Identification of multimodal brain imaging biomarkers in first-episode drugs-naive major depressive disorder through a multi-site large-scale MRI consortium data

• Published in: Journal of affective disorders 2025-01, Vol.369, p.364-372
• Main Authors: Dai, Peishan, Shi, Yun, Zhou, Xiaoyan, Xiong, Tong, Luo, Jialin, Chen, Qiongpu, Liao, Shenghui, Huang, Zhongchao, Yi, Xiaoping
• Format: Article
• Language: English
• Subjects: Adult; Biomarkers; Brain - diagnostic imaging; Case-Control Studies; Depressive Disorder, Major - diagnostic imaging; Female; First-episode drugs-naive major depressive disorder; Gray Matter - diagnostic imaging; Gray Matter - pathology; Humans; Machine learning; Magnetic Resonance Imaging; Male; Multimodal Imaging; Muti-modal fusion; Statistical analysis; Young Adult
• ISSN: 0165-0327; 1573-2517; 1573-2517
• DOI: 10.1016/j.jad.2024.10.006

Major depressive disorder (MDD) is a severe and common mental illness. The first-episode drugs-naive MDD (FEDN-MDD) patients, who have not undergone medication intervention, contribute to understanding the biological basis of MDD. Multimodal Magnetic Resonance Imaging can provide a comprehensive understanding of brain functional and structural abnormalities in MDD. However, most MDD studies use single-modal, small-scale MRI data. And several multimodal studies of MDD are limited to simple linear combinations of functional and structural features. We screened a large sample of FEDN-MDD patients and healthy controlsmultimodal MRI data. Extracting the fractional amplitude of low-frequency fluctuations (fALFF) feature from functional magnetic resonance imaging and the gray matter volume (GMV) feature from structural magnetic resonance imaging. The mCCA-jICA method was used to integrate these two modal features to investigate the functional-structural co-variation abnormalities in MDD. To validate the stability of the extracted functional-structural covariant abnormalities features, we apply them to identify FEDN-MDD patients. The results show that compared to healthy controls, FEDN-MDD patients exhibit joint group-discriminative independent component and modality-specific group-discriminative independent component, suggesting functional-structural covariant abnormalities in MDD patients. Using lightGBM classifier, we achieve a classification accuracy of 99.84 %. We use GMV and fALFF for multimodal fusion shows promise, but requires further validation with other datasets and exploration of additional multimodal features. This may indicate that multimodal fusion features can effectively explore information between different modalities and can accurately identify FEDN-MDD patients, suggesting their potential as multimodal brain imaging biomarkers for MDD. •Multimodal MRI reveals key MDD abnormalities.•Fusion features proposed as MDD biomarkers.•mCCA-jICA method integrates MRI data effectively.•Identifying MDD with 99.84 % accuracy.