Accurate autocorrelation modeling substantially improves fMRI reliability

Wiktor Olszowy; John Aston; Catarina Rua; Guy B. Williams

doi:10.1038/s41467-019-09230-w

Nature Communications (Mar 2019)

Accurate autocorrelation modeling substantially improves fMRI reliability

Wiktor Olszowy,
John Aston,
Catarina Rua,
Guy B. Williams

Affiliations

Wiktor Olszowy: Wolfson Brain Imaging Centre, Department of Clinical Neurosciences, University of Cambridge
John Aston: Statistical Laboratory, Department of Pure Mathematics and Mathematical Statistics, University of Cambridge
Catarina Rua: Wolfson Brain Imaging Centre, Department of Clinical Neurosciences, University of Cambridge
Guy B. Williams: Wolfson Brain Imaging Centre, Department of Clinical Neurosciences, University of Cambridge

DOI: https://doi.org/10.1038/s41467-019-09230-w
Journal volume & issue: Vol. 10, no. 1
pp. 1 – 11

Abstract

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There has been recent controversy over the validity of commonly-used software packages for functional MRI (fMRI) data analysis. Here, the authors compare the performance of three leading packages (AFNI, FSL, SPM) in terms of temporal autocorrelation modeling, a key statistical step in fMRI analysis.

Published in Nature Communications

ISSN: 2041-1723 (Online)
Publisher: Nature Portfolio
Country of publisher: United Kingdom
LCC subjects: Science
Website: https://www.nature.com/ncomms/

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