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

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.

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Item Permalink: http://hdl.handle.net/21.11116/0000-0006-D8F4-3 Version Permalink: http://hdl.handle.net/21.11116/0000-0006-D8F5-2
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Sobczak, F1, 2, Author              
He, Y1, 2, Author              
Sejnowski, TJ, Author
Yu, X1, 2, Author              
Affiliations:
1Research Group Translational Neuroimaging and Neural Control, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_2528695              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497794              

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 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.

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 Dates: 2020-08
 Publication Status: Published online
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Title: 2020 ISMRM & SMRT Virtual Conference & Exhibition
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Start-/End Date: 2020-08-08 - 2020-08-14

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Title: 2020 ISMRM & SMRT Virtual Conference & Exhibition
Source Genre: Proceedings
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Pages: - Volume / Issue: - Sequence Number: 1874 Start / End Page: - Identifier: -