Improved autoregressive model for correction of noise serial correlation in fast fMRI

摘要

In rapidly acquired functional MRI (fast fMRI) data, the noise serial correlations (SC) can produce problematically overestimated T‐statistics which lead to invalid statistical inferences. This study aims to evaluate and improve the accuracy of high‐order autoregressive model (AR( ), where is the model order) based prewhitening method in the SC correction.