Population modelling of the functional connectome in autism spectrum disorder

Background. Autism spectrum disorder (ASD) is marked by heterogeneity in cognitive and social functioning, yet traditional case-control approaches often overlook this variability. In this study, we apply population reference modeling to functional connectivity (FC) data to better capture individual differences in ASD. Methods. Using movie-watching fMRI data from neurotypical (NT) participants, we derived age- and sex-specific trajectories of system segregation across large-scale brain networks. We then computed deviation indices for autistic individuals and examined their relationship to behavioural measures including IQ, memory, language, and social responsiveness. Results. Our results show that autistic participants exhibit significantly negative deviations in system segregation across most networks, suggesting delayed functional specialization. These deviations were behaviourally meaningful: greater negative deviation in the salience network correlated with higher social symptom severity, while positive deviations in control and default networks were associated with stronger nonverbal IQ and language skills. Episodic memory accuracy from a self-referential encoding task also tracked with deviations in the salience network, highlighting the relevance of network development to self- and other-referential processing.Limitations.Although our population reference model was derived from the moderate-sized, diverse HBN dataset, its generalizability is limited by demographic and geographic biases. Additionally, the small McGill dataset may have constrained our ability to detect additional effects. Conclusions. These findings support the utility of FC-based population modeling with child-friendly movie-fMRI in identifying meaningful individual differences in ASD. By moving beyond group averages, we can begin to characterize distinct developmental trajectories and cognitive profiles within the spectrum. This approach may inform early detection and personalized intervention strategies and ultimately help refine our understanding of the neural basis of autism.