Open AccessResearch on the MEG of depression patients based on multivariate TE partial information decomposition
Author(s) -
Zihan Chen,
Yunjie Fang,
Wei Yan,
Jun Wang,
Jin Li,
Fengzhen Hou
Publication year - 2020
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/768/5/052053
Subject(s) - depression (economics) , multivariate statistics , psychology , multivariate analysis , entropy (arrow of time) , significant difference , clinical psychology , psychiatry , neuroscience , computer science , medicine , machine learning , physics , thermodynamics , economics , macroeconomics
Transfer entropy (TE) has been broadly used in the field of neurosciences. In this paper, the partial information decomposition algorithm is employed to decompose multivariate TE into synergistic, redundant and unique parts. In this work, the synergistic part is believed as more suitable as the computation method. We recorded the magnetoencephalogram (MEG) data of 6 subjects with depression and 13 normal subjects under different emotional stimulations, and studied the coupling between multiple symmetric channels in the frontal area in the brain of subject. The experimental results show that under different emotional stimulations, normal people present significant difference from the depression patients, especially in the right frontal area. Furthermore, under negative emotional stimulation, the difference in synergistic value between normal people and depression patients is smaller. The synergistic value of depression patients has become bigger, which indicates that the brain complexity of depression patients has grown, and their brain activities have increased.