English
 
User Manual Privacy Policy Disclaimer Contact us
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT

Released

Meeting Abstract

Predict the slow oscillation of the single-vessel resting-state fMRI signal of rats and humans with echo state networks

MPS-Authors
/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;

External Ressource

Link
(Abstract)

Fulltext (public)
There are no public fulltexts stored in PuRe
Supplementary Material (public)
There is no public supplementary material available
Citation

Sobczak, F., He, Y., & Yu, X. (2018). Predict the slow oscillation of the single-vessel resting-state fMRI signal of rats and humans with echo state networks. In Joint Annual Meeting ISMRM-ESMRMB 2018.


Cite as: http://hdl.handle.net/21.11116/0000-0001-7DB3-9
Abstract
Single-vessel fMRI has enabled the detection of slow fluctuations (<0.1Hz) of the hemodynamic fMRI signal from individual vessels in both rat and human brains. The Echo State Network (ESN) has been used to encode the slowly changing temporal dynamics of individual vessels by training the network to predict the oscillatory signals from individual vessels 10 seconds ahead in time. Distinct network reservoirs are optimized for human and animal vascular signals, showing high correlation for the ESN-predictive signal with the original fresh data. This work establishes ESN-based signal prediction for the slow-oscillatory brain fMRI signal in real-time.