Machine learning-based morphological brain analysis in schizophrenia and unaffected siblings: a multisite study of potential risk markers
Akihiko Shiino
Background and hypothesisAssessing schizophrenia risk factors is crucial for developing early preventive interventions. We hypothesized that unaffected siblings, who share high genetic risk, exhibit neuroanatomical signatures similar to affected patients, potentially reflecting early pathogenic processes.Study designTo overcome single-center limitations, we analyzed 1,018 participants from five independent, public databases. Brain MRIs were standardized via voxel-based morphometry, and covariate