Individualized morphometric-similarity deviations in autism linked to cortical hierarchy and microarchitecture

Autism spectrum disorder (ASD) is marked by profound neurobiological heterogeneity, yet it remains unclear whether atypical brain organization reflects a diffuse low-amplitude pattern shared broadly across individuals or distinct spatially specific deviations that vary from person to person. Resolving this question requires methods that move beyond group averages to map individualized cortical atypicality against normative expectations. We built subject-level morphometric similarity networks in which edges quantify multivariate morphometric similarity between cortical regions. We then applied hierarchical Bayesian regression (HBR) normative models to estimate region-wise deviations from age- and sex-adjusted norms while accounting for site variation. Individuals with ASD carried a greater burden of extreme regional deviations, yet those deviations were focal and idiosyncratic rather than uniformly distributed. The spatial pattern of deviation aligned with canonical cortical hierarchies, shifting similarity toward sensory and visual poles and away from association cortex. Moreover, edge-level testing identified a sparse reconfiguration concentrated in occipito-temporal and cross-network connections. Clustering of individual deviation maps identified two robust subgroups that differ in the global sign of deviation and in their cognitive and molecular correlates. These results show that cortical atypicality in ASD is constrained by brain gradients, expressed in subgroup-specific forms, and linked to distinct biological and cognitive axes.