Distinguishing bipolar and major depressive disorders by brain structural morphometry: a pilot study

BMC Psychiatry

Research Article

Zhi-ren Wang¹  Miao Qu²  Ya-wei Zeng³  Ke Li³  Zhen Jin³  Yi Deng⁴  Germaine Fung⁵  Raymond C. K. Chan⁶  Qing Zhao⁷  Zhi Li⁸  Yan-tao Ma⁹  Xin Yu⁹  David H. K. Shum^1,10 

[1] Center for Biological Psychiatry, Beijing Hui-Long-Guan Hospital, Beijing, China;Department of Encephalopathy, Beijing University of Chinese Medicine the 3rd Affiliated Hospital, Beijing, China;MRI Center of Beijing 306 Hospital, Beijing, China;Neuropsychology and Applied Cognitive Neuroscience Laboratory, Key Laboratory of Mental Health, Institute of Psychology, Chinese Academy of Sciences, Beijing, China;Cognitive Analysis & Brain Imaging Laboratory, MIND Institute, University of California, Davis, USA;Neuropsychology and Applied Cognitive Neuroscience Laboratory, Key Laboratory of Mental Health, Institute of Psychology, Chinese Academy of Sciences, Beijing, China;Department of Psychology, the Chinese University of Hong Kong, Hong Kong Special Administrative Region, China;Neuropsychology and Applied Cognitive Neuroscience Laboratory, Key Laboratory of Mental Health, Institute of Psychology, Chinese Academy of Sciences, Beijing, China;Institute of Psychology, Chinese Academy of Sciences, 16 Lincui Road, 100101, Beijing, China;Neuropsychology and Applied Cognitive Neuroscience Laboratory, Key Laboratory of Mental Health, Institute of Psychology, Chinese Academy of Sciences, Beijing, China;School of Applied Psychology and Behavioral Basis of Health Program, Griffith Health Institute, Griffith University, Brisbane, Australia;Neuropsychology and Applied Cognitive Neuroscience Laboratory, Key Laboratory of Mental Health, Institute of Psychology, Chinese Academy of Sciences, Beijing, China;University of Chinese Academy of Sciences, Beijing, China;Peking University Sixth Hospital, Beijing, China;Peking University Institute of Mental Health, Beijing, China;Key Laboratory of Mental Health, Ministry of Health (Peking University), Beijing, China;School of Applied Psychology and Behavioral Basis of Health Program, Griffith Health Institute, Griffith University, Brisbane, Australia;Menzies Health Institute Queensland and School of Applied Psychology, Griffith University, Gold Coast, Australia;

关键词: Bipolar disorder;    Depression;    Cortical Thickness;    Surface area;    Support vector machine;   

DOI  :  10.1186/s12888-015-0685-5

 received in 2015-03-22, accepted in 2015-11-18,  发布年份 2015

来源: Springer

【 摘 要 】

BackgroundThe clinical presentation of common symptoms during depressive episodes in bipolar disorder (BD) and major depressive disorder (MDD) poses challenges for accurate diagnosis. Disorder-specific neuroanatomical features may aid the development of reliable discrimination between these two clinical conditions.MethodsFor our sample of 16 BD patients, 19 MDD patients and 29 healthy volunteers, we adopted vertex-wise cortical based brain imaging techniques to examine cortical thickness and surface area, two components of cortical volume with distinct genetic determinants. Based on specific characteristics of neuroanatomical features, we then used support vector machine (SVM) algorithm to discriminate between patients with BD and MDD.ResultsCompared to MDD patients, BD patients showed significantly larger cortical surface area in the left bankssts, precuneus, precentral, inferior parietal, superior parietal and the right middle temporal gyri. In addition, larger volumes of subcortical regions were found in BD patients. In SVM discriminative analyses, the overall accuracy was 74.3 %, with a sensitivity of 62.5 % and a specificity of 84.2 % (p = 0.028). Compared to controls, larger surface area in the temporo-parietal regions were observed in BD patients, and thinner cortices in fronto-temporal regions were observed in MDD patients, especially in the medial orbito-frontal area.ConclusionsThese findings have demonstrated distinct spatially distributed variations in cortical thickness and surface area in patients with BD and MDD, suggesting potentially varying etiological and neuropathological processes in these two conditions. The employment of multimodal classification on disorder-specific biological features has shed light to the development of potential classification tools that could aid diagnostic decisions.

【 授权许可】

CC BY   
© Fung et al. 2015