The invention discloses a
brain function network classification method based on a variational
autoencoder. The method comprises the following steps: The method comprises the following steps of: acquiring T1 weighted MRI and rs-fMRI of a plurality of
normal people and patients with brain
cognitive impairment; carrying out pretreatment; carrying out double
regression analysis by taking the preprocessed rs-fMRI as a regression dependent variable and the
brain function network as a regression independent variable to obtain an
individual level brain function network; constructing a deep variationalautoencoder (VAE) model, taking the obtained
individual level brain function network diagram as the input and output of the VAE, and taking the
encoder part as a
feature extraction module for obtaining the implicit code of the individual function network; constructing a multi-layer sensor network to classify the codes obtained by the VAE in the step 4; and deducing samples in the
test set by using the trained classifiers for different brain function networks, and fusing deduction results of the classifiers to obtain a final
classification result.acquiring T1 weighted magnetic
resonance imagesT1 Weighted MRI and resting state
functional magnetic resonance images rs-of a plurality of normal persons and patients with brain
cognitive impairment; fMRI; carrying out pretreatment; pretreated rs- Performing double
regression analysis by taking fMRI as a regression dependent variable and taking the brain function network as a regression independent variable to obtain an
individual level brainfunction network; constructing a depth variation auto-
encoder (VAE) model, taking the obtained individual level brain function network diagram as input and output of the VAE, and taking the
encoder part as a
feature extraction module for obtaining hidden codes of the individual function network; constructing a multi-layer
perceptron network to classify the codes obtained by the VAE in the step 4;inferring samples in the
test set by utilizing a plurality of trained classifiers for different brain function networks, and fusing
inference results of the plurality of classifiers to obtain a finalclassification result; according According to the invention, the classification accuracy is improved.