A critical role of brain network architecture in a continuum model of autism spectrum disorders spanning from healthy individuals with genetic liability to individuals with ASD.

Researchers studied brain scans from over 900 people to understand how brain connections differ in autism. They found that autism affects the brain's 'hub' regions - areas that are highly connected and important for communication between different brain parts. The study showed that both people with autism and those with genetic risk for autism have similar changes in these hub regions. The research supports the idea that autism exists on a continuum, with genetic risk and diagnosed autism sharing similar brain connectivity patterns.

This neuroimaging study examined brain network architecture in 560 males with and without autism spectrum disorder (ASD), plus 391 healthy individuals, to understand how brain connectivity patterns relate to both ASD diagnosis and genetic risk. Researchers found that ASD-related brain changes and genetic risk factors particularly affected brain network 'hubs' - highly connected regions that are central to brain communication. Machine learning models could predict genetic ASD risk from brain connectivity patterns with moderate accuracy (r=0.30). Two specific brain regions (left inferior parietal and left supramarginal) emerged as key predictive areas and potential disease epicenters.

The findings support a continuum model where genetic risk and clinical ASD exist on a spectrum, both characterized by altered connectivity in critical brain network hubs.

Findings support brain connectivity assessment as a potential complement to clinical diagnosis and genetic testing. The identification of hub regions as preferentially affected may inform future targeted interventions. However, clinical translation requires validation in more diverse populations including females.

Study focused only on males, limiting generalizability to females with ASD. Cross-sectional design prevents understanding of causal relationships. Machine learning prediction accuracy was moderate, suggesting additional factors influence ASD risk beyond brain connectivity patterns measured.

Studies have shown cortical alterations in individuals with autism spectrum disorders (ASD) as well as in individuals with high polygenic risk for ASD. An important addition to the study of altered cortical anatomy is the investigation of the underlying brain network architecture that may reveal brain-wide mechanisms in ASD and in polygenic risk for ASD. Such an approach has been proven useful in other psychiatric disorders by revealing that brain network architecture shapes (to an extent) the disorder-related cortical alterations. This study uses data from a clinical dataset-560 male subjects (266 individuals with ASD and 294 healthy individuals, CTL, mean age at 17.2 years) from the Autism Brain Imaging Data Exchange database, and data of 391 healthy individuals (207 males, mean age at 12.1 years) from the Pediatric Imaging, Neurocognition and Genetics database.

ASD-related cortical alterations (group difference, ASD-CTL, in cortical thickness) and cortical correlates of polygenic risk for ASD were assessed, and then statistically compared with structural connectome-based network measures (such as hubs) using spin permutation tests. Next, we investigated whether polygenic risk for ASD could be predicted by network architecture by building machine-learning based prediction models, and whether the top predictors of the model were identified as disease epicenters of ASD. We observed that ASD-related cortical alterations as well as cortical correlates of polygenic risk for ASD implicated cortical hubs more strongly than non-hub regions. We also observed that age progression of ASD-related cortical alterations and cortical correlates of polygenic risk for ASD implicated cortical hubs more strongly than non-hub regions.

Further investigation revealed that structural connectomes predicted polygenic risk for ASD (r = 0.30, p < 0.0001), and two brain regions (the left inferior parietal and left suparmarginal) with top predictive connections were identified as disease epicenters of ASD. Our study highlights a critical role of network architecture in a continuum model of ASD spanning from healthy individuals with genetic risk to individuals with ASD. Our study also highlights the strength of investigating polygenic risk scores in addition to multi-modal neuroimaging measures to better understand the interplay between genetic risk and brain alterations associated with ASD.