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Abstract #1873

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

Filip Sobczak1,2, Patricia Pais-Roldán1,3, Xiaoning Zhao1, and Xin Yu1,4
1Translational Neuroimaging and Neural Control Group, High Field Magnetic Resonance Department, Max Planck Institute for Biological Cybernetics, Tuebingen, Germany, 2Graduate Training Centre of Neuroscience, International Max Planck Research School, University of Tuebingen, Tuebingen, Germany, 3Forschungszentrum Jülich, Jülich, Germany, 4Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA, United States

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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