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

Publisher: Nature Publishing Group UK Country of Publication: England NLM ID: 101719179 Publication Model: Electronic Cited Medium: Internet ISSN: 2399-3642 (Electronic) Linking ISSN: 23993642 NLM ISO Abbreviation: Commun Biol Subsets: MEDLINE

Original Publication: London, United Kingdom : Nature Publishing Group UK, [2018]-

Artificial Intelligence* ; Brain Mapping* ; Image Interpretation, Computer-Assisted* ; Magnetic Resonance Imaging*; Neocortex/*diagnostic imaging ; Nerve Net/*diagnostic imaging; Diffusion Tensor Imaging ; Humans ; Neocortex/physiology ; Nerve Net/physiology ; Predictive Value of Tests ; Reproducibility of Results ; Workflow

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.

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U54 MH091657 United States MH NIMH NIH HHS

Date Created: 20200307 Date Completed: 20210614 Latest Revision: 20240329

20240329

PMC7058611

10.1038/s42003-020-0794-7

32139786