Deterministic Estimation of Spatiotemporal Motifs in Resting-State fMRI
Alican Nalci1,2 and Thomas Liu2
1Electrical and Computer Engineering, University of California San Diego, La Jolla, CA, United States, 2Center for Functional MRI, University of California San Diego, La Jolla, CA, United States
In resting-state fMRI data, dynamic quasi-periodic spatio-temporal patterns have previously been identified in both animal and humans with potential links to infra slow electrical activity. These prior studies used an iterative pattern-finding algorithm that employed heuristic learning rules for the dynamic adjustment of correlation thresholds. Here we present a novel deterministic non-iterative approach for estimating spatiotemporal motifs in resting-state fMRI data without the need for heuristic learning rules.
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