Mou Lan, Shen Yuqi, Wu Boyuan, Zhang Chengyu, Zhu Jiayun, Tan Qian, Zhang Xiaomei, Wang Zefeng, Shen Zhongxia
Sleep Medical Center of Huzhou Third Municipal Hospital, the Affiliated Hospital of Huzhou University, Huzhou, 313000, People's Republic of China.
School of Mental Health, Wenzhou Medical University, Wenzhou, 325000, People's Republic of China.
Sci Rep. 2025 Jul 1;15(1):21257. doi: 10.1038/s41598-025-05896-z.
This study aimed to investigate the functionality of the prefrontal cortex in patients with unipolar depression (UD) and bipolar depression (BD) using functional near-infrared spectroscopy (fNIRS) during a verbal fluency task (VFT). Additionally, it evaluated the reliability of fNIRS as a diagnostic tool for cognitive assessments through a deep learning approach using one-dimensional convolutional networks. The study included 73 patients with UD, 59 patients with BD, and 40 healthy controls (HC). Hemodynamic responses in the prefrontal cortex were recorded using fNIRS during the VFT. Differences in oxygenated hemoglobin concentrations across the three groups were compared, and receiver operating characteristic (ROC) curves were generated for each region of interest. Both UD and BD patients demonstrated significantly reduced activation in the prefrontal cortex compared to healthy controls. UD patients showed notably lower activation values than BD patients in the dorsolateral prefrontal cortex, frontopolar prefrontal cortex, left orbitofrontal cortex, and left ventrolateral prefrontal cortex. The highest classification accuracy (79.57%) was observed in the left orbitofrontal cortex. The UD group had the largest area under the ROC curve (AUC = 0.99) in the left orbitofrontal cortex, while the BD group had the largest AUC (0.91) in the right dorsolateral prefrontal cortex. The HC group exhibited the largest AUC (0.73) in the same region. The DLPFC, FPC, lOFC, and lVLPFC may serve as biomarker regions for differentiating UD from BD. The combination of fNIRS and the VFT shows promise as a supplementary diagnostic tool for mental health disorders.
本研究旨在利用功能近红外光谱技术（fNIRS），在言语流畅性任务（VFT）期间，探究单相抑郁症（UD）和双相抑郁症（BD）患者前额叶皮质的功能。此外，通过使用一维卷积网络的深度学习方法，评估fNIRS作为认知评估诊断工具的可靠性。该研究纳入了73例UD患者、59例BD患者和40名健康对照者（HC）。在VFT期间，使用fNIRS记录前额叶皮质的血流动力学反应。比较了三组之间氧合血红蛋白浓度的差异，并为每个感兴趣区域生成了受试者工作特征（ROC）曲线。与健康对照者相比，UD和BD患者在前额叶皮质的激活均显著降低。在背外侧前额叶皮质、额极前额叶皮质、左侧眶额皮质和左侧腹外侧前额叶皮质中，UD患者的激活值明显低于BD患者。在左侧眶额皮质中观察到最高的分类准确率（79.57%）。在左侧眶额皮质中，UD组的ROC曲线下面积最大（AUC = 0.99），而在右侧背外侧前额叶皮质中，BD组的AUC最大（0.91）。HC组在同一区域的AUC最大（0.73）。背外侧前额叶皮质、额极前额叶皮质、左侧眶额皮质和左侧腹外侧前额叶皮质可能作为区分UD和BD的生物标志物区域。fNIRS与VFT的结合显示出作为精神健康障碍辅助诊断工具的潜力。
