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...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-08-01
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| Series: | Frontiers in Neuroscience |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fnins.2023.1233416/full |
| _version_ | 1827860900593270784 |
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| author | Sajjad Torabian Natalia Vélez Vanessa Sochat Yaroslav O. Halchenko Emily D. Grossman |
| author_facet | Sajjad Torabian Natalia Vélez Vanessa Sochat Yaroslav O. Halchenko Emily D. Grossman |
| author_sort | Sajjad Torabian |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-03-12T13:30:31Z |
| format | Article |
| id | doaj.art-7c52756c7de0476eb6740371db55e153 |
| institution | Directory Open Access Journal |
| issn | 1662-453X |
| language | English |
| last_indexed | 2024-03-12T13:30:31Z |
| publishDate | 2023-08-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Neuroscience |
| spelling | doaj.art-7c52756c7de0476eb6740371db55e1532023-08-24T15:24:55ZengFrontiers Media S.A.Frontiers in Neuroscience1662-453X2023-08-011710.3389/fnins.2023.12334161233416The PyMVPA BIDS-App: a robust multivariate pattern analysis pipeline for fMRI dataSajjad Torabian0Natalia Vélez1Vanessa Sochat2Yaroslav O. Halchenko3Emily D. Grossman4Visual Perception and Neuroimaging Lab, Department of Cognitive Sciences, University of California, Irvine, Irvine, CA, United StatesComputational Cognitive Neuroscience Lab, Department of Psychology, Harvard University, Cambridge, MA, United StatesLawrence Livermore National Laboratory, Livermore, CA, United StatesDepartment of Psychological and Brain Sciences, Dartmouth College, Hanover, NH, United StatesVisual Perception and Neuroimaging Lab, Department of Cognitive Sciences, University of California, Irvine, Irvine, CA, United StatesWith 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.https://www.frontiersin.org/articles/10.3389/fnins.2023.1233416/fullfMRIMVPAPyMVPABIDSBIDS-App |
| spellingShingle | Sajjad Torabian Natalia Vélez Vanessa Sochat Yaroslav O. Halchenko Emily D. Grossman The PyMVPA BIDS-App: a robust multivariate pattern analysis pipeline for fMRI data Frontiers in Neuroscience fMRI MVPA PyMVPA BIDS BIDS-App |
| 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 | fMRI MVPA PyMVPA BIDS BIDS-App |
| url | https://www.frontiersin.org/articles/10.3389/fnins.2023.1233416/full |
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