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

Author

Listed:
  • Andrea I. Luppi

    (University of Cambridge
    University of Cambridge
    University of Cambridge
    McGill University)

  • Helena M. Gellersen

    (German Center for Neurodegenerative Diseases
    University of Cambridge)

  • Zhen-Qi Liu

    (McGill University)

  • Alexander R. D. Peattie

    (University of Cambridge
    University of Cambridge)

  • Anne E. Manktelow

    (University of Cambridge
    University of Cambridge)

  • Ram Adapa

    (University of Cambridge
    University of Cambridge)

  • Adrian M. Owen

    (Western Institute for Neuroscience (WIN), Western University
    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
    University of Barcelona
    Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, College of Biomedical and Life Sciences
    School of Medicine, College of Biomedical and Life Sciences, Cardiff University)

  • Emmanuel A. Stamatakis

    (University of Cambridge
    University of Cambridge)

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.

Suggested Citation

  • 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 & Emmanuel A. Sta, 2024. "Systematic evaluation of fMRI data-processing pipelines for consistent functional connectomics," Nature Communications, Nature, vol. 15(1), pages 1-24, December.
    • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-48781-5
    DOI: 10.1038/s41467-024-48781-5
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    References listed on IDEAS

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