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Given the vascular origin of the fMRI signal, it remains challenging to separate the neural correlates of global rs-fMRI signal fluctuations from other confounding sources. However, the slow-oscillation detected from individual vessels by single-vessel fMRI presents strong correlation to neural oscillations. Here, we use recurrent neural networks (RNNs) to predict the future temporal evolution of the rs-fMRI slow oscillation from both rodent and human brains. The RNNs trained with vessel-specific rs-fMRI signals encode the unique brain oscillatory dynamic feature, presenting more effective prediction than the conventional autoregressive model. This RNN-based predictive modeling of rs-fMRI datasets from the Human Connectome Project (HCP) reveals brain state-specific characteristics, demonstrating an inverse relationship between the global rs-fMRI signal fluctuation with the internal default-mode network (DMN) correlation. The RNN prediction method presents a unique data-driven encoding scheme to specify potential brain state differences based on the global fMRI signal fluctuation, but not solely dependent on the global variance.<\/jats:p>","DOI":"10.1093\/cercor\/bhaa260","type":"journal-article","created":{"date-parts":[[2020,8,18]],"date-time":"2020-08-18T16:14:10Z","timestamp":1597767250000},"page":"826-844","source":"Crossref","is-referenced-by-count":14,"title":["Predicting the fMRI Signal Fluctuation with Recurrent Neural Networks Trained on Vascular Network Dynamics"],"prefix":"10.1093","volume":"31","author":[{"given":"Filip","family":"Sobczak","sequence":"first","affiliation":[{"name":"Translational Neuroimaging and Neural Control Group, High Field Magnetic Resonance Department, Max Planck Institute for Biological Cybernetics, 72076 Tuebingen, Germany"},{"name":"Graduate Training Centre of Neuroscience, International Max Planck Research School, University of Tuebingen, 72074 Tuebingen, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yi","family":"He","sequence":"additional","affiliation":[{"name":"Translational Neuroimaging and Neural Control Group, High Field Magnetic Resonance Department, Max Planck Institute for Biological Cybernetics, 72076 Tuebingen, Germany"},{"name":"Danish Research Centre for Magnetic Resonance, 2650, Hvidovre, Denmark"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Terrence J","family":"Sejnowski","sequence":"additional","affiliation":[{"name":"Howard Hughes Medical Institute, Computational Neurobiology Laboratory, Salk Institute for Biological Studies, La Jolla, CA 92037, USA"},{"name":"Division of Biological Sciences, University of California, San Diego, La Jolla, CA 92093, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xin","family":"Yu","sequence":"additional","affiliation":[{"name":"Translational Neuroimaging and Neural Control Group, High Field Magnetic Resonance Department, Max Planck Institute for Biological Cybernetics, 72076 Tuebingen, Germany"},{"name":"Athinoula A. 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