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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...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2023
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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. |
format | Online Article Text |
id | pubmed-10483824 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
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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