关键词: 血氧水平依赖功能磁共振成像信号/
模糊近似熵/
样本熵/
抑郁症
English Abstract
Analysis of resting state functional magnetic resonance imaging signal complexity of adult major depressive disorder based on fuzzy approximate entropy
Yang Xiao-Jing1,Yang Yang1,2,3,
Li Huai-Zhou1,
Zhong Ning1,2,3
1.Institute of International WIC, Beijing University of Technology, Beijing 100124, China;
2.Dept. of Life Science and Informatics, Maebashi Institute of Technology, Maebashi 371-0816, Japan;
3.Anding Hospital, Capital Medical University, Beijing 100124, China
Fund Project:Project supported by the National Basic Research Program of China(Grant No. 2014CB744600) and the National Natural Science Foundation of China(Grant Nos. 61272345, 61105118).Received Date:26 June 2016
Accepted Date:23 July 2016
Published Online:05 November 2016
Abstract:Major depressive disorder (MDD) is a kind of mental disease which has characteristics of the low mood,sense of worthless,less interest in the surrounding things,sadness or hopeless,slow thinking,intelligence,language,action,etc. The aim of this research is to find the differences between entropy values and ages,genders of MDD patients,MDD patients and healthy controls.Twenty-two MDD patients (male 11;age 18-65) and their matched healthy controls in gender,age,and education are examined by analyzing (blood oxygenation level dependent-functional magnetic resonance imaging,BOLD-fMRI) signals from nonlinear complexity perspective.As the BOLD-fMRI signals have limited time resolution,so they are very difficult to quantify the complexities of fMRI signals.We extract the corresponding signals from the fMRI signals.The complexities of the age,gender,MDD patients and healthy controls can be predicted by the proposed approach.However,information redundancy and other issues may exist in non-linear dynamic signals. These issues will cause an increase in computational complexity or a decrease in computational accuracy.To solve the above problems,we propose a method of fuzzy approximate entropy (fApEn),and compare it with sample entropy (SampEn).The addition and subtraction under different emotional stimuli as a multi-task are used to coordinate brain sense with motion control.The 12-channel fMRI signals are obtained involving the BOLD signals on resting signals (about 24 s).The methods of the fApEn and SampEn are proposed to deal with the BOLD-fMRI signals in the different ages and genders,and those between MDD patients and healthy controls from the differences between fApEn and SampEn of different genders,main effect and interaction effect analysis of fApEn and SampEn measures, regression curve between entropy and age of the whole sample,correlations of fApEn and SampEn with age,fApEn-age correlation and magnitude in gray matter and white matter,multiple regression analysis of fApEn with age for the whole sample,also the receiver operating characteristic analyses of fApEn and SampEn,the relationship between fAPEn and N aspects.The results show that 1) the complexities of the resting state fMRI signals measured are consistent with those from the Goldberger/Lipsitz model:the more the health,the greater the complexity is;2) the mean whole brain fApEn demonstrates significant negative correlation (r=-0.512,P0.001) with age,SampEn produces a non-significant negative correlation (r=-0.102,p=0.412),and fApEn also demonstrates a significant (P0.05) negative correlation with age-region (frontal,parietal,limbic,temporal and cerebellum parietal lobes),there is non-significant region between the SampEn maps and age;3) the fuzzy approximate entropy values of major depressive disorder patients are lower than those of healthy controls during resting.These results support the Goldberger/Lipsitz model,and the results also show that the fApEn is a new effective method to analyze the complexity of BOLD-fMRI signals.
Keywords: blood oxygenation level dependent-functional magnetic resonance imaging signals/
fuzzy approximate entropy/
sample entropy/
major depressive disorder
