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Abstract #2384

Optimization of Serial Correlation Correction Methods Based on Autoregressive Model in Fast fMRI

Qingfei Luo1, Masaya Misaki1, Beni Mulyana1, Chung-Ki Wong1, and Jerzy Bodurka1,2

1Laureate Institute for Brain Research, Tulsa, OK, United States, 2Stephenson School for Biomedical Engineering, University of Oklahoma, Norman, OK, United States

Serial correlation (SC) of noise inflates T-statistics in simultaneous multi-slice excitation (SMS) fMRI studies with short repetition times (TR<2s). The SC can be corrected using noise pre-whitening methods based on the high-order autoregressive (AR) model. This study aims to determine the optimal order selection (OS) method of AR model to achieve the best SC correction accuracy. By evaluating the false positive characteristics in rest/null datasets, our study showed that the corrected Akaike information criterion (AICc) has the best performance among the OS criteria. We recommend use the AR model with AICc to correct the SC in SMS fMRI experiments.

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