Systematic evaluation of fMRI data-processing pipelines for consistent functional connectomics

Andrea I. Luppi (), Helena M. Gellersen, Zhen-Qi Liu, Alexander R. D. Peattie, Anne E. Manktelow, Ram Adapa, Adrian M. Owen, Lorina Naci, David K. Menon, Stavros I. Dimitriadis and Emmanuel A. Stamatakis
Additional contact information
Andrea I. Luppi: University of Cambridge
Helena M. Gellersen: German Center for Neurodegenerative Diseases
Zhen-Qi Liu: McGill University
Alexander R. D. Peattie: University of Cambridge
Anne E. Manktelow: University of Cambridge
Ram Adapa: University of Cambridge
Adrian M. Owen: Western Institute for Neuroscience (WIN), Western University
Lorina Naci: School of Psychology, Trinity College Dublin
David K. Menon: University of Cambridge
Stavros I. Dimitriadis: University of Barcelona
Emmanuel A. Stamatakis: University of Cambridge

Nature Communications, 2024, vol. 15, issue 1, 1-24

Abstract: Abstract Functional interactions between brain regions can be viewed as a network, enabling neuroscientists to investigate brain function through network science. Here, we systematically evaluate 768 data-processing pipelines for network reconstruction from resting-state functional MRI, evaluating the effect of brain parcellation, connectivity definition, and global signal regression. Our criteria seek pipelines that minimise motion confounds and spurious test-retest discrepancies of network topology, while being sensitive to both inter-subject differences and experimental effects of interest. We reveal vast and systematic variability across pipelines’ suitability for functional connectomics. Inappropriate choice of data-processing pipeline can produce results that are not only misleading, but systematically so, with the majority of pipelines failing at least one criterion. However, a set of optimal pipelines consistently satisfy all criteria across different datasets, spanning minutes, weeks, and months. We provide a full breakdown of each pipeline’s performance across criteria and datasets, to inform future best practices in functional connectomics.

Date: 2024
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DOI: 10.1038/s41467-024-48781-5

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