Gradients of brain organization: Smooth sailing from methods development to user community

TL;DR

This paper discusses how community-driven efforts, open-source tools, and data sharing have accelerated the adoption of gradient methods in neuroimaging, advancing understanding of brain organization across multiple scales.

Contribution

It highlights the role of community efforts and open science in making gradient methods accessible and influential in neuroscience research.

Findings

Community engagement propelled gradient methods to prominence.

Open-source software and workshops facilitated widespread adoption.

Gradient techniques now central to understanding brain organization.

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.