Depression and bipolar disorder brain network analysis method based on dual-channel phase synchronization feature fusion

Abstract

The invention discloses a depression and bipolar disorder brain network analysis method based on dual-channel phase synchronization feature fusion. The method comprises the following steps: constructing phase synchronization matrixes by using resting scalp electroencephalogram signals and respectively adopting a phase delay index, a weighted phase delay index and a phase lock value synchronization index, and fusing the phase synchronization matrixes, so that brain lesion areas of patients with depression and bipolar disorder can be effectively distinguished. Compared with the prior art, the method provided by the invention has the following advantages that more effective information can be obtained by fusing the three synchronous features to detect a weak interaction relationship among the signals, and whether the electrode signals of the brain region are in a synchronous state or not can be found favorably, and thus the lesion brain region can be identified effectively. Experiments show that compared with a healthy control group, the method has the advantage that the difference between the frontal lobe and the parietal lobe of a patient with depression and bipolar disorder is relatively large.

Description

technical field [0001] The invention belongs to the field of computational neuroscience, and specifically relates to a brain network analysis method for depression and bipolar disorder based on phase synchronization indicators: phase delay index, phase lock value and weighted phase delay index. Construct the functional connectivity matrix of the three indicators in the α, β, θ, and δ frequency bands for further research and analysis of differences in functional connectivity and brain networks of different types of subjects. Background technique [0002] With the acceleration of the pace of life, social competitiveness is increasing, and people's nervousness and anxiety occur from time to time, so more and more patients are caused by depressive disorder. Depressive disorder is characterized by significant and persistent depression of mood, accompanied by There are varying degrees of cognitive and behavioral impairment and a higher suicide rate. People with bipolar disorder f...

AI Technical Summary

Problems solved by technology

However, the existing phase lag index (Phase Lag Index, PLI) is very susceptible to slight fluctuations and signal reversal occurs, and the phase lock value (PhaseLocking Values, PLV) is easily affected by volume conduction effects. , WPLI) are relatively insensitive to real changes in phase synchronization

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