Connectome-based symptom mapping and in silico related gene expression in children with autism and/or attention-deficit/hyperactivity disorder.

Abstract

Clinical, neuroimaging and genomics evidence have increasingly underscored a degree of overlap between autism and attention-deficit/hyperactivity disorder (ADHD). This study explores the specific contribution of their core symptoms to shared biology in N = 166 verbal children (6-12 years) with rigorously-established primary diagnoses of either autism or ADHD (without autism). We investigated the associations between inter-individual differences in low motion whole-brain intrinsic functional connectivity (iFC) and dimensional measures of autism and ADHD symptoms indexed by clinician-based observation and parent interview, respectively. Additionally, we explored their linked gene expression patterns in silico. Whole-brain multivariate distance matrix regression revealed a transdiagnostic association between autism severity and iFC of two nodes primarily on the left hemisphere: the middle frontal gyrus of the frontoparietal network and the posterior cingulate cortex of the default mode network. Across children, the greater the iFC between these nodes, the more severe the autism symptoms, even after controlling for ADHD ratings. Results from secondary segregation analyses were consistent with primary findings, underscoring the significance of internetwork iFC for autism symptom severity across diagnoses. No statistically significant brain-behavior relationships were observed for ADHD symptoms. Genetic enrichment analyses of the iFC maps associated with autism symptoms implicated genes known to: (i) have greater rate of variance in autism and ADHD, and (ii) be involved in neuron projections, suggesting shared genetic mechanisms for this specific brain-clinical phenotype. These findings underscore the relevance of transdiagnostic dimensional approaches in linking clinically-defined and observation-based phenomena to shared presentations at the macroscale circuit- and genomic-levels across diagnoses.