Brain function connection module division method based on weighted network

Abstract

The invention relates to a brain function connection module division method based on the weighted network. The method mainly comprises steps that brain function magnetic resonance imaging pre-processing and partition template matching standardizing are carried out, and the time sequence corresponding to each brain zone is extracted; the sub time sequence corresponding to each window is separated through the sliding window method, correlation coefficient matrixes of all the windows are combined, and the dynamic weighted network of brain function connection is constructed; the edge betweenness of the weighted network during weight considering is acquired through the betweenness rate, the ratio of the edge betweenness ignoring weight, the betweenness rate and the connection edge weight is calculated, and the connection edge with the highest ratio is removed; the module division result is outputted and a modularity value is calculated till no connection edge in the network can be removed;the module division result corresponding to the largest modularity value is outputted. The method is advantaged in that dynamic characteristics and weight change of brain function connection are comprehensively considered, and shortcomings of the traditional module division method of ignoring time-varying characteristics and simple thresholding of the weight are made up.

Description

technical field [0001] The invention relates to a module division method of brain function connection, in particular to a weighted network-based method for division of brain function connection modules, belonging to the technical field of biomedical information processing. Background technique [0002] In recent years, brain networks have become an important field of brain science research. A brain network is an interactive integration of dynamic activities between different neurons, neuron clusters, or brain regions in a structural network. People apply complex network theory to brain science research, use the basic principles of complex network combined with statistical research methods to conduct statistical analysis, and discover the group attributes of brain networks and the potential topological relationships between nodes. A module is a group of nodes in a complex network with dense internal connections but sparse external connections. Real networks often have sever...

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Problems solved by technology

Because the complexity of the weighted network is much higher than that of the unweighted network, the weights are usually simply thresholded in previous studies, which leads to the loss of weight information and also brings certain errors to the module division results.

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