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

By Reinder Vos de Wael, O. Benkarim, C. Paquola, S. Larivière, J. Royer, Shahin Tavakol, Ting Xu, Seokjun Hong, S. Valk, Bratislav Misic and 4 more. Communications Biology, 2019

599 citations recorded by the source corpus.

Understanding how cognitive functions emerge from brain structure depends on quantifying how discrete regions are integrated within the broader cortical landscape. Recent work established that macroscale brain organization and function can be described in a compact manner with multivariate machine learning approaches that identify manifolds often described as cortical gradients. By quantifying topographic principles of macroscale organization, cortical gradients lend an analytical framework to study structural and functional brain organization across species, throughout development and aging, and its perturbations in disease. Here, we present BrainSpace, a Python/Matlab toolbox for (i) the identification of gradients, (ii) their alignment, and (iii) their visualization. Our toolbox furthermore allows for controlled association studies between gradients with other brain-level features, adjusted with respect to null models that account for spatial autocorrelation. Validation experiments demonstrate the usage and consistency of our tools for the analysis of functional and microstructural gradients across different spatial scales. Vos de Wael et al. developed an open source tool called BrainSpace to quantify cortical gradients using 3 structural or functional imaging data. Their toolbox enables gradient identification, comparison, 4 visualization, and association with other brain features.

Subfields: Cognitive Neuroscience, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/52304621a2fa4894ce6820850d09056f1ad67b9f

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In Cognitive Neuroscience

In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

The default mode network in cognition: a topographical perspective (2021, unknown): “This more complex perspective regarding brain function 34 has been formalized through the application of a class of cortex-wise decomposition techniques, linked to principal components analysis, to measures of brain activity and connectivity 35,36 .”
neuromaps: structural and functional interpretation of brain maps (2022, methodology): “The three parametrized data models circumvent spatial rotations by applying generative frameworks such as a spatial lag model 4 , spectral randomization 44 or variogram matching 43 .”
Frequency-specific maturation of cortical organization and social-cognitive links from childhood to adolescence (2025, unknown): “Gradient analyses were conducted using the BrainSpace toolkit (Wael et al., 2020).”