Jessica Royer, Casey Paquola, Sofie L. Valk, Matthias Kirschner, Seok-Jun Hong, Bo-yong Park, Richard A. I. Bethlehem, Robert Leech, B. T. Thomas Yeo, Elizabeth Jefferies, Jonathan Smallwood, Daniel Margulies, Boris C. Bernhardt
{"title":"Gradients of brain organization: Smooth sailing from methods development to user community","authors":"Jessica Royer, Casey Paquola, Sofie L. Valk, Matthias Kirschner, Seok-Jun Hong, Bo-yong Park, Richard A. I. Bethlehem, Robert Leech, B. T. Thomas Yeo, Elizabeth Jefferies, Jonathan Smallwood, Daniel Margulies, Boris C. Bernhardt","doi":"arxiv-2402.11055","DOIUrl":null,"url":null,"abstract":"Multimodal neuroimaging grants a powerful in vivo window into the structure\nand function of the human brain. Recent methodological and conceptual advances\nhave enabled investigations of the interplay between large-scale spatial\ntrends, or gradients, in brain structure and function, offering a framework to\nunify principles of brain organization across multiple scales. Strong community\nenthusiasm for these techniques has been instrumental in their widespread\nadoption and implementation to answer key questions in neuroscience. Following\na brief review of current literature on this framework, this perspective paper\nwill highlight how pragmatic steps aiming to make gradient methods more\naccessible to the community propelled these techniques to the forefront of\nneuroscientific inquiry. More specifically, we will emphasize how interest for\ngradient methods was catalyzed by data sharing, open-source software\ndevelopment, as well as the organization of dedicated workshops led by a\ndiverse team of early career researchers. To this end, we argue that the\ngrowing excitement for brain gradients is the result of coordinated and\nconsistent efforts to build an inclusive community and can serve as a case in\npoint for future innovations and conceptual advances in neuroinformatics. We\nclose this perspective paper by discussing challenges for the continuous\nrefinement of neuroscientific theory, methodological innovation, and real-world\ntranslation to maintain our collective progress towards integrated models of\nbrain organization.","PeriodicalId":501219,"journal":{"name":"arXiv - QuanBio - Other Quantitative Biology","volume":"3 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"arXiv - QuanBio - Other Quantitative Biology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/arxiv-2402.11055","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Multimodal neuroimaging grants a powerful in vivo window into the structure
and function of the human brain. Recent methodological and conceptual advances
have enabled investigations of the interplay between large-scale spatial
trends, or gradients, in brain structure and function, offering a framework to
unify principles of brain organization across multiple scales. Strong community
enthusiasm for these techniques has been instrumental in their widespread
adoption and implementation to answer key questions in neuroscience. Following
a brief review of current literature on this framework, this perspective paper
will highlight how pragmatic steps aiming to make gradient methods more
accessible to the community propelled these techniques to the forefront of
neuroscientific inquiry. More specifically, we will emphasize how interest for
gradient methods was catalyzed by data sharing, open-source software
development, as well as the organization of dedicated workshops led by a
diverse team of early career researchers. To this end, we argue that the
growing excitement for brain gradients is the result of coordinated and
consistent efforts to build an inclusive community and can serve as a case in
point for future innovations and conceptual advances in neuroinformatics. We
close this perspective paper by discussing challenges for the continuous
refinement of neuroscientific theory, methodological innovation, and real-world
translation to maintain our collective progress towards integrated models of
brain organization.