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

ABSTRACT:

Purpose

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(p), where p is the model order) based prewhitening method in the SC correction.

Methods

Fast fMRI images were acquired at rest (null data) using a multiband simultaneous multi-slice echo planar imaging pulse sequence with repetition time (TR) = 300 and 500 ms. The SC effect in the fast fMRI data was corrected using the prewhitening method based on two AR(p) models: (1) the conventional model (fixed AR(p)) which preselects a constant p for all the image voxels; (2) an improved model (AR_A

SUBMITTER: Luo Q 

PROVIDER: S-EPMC7263980 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature