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

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.

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Item Permalink: http://hdl.handle.net/21.11116/0000-0006-D8EF-A Version Permalink: http://hdl.handle.net/21.11116/0000-0006-D8F0-7
Genre: Poster

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Sobczak, F1, 2, Author              
Pais-Roldán, P1, 2, Author              
Zhao, X1, 2, 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: 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.

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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: 1873 Start / End Page: - Identifier: -