Evidence map›Paper›PMID 37694123›Full record

ArticleFrontiers in neuroscience2023

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

Sajjad Torabian, Natalia Vélez, Vanessa Sochat, Yaroslav O Halchenko, Emily D Grossman

Open access · goldAbstract read
In one paragraph

Article in Frontiers in neuroscience, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.4field-weighted citation impact, top 42% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed, 2 citations in OpenAlex.

  1. Article
  2. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors at 4 institutions in 1 country.

Sajjad TorabianVisual Perception and Neuroimaging Lab, Department of Cognitive Sciences, University of California, Irvine, Irvine, CA, United States.
Natalia VélezComputational Cognitive Neuroscience Lab, Department of Psychology, Harvard University, Cambridge, MA, United States.
Vanessa SochatLawrence Livermore National Laboratory, Livermore, CA, United States.
Yaroslav O HalchenkoDepartment of Psychological and Brain Sciences, Dartmouth College, Hanover, NH, United States.
Emily D GrossmanVisual Perception and Neuroimaging Lab, Department of Cognitive Sciences, University of California, Irvine, Irvine, CA, United States.
University of California, Irvine · USDartmouth College · USHarvard University · USLawrence Livermore National Laboratory · US

Funding

Resource DiscoveryP41EB019936 · NIBIB · UNIV OF MASSACHUSETTS MED SCH WORCESTER · PI David Nelson Kennedy · 2016 to 2026
$15.2M
NIBIB NIH HHS P41 EB019936
6 · The paper itself

Abstract

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.

Indexed as

BIDSBIDS-AppfMRIMVPAPyMVPA

Identifiers

PMID37694123
PMCPMC10483824
OpenAlexW4386141800

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.