Relationships between resting state brain networks and cognition across psychosis, depression, and clinical high-risk for psychosis
Dilara Steenken, Madalina Buciuman, David Popovic, Shalaila Haas, Linda Antonucci, Lana Kambeitz-Ilankovic, Anne Ruef, Stefan Borgwardt, Joseph Kambeitz, Christos Pantelis, Rebekka Lencer, Alessandro Bertolino, Paolo Brambilla, Rachel Upthegrove, Stephan J. Wood, Peter Falkai, Anita Riecher-Rössler, Stephan Ruhrmann, Frauke Schultze-Lutter, Eva Meisenzahl, Jarmo Hietala, Raimo K. Salokangas, Nikolaos Koutsouleris · FENS Forum 2024
Individuals with depression, psychosis and clinically high risk for psychosis face cognitive decline and show altered functional connectivity in resting-state networks. An unsupervised machine-learning method, sparse partial least squares analysis (SPLS) captures latent variables from clinical features and brain mechanisms, allowing the examination of relationships between functional connectivity and cognitive functions across disorders. This study aimed to explore relationships between functional connectivity, cognitive domains and study groups using SPLS. 990 participants with recent-onset p
ePoster · Neuroscience