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

Autor: Tarek Lajnef, Dmitrii Altukhov, David Meunier, Annalisa Pascarella, Mainak Jas, Golnoush Alamian, Etienne Combrisson, Jordan Alves, Vanessa Hadid, Arthur Dehgan, Anne-Lise Saive, Fanny Barlaam, Daphné Bertrand-Dubois, Karim Jerbi
Přispěvatelé: Institut de Neurosciences de la Timone (INT), Aix Marseille Université (AMU)-Centre National de la Recherche Scientifique (CNRS), Istituto per le Applicazioni del Calcolo 'Mauro Picone' (IAC), Consiglio Nazionale delle Ricerche [Roma] (CNR), Centre for Cognition and Decision Making [HSE, Moscow], Institut of Cognitive Neuroscience [HSE, Moscow] (ICN), Vysšaja škola èkonomiki = National Research University Higher School of Economics [Moscow] (HSE)-Vysšaja škola èkonomiki = National Research University Higher School of Economics [Moscow] (HSE), Signal, Statistique et Apprentissage (S2A), Laboratoire Traitement et Communication de l'Information (LTCI), Institut Mines-Télécom [Paris] (IMT)-Télécom Paris-Institut Mines-Télécom [Paris] (IMT)-Télécom Paris, Département Images, Données, Signal (IDS), Télécom ParisTech, Institut Polytechnique de Paris (IP Paris), Centre de recherche en neurosciences de Lyon - Lyon Neuroscience Research Center (CRNL), Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Université Jean Monnet - Saint-Étienne (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Université de Montréal (UdeM), Université du Québec à Montréal = University of Québec in Montréal (UQAM), University of Montreal, Aarhus University [Aarhus], Canada Research Chairs program and aDiscovery Grant (RGPIN-2015-04854) from the Natural Sciences andEngineering Research Council of Canada, a New Investigators Awardfrom the Fonds de Recherche du Quebec - Nature et Technologies (2018-NC-206005) and an IVADO-Apogee fundamental research project grant.This research is also supported in part by the FRQNT Strategic ClustersProgram (2020-RS4-265502 - Centre UNIQUE - Union Neurosciences&Artificial Intelligence - Quebec), Meunier, David, Centre National de la Recherche Scientifique (CNRS)-Aix Marseille Université (AMU), Centre de recherche en neurosciences de Lyon (CRNL), Université de Lyon-Université de Lyon-Université Jean Monnet [Saint-Étienne] (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), National Research University Higher School of Economics [Moscow] (HSE)-National Research University Higher School of Economics [Moscow] (HSE), National Research Council of Italy | Consiglio Nazionale delle Ricerche (CNR)
Jazyk: angličtina
Rok vydání: 2020
Předmět:
Computer science
Source reconstruction
Functional connectivity
0302 clinical medicine
Software
Magnetoencephalography (MEG)
Multi-modality
MATLAB
computer.programming_language
0303 health sciences
[SDV.BIBS] Life Sciences [q-bio]/Quantitative Methods [q-bio.QM]
05 social sciences
Brain
Magnetoencephalography
Electroencephalography
Magnetic Resonance Imaging
[SDV.BIBS]Life Sciences [q-bio]/Quantitative Methods [q-bio.QM]
Toolbox
Electrophysiology
Neurology
[SDV.NEU]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC]
Algorithms
MNE
MRI
Brain networks
Cognitive Neuroscience
Neuroimaging
Brain imaging
050105 experimental psychology
Nipype
lcsh:RC321-571
03 medical and health sciences
Humans
0501 psychology and cognitive sciences
[SDV.NEU] Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC]
Electroencephalography (EEG)
Implementation
MNE Source reconstruction
lcsh:Neurosciences. Biological psychiatry. Neuropsychiatry
030304 developmental biology
Pipelines
business.industry
Reproducibility of Results
Reproducible science
Modular design
Python (programming language)
Graph theory
Computer architecture
Nerve Net
business
computer
030217 neurology & neurosurgery
Electrophysiology MRI
Python
Zdroj: NeuroImage, Vol 219, Iss, Pp 117020-(2020)
NeuroImage
NeuroImage, 2020, 219, pp.117020. ⟨10.1016/j.neuroimage.2020.117020⟩
NeuroImage, Elsevier, 2020, 219, pp.117020. ⟨10.1016/j.neuroimage.2020.117020⟩
