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A network correspondence toolbox for quantitative evaluation of novel neuroimaging results

Author

Listed:
  • Ru Kong

    (National University of Singapore)

  • R. Nathan Spreng

    (McGill University)

  • Aihuiping Xue

    (National University of Singapore)

  • Richard F. Betzel

    (Indiana University)

  • Jessica R. Cohen

    (University of North Carolina)

  • Jessica S. Damoiseaux

    (Wayne State University
    Wayne State University)

  • Felipe De Brigard

    (Duke University)

  • Simon B. Eickhoff

    (Heinrich Heine University Düsseldorf
    Research Centre Jülich)

  • Alex Fornito

    (Monash University
    Monash University
    Monash University)

  • Caterina Gratton

    (University of Illinois
    University of Illinois)

  • Evan M. Gordon

    (Washington University)

  • Avram J. Holmes

    (Rutgers University
    Rutgers University)

  • Angela R. Laird

    (Florida International University)

  • Linda Larson-Prior

    (University of Arkansas for Medical Sciences
    University of Arkansas for Medical Sciences)

  • Lisa D. Nickerson

    (McLean Hospital)

  • Ana Luísa Pinho

    (Western University
    Western University)

  • Adeel Razi

    (Monash University
    Monash University
    Monash University)

  • Sepideh Sadaghiani

    (University of Illinois
    University of Illinois)

  • James M. Shine

    (University of Sydney)

  • Anastasia Yendiki

    (Massachusetts General Hospital)

  • B. T. Thomas Yeo

    (National University of Singapore)

  • Lucina Q. Uddin

    (University of California Los Angeles
    University of California Los Angeles)

Abstract

The brain can be decomposed into large-scale functional networks, but the specific spatial topographies of these networks and the names used to describe them vary across studies. Such discordance has hampered interpretation and convergence of research findings across the field. We have developed the Network Correspondence Toolbox (NCT) to permit researchers to examine and report spatial correspondence between their novel neuroimaging results and multiple widely used functional brain atlases. We provide several exemplar demonstrations to illustrate how researchers can use the NCT to report their own findings. The NCT provides a convenient means for computing Dice coefficients with spin test permutations to determine the magnitude and statistical significance of correspondence among user-defined maps and existing atlas labels. The adoption of the NCT will make it easier for network neuroscience researchers to report their findings in a standardized manner, thus aiding reproducibility and facilitating comparisons between studies to produce interdisciplinary insights.

Suggested Citation

  • Ru Kong & R. Nathan Spreng & Aihuiping Xue & Richard F. Betzel & Jessica R. Cohen & Jessica S. Damoiseaux & Felipe De Brigard & Simon B. Eickhoff & Alex Fornito & Caterina Gratton & Evan M. Gordon & A, 2025. "A network correspondence toolbox for quantitative evaluation of novel neuroimaging results," Nature Communications, Nature, vol. 16(1), pages 1-16, December.
  • Handle: RePEc:nat:natcom:v:16:y:2025:i:1:d:10.1038_s41467-025-58176-9
    DOI: 10.1038/s41467-025-58176-9
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