Spatiotemporal feature-based investigation of major depressive disorder and emotion recognition using functional near infrared spectroscopy.
- 1School of Computer & Artifical Intelligence (College Of Software), Lanzhou University of Technology, Lanzhou, China.
- 2Guangyuan Mental Health Center, GuangYuan, China.
- 3School of Information & Engineering, Lanzhou University, Lanzhou, China.
Abstract
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.
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BibTeX
@article{chao2026spatiotemporal,
title = {{Spatiotemporal feature-based investigation of major depressive disorder and emotion recognition using functional near infrared spectroscopy.}},
author = {Jinlong Chao and Rui Yang and Hong Peng and Jian Shi and Qiuxia Shi},
journal = {Frontiers in psychiatry},
year = {2026},
month = oct,
doi = {10.3389/fpsyt.2026.1907020},
url = {https://europepmc.org/article/MED/42903202},
note = {CC BY},
}
RIS
TY - JOUR
TI - Spatiotemporal feature-based investigation of major depressive disorder and emotion recognition using functional near infrared spectroscopy.
AU - Jinlong Chao
AU - Rui Yang
AU - Hong Peng
AU - Jian Shi
AU - Qiuxia Shi
PY - 2026
DA - 2026/10/06
JO - Frontiers in psychiatry
DO - 10.3389/fpsyt.2026.1907020
UR - https://europepmc.org/article/MED/42903202
AB - 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.
SN - 1664-0640
ER -