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Abstract #2255

Quantification of Reproducible Spatiotemporal Dynamic Patterns Using Bootstrapping from Randomized Phase Data in the Rodent Model

Matthew E. Magnuson1, Garth Thompson1, Wen-Ju Pan1, Shella Keilholz1

1Biomedical Engineering, Georgia Institute of Technology / Emory University, Atlanta, GA, United States

Dynamic analysis of functional MRI data is gaining traction over the traditional static based analysis, primarily because it provides complex information regarding the spatial and temporal properties of functional activity as opposed to a single average of those dynamic components. Majeed et al. previously published a technique for creating reproducible spatiotemporal dynamic templates from functional data based on a specified window length. In the work presented here we expand on the previous work using a bootstrapping algorithm to statistically threshold the template output allowing for quantification, clustering, and finally inter-subject comparisons.