A network correspondence toolbox for quantitative evaluation of novel neuroimaging results

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, Avram J. Holmes, Angela R. Laird, Linda Larson-Prior, Lisa D. Nickerson, Ana Luísa Pinho, Adeel Razi, Sepideh Sadaghiani, James M. Shine, Anastasia Yendiki, B. T. Thomas Yeo () and Lucina Q. Uddin ()
Additional contact information
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
Felipe De Brigard: Duke University
Simon B. Eickhoff: Heinrich Heine University Düsseldorf
Alex Fornito: Monash University
Caterina Gratton: University of Illinois
Evan M. Gordon: Washington University
Avram J. Holmes: Rutgers University
Angela R. Laird: Florida International University
Linda Larson-Prior: University of Arkansas for Medical Sciences
Lisa D. Nickerson: McLean Hospital
Ana Luísa Pinho: Western University
Adeel Razi: Monash University
Sepideh Sadaghiani: 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

Nature Communications, 2025, vol. 16, issue 1, 1-16

Abstract: 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.

Date: 2025
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DOI: 10.1038/s41467-025-58176-9

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