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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,...
Autores principales: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10296102/ https://www.ncbi.nlm.nih.gov/pubmed/37371350 http://dx.doi.org/10.3390/brainsci13060870 |
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author | Huth, Fabian Tozzi, Leonardo Marxen, Michael Riedel, Philipp Bröckel, Kyra Martini, Julia Berndt, Christina Sauer, Cathrin Vogelbacher, Christoph Jansen, Andreas Kircher, Tilo Falkenberg, Irina Thomas-Odenthal, Florian Lambert, Martin Kraft, Vivien Leicht, Gregor Mulert, Christoph Fallgatter, Andreas J. Ethofer, Thomas Rau, Anne Leopold, Karolina Bechdolf, Andreas Reif, Andreas Matura, Silke Biere, Silvia Bermpohl, Felix Fiebig, Jana Stamm, Thomas Correll, Christoph U. Juckel, Georg Flasbeck, Vera Ritter, Philipp Bauer, Michael Pfennig, Andrea Mikolas, Pavol |
author_facet | Huth, Fabian Tozzi, Leonardo Marxen, Michael Riedel, Philipp Bröckel, Kyra Martini, Julia Berndt, Christina Sauer, Cathrin Vogelbacher, Christoph Jansen, Andreas Kircher, Tilo Falkenberg, Irina Thomas-Odenthal, Florian Lambert, Martin Kraft, Vivien Leicht, Gregor Mulert, Christoph Fallgatter, Andreas J. Ethofer, Thomas Rau, Anne Leopold, Karolina Bechdolf, Andreas Reif, Andreas Matura, Silke Biere, Silvia Bermpohl, Felix Fiebig, Jana Stamm, Thomas Correll, Christoph U. Juckel, Georg Flasbeck, Vera Ritter, Philipp Bauer, Michael Pfennig, Andrea Mikolas, Pavol |
author_sort | Huth, Fabian |
collection | PubMed |
description | 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, we used structural MRI (n = 271, 7 sites) to compare volumes of hippocampus, amygdala and their subfields/nuclei between help-seeking subjects divided into 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 a 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 alterations of the hippocampus and amygdala may not be as pronounced in young people at risk; nonetheless, machine learning can predict the 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. |
format | Online Article Text |
id | pubmed-10296102 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-102961022023-06-28 Machine Learning Prediction of Estimated Risk for Bipolar Disorders Using Hippocampal Subfield and Amygdala Nuclei Volumes Huth, Fabian Tozzi, Leonardo Marxen, Michael Riedel, Philipp Bröckel, Kyra Martini, Julia Berndt, Christina Sauer, Cathrin Vogelbacher, Christoph Jansen, Andreas Kircher, Tilo Falkenberg, Irina Thomas-Odenthal, Florian Lambert, Martin Kraft, Vivien Leicht, Gregor Mulert, Christoph Fallgatter, Andreas J. Ethofer, Thomas Rau, Anne Leopold, Karolina Bechdolf, Andreas Reif, Andreas Matura, Silke Biere, Silvia Bermpohl, Felix Fiebig, Jana Stamm, Thomas Correll, Christoph U. Juckel, Georg Flasbeck, Vera Ritter, Philipp Bauer, Michael Pfennig, Andrea Mikolas, Pavol Brain Sci Article 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, we used structural MRI (n = 271, 7 sites) to compare volumes of hippocampus, amygdala and their subfields/nuclei between help-seeking subjects divided into 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 a 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 alterations of the hippocampus and amygdala may not be as pronounced in young people at risk; nonetheless, machine learning can predict the 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. MDPI 2023-05-27 /pmc/articles/PMC10296102/ /pubmed/37371350 http://dx.doi.org/10.3390/brainsci13060870 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Huth, Fabian Tozzi, Leonardo Marxen, Michael Riedel, Philipp Bröckel, Kyra Martini, Julia Berndt, Christina Sauer, Cathrin Vogelbacher, Christoph Jansen, Andreas Kircher, Tilo Falkenberg, Irina Thomas-Odenthal, Florian Lambert, Martin Kraft, Vivien Leicht, Gregor Mulert, Christoph Fallgatter, Andreas J. Ethofer, Thomas Rau, Anne Leopold, Karolina Bechdolf, Andreas Reif, Andreas Matura, Silke Biere, Silvia Bermpohl, Felix Fiebig, Jana Stamm, Thomas Correll, Christoph U. Juckel, Georg Flasbeck, Vera Ritter, Philipp Bauer, Michael Pfennig, Andrea Mikolas, Pavol Machine Learning Prediction of Estimated Risk for Bipolar Disorders Using Hippocampal Subfield and Amygdala Nuclei Volumes |
title | Machine Learning Prediction of Estimated Risk for Bipolar Disorders Using Hippocampal Subfield and Amygdala Nuclei Volumes |
title_full | Machine Learning Prediction of Estimated Risk for Bipolar Disorders Using Hippocampal Subfield and Amygdala Nuclei Volumes |
title_fullStr | Machine Learning Prediction of Estimated Risk for Bipolar Disorders Using Hippocampal Subfield and Amygdala Nuclei Volumes |
title_full_unstemmed | Machine Learning Prediction of Estimated Risk for Bipolar Disorders Using Hippocampal Subfield and Amygdala Nuclei Volumes |
title_short | Machine Learning Prediction of Estimated Risk for Bipolar Disorders Using Hippocampal Subfield and Amygdala Nuclei Volumes |
title_sort | machine learning prediction of estimated risk for bipolar disorders using hippocampal subfield and amygdala nuclei volumes |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10296102/ https://www.ncbi.nlm.nih.gov/pubmed/37371350 http://dx.doi.org/10.3390/brainsci13060870 |
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