A human brain function network classification method based on a convolution neural network

A convolutional neural network and functional network technology, applied in biological neural network models, neural architectures, instruments, etc., can solve problems that affect the diagnosis of brain diseases, convolutional neural network models cannot make full use of brain network topology information, etc.

Active Publication Date: 2019-02-22
BEIJING UNIV OF TECH
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Problems solved by technology

However, this method assumes that the edges from the same node have the same importance to different nodes. This assumption leads to the inability of the

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  • A human brain function network classification method based on a convolution neural network
  • A human brain function network classification method based on a convolution neural network
  • A human brain function network classification method based on a convolution neural network

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[0045] Take the simulation data set and the real fMRI data set as examples below to illustrate the specific implementation steps of the present invention: Step (1) obtain the resting state fMRI data and preprocess:

[0046] Step (1.1) Resting-state fMRI data acquisition: We obtained autism (Autismspectrum disorder, ASD) data from ABIDE (Autism Brain Imaging DataExchange, http: / / fcon_1000.projects.nitrc.org / indi / abide / ) for analysis , including the resting-state functional magnetic resonance imaging (rs-fMRI) data of 1112 subjects.

[0047] Step (1.2) Data preprocessing: In order to be able to easily reproduce and extend the method, all preprocessed data were obtained from the Preprocessed Connectomes Project (PCP, http: / / preprocessed-connectomes-project.org / abide / ). The PCP project publicly released and shared the preprocessed data of each site in ABIDE by four different preprocessing processes. The data used in the present invention are preprocessed by Data Processing Assist...

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Abstract

The invention discloses a human brain function network classification method based on a convolution neural network, and belongs to the field of brain science research. the method comprises the following steps: obtaining resting state fMRI data and preprocessing the data; generating simulation data; performing data set partitioning; performing classification of human brain functional network basedon convolution neural network. The method of the invention is based on a convolution neural network, and uses Element-wise Filters to give unique weights to each edge and node of the human brain functional network data, thereby constructing a multi-layer neural network comprising an edge-to-node layer and a node-to-graph layer. The method of the invention can better utilize the topological structure information of the data of the human brain function network and carry out the feature expression, thereby improving the classification effect, and the method is reasonable and reliable, and can provide powerful help for the diagnosis of neuropsychiatric diseases.

Description

technical field [0001] The invention belongs to the field of brain science research. Specifically, the invention relates to a method for classifying human brain function networks based on convolutional neural networks. Background technique [0002] The human brain is one of the most important organs of the human body, containing a large number of neuron cells. Through the interaction between multiple neurons, neuron clusters or multiple brain regions, the human brain can complete various complex tasks. The structure and function of the human brain are extremely complex, far beyond our current cognitive capabilities. Therefore, it is undoubtedly very meaningful to explore and understand the working mechanism of the human brain and unravel the mystery of the brain. In recent years, with the continuous development of science and technology, more and more brain imaging techniques have been applied to brain research, such as magnetic resonance imaging (Magnetic Resonance Imagin...

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Application Information

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IPC IPC(8): G06K9/62G06N3/04G16H50/20
CPCG16H50/20G06N3/045G06F18/241G06F18/214
Inventor 邢新颖冀俊忠姚垚
Owner BEIJING UNIV OF TECH
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