Innovations and approaches in depression detection via functional near-infrared spectroscopy

Depression, a leading contributor to global disability, lacks objective diagnostic biomarkers. This review evaluates functional near-infrared spectroscopy (fNIRS) as a portable neuroimaging tool for depression detection, highlighting its algorithmic innovations and clinical translation potential. Machine learning techniques effectively decode hemodynamic patterns of the prefrontal cortex during emotional or cognitive tasks to achieve high classification accuracy in controlled studies. Clinically, fNIRS identifies prefrontal cortex hypoactivation as correlated with symptom severity and tracks neuroplasticity during psychotherapy. However, heterogeneity across symptom subtypes, cultural backgrounds, and age groups limits the generalizability of the model. Technical challenges include signal noise from motion artifacts and interference from superficial tissues. Future research should prioritize standardized multicenter trials, multimodal integration to enhance biomarker specificity, and interpretable artificial intelligence frameworks for clinical translation. fNIRS demonstrates unique advantages for scalable, noninvasive depression screening but necessitates rigorous validation to transition from research to point-of-care applications. This review provides insights into the optimization of fNIRS-based tools for precision psychiatry.