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Abstract #5465

Serial Correlations in fMRI Time-Series Arise from Non-Stochastic Signals Related to Brain Function

Kaundinya Gopinath1, Venkatagiri Krishnamurthy1, and K Sathian2,3

1Department of Radiology, Emory University, Atlanta, GA, United States, 2VA RR&D Center of Excellence, Atlanta VAMC, Decatur, GA, United States, 3Department of Neurology, Emory University, Atlanta, GA, United States

In this study, we first demonstrate using resting state fMRI (rsfMRI) “null” datasets, that serial correlation in fMRI time-series arises from non-stochastic signals (e.g., coordinated activity within brain function networks unrelated to the fMRI paradigm of interest). Using this principle, we then advance a method to obtain whitened GLM first-level analysis regression residuals in task fMRI studies, by accounting for non-stochastic brain signals through principal components analysis. Importantly, the proposed methods is insensitive to the temporal resolution of fMRI time-series, unlike conventional stochastic models of serial correlation, whose parameters have to be modified depending on fMRI scan-TR.

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