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Abstract #5344

Confound Suppression in Resting State fMRI using Sliding Windows and Running Mean

Cameron Trapp1, Kishore Vakamudi1, and Stefan Posse1,2,3

1Neurology, University of New Mexico, Albuquerque, NM, United States, 2Physics and Astronomy, University of New Mexico, 3Electrical Engineering, University of New Mexico

We analytically investigate the characteristics of a recently developed sliding window methodology designed for real time analysis of resting state connectivity. The suppression of various types of confounds is investigated both in this analytical framework and numerically. It is shown that this methodology not only acts as a high pass filter and denoiser, but behaves as a model free despiking and confound suppression tool.

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