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  Loading and plotting of cortical surface representations in Nilearn

Huntenburg, J. M., Abraham, A., Loula, J., Liem, F., Dadi, K., & Varoquaux, G. (2017). Loading and plotting of cortical surface representations in Nilearn. Research Ideas and Outcomes, 3: e12342. doi:10.3897/rio.3.e12342.

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Item Permalink: https://hdl.handle.net/11858/00-001M-0000-002C-8886-5 Version Permalink: https://hdl.handle.net/21.11116/0000-0003-5EE8-F
Genre: Journal Article

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Huntenburg_Abraham_2017.pdf (Publisher version), 3MB
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Huntenburg, Julia M.1, 2, Author           
Abraham, Alexandre3, 4, Author
Loula, João3, 5, Author
Liem, Franz1, Author           
Dadi, Kamalaker3, 4, Author
Varoquaux, Gaël3, 4, Author
Affiliations:
1Max Planck Research Group Neuroanatomy and Connectivity, MPI for Human Cognitive and Brain Sciences, Max Planck Society, ou_1356546              
2Neurocomputation and Neuroimaging Unit, FU Berlin, Germany, ou_persistent22              
3Inria Parietal, Saclay, France, ou_persistent22              
4CEA Neurospin, Gif-Sur-Yvette, France, ou_persistent22              
5Department of Computer Science, École Polytechnique, Palaiseau, France, ou_persistent22              

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Free keywords: Cortical surfaces; Surface plotting; Python
 Abstract: Processing neuroimaging data on the cortical surface traditionally requires dedicated heavy-weight software suites. Here, we present an initial support of cortical surfaces in Python within the neuroimaging data processing toolbox Nilearn. We provide loading and plotting functions for different surface data formats with minimal dependencies, along with examples of their application. Limitations of the current implementation and potential next steps are discussed.

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Language(s): eng - English
 Dates: 2017-02-202017-02-23
 Publication Status: Published online
 Pages: -
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 Rev. Type: -
 Identifiers: DOI: 10.3897/rio.3.e12342
BibTex Citekey: Huntenburg2017
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Title: Research Ideas and Outcomes
Source Genre: Journal
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Publ. Info: Sofia : Pensoft
Pages: - Volume / Issue: 3 Sequence Number: e12342 Start / End Page: - Identifier: ISSN: 2367-7163
CoNE: https://pure.mpg.de/cone/journals/resource/2367-7163