Machine Learning Prediction of Estimated Risk for Bipolar Disorders Using Hippocampal Subfield and Amygdala Nuclei Volumes

The pathophysiology of bipolar disorder (BD) remains mostly unclear. Yet, a valid biomarker is necessary to improve upon the early detection of this serious disorder. Patients with manifest BD display reduced volumes of the hippocampal subfields and amygdala nuclei. In this pre-registered analysis,...

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Autori principali:	Fabian Huth, Leonardo Tozzi, Michael Marxen, Philipp Riedel, Kyra Bröckel, Julia Martini, Christina Berndt, Cathrin Sauer, Christoph Vogelbacher, Andreas Jansen, Tilo Kircher, Irina Falkenberg, Florian Thomas-Odenthal, Martin Lambert, Vivien Kraft, Gregor Leicht, Christoph Mulert, Andreas J. Fallgatter, Thomas Ethofer, Anne Rau, Karolina Leopold, Andreas Bechdolf, Andreas Reif, Silke Matura, Silvia Biere, Felix Bermpohl, Jana Fiebig, Thomas Stamm, Christoph U. Correll, Georg Juckel, Vera Flasbeck, Philipp Ritter, Michael Bauer, Andrea Pfennig, Pavol Mikolas
Natura:	Artigo
Lingua:	Inglês
Pubblicazione:	MDPI AG 2023-05-01
Serie:	Brain Sciences
Soggetti:	bipolar risk hippocampal subfields amygdala nuclei MRI machine learning
Accesso online:	https://www.mdpi.com/2076-3425/13/6/870
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