NeuroPycon: An open-source python toolbox for fast multi-modal and reproducible brain connectivity pipelines

Recent years have witnessed a massive push towards reproducible research in neuroscience. Unfortunately, this endeavor is often challenged by the large diversity of tools used, project-specific custom code and the difficulty to track all user-defined parameters. NeuroPycon is an open-source multi-mo...

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Vydané v:	NeuroImage (Orlando, Fla.) Ročník 219; s. 117020
Hlavní autori:	Meunier, David, Pascarella, Annalisa, Altukhov, Dmitrii, Jas, Mainak, Combrisson, Etienne, Lajnef, Tarek, Bertrand-Dubois, Daphné, Hadid, Vanessa, Alamian, Golnoush, Alves, Jordan, Barlaam, Fanny, Saive, Anne-Lise, Dehgan, Arthur, Jerbi, Karim
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	United States Elsevier Inc 01.10.2020 Elsevier Limited Elsevier
Predmet:	Algorithms Brain - diagnostic imaging Brain imaging Brain mapping Brain networks Brain research Coding standards Computational neuroscience Computer programs Data analysis Data processing Documentation EEG Electroencephalography Electroencephalography (EEG) Electrophysiology Functional connectivity Functional magnetic resonance imaging Graph theory Humans Interfaces Life Sciences Magnetic Resonance Imaging Magnetoencephalography Magnetoencephalography (MEG) Medical imaging MNE MRI Multi-modality Nerve Net - diagnostic imaging Nervous system Neural networks Neuroimaging Neuroimaging - methods Neurons and Cognition Nipype Packaging Pipelines Python Quantitative Methods Replication Reproducibility of Results Reproducible science Signal processing Software Software packages Source reconstruction Functional connectivity Brain imaging MRI MNE Brain networks Pipelines Multi-modality Electrophysiology Source reconstruction Graph theory Reproducible science Nipype Magnetoencephalography (MEG) Electroencephalography (EEG) Python Electrophysiology MRI MNE Source reconstruction
ISSN:	1053-8119, 1095-9572, 1095-9572
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