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

Reinder Vos de Wael, Oualid Benkarim, Casey Paquola, Sara Lariviere, Jessica Royer, Shahin Tavakol, Ting Xu, Seok-Jun Hong, Sofie L. Valk, Bratislav Misic, Michael P. Milham, Daniel S. Margulies, Jonathan Smallwood, View ORCID ProfileBoris C. Bernhardt
doi: https://doi.org/10.1101/761460
Reinder Vos de Wael
aMcConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Canada
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Oualid Benkarim
aMcConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Canada
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Casey Paquola
aMcConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Canada
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Sara Lariviere
aMcConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Canada
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Jessica Royer
aMcConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Canada
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Shahin Tavakol
aMcConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Canada
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Ting Xu
aMcConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Canada
bCenter for the Developing Brain, Child Mind Institute; New York, USA
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Seok-Jun Hong
aMcConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Canada
bCenter for the Developing Brain, Child Mind Institute; New York, USA
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Sofie L. Valk
dInstitute for Neuroscience and Medicine 7/Institute of Systems Neuroscience, Forschungszentrum Juelich - Heinrich Heine Universitaet Duesseldorf, Juelich, Germany
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Bratislav Misic
aMcConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Canada
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Michael P. Milham
aMcConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Canada
bCenter for the Developing Brain, Child Mind Institute; New York, USA
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Daniel S. Margulies
cFrontlab, Institut du Cerveau et de la Moelle épinière, UPMC UMRS 1127, Inserm U 1127, CNRS UMR 7225, Paris, France
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Jonathan Smallwood
eDepartment of Psychology, University of York, Heslington, England, United Kingdom
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Boris C. Bernhardt
aMcConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Canada
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  • ORCID record for Boris C. Bernhardt
  • For correspondence: boris.bernhardt@mcgill.ca
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Abstract

Understanding how higher order cognitive function emerges from the underlying brain structure depends on quantifying how the behaviour of discrete regions are integrated within the broader cortical landscape. Recent work has established that this macroscale brain organization and function can be quantified in a compact manner through the use of 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. More generally, its macroscale perspective on brain organization offers novel possibilities to investigate the complex relationships between brain structure, function, and cognition in a quantified manner. Here, we present a compact workflow and open-access toolbox that allows for (i) the identification of gradients (from structural or functional imaging data), (ii) their alignment (across subjects or modalities), and (iii) their visualization (in embedding or cortical space). Our toolbox also allows for controlled association studies between gradients with other brain-level features, adjusted with respect to several null models that account for spatial autocorrelation. The toolbox is implemented in both Python and Matlab, programming languages widely used by the neuroimaging and network neuroscience communities. Several use-case examples and validation experiments demonstrate the usage and consistency of our tools for the analysis of functional and microstructural gradients across different spatial scales.

Footnotes

  • http://BrainSpace.readthedocs.io

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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BrainSpace: a toolbox for the analysis of macroscale gradients in neuroimaging and connectomics datasets
Reinder Vos de Wael, Oualid Benkarim, Casey Paquola, Sara Lariviere, Jessica Royer, Shahin Tavakol, Ting Xu, Seok-Jun Hong, Sofie L. Valk, Bratislav Misic, Michael P. Milham, Daniel S. Margulies, Jonathan Smallwood, Boris C. Bernhardt
bioRxiv 761460; doi: https://doi.org/10.1101/761460
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BrainSpace: a toolbox for the analysis of macroscale gradients in neuroimaging and connectomics datasets
Reinder Vos de Wael, Oualid Benkarim, Casey Paquola, Sara Lariviere, Jessica Royer, Shahin Tavakol, Ting Xu, Seok-Jun Hong, Sofie L. Valk, Bratislav Misic, Michael P. Milham, Daniel S. Margulies, Jonathan Smallwood, Boris C. Bernhardt
bioRxiv 761460; doi: https://doi.org/10.1101/761460

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