A hybrid learning framework for fine-grained interpretation of brain spatiotemporal patterns during naturalistic functional magnetic resonance imaging

HIGHLIGHTS

SUMMARY

For the temporal analysis, Chen and Hu applied recurrent neural_network (RNN) model based on GRU to capture the sequential information in fMRI data, which can extract GRU patterns and identify subjects. Relative to a resting-state or single-task state, naturalistic stimuli can evoke more intense brain activities and have been proved to possess higher test-retest reliability, suggesting greater potential to study adaptive human brain function (Martinez-Garcia et_al, 2012; Sonkusare et_al, 2019; Simony and Chang, 2020). Although these researches brought a lot of new views, they had a limited contribution to bridge . . .

 



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