A comparative machine learning study of schizophrenia biomarkers derived from functional connectivity.

ABSTRACT: Functional connectivity holds promise as a biomarker of schizophrenia. Yet, the high dimensionality of predictive models trained on functional connectomes, combined with small sample sizes in clinical research, increases the risk of overfitting. Recently, low-dimensional representations of the connectome such as macroscale cortical gradients and gradient dispersion have been proposed, with studies noting consistent gradient and dispersion differences in psychiatric conditions. However, it is unknown which of these derived measures has the highest predictive capacity and how they compare to raw functional connectivity specifically in the case of schizophrenia. Our study evaluates which connectome features derived from resting state functional MRI - functional connectivity, gradients, or gra

SUBMITTER: Shevchenko V 

PROVIDER: S-EPMC11754439 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature