TY  - JOUR
AU  - Royer, Jessica
AU  - Paquola, Casey
AU  - Valk, Sofie L.
AU  - Kirschner, Matthias
AU  - Hong, Seok-Jun
AU  - Park, Bo-yong
AU  - Bethlehem, Richard A. I.
AU  - Leech, Robert
AU  - Yeo, B. T. Thomas
AU  - Smallwood, Jonathan
AU  - Jefferies, Elizabeth
AU  - Margulies, Daniel
AU  - Bernhardt, Boris C.
TI  - Gradients of Brain Organization: Smooth Sailing from Methods Development to User Community
JO  - Neuroinformatics
VL  - 22
SN  - 1539-2791
CY  - New York, NY
PB  - Springer
M1  - FZJ-2024-05846
SP  - 623-634
PY  - 2024
AB  - 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.
LB  - PUB:(DE-HGF)16
C6  - 38568476
UR  - <Go to ISI:>//WOS:001195996900001
DO  - DOI:10.1007/s12021-024-09660-y
UR  - https://juser.fz-juelich.de/record/1031831
ER  -