ScholarWorks@숙명여자대학교

초록

Background: Functional near-infrared spectroscopy (fNIRS) is increasingly used to examine cognitive abilities and support the diagnosis of bipolar disorder (BD). Despite its growing application, research on functional connectivity in BD remains limited, and task-induced functional connectivity remains under-investigated. This study aims to differentiate patients with BD from healthy controls (HCs) based on fNIRS-derived functional connectivity. Methods: We analyzed fNIRS data from 50 patients with BD and 50 HCs during resting state and two cognitive tasks, including the verbal fluency test (VFT) and Stroop color-word test (Stroop). Functional connectivity was quantified using coherence and correlation evaluations, and four network characteristics, such as local efficiency, global efficiency, global clustering coefficient, and average closeness centrality, were extracted. A thresholding method was applied by filtering the top 10 % to 50 % to highlight significant network connections. Group differences were evaluated using t-tests with false discovery rate. Findings: Patients with BD showed significantly altered coherence values, especially between channels 4 and 10 (p < 0.001, t = −5.534, η2 = 0.562) and lower global clustering coefficients (p < 0.001, t = −5.666, η2 = 0.578) compared to HCs. The k-nearest neighbor (KNN) classifier using coherence-based network metrics during the VFT achieved the highest classification performance with an accuracy of 0.818, precision of 0.819, recall of 0.824, and an F1-score of 0.817. Conclusion: Our findings suggest that task-induced functional connectivity, particularly coherence-based metrics derived during the VFT, reflects distinct functional connectivity patterns that differentiate patients with BD from HCs. These findings support the utility of network-based approaches under cognitive task conditions in characterizing functional connectivity alterations associated with BD.

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