Exploring the predictive value of structural covariance networks for the diagnosis of schizophrenia

IntroductionSchizophrenia is a psychiatric disorder hypothesized to result from disturbed brain connectivity. Structural covariance networks (SCN) describe the shared variation in morphological properties emerging from coordinated neurodevelopmental processes, This study evaluates the potential of S...

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Hauptverfasser:	Clara S. Vetter, Annika Bender, Dominic B. Dwyer, Max Montembeault, Anne Ruef, Katharine Chisholm, Lana Kambeitz-Ilankovic, Linda A. Antonucci, Stephan Ruhrmann, Joseph Kambeitz, Marlene Rosen, Theresa Lichtenstein, Anita Riecher-Rössler, Rachel Upthegrove, Raimo K. R. Salokangas, Jarmo Hietala, Christos Pantelis, Rebekka Lencer, Eva Meisenzahl, Stephen J. Wood, Paolo Brambilla, Stefan Borgwardt, Peter Falkai, Alessandro Bertolino, Nikolaos Koutsouleris, PRONIA Consortium
Format:	Artigo
Sprache:	Inglês
Veröffentlicht:	Frontiers Media S.A. 2025-06-01
Schriftenreihe:	Frontiers in Psychiatry
Schlagworte:	precision psychiatry schizophrenia structural covariance machine learning neuroimaging brain connectivity
Online-Zugang:	https://www.frontiersin.org/articles/10.3389/fpsyt.2025.1570797/full
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