ArticleAlpha psychiatry2025
Machine Learning Based Identification of Depressive Symptoms Among Students in a Chinese University Using Functional Near-Infrared Spectroscopy.
Article in Alpha psychiatry, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Background: Individuals suffer from depression at a high rate on university campuses and current assessment methods primarily rely on subjective questionnaires. Therefore, there is a pressing need to develop objective measures for the automatic detection of depression. This study aimed to investigate the functional near-infrared spectroscopy (fNIRS) changes associated with depression and assess the potential of fNIRS signals in detecting depression among university students. Methods: A total of 192 participants were recruited for psychological assessment. A 48-channel fNIRS system was employed to measure cerebral blood oxygenation signals during the verbal fluency task (VFT). Two-sample Results: Significant hemodynamic alterations were observed in the depression group at channels 4, 16, 21, 26, 32, 43, 44, and 47, in comparison with the control group. The bilateral medial prefrontal cortices (MPFC), left dorsolateral prefrontal cortex, and left temporal lobe, represented by channels 4, 16, 43, and 44, were associated with depression. Among the five machine learning algorithms, K-Nearest-Neighbors (KNN) exhibited superior classification performance (AUC = 66.51%). The left MPFC was the most significant contributor to the classification efficacy of the KNN model. Conclusion: fNIRS-VFT may serve as an objective tool for evaluating depressive symptoms in university students. The findings underscore the central role of the left MPFC in the neural mechanisms underlying depression. This work developed an fNIRS-based identification system for depression in university students.
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