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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