Individual brain function network extraction method suitable for multi-subject brain function data analysis

Abstract

The invention discloses a method for extracting a brain function network of an individual based on the analysis of multiple tested brain function data. The method comprises the following steps of: calculating the tested independent components of the individual, having correspondence among the different tested, based on the tested brain function data of the individual; using a provided algorithm for analyzing the independent components with reference signals based on a multi-target function optimization framework, meanwhile, optimizing the correspondence between the tested independent components of the individual and the reference signals and the independence among the different tested components of the individual, wherein the reference signals are obtained by jointly analyzing the independent components of the tested brain function data of the individual, and can also be obtained from a brain network pattern and the like obtained through the brain network analysis or meta analysis of other modal imaging data; after the tested independent components of the individual are obtained, using a provided time sequence calculation method to calculate a time sequence corresponding to each independent component; and judging the obtained independent components to obtain a brain function network, wherein the time sequence corresponding to the independent component is an activating mode corresponding to the brain function network.

Description

technical field [0001] The invention relates to the technical field of medical image processing, in particular to an individual brain function network extraction method suitable for multi-subject brain function data analysis. Background technique [0002] Analyzing brain function networks based on brain function data is of great significance to neuroscience research and medical diagnosis. Due to the unpredictability of multi-subject data, the independent component analysis of multi-subject data is a great challenge. The main difficulty lies in how to establish correspondence between multi-subject brain networks to facilitate subsequent statistical analysis, while maintaining individual subject brain networks. Network specificity to facilitate individual diagnosis. [0003] At present, there are two types of methods for multi-subject data analysis using independent component analysis (ICA). Correspondence was established between independent components of different subjects....

AI Technical Summary

Problems solved by technology

[0004] The Back-reconstruction method uses the component information and the dimensionality reduction results of PCA to restore the components of individual subjects, so it is greatly affected by different PCA strategies and cannot be used for brain network analysis of brain function data of new subjects

The dual regression method first obtains the time series of individual subjects through linear regression based on the components, and then obtains the independent components of individual subjects based on the obtained individual time series linear regression. The components obtained by this method cannot guarantee independence, which in turn affects the brain. Accuracy of functional networks

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