Machine learning-based assessment of morphometric abnormalities distinguishes bipolar disorder and major depressive disorder

Kewei He, Jingbo Zhang, Yang Huang, Xue Mo, Renqiang Yu, Jing Min, Tong Zhu, Yunfeng Ma, Xiangqian He, Fajin Lv, Jianguang Zeng, Chao Li, Robert McNamara, Du Lei (Lead / Corresponding author), Mengqi Liu (Lead / Corresponding author)

Research output: Contribution to journalArticlepeer-review

Abstract

Introduction
Bipolar disorder (BD) and major depressive disorder (MDD) have overlapping clinical presentations which may make it difficult for clinicians to distinguish them potentially resulting in misdiagnosis. This study combined structural MRI and machine learning techniques to determine whether regional morphological differences could distinguish patients with BD and MDD.

Methods
A total of 123 participants, including BD (n=31), MDD (n=48), and healthy controls (HC, n=44), underwent high-resolution 3D T1-weighted imaging. Cortical thickness, surface area, and subcortical volumes were measured using FreeSurfer software. Common and classic machine learning models were utilized to identify distinct morphometric alterations between BD and MDD.

Results
Significant morphological differences were observed in both common and distinct brain regions between BD, MDD, and HC. Specifically, abnormalities in the amygdala, thalamus, medial orbitofrontal cortex and fusiform were observed in both BD and MDD compared with HC. Relative to HC, unique differences in BD were identified in the lateral occipital and inferior/middle temporal regions, whereas MDD exhibited differences in nucleus accumbens and middle temporal regions.BD exhibited larger surface area in right middle temporal gyrus and greater right nucleus accumbens volume compared to MDD. The integration of two-stage models, including deep neural network (DNN) and support vector machine (SVM),achieved an accuracy rate of 91.2% in discriminating individuals with BD from MDD.

Conclusion
These findings demonstrate that structural MRI combined with machine learning techniques can accurately discriminate individuals with BD from MDD, and provide a foundation supporting the potential of this approach to improve diagnostic accuracy.
Original languageEnglish
Number of pages10
JournalNeuroradiology
Early online date18 Jan 2025
DOIs
Publication statusE-pub ahead of print - 18 Jan 2025

Keywords

  • Bipolar disorder
  • Major depressive disorder
  • Cortical thickness
  • Morphometric
  • Machine learning

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