Depression recognition using functional connectivity based on dynamic causal model

作     者：罗国平 刘刚 赵竟 姚志剑 卢青 Luo Guoping;Liu Gang;Zhao Jing;Yao Zhijian;Lu Qing

作者机构：东南大学学习科学研究中心南京210096 南京医科大学附属脑科医院南京210029 

出 版 物：《Journal of Southeast University(English Edition)》 (东南大学学报（英文版）)

年 卷 期：2011年第27卷第4期

页      面：367-369页

核心收录：

学科分类：071011[理学-生物物理学] 0710[理学-生物学] 07[理学] 08[工学] 0835[工学-软件工程] 081202[工学-计算机软件与理论] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：The National Natural Science Foundation of China(No.30900356 81071135) 

主　　题：depression recognition fMRI dynamic causal model Bayesian model selection 

摘      要：Dynamic casual modeling of functional magnetic resonance imaging（fMRI） signals is employed to explore critical emotional neurocircuitry under sad stimuli. The intrinsic model of emotional loops is built on the basis of Papez＇s circuit and related prior knowledge, and then three modulatory connection models are established. In these models, stimuli are placed at different points, which represents they affect the neural activities between brain regions, and these activities are modulated in different ways. Then, the optimal model is selected by Bayesian model comparison. From group analysis, patients＇ intrinsic and modulatory connections from the anterior cingulate cortex （ACC） to the right inferior frontal gyrus （rlFG） are significantly higher than those of the control group. Then the functional connection parameters of the model are selected as classifier features. The classification accuracy rate from the support vector machine（SVM） classifier is 80.73%, which, to some extent, validates the effectiveness of the regional connectivity parameters for depression recognition and provides a new approach for the clinical diagnosis of depression.