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Classifying Major Depressive Disorder Using fNIRS During Motor Rehabilitation
Summary
This study introduces a new method using functional near-infrared spectroscopy (fNIRS) to quickly assess major depressive disorder (MDD) during physical rehabilitation. The technique achieved high accuracy in identifying depression, potentially improving patient recovery guidance.
Area of Science:
- Neuroscience
- Medical Technology
- Psychiatry
Background:
- Major depressive disorder (MDD) significantly hinders physical recovery after events like stroke.
- Current depression assessments are subjective and rarely integrated into rehabilitation planning.
Purpose of the Study:
- To develop a predictive depression assessment using functional near-infrared spectroscopy (fNIRS).
- To integrate fNIRS assessment into physical rehabilitation for real-time monitoring.
Main Methods:
- Thirty-one participants (14 with MDD, 17 healthy) underwent fNIRS during a motor task (Grasp and Release Test).
- Brain oxy-hemodynamic (HbO) responses were analyzed using XGBoost and Random Forest algorithms with extracted signal features.
- A 16-channel wearable fNIRS device was utilized for data acquisition.
Main Results:
- The predictive model achieved 92.6% classification accuracy, 84.8% sensitivity, and 91.7% specificity.
- Key neuromarkers identified include mean HbO, full width half maximum, and kurtosis in prefrontal cortex regions.
- The method demonstrated rapid setup and assessment capabilities.
Conclusions:
- Wearable fNIRS can enable rapid, objective depression assessment during rehabilitation.
- This technology can actively guide motor therapy decisions for patients with MDD.
- The findings support embedding predictive neurotechnology in post-stroke and post-spinal-cord-injury care.
