Classification of psychosis spectrum disorders using graph convolutional networks with structurally constrained functional connectomes.

ABSTRACT: This article considers the problem of classifying individuals in a dataset of diverse psychosis spectrum conditions, including persons with subsyndromal psychotic-like experiences (PLEs) and healthy controls. This task is more challenging than the traditional problem of distinguishing patients with a diagnosed disorder from controls using brain network features, since the neurobiological differences between PLE individuals and healthy persons are less pronounced. Further, examining a transdiagnostic sample compared to controls is concordant with contemporary approaches to understanding the full spectrum of neurobiology of psychoses. We consider both support vector machines (SVMs) and graph convolutional networks (GCNs) for classification, with a variety of edge selection methods for proces

SUBMITTER: Lewis M 

PROVIDER: S-EPMC11655243 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature