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Laminar Python: Tools for cortical depth-resolved analysis of high-resolution brain imaging data in Python

MPS-Authors
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Huntenburg,  Julia M.
Max Planck Research Group Neuroanatomy and Connectivity, MPI for Human Cognitive and Brain Sciences, Max Planck Society;
FU Berlin, Germany;

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Steele,  Christopher
Department Neurology, MPI for Human Cognitive and Brain Sciences, Max Planck Society;
Douglas Mental Health University Institute, McGill University, Montréal, QC, Canada;

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Bazin,  Pierre-Louis
FU Berlin, Germany;
Department Neurology, MPI for Human Cognitive and Brain Sciences, Max Planck Society;
Department Neurophysics (Weiskopf), MPI for Human Cognitive and Brain Sciences, Max Planck Society;

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Huntenburg_Wagstyl_2017.pdf
(Publisher version), 13MB

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Citation

Huntenburg, J. M., Wagstyl, K., Steele, C., Funck, T., Bethlehem, R., Foubet, O., et al. (2017). Laminar Python: Tools for cortical depth-resolved analysis of high-resolution brain imaging data in Python. Research Ideas and Outcomes, 3: e12346. doi:10.3897/rio.3.e12346.


Cite as: http://hdl.handle.net/11858/00-001M-0000-002C-887E-A
Abstract
Increasingly available high-resolution brain imaging data require specialized processing tools that can leverage their anatomical detail and handle their size. Here, we present user-friendly Python tools for cortical depth resolved analysis in such data. Our implementation is based on the CBS High-Res Brain Processing framework, and aims to make high-resolution data processing tools available to the broader community.