Fig. 4: Functional network heterogeneity of extreme negative GMV deviations in each disorder.

a–d, Workflow for characterizing network-level GMV heterogeneity. For each individual in the HC_test and each clinical group (a), we assigned each brain region showing an extreme deviation to one of seven canonical cortical functional networks or three subcortical nuclei (b), such that the entire network was considered deviant if it contained at least one region with an extreme deviation. The cortical surface renderings show the resulting network-level extreme deviation maps (c). We quantified the proportion of individuals in each group showing a deviation within each network and compared these proportions to the network overlap in HC_test (d). e, Group differences in network-level overlap were evaluated with respect to two empirical null models (for details, see Extended Data Fig. 3). f,g, The network-level −log₁₀ P values associated with the difference in percent overlap for extreme negative GMV deviations between each clinical group and the HC_test cohort (gray) under group-based (f) or spatial permutation (g) testing, respectively. ** corresponds to \({P}_{{{\mathrm{FDR}}}} < 0.05\), two tailed, cases > controls, * corresponds to \({P}_{{{\mathrm{uncorrected}}}} < 0.05\), two tailed, cases > controls. The solid black line indicates −log₁₀ P = 1.6 (P = 0.05, two tailed, uncorrected). VIS, visual; SM, somatomotor; DA, dorsal attention; SAL/VA, salience/ventral attention; L, limbic; F, frontoparietal; DM, default mode; MeTe, medial temporal; Tha, thalamus; Bas, basal ganglia). Data used to generate this figure can be found in Supplementary Data 1 (Network_neg_10network).