NeuroImage (Orlando Fla., Print) 219 (2020). doi:10.1016/j.neuroimage.2020.117020
info:cnr-pdr/source/autori:Meunier, David; Pascarella, Annalisa; Altukhov, Dmitrii; Jas, Mainak; Combrisson, Etienne; Lajnef, Tarek; Bertrand-Dubois, Daphne; Hadid, Vanessa; Alamian, Golnoush; Alves, Jordan; Barlaam, Fanny; Saive, Anne-Lise; Dehgan, Arthur; Jerbi, Karim/titolo:NeuroPycon: An open-source python toolbox for fast multi-modal and reproducible brain connectivity pipelines/doi:10.1016%2Fj.neuroimage.2020.117020/rivista:NeuroImage (Orlando Fla., Print)/anno:2020/pagina_da:/pagina_a:/intervallo_pagine:/volume:219
Meunier, D, Pascarella, A, Altukhov, D, Jas, M, Combrisson, E, Lajnef, T, Bertrand-Dubois, D, Hadid, V, Alamian, G, Alves, J, Barlaam, F, Saive, A L, Dehgan, A & Jerbi, K 2020, ' NeuroPycon : An open-source python toolbox for fast multi-modal and reproducible brain connectivity pipelines ', NeuroImage, vol. 219, 117020 . https://doi.org/10.1016/j.neuroimage.2020.117020
ISSN: 1095-9572
1053-8119
Popis: 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-modal brain data analysis toolkit which provides Python-based template pipelines for advanced multi-processing of MEG, EEG, functional and anatomical MRI data, with a focus on connectivity and graph theoretical analyses. Importantly, it provides shareable parameter files to facilitate replication of all analysis steps. NeuroPycon is based on the NiPype framework which facilitates data analyses by wrapping many commonly-used neuroimaging software tools into a common Python environment. In other words, rather than being a brain imaging software with is own implementation of standard algorithms for brain signal processing, NeuroPycon seamlessly integrates existing packages (coded in python, Matlab or other languages) into a unified python framework. Importantly, thanks to the multi-threaded processing and computational efficiency afforded by NiPype, NeuroPycon provides an easy option for fast parallel processing, which critical when handling large sets of multi-dimensional brain data. Moreover, its flexible design allows users to easily configure analysis pipelines by connecting distinct nodes to each other. Each node can be a Python-wrapped module, a user-defined function or a well-established tool (e.g. MNE-Python for MEG analysis, Radatools for graph theoretical metrics, etc.). Last but not least, the ability to use NeuroPycon parameter files to fully describe any pipeline is an important feature for reproducibility, as they can be shared and used for easy replication by others. The current implementation of NeuroPycon contains two complementary packages: The first, called ephypype, includes pipelines for electrophysiology analysis and a command-line interface for on the fly pipeline creation. Current implementations allow for MEG/EEG data import, pre-processing and cleaning by automatic removal of ocular and cardiac artefacts, in addition to sensor or source-level connectivity analyses. The second package, called graphpype, is designed to investigate functional connectivity via a wide range of graph-theoretical metrics, including modular partitions. The present article describes the philosophy, architecture, and functionalities of the toolkit and provides illustrative examples through interactive notebooks. NeuroPycon is available for download via github (https://github.com/neuropycon) and the two principal packages are documented online (https://neuropycon.github.io/ephypype/index.html. and https://neuropycon.github.io/graphpype/index.html). Future developments include fusion of multi-modal data (eg. MEG and fMRI or intracranial EEG and fMRI). We hope that the release of NeuroPycon will attract many users and new contributors, and facilitate the efforts of our community towards open source tool sharing and development, as well as scientific reproducibility.
Databáze: OpenAIRE
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