A brain structural connectivity biomarker for diagnosis of autism spectrum disorder in early childhood

Abstract

AbstractObjectiveAutism spectrum disorder (ASD) is associated with altered brain development, but it is unclear which specific structural changes may serve as potential diagnostic markers. This study aimed to identify and model brain-wide differences in structural connectivity using MRI diffusion tensor imaging (DTI) in young ASD and typically developing (TD) children (3·5-6 years old).MethodsNinety-three ASD and 26 TD children were included in a discovery dataset and 12 ASD and 9 TD children from different sites included as independent validation datasets. Brain-wide (294 regions) structural connectivity was measured using DTI (fractional anisotropy, FA) under sedation together with symptom severity and behavioral and cognitive development. A connection matrix was constructed for each child for comparisons between ASD and TD groups. Pattern classification was performed and the resulting model tested on two independent datasets.ResultsThirty-three structural connections showed increased FA in ASD compared to TD children and associated with both symptom severity and general cognitive development. The majority (29/33) involved the frontal lobe and comprised five different networks with functional relevance to default mode, motor control, social recognition, language and reward. Overall, classification accuracy is very high in the discovery dataset 96.77%, and 91·67% and 88·89% in the two independent validation datasets.ConclusionsIdentified structural connectivity differences primarily involving the frontal cortex can very accurately distinguish individual ASD from TD children and may therefore represent a robust early brain biomarker.