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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},
}