Abstract #3700

Identification and Characterization of Resting State Networks in the Translational Pig Model

Gregory Simchick^1,2, Alice Shen³, Hea Jin Park⁴, Franklin West⁵, and Qun Zhao^1,2

¹Physics and Astronomy, University of Georgia, Athens, GA, United States, ²Bio-Imaging Research Center, University of Georgia, Athens, GA, United States, ³University of Georgia, Athens, GA, United States, ⁴Foods and Nutrition, University of Georgia, Athens, GA, United States, ⁵Animal and Dairy Science, University of Georgia, Athens, GA, United States

Due to the similar size, structure, composition, and neurodevelopment of the pig brain in comparison to the human brain, the pig serves as a valuable large animal model for studying brain connectivity. Presented here are five resting state networks (RSNs) identified within the three-week-old piglet brain determined using temporal sparse dictionary learning. Each RSN’s learned activation map correlates well with a constructed pig RSN atlas with Pearson spatial correlation coefficients in the range of [0.30 0.53], and clear differences in the power spectra, as well as unique characteristic frequencies, associated with the learned signal for each RSN are observed.

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