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

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 taskfocused 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 generates scatterplots that illustrate the relationship between cerebellar data (e.g., volumetric patient study clusters, task activation maps, etc.) and cerebellar gradients 1 and 2. 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.

BrainSpace: a toolbox for the analysis of macroscale gradients in neuroimaging and connectomics datasets

“…While tools for unsupervised manifold identification and their alignment are extensively used in data science across multiple research domains 47 , and while few prior connectome level studies made their workflow openly accessible (see refs. 11,15,48 ), we currently lack a unified software package that incorporates the major steps of gradient construction and evaluation for neuroimaging and connectome datasets. We filled this gap with Brain-Space, a compact open-access Matlab/Python toolbox for the identification and analysis of low-dimensional gradients for any given regional or connectome-level feature.…”

Convergence of three parcellation approaches demonstrating cerebellar lobule volume deficits in Alcohol Use Disorder

“…The possibility of quantifying the volume individual cerebellar lobules provides the basis for devising studies to test selective circuitry using multimodal neuroimaging with structural MRI [as attempted herein and (Guell et al, 2019; Guell et al, 2018)], diffusion tensor imaging to identify structural connections between hypothesized neural nodes (e.g., Sullivan et al, 2015), and resting-state functional MRI to seek functional correlated activity between brain regions and structures (e.g., Habas et al, 2009; Krienen and Buckner, 2009). Clearly, all three cerebellar quantification methods examined herein produced similar patterns of group differences and relations with performance and biological data, suggesting that any of the three methods may be used in human studies to seek diagnostic effects or functional correlates of regional cerebellar gray matter volumes.…”

Cerebellar Functional Anatomy: a Didactic Summary Based on Human fMRI Evidence

“…Functional gradient 2 captures a smaller portion of data variability and reveals that a secondary axis in cerebellar macroscale functional neuroanatomy isolates attentional/ executive processing. These two dimensions (functional gradients 1 and 2) provide an alternative functional rather than anatomical space for the visualization of the results of cerebellar neuroimaging [23].…”