A dynamic function mode learning method inspired by an fMRI brain network mechanism, comprising: collecting resting-state fMRI brain image data of several subjects; performing a preprocessing operation on the resting-state fMRI brain image data; according to the preprocessed resting-state fMRI brain image data, using a GICA-IR method to separately obtain a group-level resting-state brain function network and an individual-level resting-state brain function network, as well as time sequences corresponding thereto; using a sliding time window method to calculate a dynamic function connection matrix between resting-state brain function networks corresponding to each subject, and stretching triangular elements on the matrix into dynamic function connection vectors, so as to obtain a set of dynamic function connection vectors corresponding to all of the subjects; and using a deep neural network model and an affine propagation clustering algorithm to extract a dynamic function connection pattern intrinsic to the brain that is implied in the set of dynamic function connection vectors. The described method provides a solid basis for revealing the basic principles of cognitive activities of the brain and the damage mechanism of cranial nerves diseases, and exploring the features of occupational brain plasticity reorganization.
DYNAMIC FUNCTION MODE LEARNING METHOD INSPIRED BY FMRI BRAIN NETWORK MECHANISM