Abstract #3824

Sparse Estimation of Quasi-periodic Spatiotemporal Components in Resting-State fMRI

Alican Nalci^1,2, Bhaskar D. Rao², and Thomas T. Liu¹

¹Center for Functional MRI, University of California, San Diego, La Jolla, CA, United States, ²Electrical and Computer Engineering, University of California, San Diego, La Jolla, CA, United States

Recent studies suggest the presence of complex recurrent spatiotemporal patterns in resting-state fMRI. These patterns may affect the performance of existing preprocessing and analysis approaches, such as global signal regression and ICA. In this work we present an approach for the sparse estimation of quasi-periodic spatiotemporal components in resting state fMRI. Our algorithm successfully estimates spatiotemporal components in a sample resting-state fMRI dataset and our results suggest that the removal of these components may represent an alternative to global signal regression.

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