Research On The Meg Of Depression Patients Based On Multivariate Sympolic Transfer Entropy And Partial Information Decomposition

Depression has complicated pathogenesis,and its current medical diagnosis measure for this disease is simple.Severe depression patient may suffer from immense physical pain and present suicidal tendency.MEG has the characteristics of very high temporal-spatial resolution,safety and non-invasion,which has provided a great medical measure for depression diagnosis.In this paper,the multivariate symbol transfer entropy and partial information decomposition algorithms are adopted to study the MEG of depression patient.The research objects are divided into multi-channel combinations in the same brain area and between different brain areas to calculate the multivariate symbol transfer entropy of healthy control group and depression patient group under different emotional stimulations(coordination value of multivariate transfer entropy partial information decomposition).Finally,the T-test results of independent samples prove that there is significant distinction between the two groups of experimental samples.The research of this paper consists of the following three parts.Firstly,under different emotional stimulations,the multivariate symbol transfer entropy algorithm is used to calculate the values of multivariate symbol transfer entropy of the healthy control group and depression patient group under the three MEG channel combinations in the same brain area and different brain areas.The experimental results show that the multivariate symbol transfer entropy of healthy control group is bigger than depression patient group.For the channel combination in the same brain area,under the stimulation of negative emotion,the distinction in the channel from the left frontal area to the right frontal area is the most significant between the healthy control group and depression patient group;Under the stimulation of positive emotion,except for the left occipital region ? right occipital region channel combination,there is significant distinction between the healthy control group and depression patient group under all other channel combinations and among them,the distinction under the left frontal area?right frontal area channel combination is the most significant;Under neutral stimulation,the distinction between the depression patients and the healthy control group is significant in various brain areas,and the intensity of distinction degree is in the order of frontal area> temporal region >occipital region> central area.Under three different emotional stimulations,in the healthy control group,the multivariate symbol transfer entropies in various brain areas do not present significant difference;while in the depression patient group,the multivariate symbol transfer entropy significantly increases under negative stimulation.For different brain areas,under positive and neutral stimulations,the healthy control group can be obviously distinguished from the depression patient group in the channels of left occipital region ?frontal area and left occipital region?central area.Secondly,the dynamic adaptive symbolic method is used to replace the traditional static scale symbolic method to improve the multivariate symbol transfer entropy algorithm.The result of this part is basically consistent with the above obtained experimental conclusion with multivariate symbol transfer entropy algorithm,but the relative difference between the healthy control group and depression patient group in terms of multivariate symbol transfer entropy is bigger.In the same brain area,under the negative emotional stimulation,the healthy control group can be significantly distinguished from the depression patient group according to the temporal region and frontal area;under the positive and neutral stimulations,the relative difference of improved multivariate symbol transfer entropy between the healthy control group and depression patient group is bigger.For channel combinations of different brain areas,under the positive and neutral stimulations,the healthy control group can be significantly distinguished from the depression patient group through right occipital region?frontal area and right occipital region?central area.Thirdly,the partial information decomposition and multivariate symbol transfer entropy algorithms are employed to conduct connectivity analysis of three brain areas(occipital region,temporal region and frontal area)of the healthy control group and depression patient group.Under negative and positive stimulations,there are more channel combinations in the three brain areas which satisfy significant different than the combinations under neutral stimulation.Under negative and positive stimulations,the occipital region/ temporal region?frontal area channel combinations can be used to better distinguish the healthy control group and depression patient group than the region/ frontal area ? temporal region channel combinations.By comparing the relative difference in connectivity for the healthy control group and depression patient group,we find that under negative stimulation,the left occipital region/ right temporal region?right frontal area can provide the most significant distinction;under positive stimulation,the channel combination of left occipital region/ left temporal region?right frontal area can provide the most obvious distinction;under neutral stimulation,the channel combination of left occipital region/ left temporal region?left frontal area can provide more notable distinction.