littlebrain: a gradient-based tool for the topographical interpretation of cerebellar neuroimaging findings.

Clicks: 192
ID: 132633
2019
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Ranked #118 of 234 articles by views in ensaio pesquisa em educação em ciências

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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.
Reference Key
guell2019ploslittlebrain: Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors ;Xavier Guell;Mathias Goncalves;Jakub R Kaczmarzyk;John D E Gabrieli;Jeremy D Schmahmann;Satrajit S Ghosh
Journal ensaio pesquisa em educação em ciências
Year 2019
DOI
10.1371/journal.pone.0210028
URL
https://doi.org/10.1371/journal.pone.0210028
Keywords
medicinescience

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