Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Video

Updated: May 14, 2026

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
14:27

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

PRoNTo: pattern recognition for neuroimaging toolbox.

J Schrouff1, M J Rosa, J M Rondina

  • 1Cyclotron Research Centre, University of Liège, Liège, Belgium.

Neuroinformatics
|February 19, 2013
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

[Effectiveness and safety of rivaroxaban in pediatric patients with coronary artery diseases: a case series study].

Zhonghua er ke za zhi = Chinese journal of pediatrics·2026
Same author

[Artificial intelligence in hypertension: advances and challenges].

Zhonghua xin xue guan bing za zhi·2025
Same author

Dissecting heterogeneity in cortical thickness abnormalities in major depressive disorder: a large-scale ENIGMA MDD normative modelling study.

bioRxiv : the preprint server for biology·2025
Same author

[Analysis of 41 cases of myocardial infarction in children with coronary artery lesion after Kawasaki disease].

Zhonghua er ke za zhi = Chinese journal of pediatrics·2025
Same author

[Relationship between clopidogrel resistance and genetic variability in Kawasaki disease children with coronary artery lesions].

Zhonghua er ke za zhi = Chinese journal of pediatrics·2024
Same author

A neuroimaging measure to capture heterogeneous patterns of atrophy in Parkinson's disease and dementia with Lewy bodies.

NeuroImage. Clinical·2024
Same journal

Metabolically Faithful 3D PET Restoration via Volumetric Swin Transformers.

Neuroinformatics·2026
Same journal

CytoCLIP: Learning Cytoarchitectural Characteristics in Developing Human Brain Using Contrastive Language Image Pre-Training.

Neuroinformatics·2026
Same journal

Increasing the Reliability of Functional Connectivity by Predicting Long-Scan Functional Connectivity based on Short-Scan Functional Connectivity: Model Exploration, Explanation, Validation, and Application.

Neuroinformatics·2026
Same journal

HESREN: A Derivative-Informed Reservoir Framework for Detecting Transient Neural Events and Windowless Estimation of Dynamic Functional Connectivity.

Neuroinformatics·2026
Same journal

Computational Morphometry of Peripheral Nerves: A Pipeline Perspective on Reproducibility and Generalization.

Neuroinformatics·2026
Same journal

Multimodal Branched Transport Infers Anatomically Aligned Brain Reaction Maps.

Neuroinformatics·2026
See all related articles

A new open-source software toolbox, Pattern Recognition for Neuroimaging Toolbox (PRoNTo), enables advanced machine learning analyses for neuroimaging data. This facilitates sensitive detection of distributed effects in cognitive and clinical neuroscience research.

Area of Science:

  • Neuroscience
  • Machine Learning
  • Data Analysis

Background:

  • Mass univariate statistical analyses are common for neuroimaging data.
  • Multivariate pattern analyses (MPA) using machine learning offer increased sensitivity for detecting spatially distributed effects.
  • A lack of accessible software frameworks hinders MPA adoption in neuroimaging.

Purpose of the Study:

  • To develop an open-source, user-friendly software toolbox for multivariate pattern analyses of neuroimaging data using machine learning.
  • To provide a framework that integrates machine learning functionalities for neuroimaging research.
  • To bridge the gap between the neuroimaging and machine learning communities.

Main Methods:

  • Development of the Pattern Recognition for Neuroimaging Toolbox (PRoNTo).

More Related Videos

Neuroimaging-Guided TMS–EEG for Real-Time Cortical Network Mapping
09:55

Neuroimaging-Guided TMS–EEG for Real-Time Cortical Network Mapping

Published on: June 13, 2025

Related Experiment Videos

Last Updated: May 14, 2026

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
14:27

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

Neuroimaging-Guided TMS–EEG for Real-Time Cortical Network Mapping
09:55

Neuroimaging-Guided TMS–EEG for Real-Time Cortical Network Mapping

Published on: June 13, 2025

  • PRoNTo is a MATLAB-based, cross-platform, and SPM-compatible toolbox.
  • The toolbox incorporates various machine learning models for neuroimaging data analysis.

    • Main Results:

    • PRoNTo offers a comprehensive suite of tools for multivariate neuroimaging analysis.
    • The toolbox enables the investigation of research questions not easily addressed by mass univariate methods.
    • PRoNTo is designed to be extensible, encouraging community contributions.

    Conclusions:

    • PRoNTo provides a valuable, accessible framework for advanced machine learning applications in neuroimaging.
    • The toolbox enhances the capabilities for analyzing complex patterns in cognitive and clinical neuroscience.
    • PRoNTo aims to foster collaboration and innovation at the intersection of neuroimaging and machine learning.