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

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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Main Authors: 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: Article
Language:English
Published: Frontiers Media S.A. 2025-06-01
Series:Frontiers in Psychiatry
Subjects:
precision psychiatry
schizophrenia
structural covariance
machine learning
neuroimaging
brain connectivity
Online Access:https://www.frontiersin.org/articles/10.3389/fpsyt.2025.1570797/full
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https://www.frontiersin.org/articles/10.3389/fpsyt.2025.1570797/full

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