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  Decoding the brain state-dependent relationship between pupil dynamics and resting state fMRI signal fluctuation

Sobczak, F., Pais-Roldán, P., Takahashi, K., & Yu, X. (2021). Decoding the brain state-dependent relationship between pupil dynamics and resting state fMRI signal fluctuation. eLife, 10: e68980, pp. 1-21. doi:10.7554/eLife.68980.

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https://elifesciences.org/articles/68980 (Publisher version)
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Sobczak, F1, 2, Author              
Pais-Roldán, P1, 2, Author              
Takahashi, K1, 2, Author              
Yu, X, 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: Pupil dynamics serve as a physiological indicator of cognitive processes and arousal states of the brain across a diverse range of behavioral experiments. Pupil diameter changes reflect brain state fluctuations driven by neuromodulatory systems. Resting state fMRI (rs-fMRI) has been used to identify global patterns of neuronal correlation with pupil diameter changes, however, the linkage between distinct brain state-dependent activation patterns of neuromodulatory nuclei with pupil dynamics remains to be explored. Here, we identified four clusters of trials with unique activity patterns related to pupil diameter changes in anesthetized rat brains. Going beyond the typical rs-fMRI correlation analysis with pupil dynamics, we decomposed spatiotemporal patterns of rs-fMRI with principal components analysis (PCA) and characterized the cluster-specific pupil-fMRI relationships by optimizing the PCA component weighting via decoding methods. This work shows that pupil dynamics are tightly coupled with different neuromodulatory centers in different trials, presenting a novel PCA-based decoding method to study the brain state-dependent pupil-fMRI relationship.

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 Dates: 2021-08
 Publication Status: Published online
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 Rev. Type: -
 Identifiers: DOI: 10.7554/eLife.68980
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Title: eLife
Source Genre: Journal
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Publ. Info: Cambridge : eLife Sciences Publications
Pages: - Volume / Issue: 10 Sequence Number: e68980 Start / End Page: 1 - 21 Identifier: ISSN: 2050-084X
CoNE: https://pure.mpg.de/cone/journals/resource/2050-084X