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Predicting the brain state index, pupil dynamics, with rs-fMRI signal-trained models

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/persons214931

Pais-Roldán,  P
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/persons215737

Zhao,  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;

/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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https://www.ismrm.org/20/program_files/DP01-07.htm
(Abstract)

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Citation

Sobczak, F., Pais-Roldán, P., Zhao, X., & Yu, X. (2020). Predicting the brain state index, pupil dynamics, with rs-fMRI signal-trained models. Poster presented at 2020 ISMRM & SMRT Virtual Conference & Exhibition.


Cite as: https://hdl.handle.net/21.11116/0000-0006-D8EF-A
Abstract
Lately, we have acquired the resting state fMRI (rs-fMRI) signal with pupillometry from anesthetized rats to investigate specific resting-state network correlations with brain state-specific pupil dynamics. Here we used the acquired data to estimate the instantaneous arousal index based on the rs-fMRI signal. We evaluated predicting pupil dynamics using three methods: linear regression (LR), gated recurrent unit (GRU) neural networks and a previously proposed correlation-based (CC) approach. LR and GRU provided much better predictions than CC method. Also, using weighted PCA components, we can identify specific regions of the brain related to pupil dynamics as the brain state index.