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

Abstract

'Jessica Royer' 'Shahin Tavakol' 'Ting Xu' 'Seok-Jun Hong' 'Georg Langs' 'Sofie Valk' 'Bratislav Misic' 'Michael Milham' 'Daniel Margulies' 'Jonathan Smallwood' 'Boris C. Bernhardt'] 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.