LittleBrain: A gradient-based tool for the topographical interpretation of cerebellar neuroimaging findings
Name
journal.pone.0210028.pdf
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3.15 MB
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Author(s)
Schmahmann, Jeremy D.
Guell Paradis, Xavier
Goncalves, Mathias
Kaczmarzyk, Jakub
Gabrieli, John D. E.
Ghosh, Satrajit S
Date Issued
January 2019
Journal
PLOS ONE
Publisher
Public Library of Science
Citation
Guell, Xavier, Mathias Goncalves, Jakub R. Kaczmarzyk, John D. E. Gabrieli, Jeremy D. Schmahmann, and Satrajit S. Ghosh. “LittleBrain: A Gradient-Based Tool for the Topographical Interpretation of Cerebellar Neuroimaging Findings.” Edited by Daniel S. Margulies. PLOS ONE 14, no. 1 (January 16, 2019):
Version
Final published version
Abstract
Gradient-based approaches to brain function have recently unmasked fundamental properties of brain organization. Diffusion map embedding analysis of resting-state fMRI data revealed a primary-to-transmodal axis of cerebral cortical macroscale functional organization. The same method was recently used to analyze resting-state data within the cerebellum, revealing for the first time a sensorimotor-fugal macroscale organization principle of cerebellar function. Cerebellar gradient 1 extended from motor to non-motor task-unfocused (default-mode network) areas, and cerebellar gradient 2 isolated task-focused processing regions. Here we present a freely available and easily accessible tool that applies this new knowledge to the topographical interpretation of cerebellar neuroimaging findings. LittleBrain illustrates the relationship between cerebellar data (e.g., volumetric patient study clusters, task activation maps, etc.) and cerebellar gradients 1 and 2. Specifically, LittleBrain plots all voxels of the cerebellum in a two-dimensional scatterplot, with each axis corresponding to one of the two principal functional gradients of the cerebellum, and indicates the position of cerebellar neuroimaging data within these two dimensions. This novel method of data mapping provides alternative, gradual visualizations that complement discrete parcellation maps of cerebellar functional neuroanatomy. We present application examples to show that LittleBrain can also capture subtle, progressive aspects of cerebellar functional neuroanatomy that would be difficult to visualize using conventional mapping techniques. Download and use instructions can be found at https://xaviergp.github.io/littlebrain.
MIT Department
Harvard University--MIT Division of Health Sciences and Technology
Massachusetts Institute of Technology. Clinical Research Center
Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences
Massachusetts Institute of Technology. Office of Digital Learning
Massachusetts Institute of Technology. Research Laboratory of Electronics
McGovern Institute for Brain Research at MIT
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Creative Commons Attribution 4.0 International license
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DOI of Published Version
https://doi.org/10.1371/journal.pone.0210028
