BrainStat: A toolbox for brain-wide statistics and multimodal feature associations
Sara Larivière,
Şeyma Bayrak,
Reinder Vos de Wael,
Oualid Benkarim,
Peer Herholz,
Raul Rodriguez-Cruces,
Casey Paquola,
Seok-Jun Hong,
Bratislav Misic,
Alan C. Evans,
Sofie L. Valk,
Boris C. Bernhardt
Affiliations
Sara Larivière
McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
Şeyma Bayrak
Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
Reinder Vos de Wael
McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
Oualid Benkarim
McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
Peer Herholz
McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
Raul Rodriguez-Cruces
McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
Casey Paquola
Institute of Neuroscience and Medicine (INM-1), Forschungszentrum Jülich, Germany
Seok-Jun Hong
Child Mind Institute, New York, USA; Center for Neuroscience Imaging Research, Institute for Basic Science, and Department of Biomedical Engineering, Sungkyunkwan University, Suwon, South Korea
Bratislav Misic
McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
Alan C. Evans
McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
Sofie L. Valk
Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany; Institute of Neuroscience and Medicine (INM-1), Forschungszentrum Jülich, Germany
Boris C. Bernhardt
McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada; Corresponding authors at: Montreal Neurological Institute, McGill University, Montreal, QC, Canada.
Analysis and interpretation of neuroimaging datasets has become a multidisciplinary endeavor, relying not only on statistical methods, but increasingly on associations with respect to other brain-derived features such as gene expression, histological data, and functional as well as cognitive architectures. Here, we introduce BrainStat - a toolbox for (i) univariate and multivariate linear models in volumetric and surface-based brain imaging datasets, and (ii) multidomain feature association of results with respect to spatial maps of post-mortem gene expression and histology, task-based fMRI meta-analysis, as well as resting-state fMRI motifs across several common surface templates. The combination of statistics and feature associations into a turnkey toolbox streamlines analytical processes and accelerates cross-modal research. The toolbox is implemented in both Python and MATLAB, two widely used programming languages in the neuroimaging and neuroinformatics communities. BrainStat is openly available and complemented by an expandable documentation.
