Method for extracting brain function states

A technology of functional state and extraction method, which is applied in the field of biomedical information, can solve the problems of dimensionality disaster and fail to improve clustering performance, and achieve the effect of improving accuracy and performance and improving deep autoencoder

Pending Publication Date: 2020-08-11
CHANGZHOU UNIV
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Problems solved by technology

For example, the dimensionality reduction method of brain network state observation matrix based on deep autoencoder (Yang Baojie, Wang Bin, Xue Jie, Dai Zhaokun, Liu Hui, Xiong Xin. The dimensionality reduction method of brain network state observation matrix based on deep autoencoder. Sensor and Mi...

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  • Method for extracting brain function states
  • Method for extracting brain function states
  • Method for extracting brain function states

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Embodiment Construction

[0031] In order to make the object, technical solution and advantages of the present invention clearer, the present invention will be described in detail below in conjunction with the accompanying drawings and specific embodiments.

[0032] like figure 1 As shown, a method for extracting brain functional states of the present invention, combined with a depth autoencoder model, comprises the following steps:

[0033](1) Read and format-convert the collected fMRI lines of each subject, and then perform preprocessing, including: time correction, head movement correction, spatial registration, spatial standardization, smoothing, and filtering operations. In this embodiment, 30 volunteers in the normal group (20 males and 10 females) and 30 early mild cognitive impairment subjects (15 males and 15 females) were selected, and the signal low-frequency filtering range was 0.01 Hz to 0.01 Hz. 0.08Hz. First, the resting-state functional magnetic resonance imaging of each subject's bra...

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Abstract

The present invention discloses a method for extracting brain function states. The method comprises the following steps: 1) extracting time series from functional magnetic resonance imaging; 2) dividing the time series into overlapping sub-segments of fixed length and calculating correlation coefficients among the sub-segments to construct a time-dependent dynamic function network; 3) vectorizingthe dynamic function network and matching the vectorized dynamic function network into a functional connection aggregation matrix; 4) improving and training a deep autoencoder model and inputting thefunctional connection aggregation matrix into the deep autoencoder for dimensionality reduction to obtain several feature vectors; 5) determining the optimal number of clusters and clustering the matrix composed of the feature vectors to obtain public functional networks of different functional states; and 6) analyzing residence time, conversion times and stability of the various functional statesand determining the brain function states. The method has reference value for study of brain cognitive functions and is helpful for discovering the differences among different functional states and the changing laws among the functional states.

Description

technical field [0001] The invention belongs to the technical field of biomedical information, and specifically relates to a method for extracting brain function states. [0002] technical background [0003] The human brain is an extremely complex system that exists in nature. All kinds of neurons are connected together through synapses, forming a very complex network structure, which is the structural basis for various physiological and cognitive activities of the brain. During the active or passive activity of the brain caused by external stimuli, each neuron or neural dynamic process extends into a complex brain functional network, which is an intuitive description of the changes in brain neural activity. In previous studies, functional connectivity in brain functional networks was a research hotspot. Functional connectivity is closely related to structural connectivity and is limited by structural connectivity, but structural connectivity can also be used to predict the...

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Application Information

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IPC IPC(8): A61B5/055G06K9/62
CPCA61B5/055A61B5/7267A61B2576/026G06F18/23
Inventor 焦竹青季一新高鹏焦庭轩张珈豪
Owner CHANGZHOU UNIV
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