Fig. 1: Overview of the methodological approach.

a, The features for each modality were extracted from the right and the left FFG. These were (1) GM volume based on VBM for structural MRI, (2) T-maps contrasting the faces condition to the shapes condition reflecting sensitivity to faces from the Hariri paradigm for task-fMRI, (3) seed-based connectivity analysis (SCA) between the FFA and all other intra-FFG voxels for rs-fMRI and (4) the principal component of source-reconstructed time series for EEG. b, Next, normative modeling was applied to each imaging modality using Bayesian linear regression. The depicted trajectories per modality are schematic and the actual modality-specific normative models are depicted in Extended Data Fig. 1. c, To model cross-subject individual-level variation, resulting Z-deviation maps per modality were statistically merged using LICA resulting in measures of modality contributions and subject loadings. d, Next, we tested for group differences in ICs and group separability using either multi- or unimodal ICs. e, Finally, we computed multivariate associations (that is, CCA) between subject loadings and clinical, cognitive measures related to either social–communicative or nonsocial features.