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The PyMVPA BIDS-App: a robust multivariate pattern analysis pipeline for fMRI data

With the advent of multivariate pattern analysis (MVPA) as an important analytic approach to fMRI, new insights into the functional organization of the brain have emerged. Several software packages have been developed to perform MVPA analysis, but deploying them comes with the cost of adjusting data...

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Autores principales: Torabian, Sajjad, Vélez, Natalia, Sochat, Vanessa, Halchenko, Yaroslav O., Grossman, Emily D.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10483824/
https://www.ncbi.nlm.nih.gov/pubmed/37694123
http://dx.doi.org/10.3389/fnins.2023.1233416
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author Torabian, Sajjad
Vélez, Natalia
Sochat, Vanessa
Halchenko, Yaroslav O.
Grossman, Emily D.
author_facet Torabian, Sajjad
Vélez, Natalia
Sochat, Vanessa
Halchenko, Yaroslav O.
Grossman, Emily D.
author_sort Torabian, Sajjad
collection PubMed
description With the advent of multivariate pattern analysis (MVPA) as an important analytic approach to fMRI, new insights into the functional organization of the brain have emerged. Several software packages have been developed to perform MVPA analysis, but deploying them comes with the cost of adjusting data to individual idiosyncrasies associated with each package. Here we describe PyMVPA BIDS-App, a fast and robust pipeline based on the data organization of the BIDS standard that performs multivariate analyses using powerful functionality of PyMVPA. The app runs flexibly with blocked and event-related fMRI experimental designs, is capable of performing classification as well as representational similarity analysis, and works both within regions of interest or on the whole brain through searchlights. In addition, the app accepts as input both volumetric and surface-based data. Inspections into the intermediate stages of the analyses are available and the readability of final results are facilitated through visualizations. The PyMVPA BIDS-App is designed to be accessible to novice users, while also offering more control to experts through command-line arguments in a highly reproducible environment.
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spelling pubmed-104838242023-09-08 The PyMVPA BIDS-App: a robust multivariate pattern analysis pipeline for fMRI data Torabian, Sajjad Vélez, Natalia Sochat, Vanessa Halchenko, Yaroslav O. Grossman, Emily D. Front Neurosci Neuroscience With the advent of multivariate pattern analysis (MVPA) as an important analytic approach to fMRI, new insights into the functional organization of the brain have emerged. Several software packages have been developed to perform MVPA analysis, but deploying them comes with the cost of adjusting data to individual idiosyncrasies associated with each package. Here we describe PyMVPA BIDS-App, a fast and robust pipeline based on the data organization of the BIDS standard that performs multivariate analyses using powerful functionality of PyMVPA. The app runs flexibly with blocked and event-related fMRI experimental designs, is capable of performing classification as well as representational similarity analysis, and works both within regions of interest or on the whole brain through searchlights. In addition, the app accepts as input both volumetric and surface-based data. Inspections into the intermediate stages of the analyses are available and the readability of final results are facilitated through visualizations. The PyMVPA BIDS-App is designed to be accessible to novice users, while also offering more control to experts through command-line arguments in a highly reproducible environment. Frontiers Media S.A. 2023-08-24 /pmc/articles/PMC10483824/ /pubmed/37694123 http://dx.doi.org/10.3389/fnins.2023.1233416 Text en Copyright © 2023 Torabian, Vélez, Sochat, Halchenko and Grossman. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Neuroscience
Torabian, Sajjad
Vélez, Natalia
Sochat, Vanessa
Halchenko, Yaroslav O.
Grossman, Emily D.
The PyMVPA BIDS-App: a robust multivariate pattern analysis pipeline for fMRI data
title The PyMVPA BIDS-App: a robust multivariate pattern analysis pipeline for fMRI data
title_full The PyMVPA BIDS-App: a robust multivariate pattern analysis pipeline for fMRI data
title_fullStr The PyMVPA BIDS-App: a robust multivariate pattern analysis pipeline for fMRI data
title_full_unstemmed The PyMVPA BIDS-App: a robust multivariate pattern analysis pipeline for fMRI data
title_short The PyMVPA BIDS-App: a robust multivariate pattern analysis pipeline for fMRI data
title_sort pymvpa bids-app: a robust multivariate pattern analysis pipeline for fmri data
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10483824/
https://www.ncbi.nlm.nih.gov/pubmed/37694123
http://dx.doi.org/10.3389/fnins.2023.1233416
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