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

Publikation: Beitrag in FachzeitschriftForschungsartikelBeigetragenBegutachtung

Beitragende

Abstract

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.

Details

OriginalspracheEnglisch
Aufsatznummer870
FachzeitschriftBrain sciences
Jahrgang13
Ausgabenummer6
PublikationsstatusVeröffentlicht - Juni 2023
Peer-Review-StatusJa

Externe IDs

ORCID /0000-0001-8870-0041/work/142251346
ORCID /0000-0001-9298-2125/work/143074524
ORCID /0000-0002-2666-859X/work/146643978
ORCID /0000-0003-4286-5830/work/148143969
ORCID /0000-0002-3974-7115/work/148144896
ORCID /0000-0002-3415-5583/work/150329693

Schlagworte

ASJC Scopus Sachgebiete

Schlagwörter

  • amygdala nuclei, bipolar risk, hippocampal subfields, machine learning, MRI