Iterative Data-adaptive Autoregressive (IDAR) whitening procedure for long and short TR fMRI.

ABSTRACT:

Introduction

Functional magnetic resonance imaging (fMRI) has become a fundamental tool for studying brain function. However, the presence of serial correlations in fMRI data complicates data analysis, violates the statistical assumptions of analyses methods, and can lead to incorrect conclusions in fMRI studies.

Methods

In this paper, we show that conventional whitening procedures designed for data with longer repetition times (TRs) (>2 s) are inadequate for the increasing use of short-TR fMRI data. Furthermore, we comprehensively investigate the shortcomings of existing whitening methods and introduce an iterative whitening approach named "IDAR" (Iterative Data-adaptive Autoregressive model) to address these shortcomings. IDAR employs high-order autoregressive (AR) models

SUBMITTER: Yue K 

PROVIDER: S-EPMC11327036 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature