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

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.