Human brain function network classification method based on convolutional neural network

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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Problems solved by technology

However, these methods do not consider the modular structure in brain networks, which limits their performance.

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