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@ARTICLE{Kong:1029130,
      author       = {Kong, Ru Q and Spreng, R. Nathan and XUE, AIHUIPING and
                      Betzel, Richard and Cohen, Jessica R and Damoiseaux, Jessica
                      and De Brigard, Felipe and Eickhoff, Simon B and Fornito,
                      Alex and Gratton, Caterina and Gordon, Evan M and Holmes,
                      Avram J and Laird, Angela R and Larson-Prior, Linda and
                      Nickerson, Lisa D and Pinho, Ana Luisa and Razi, Adeel and
                      Sadaghiani, Sepideh and Shine, James and Yendiki, Anastasia
                      and Yeo, B. T. Thomas and Uddin, Lucina Q},
      title        = {{A} network correspondence toolbox for quantitative
                      evaluation of novel neuroimaging results},
      journal      = {bioRxiv beta},
      address      = {Cold Spring Harbor},
      publisher    = {Cold Spring Harbor Laboratory, NY},
      reportid     = {FZJ-2024-04986},
      year         = {2024},
      abstract     = {Decades of neuroscience research has shown that macroscale
                      brain dynamics can be reliably decomposed into a subset of
                      large-scale functional networks, but the specific spatial
                      topographies of these networks and the names used to
                      describe them can vary across studies. Such discordance has
                      hampered interpretation and convergence of research findings
                      across the field. To address this problem, we have developed
                      the Network Correspondence Toolbox (NCT) to permit
                      researchers to examine and report spatial correspondence
                      between their novel neuroimaging results and sixteen widely
                      used functional brain atlases, consistent with recommended
                      reporting standards developed by the Organization for Human
                      Brain Mapping. The atlases included in the toolbox show some
                      topographical convergence for specific networks, such as
                      those labeled as default or visual. Network naming varies
                      across atlases, particularly for networks spanning
                      frontoparietal association cortices. For this reason,
                      quantitative comparison with multiple atlases is recommended
                      to benchmark novel neuroimaging findings. We provide several
                      exemplar demonstrations using the Human Connectome Project
                      task fMRI results and UK Biobank independent component
                      analysis maps to illustrate how researchers can use the NCT
                      to report their own findings through quantitative evaluation
                      against multiple published atlases. 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 NCT also includes functionality
                      to incorporate additional atlases in the future. 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.},
      cin          = {INM-7},
      ddc          = {570},
      cid          = {I:(DE-Juel1)INM-7-20090406},
      pnm          = {5251 - Multilevel Brain Organization and Variability
                      (POF4-525)},
      pid          = {G:(DE-HGF)POF4-5251},
      typ          = {PUB:(DE-HGF)25},
      doi          = {10.1101/2024.06.17.599426},
      url          = {https://juser.fz-juelich.de/record/1029130},
}