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The Identification of Migraine Using Functional Connectivity Pattern of Multi-Networks by Deep Learning with Genetic Optimization

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DOI: 10.23977/fbb2020.008


Yuhu Shi

Corresponding Author

Yuhu Shi


Migraine is a kind of chronic functional disorder characterized by recurrent pain, and its pathogenesis is still unclear. Therefore, researchers have conducted a large number of studies on migraine by using functional magnetic resonances imagine (fMRI) technology in recent years. On this basis, we explored the performance of functional connectivities (FCs) between resting-state brain functional networks for the identification of migraine in this paper. Specifically, eight resting-state brain functional networks were obtained from fMRI data of 34 migraine patients and 40 healthy control subjects by group independent component analysis with spatiotemporal dual-regression. Then, the FCs between these brain networks were used as the features for the classification of migraine patients and healthy subjects by back-propagation and genetic algorithms, and good prediction performance was obtained in predicting migraine, which means these resting-state networks and the FCs between them played an key role in the brain functional activity of migraine. Therefore, it may be provided a new perspective for the clinical diagnosis of migraine.


fMRI, Group independent component analysis, Back-propagation, Genetic algorithm, Migraine

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