Human brain function network classification method based on convolutional neural network

A convolutional neural network and functional network technology, applied in medical science, sensors, diagnostic recording/measurement, etc., can solve performance limitations, do not consider the brain network modular structure and other problems, and achieve the effect of accurate brain disease diagnosis

Active Publication Date: 2021-06-29
BEIJING UNIV OF TECH
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However, these methods do not consider the modular stru

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  • Human brain function network classification method based on convolutional neural network
  • Human brain function network classification method based on convolutional neural network
  • Human brain function network classification method based on convolutional neural network

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Embodiment Construction

[0062] In this embodiment, autistic patients are used as research objects, but the method is not limited thereto, and patients with other mental illnesses can also be used as research objects, and the data can be replaced with corresponding resting-state fMRI data. The following takes the fMRI data set of real autistic patients as an example to illustrate the implementation steps of this method:

[0063] Step (1) Obtain resting state fMRI data and preprocess:

[0064] Step (1.1) The present invention constructs a brain network (brain function network) using resting state fMRI data. Resting state fMRI data acquisition: Obtain autism (Autism spectrum disorder, ASD) resting state fMRI data from ABIDE (Autism Brain Imaging Data Exchange, http: / / fcon_1000.projects.nitrc.org / indi / abide / ), The dataset contains a total of N s =Data of 1112 subjects, each subject's data includes its brain imaging data and the subject's label y. Among them, in the process of brain imaging data acquis...

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Abstract

The invention relates to a human brain function network classification method based on a convolutional neural network, and is used for solving the problems that an existing method ignores the modularization characteristics of a brain network and classification accuracy is low. The human brain function network classification method specifically comprises the following steps of: obtaining resting state fMRI data, carrying out preprocessing, utilizing a preprocessed fMRI time sequence signal to calculate the function connection intensity of each brain interval, and constructing a real human brain function network dataset; independently dividing the real dataset and a simulated dataset into a training set, a verification set and a test set; constructing the convolutional neural network CNN-MF based on scale modularization characteristics for classifying the human brain function network; and carrying out model training: carrying out classification by a model which finishes the training so as to realizing discovery and diagnosis aid of brain diseases. The method disclosed by the invention can effectively utilize modularization structure information in human brain function network data so as to more accurately carry out a brain disease diagnosis.

Description

technical field [0001] The invention belongs to the field of brain science research, in particular, the invention relates to a method for classifying human brain function networks based on convolutional neural networks. Background technique [0002] The human brain is an extremely complex tissue consisting of a large number of neurons and their interconnections. Specifically, on average, each neuron is connected to thousands of other neurons, enabling the human brain to receive, transmit, process, and fuse information, and it also prompts researchers to deeply understand the working mechanism of the human brain from the perspective of brain networks . In recent years, the rapid development of brain imaging technologies such as Magnetic Resonance Imaging (MRI), Electroencephalography (EEG), Magnetoencephalography (MEG), and Computer Tomography (CT) has enabled the study of Researchers can construct and study brain networks from a functional or structural perspective. Furth...

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Application Information

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IPC IPC(8): A61B5/00
CPCA61B5/7264A61B5/0033Y02T10/40
Inventor 姚垚冀俊忠
Owner BEIJING UNIV OF TECH
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