This study investigates the relationship between brain network architecture and autism spectrum disorder (ASD), examining both individuals with ASD and those with high polygenic risk. Using data from the ABIDE and PING databases, the researchers assessed ASD-related cortical alterations and cortical correlates of polygenic risk for ASD, comparing them to structural connectome-based network measures. They found that ASD-related cortical alterations and polygenic risk correlates were more strongly implicated in cortical hubs than non-hub regions. Machine learning models predicted polygenic risk from structural connectomes (r = 0.30, p < 0.0001), with top predictors corresponding to ASD disease epicenters. The study highlights the crucial role of brain network architecture in a continuum model of ASD.

Budhachandra Khundrakpam, Neha Bhutani, Uku Vainik, Jinnan Gong, Noor Al-Sharif, Alain Dagher, Tonya White, Alan C. Evans