Brain network topology difference fast extracting method based on network synchronicity

Abstract

The invention discloses a brain network topology difference fast extracting method based on network synchronicity. By the method, work load and time can be reduced evidently, difference of brain network topology structures can be found fast, the difference connecting map can be built fast and accurately, and a 64-lead brain functional network can be completed in a few seconds. Synchronicity coefficients of brainwave lead time sequences are calculated to build a synchronicity coefficient matrix, the synchronicity coefficient matrix is converted into a binary network by using the self feature threshold of the brain network, and the topology structure difference connecting map is built by chi-square test and cross verification, and accordingly complex network analyzing can be reduced, experiment time and cost can be shortened, and the different connections are evident by comparing the topology structure difference connecting map with traditional network difference connecting maps to facilitate observation.

Description

technical field [0001] The invention relates to a rapid extraction technology of brain network topology differences, which belongs to the field of neural information technology, and specifically relates to a rapid extraction technology of brain network topology differences based on network synchronization. Background technique [0002] The EEG acquisition system is a kind of spontaneous and rhythmic electrical activity of brain cell groups that is non-invasively recorded at the scalp using sophisticated electronic equipment; it is a technology that amplifies and records the bioelectrical activity of human brain tissue. In the examination of neurological diseases, because it reflects the "living" brain function state, it has been playing an important role in the diagnosis of neurological diseases in the past few decades. [0003] In the past ten years, researchers have used MRI and EEG technologies to study brain networks, and brain functional networks are an important part o...

AI Technical Summary

Problems solved by technology

However, when calculating these parameters, it is basically for the entire network, which does not reflect some characteristics of the local network well.

Moreover, it is cumbersome and time-consuming to calculate these parameters, it is not easy to understand, and it cannot intuitively express the differences between brain networks.

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