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Abstract #2396

Using Temporal ICA to Selectively Remove Global Noise While Preserving Global Signal in Functional MRI Data

Matthew F. Glasser1,2, Timothy S. Coalson1, Janine D. Bijsterbosch3, Samuel J. Harrison3, Michael P. Harms1, Alan Anticevic4, David C. Van Essen1, and Stephen M. Smith3

1Washington University in St. Louis, Saint Louis, MO, United States, 2St. Luke's Hospital, Saint Louis, MO, United States, 3University of Oxford., Oxford, United Kingdom, 4Yale University, New Haven, CT, United States

A major unresolved methodological issue in fMRI is how to address the problem of spatially global noise, particularly in resting state functional connectivity data. Global signal regression is effective at removing global noise, which largely arises from physiological sources; however, it has the drawback of additionally removing global or semi-global neural signal as well. Here we present a method to selectively remove global noise while preserving global neural signal using temporal ICA. Thus, we remove a global positive bias in functional connectivity without inducing the network-specific negative bias that results from global signal regression.

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