Multimodal AI for predicting comorbid REM sleep behavior disorder in major depressive disorder.

Authors

Lizhou Fan, Xiang Li, Xinze Wang, Jianzhang Ni, Zhixuan He, Yuhua Yang, Huizi Yu, Zhiying Liang, Shi Tang, Siyi Gong, Ningning Li, Xinxin Lin, Xin Ma, Yaping Liu, Jihui Zhang, Jing Wang, Joey W Y Chan, Ngan Yin Chan, Tim M H Li, Bei Huang, Yun Kwok Wing

Published in

NPJ digital medicine. Jul 14, 2026. Epub Jul 14, 2026.

Abstract

REM sleep behavior disorder (RBD) is a robust prodromal marker of α-synucleinopathies: idiopathic RBD carries a 10-15-year phenoconversion risk of 80-90% to Parkinson's disease (PD) and related disorders. In major depressive disorder (MDD), comorbid RBD marks a subgroup at elevated prodromal PD risk, yet is frequently missed in psychiatric practice. Here, we developed a multimodal AI framework to detect comorbid RBD in MDD. From a clinical cohort of 329 patients, we obtained 261 video clips in 31 patients during reading and spontaneous-speech tasks, including 19 patients with MDD-RBD and 12 demographically and medication-matched MDD-only controls that, to our knowledge, formed the largest cohort of its kind worldwide. We used a dual-stream multimodal model that learned facial dynamics from video and vocal features from speech, and then combined both signals to predict comorbid RBD. In 5-fold cross-validation, our best model achieved 80.5% accuracy and 0.848 F1-score. Explainability analysis highlighted lower-face tension and variability, together with brow lowering, as candidate biomarkers requiring further validation. Predicted risk correlated with RBDQ score (r = 0.53, p = 0.005) and weakly with UPDRS motor score (r = 0.34, p = 0.067). Out-of-distribution evaluation showed broadly similar patterns, supporting the promise of multimodal AI for predicting RBD in MDD and identifying interpretable potential digital markers of prodromal synucleinopathy.

PMID:
42449188
Bibliographic data and abstract were imported from PubMed on 15 Jul 2026.

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