Individual cognitive ability prediction method and system based on dynamic function connection

Abstract

The invention discloses an individual cognitive ability prediction method and system based on dynamic function connection. The method comprises the following steps: acquiring functional magnetic resonance imaging data in a resting state; extracting an average time sequence signal of each brain region through a brain template after preprocessing of the functional magnetic resonance imaging data; calculating and constructing a dynamic function connection matrix by using a sliding time window; with the dynamic function connection matrix as input high-dimensional dynamic brain description originaldata, constructing a model combining convolution and a long-short-term memory network; and substituting a dynamic function matrix and corresponding cognitive behavior data into the model for trainingand testing so as to realize prediction of cognitive competence. According to the invention, dynamic information of brain function connection is fully utilized, the advantages of a deep learning method in big data processing are utilized, calculation is rapid, prediction effect is ideal, various behavior data of a population can be predicted, dependence on the absolute value of the amplitude of imaging signals is avoided, migrations among different MRI machines can be realized, and prediction performance is excellent.

Description

technical field [0001] The present invention relates to an individual cognitive ability prediction technology based on functional magnetic resonance imaging (fMRI) data, in particular to a method and system for individual cognitive ability prediction based on dynamic functional connectivity. Background technique [0002] The principle of functional magnetic resonance imaging is to measure hemodynamic changes caused by neuronal activity through magnetic resonance imaging. For example, when certain sense organs are stimulated, certain specific brain functional areas of the cerebral cortex are activated, and these activated brain areas send out neural signals, which are obtained by using MRI to obtain a series of images. When people perform various complex tasks such as movement, sensation, and advanced cognition, certain neural signals in the brain will be aroused. This physiological brain activity will cause changes in local cerebral blood flow, cerebral blood volume, and ene...

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Problems solved by technology

[0006] A large number of current studies on individual differences implicitly assume that the functional coupling between brain regions is static, the actual brain functional connectivity is constantly changing, and the dynamic functional connectivity data is high-dimensional and complex, which is difficult for ordinary machine learning methods. Direct processing of high-dimensional data fails to capture the relationship between individual differences and dynamic functional connectivity

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