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

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

doi:10.20944/preprints202304.1086.v1

A peer-reviewed article of this Preprint also exists.

Huth, F.; Tozzi, L.; Marxen, M.; Riedel, P.; Bröckel, K.; Martini, J.; Berndt, C.; Sauer, C.; Vogelbacher, C.; Jansen, A.; Kircher, T.; Falkenberg, I.; Thomas-Odenthal, F.; Lambert, M.; Kraft, V.; Leicht, G.; Mulert, C.; Fallgatter, A.J.; Ethofer, T.; Rau, A.; Leopold, K.; Bechdolf, A.; Reif, A.; Matura, S.; Biere, S.; Bermpohl, F.; Fiebig, J.; Stamm, T.; Correll, C.U.; Juckel, G.; Flasbeck, V.; Ritter, P.; Bauer, M.; Pfennig, A.; Mikolas, P. Machine Learning Prediction of Estimated Risk for Bipolar Disorders Using Hippocampal Subfield and Amygdala Nuclei Volumes. Brain Sci. 2023, 13, 870. Huth, F.; Tozzi, L.; Marxen, M.; Riedel, P.; Bröckel, K.; Martini, J.; Berndt, C.; Sauer, C.; Vogelbacher, C.; Jansen, A.; Kircher, T.; Falkenberg, I.; Thomas-Odenthal, F.; Lambert, M.; Kraft, V.; Leicht, G.; Mulert, C.; Fallgatter, A.J.; Ethofer, T.; Rau, A.; Leopold, K.; Bechdolf, A.; Reif, A.; Matura, S.; Biere, S.; Bermpohl, F.; Fiebig, J.; Stamm, T.; Correll, C.U.; Juckel, G.; Flasbeck, V.; Ritter, P.; Bauer, M.; Pfennig, A.; Mikolas, P. Machine Learning Prediction of Estimated Risk for Bipolar Disorders Using Hippocampal Subfield and Amygdala Nuclei Volumes. Brain Sci. 2023, 13, 870.

Abstract

The pathophysiology of bipolar disorder (BD) remains mostly unclear. Yet, a valid biomarker is necessary to improve 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, we used structural MRI (N=271, 7 sites), to compare volumes of hippocampus, amygdala, and their subfields/nuclei between help-seeking subjects divided in risk groups for BD as estimated by BPSS-P, BARS and EPIbipolar. We performed between-group comparisons using linear mixed effects models for all three risk assessment tools. Additionally, we aimed to differentiate the risk groups using linear support vector machine. We found no significant volume differences between the risk groups for all limbic structures during the main analysis. However, the SVM could still classify subjects at risk according to BPSS-P criteria with a balanced accuracy of 66.90% (95% CI 59.2– 74.6) for 10-fold cross-validation and 61.9% (95% CI 52.0– 71.9) for leave-one-site-out. Structural altera-tions of hippocampus and amygdala may not be as pronounced in young people at risk, nonetheless, ma-chine learning can predict estimated risk for BD above chance. This suggests that neural changes may not merely be a consequence of BD and may have prognostic clinical value.

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

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