Spatiotemporal feature-based investigation of major depressive disorder and emotion recognition using functional near infrared spectroscopy

Major Depressive Disorder (MDD) remains one of the most prevalent psychiatric conditions worldwide, with a marked increase in incidence and severity following the COVID-19 pandemic. Despite its rising burden, clinical diagnosis continues to rely on subjective assessments, lacking objective and reproducible neurobiological markers. Functional Near-Infrared Spectroscopy (fNIRS) has emerged as a promising non-invasive imaging technique capable of capturing the spatiotemporal dynamics of cerebral hemodynamics, particularly within the prefrontal cortex—a key region involved in emotional regulation and cognitive control. In this study, we propose a spatiotemporal feature-based framework for MDD classification and emotion-related brain state recognition. Thirty MDD patients and thirty demographically matched healthy controls underwent fNIRS recording while exposed to emotional auditory stimuli. By analyzing group-level differences in oxyhemoglobin concentrations and β _values derived from a general linear model (GLM), we identified significant prefrontal abnormalities in the MDD group, particularly under negative emotional conditions. These alterations were most prominent in the dorsolateral prefrontal cortex (DLPFC), a region consistently implicated in emotion dysregulation. The classification model, based on fNIRS-derived activation features, achieved 90% accuracy for MDD detection and 85% for emotion recognition. These results demonstrate that integrating spatial and temporal features improves sensitivity to depression-related cortical dysfunction, offering a potential objective biomarker for diagnosis and novel insight into emotion-specific neurocircuitry in MDD. This work supports the clinical feasibility of fNIRS-based biomarkers and advances understanding of affective brain processing in depression.