Electroconvulsive Therapy Induces Cortical Morphological Alterations in Major Depressive Disorder Revealed with Surface-Based Morphometry Analysis.

Journal: International journal of neural systems
Published Date:

Abstract

Although electroconvulsive therapy (ECT) is one of the most effective treatments for major depressive disorder (MDD), the mechanism underlying the therapeutic efficacy and side effects of ECT remains poorly understood. Here, we investigated alterations in the cortical morphological measurements including cortical thickness (CT), surface area (SA), and local gyrification index (LGI) in 23 MDD patients before and after ECT. Furthermore, multivariate pattern analysis using linear support vector machine (SVM) was applied to investigate whether the changed morphological measurements can be effective indicators for therapeutic efficacy of ECT. Surface-based morphometry (SBM) analysis found significantly increased vertex-wise and regional cortical thickness (CT) and surface area (SA) in widespread regions, mainly located in the left insula (INS) and left fusiform gyrus, as well as hypergyrification in the left middle temporal gyrus (MTG) in MDD patients after ECT. Partial correlational analyses identified associations between the morphological properties and depressive symptom scores and impaired memory scores. Moreover, SVM result showed that the changed morphological measurements were effective to classify the MDD patients before and after ECT. Our findings suggested that ECT may enhance cortical neuroplasticity to facilitate neurogenesis to remit depressive symptoms and to impair delayed memory. These findings indicated that the cortical morphometry is a good index for therapeutic efficacy of ECT.

Authors

  • Jinping Xu
    1Institute of Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, P. R. China.
  • Jiaojian Wang
    3The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu 625014, P. R. China.
  • Tongjian Bai
    4Department of Neurology, The First Hospital of Anhui Medical University, Hefei 230022, P. R. China.
  • Xiaodong Zhang
    The Center of Psychosomatic Medicine, Sichuan Provincial Center for Mental Health, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu 611731, China.
  • Tian Li
    College of Plant Protection, Southwest University, Chongqing, China.
  • Qingmao Hu
    Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, 1068 Xueyuan Boulevard, Shenzhen 518055, China; Key Laboratory of Human-Machine Intelligence Synergy Systems, 1068 Xueyuan Boulevard, Shenzhen 518055, China. Electronic address: qm.hu@siat.ac.cn.
  • Hongming Li
    6Center for Biomedical Image Computing and Analytics, Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA.
  • Li Zhang
    Department of Animal Nutrition and Feed Science, College of Animal Science and Technology, Huazhong Agricultural University, Wuhan 430070, China.
  • Qiang Wei
    School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, USA.
  • Yanghua Tian
    4Department of Neurology, The First Hospital of Anhui Medical University, Hefei 230022, P. R. China.
  • Kai Wang
    Department of Rheumatology, The Affiliated Huai'an No. 1 People's Hospital of Nanjing Medical University, Huai'an, Jiangsu, China.