Brain function network modeling method for resting state synchronization EEG-fMRI

Abstract

The invention discloses a brain function network modeling method for resting state synchronization EEG-fMRI, and relates to the research field of neural signal processing. The brain function network modeling method includes the steps that firstly, EEG signal preprocessing is conducted, a tape limit energy signal is extracted and a regression item is established; secondly, fMRI signal preprocessing is conducted, and a BOLD signal of each brain region is extracted; thirdly, the BOLD signal, extracted through fMRI, of each brain region and the regression item obtained from EEG are subjected to principal component analysis; fourthly, canonical correlation analysis is conducted on principal components of the two signals obtained from the last step; fifthly, modeling is conducted on a brain function network of resting state synchronization EEG-fMRI. By means of the brain function network modeling method, a plurality of the brain function networks can be obtained; the brain function networks and existing research results have high coherence so that the effectiveness of the modeling method can be proved, and thus a new thought and a scheme are provided for the resting state brain function research.

Description

technical field [0001] The invention belongs to the field of neural signal processing research, relates to a brain function network modeling method, in particular to a resting state synchronous EEG-fMRI brain function network modeling method. Background technique [0002] All human advanced cognitive functions such as thinking, emotion and consciousness depend on the brain, which is an extremely complex system. Although the brain accounts for only 2% of the mass of the human body, it consumes 20% of the energy. When there is no task, the brain will also be active. People call the state of the brain, which is not caused by external stimuli, not controlled by the subject's will, and spontaneously generates neural activity, "resting state". Studies have found that the increased energy consumption of the brain in the task state is usually no more than 5% compared with the rest state, and more than 60% of the brain's total energy consumption is used for spontaneous neural activi...

AI Technical Summary

Problems solved by technology

The comparison method only compares and analyzes EEG and fMRI data, without data fusion between modalities; the asymmetric fusion method assumes that the neural activities measured by EEG and fMRI are exactly the same, and this assumption lacks a certain physiological basis; the existing Most of the symmetric fusion methods only focus on the fusion of event-related potentials and fMRI data, and there is no research on the symmetric fusion method of resting-state synchronous EEG-fMRI

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