Abstract #3833

Deterministic Estimation of Spatiotemporal Motifs in Resting-State fMRI

Alican Nalci^1,2 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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