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Abstract #5323

Accounting for serial correlation in GLM residuals during resting state fMRI nuisance regression

Molly G Bright1,2, Christopher R Tench2, and Kevin Murphy3

1Sir Peter Mansfield Imaging Centre, School of Medicine, University of Nottingham, Nottingham, United Kingdom, 2Division of Clinical Neurosciences, School of Medicine, University of Nottingham, United Kingdom, 3CUBRIC, School of Physics and Astronomy, Cardiff University, United Kingdom

In resting-state fMRI nuisance regression, a General Linear Model (GLM) is employed to fit and remove the variance associated with a noise model. Without "ground-truth" knowledge, the noise models must be tested and improved to obtain accurately cleaned datasets without "throwing the baby out with the bath-water." Valid statistical inference on a GLM fit requires normally-distributed residuals, which is not the case when intrinsic brain fluctuations are present. We demonstrate that existing pre-whitening tools can be appropriately applied to account for serial autocorrelation in resting-state fluctuations during nuisance regression, allowing statistical differentiation of true and simulated noise models.

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