Brain function network feature classification method

Abstract

The invention relates to a brain function network feature classification method, which comprises the following steps of: performing format conversion and preprocessing on functional magnetic resonanceimaging, and extracting a time sequence of each brain region; dividing the time sequence into overlapping sub-segments with fixed lengths, and calculating correlation coefficients among the sub-segments to construct a plurality of dynamic function networks; splicing the column vectors of the upper triangular elements of each dynamic brain function network into a function connection vector, and combining all the tested function connection vectors into a successful aggregation matrix; dividing all the tested aggregation matrixes into three parts as samples, wherein each sample is used as a feature subspace; learning and classifying each feature subspace by the training set to obtain a training result; evaluating the network model by the verification set, and adjusting network parameter by the verification set; and classifyings each feature subspace by the test set to obtain a final classification result. The method has a certain reference value for researching cognitive impairment of the brain.

Description

technical field [0001] The invention belongs to the technical field of biomedical information, and relates to a method for classifying features of a brain function network, in particular to a method for classifying features of a brain function network based on a deep polynomial network. Background technique [0002] The human brain is a very complex system that exists in nature. Various types of neurons are connected together through synapses to form a very complex brain structure network, which is the structural basis for various physiological and cognitive activities of the brain. . During the active or passive activity of the brain caused by external stimuli, each neuron or neural dynamic process extends into a complex brain functional network, which is an intuitive description of the changes in brain neural activity. In previous studies, functional connectivity in brain functional networks was a research hotspot. Functional connectivity is closely related to structural ...

AI Technical Summary

Problems solved by technology

Although this method is effective, it also has disadvantages

First, there may be some human errors, and because the biomarkers of the disease are not clear, there may be omissions in the division of brain regions of interest, which will affect the results of early diagnosis; second, the algorithm model requires a large amount of data for training , but this requires a great deal of time and manpower

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