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Exploring the Impact of Analysis Software on Task fMRI Results

View ORCID ProfileAlexander Bowring, Camille Maumet, Thomas E. Nichols
doi: https://doi.org/10.1101/285585
Alexander Bowring
1Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Population Health, University of Oxford, Oxford, UK
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  • ORCID record for Alexander Bowring
Camille Maumet
2Univ Rennes, Inria, CNRS, Inserm, IRISA UMR 6074, VISAGES ERL U-1228, Rennes, France
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Thomas E. Nichols
1Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Population Health, University of Oxford, Oxford, UK
3Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, OX3 9DU, UK
4Department of Statistics, University of Warwick, Coventry, CV4 7AL, UK
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  • For correspondence: thomas.nichols@bdi.ox.ac.uk
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Abstract

A wealth of analysis tools are available to fMRI researchers in order to extract patterns of task variation and, ultimately, understand cognitive function. However, this ‘methodological plurality’ comes with a drawback. While conceptually similar, two different analysis pipelines applied on the same dataset may not produce the same scientific results. Differences in methods, implementations across software packages, and even operating systems or software versions all contribute to this variability. Consequently, attention in the field has recently been directed to reproducibility and data sharing. Neuroimaging is currently experiencing a surge in initiatives to improve research practices and ensure that all conclusions inferred from an fMRI study are replicable.

In this work, our goal is to understand how choice of software package impacts on analysis results. We use publically shared data from three published task fMRI neuroimaging studies, reanalyzing each study using the three main neuroimaging software packages, AFNI, FSL and SPM, using parametric and nonparametric inference. We obtain all information on how to process, analyze, and model each dataset from the publications. We make quantitative and qualitative comparisons between our replications to gauge the scale of variability in our results and assess the fundamental differences between each software package. While qualitatively we find broad similarities between packages, we also discover marked differences, such as Dice similarity coefficients ranging from 0.000 - 0.743 in comparisons of thresholded statistic maps between software. We discuss the challenges involved in trying to reanalyse the published studies, and highlight our own efforts to make this research reproducible.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
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Posted March 20, 2018.
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Exploring the Impact of Analysis Software on Task fMRI Results
Alexander Bowring, Camille Maumet, Thomas E. Nichols
bioRxiv 285585; doi: https://doi.org/10.1101/285585
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Exploring the Impact of Analysis Software on Task fMRI Results
Alexander Bowring, Camille Maumet, Thomas E. Nichols
bioRxiv 285585; doi: https://doi.org/10.1101/285585

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