Different prefrontal cortex activity patterns in bipolar and unipolar depression during verbal fluency tasks based on functional near infrared spectroscopy study.

Journal: Scientific reports
Published Date:

Abstract

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.

Authors

  • Lan Mou
    Sleep Medical Center of Huzhou Third Municipal Hospital, the Affiliated Hospital of Huzhou University, Huzhou, 313000, People's Republic of China.
  • Yuqi Shen
    Biobehavioral Health Department, The Pennsylvania State University, State College, Pennsylvania, USA.
  • Boyuan Wu
    School of Information Engineering, Huzhou University, Huzhou, ZheJiang 313000, China.
  • Chengyu Zhang
    School of Mechanical and Automotive Engineering, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250353, China.
  • Jiayun Zhu
    School of Information Engineering, Huzhou University, Huzhou, ZheJiang 313000, China.
  • Qian Tan
    Guangdong Basic Research Center of Excellence for Ecological Security and Green Development, Guangdong Provincial Key Laboratory of Water Quality Improvement and Ecological Restoration for Watersheds, School of Ecology, Environment and Resources, Guangdong University of Technology, Guangzhou, 510006, China. Electronic address: qian_tan@gdut.edu.cn.
  • Xiaomei Zhang
    School of Electronic and Electrical Engineering, Shanghai University of Engineering Science, Shanghai 201620, China.
  • Zefeng Wang
    CAS Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, CAS Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, Shanghai 200031, China. Electronic address: wangzefeng@picb.an.cn.
  • Zhongxia Shen
    School of Medicine, Southeast University, Nanjing 210096, China.