Depression classification method based on graph embedding and multi-modal brain network

Abstract

The invention discloses a depression classification method based on graph embedding and a multi-modal brain network, and the method comprises the steps of modeling a brain into a brain network based on the thought of a graph theory, fusing a whole-brain function network and a structure network through a function-structure hierarchical mapping algorithm, and obtaining the brain network with the fusion of function and structure information, automatically learning the topological structure features and connection features of the brain network by using a graph embedding algorithm to obtain the vector representation of each node of the brain network, further combining into brain network representation, and finally performing depression classification based on each tested brain network representation by using a support vector machine model. According to the method, graph embedding and the multi-modal brain network are utilized, the defect of single-modal information is overcome, the feature vector suitable for a machine learning classification model is generated, the brain network level features related to depression are effectively mined, and the depression classification precision is improved.

Description

technical field [0001] This patent relates to the field of medical image processing and machine learning, especially a method for classification of depression based on graph embedding and multimodal brain network. Background technique [0002] Depression is a common mental illness worldwide. The core symptoms are low mood, loss of interest, inability to concentrate, difficulty in thinking, memory loss, decreased activity, and sleep disturbance. It has caused heavy burdens to patients and their families burden. Early diagnosis and treatment of depression are crucial to the recovery of depressed patients. At present, the diagnosis of depression mainly depends on the doctor's clinical interview, but depression is a heterogeneous disease with very complex clinical symptoms, similar to some symptoms of other mental diseases. Studies have found that depression is related to changes in brain structure and function, so studying the impact of depression on brain structure and funct...

AI Technical Summary

Problems solved by technology

[0005] In order to solve the problem of insufficient feature mining ability of existing brain network analysis methods, inspired by natural language processing, the present invention proposes a depression classification method based on graph embedding and multimodal brain network, which can fully, quickly and efficiently extract Multimodal brain network features for depression classification

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