Inter-subject functional variability of gray and white matter in autism spectrum disorder.

This brain imaging study looked at how brain connections vary between people with autism and those without. They found that people with autism have more variable brain connection patterns in specific brain regions. These differences were related to autism symptom severity and could help identify autism with 77% accuracy. The study also found links to specific genes involved in sensory development and brain support cells.

This neuroimaging study examined brain connectivity variability in 272 individuals with autism and 368 controls using functional MRI data. Researchers found that autistic individuals showed increased variability in brain connectivity patterns in both gray matter (particularly in default mode and attention networks) and white matter (mainly in corpus callosum regions). These connectivity differences correlated with autism symptom severity and achieved 77% accuracy in distinguishing autism from typical development when combined. Gene expression analysis revealed that gray matter changes were linked to sensory development genes, while white matter changes were associated with astrocyte-related pathways, providing insights into potential biological mechanisms underlying autism.

Findings support brain connectivity as potential autism biomarker and highlight individual variability importance. May inform personalized treatment approaches and provide therapeutic targets through identified genetic pathways, though diagnostic accuracy needs improvement for clinical implementation.

Study is cross-sectional, limiting causal inferences. Sample heterogeneity may affect generalizability. Diagnostic accuracy of 77% is modest for clinical application. Gene expression correlations are exploratory and require validation.

Autism spectrum disorder (ASD) is biologically highly heterogeneous; however, most studies focused on group-level analyses, overlooking inter-subject variability in functional connectivity (IVFC), particularly in white matter IVFC (WM-IVFC) where mechanisms and genetic influences remain unclear. Resting-state functional magnetic resonance imaging data from 272 patients with ASD and 368 typical controls (TC) were obtained from the Autism Brain Imaging Data Exchange Project (ABIDE II) database. Gray matter IVFC (GM-IVFC) and WM-IVFC were compared between groups and correlated with symptom severity. A support vector machine (SVM) model was constructed to assess the diagnostic potential of GM-IVFC, WM-IVFC, and their combination.

Transcriptome neuroimaging analyses were conducted by correlating IVFC alterations with regional gene expression data from the Allen Human Brain Atlas. Both GM-IVFC and WM-IVFC showed regionally uneven distributions across the brain. Compared to TC, patients with ASD exhibited increased GM-IVFC mainly in the default mode and attention networks, and altered WM-IVFC mainly in the genu of the corpus callosum and superior fronto-occipital fasciculus, which were significantly associated with symptom severity. The SVM model utilizing both the GM-IVFC and WM-IVFC features yielded the best diagnostic performance (accuracy = 0.77).

Transcriptome-neuroimaging associations revealed that GM-IVFC alterations were enriched in genes involved in sensory organ morphogenesis, whereas WM-IVFC alterations were linked to astrocyte-related pathways. Our findings highlight the complementary roles of GM-IVFC and WM-IVFC, supporting their potential as biomarkers and offering novel insights into the genetic and neurobiological underpinnings of ASD.