
==== Front
bioRxiv
BIORXIV
bioRxiv
2692-8205
Cold Spring Harbor Laboratory

10.1101/2024.09.09.612129
preprint
1
Article
Addressing inconsistency in functional neuroimaging: A replicable data-driven multi-scale functional atlas for canonical brain networks
Jensen Kyle M. http://orcid.org/0000-0001-7896-2232

Turner Jessica A. http://orcid.org/0000-0003-0076-8434

Calhoun Vince D. http://orcid.org/0000-0001-9058-0747

Iraji Armin http://orcid.org/0000-0002-0605-593X

10 9 2024
2024.09.09.612129https://creativecommons.org/licenses/by-nc-nd/4.0/ This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which allows reusers to copy and distribute the material in any medium or format in unadapted form only, for noncommercial purposes only, and only so long as attribution is given to the creator.
http://biorxiv.org/lookup/doi/10.1101/2024.09.09.612129
nihpp-2024.09.09.612129.pdf
Abstract

The advent of various neuroimaging methodologies has greatly aided in the conceptualization of large-scale brain networks in the field of cognitive neuroscience. However, there is inconsistency across studies in both nomenclature and the functional entities being described. There is a need for a unifying framework which standardizes terminology across studies while also bringing analyses and results into the same reference space. Here we present a functional whole-brain atlas of canonical brain networks derived from more than 100k resting-state fMRI datasets. These data-driven networks are highly replicable across datasets as well as multiple spatial scales. We have organized, labeled, and described them with terms familiar to the fields of cognitive and affective neuroscience in order to optimize their utility in future neuroimaging analyses and enhance the accessibility of new findings. The benefits of this atlas are not limited to future template-based or reference-guided analyses, but also extend to other data-driven neuroimaging approaches across modalities, such as those using blind independent component analysis (ICA). Future studies utilizing this atlas will contribute to greater harmonization and standardization in functional neuroimaging research.
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pmc
