Reversible cerebral vasoconstriction syndrome (RCVS) is a reversible segmental and multifocal vasoconstriction of the cerebral arteries and is believed to relate to autonomic network over-activity. We used Long Short-Term Memory networks (LSTMs), a type of deep neural network designed to handle time sequence data, to learn directly from the rs-fMRI time-series for classification of individuals with RCVS and healthy controls based on the regions in autonomic and other functional networks. These results provide methodological implications for rs-fMRI data of RCVS patients involved in the analysis and are a key element in future studies.
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