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

Frontiers in Neuroscience

Neuroscience

Natalia Vélez¹  Yaroslav O. Halchenko²  Vanessa Sochat³  Emily D. Grossman⁴  Sajjad Torabian⁴ 

[1] Computational Cognitive Neuroscience Lab, Department of Psychology, Harvard University, Cambridge, MA, United States;Department of Psychological and Brain Sciences, Dartmouth College, Hanover, NH, United States;Lawrence Livermore National Laboratory, Livermore, CA, United States;Visual Perception and Neuroimaging Lab, Department of Cognitive Sciences, University of California, Irvine, Irvine, CA, United States;

关键词: fMRI;    MVPA;    PyMVPA;    BIDS;    BIDS-App;   

DOI  :  10.3389/fnins.2023.1233416

 received in 2023-06-02, accepted in 2023-08-04,  发布年份 2023

来源: Frontiers

【 摘 要 】

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.

【 授权许可】

Unknown   
Copyright © 2023 Torabian, Vélez, Sochat, Halchenko and Grossman.