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Decoupling the default mode network and global state oscillation by neural network-based prediction of the fMRI signal fluctuation

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/persons/resource/persons214934

Sobczak,  F
Research Group Translational Neuroimaging and Neural Control, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

/persons/resource/persons192829

He,  Y
Research Group Translational Neuroimaging and Neural Control, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

/persons/resource/persons133486

Yu,  X
Research Group Translational Neuroimaging and Neural Control, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Citation

Sobczak, F., He, Y., Sejnowski, T., & Yu, X. (2020). Decoupling the default mode network and global state oscillation by neural network-based prediction of the fMRI signal fluctuation. Poster presented at 2020 ISMRM & SMRT Virtual Conference & Exhibition.


Cite as: http://hdl.handle.net/21.11116/0000-0006-D8F4-3
Abstract
Previously we developed an echo-state network (ESN) to predict the future temporal evolution of the rs-fMRI slow oscillatory feature from both rodent and human brains. In particular, rs-fMRI signals from individual blood vessels that were strongly correlated with neural calcium oscillations were used to train an ESN to predict brain state-specific rs-fMRI signal fluctuations. Here, the ESN-based predictive model was applied to classify rs-fMRI datasets from the Human Connectome Project (HCP). The ESN enables to decouple the brain state-dependent global rs-fMRI signal fluctuation from the intrinsic activity of the default-mode network.