Accurate autocorrelation modeling substantially improves fMRI reliability

TL;DR

This study compares fMRI analysis packages and finds that accurate autocorrelation modeling significantly enhances the reliability of task-based fMRI results, highlighting the need for better diagnostic tools.

Contribution

It provides a comprehensive comparison of autocorrelation modeling in AFNI, FSL, and SPM, demonstrating the impact on fMRI reliability and suggesting improvements.

Findings

AFNI's autocorrelation modeling outperforms FSL and SPM.

Residual autocorrelation confounds low-frequency experimental results.

SPM's FAST pre-whitening improves over default SPM methods.

Abstract

Given the recent controversies in some neuroimaging statistical methods, we compare the most frequently used functional Magnetic Resonance Imaging (fMRI) analysis packages: AFNI, FSL and SPM, with regard to temporal autocorrelation modeling. This process, sometimes known as pre-whitening, is conducted in virtually all task fMRI studies. We employ eleven datasets containing 980 scans corresponding to different fMRI protocols and subject populations. Though autocorrelation modeling in AFNI is not perfect, its performance is much higher than the performance of autocorrelation modeling in FSL and SPM. The residual autocorrelated noise in FSL and SPM leads to heavily confounded first level results, particularly for low-frequency experimental designs. Our results show superior performance of SPM's alternative pre-whitening: FAST, over SPM's default. The reliability of task fMRI studies would increase with more accurate autocorrelation modeling. Furthermore, reliability could increase if the packages provided diagnostic plots. This way the investigator would be aware of pre-whitening problems.