samples. This will eventually pave the way for more tailored behavioral interventions and improving clinical outcomes. (PsycInfo Database Record (c) 2024 APA, all rights reserved).