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Abstract #5378

Resting State Network Dynamics using Sliding-Window Detrending and Meta-Statistics: A New Approach for Real-time fMRI

Kishore Vakamudi1, Kunxiu Gao2, Cameron W Trapp3, Greg Scantlen4, and Stefan Posse5,6

1Neurology, Physics and Astronomy, University of New Mexico, Albuquerque, NM, United States, 2NeurInsight LLC, Albuquerque, 3Neurology, Physics and Astronomy, University of New Mexico, 4CreativeC LLC, Albuquerque, 5Neurology, Physics and Astronomy, Electrical and Computer Engineering, University of New Mexico, Albuquerque, NM, United States, 6NeurInsight LLC, Albuquerque, NM, United States

This study introduces a real-time confound-tolerant approach for mapping resting-state network (RSN) dynamics that is compatible with ultra-high-speed fMRI and integrates the following processing steps: (a) iterative optimization of seed selection, (b) sliding-window online detrending of confounding signals, and (c) seed-based sliding-window correlation analysis using hierarchical running averages (meta-statistics) for mapping connectivity dynamics. The method maximizes sensitivity and specificity of mapping RSNs with enhanced suppression of spurious connectivity in WM and GM. This methodology is suitable for online monitoring of data quality, for clinical applications and basic neuroscience research of resting-state connectivity, for which there are no currently available tools.

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