Abstract
Structural covariance networks provide data-driven and biologically-relevant brain parcellations, potentially increasing power to detect disease effects. Here, we utilized orthogonal projective non-negative matrix factorization (opNMF) to investigate patterns of structural covariance (PSCs) in schizophrenia (SCZ), and their clinical correlates in a large, multi-site dataset.
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