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 Concept Videos

You might also read

Related Articles

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

Sort by
Same author

Bridging the neuro-AI chasm: a framework for scalable, contextually adaptive training resources in large-scale brain data science.

Frontiers in psychology·2026
Same author

Characterization of peptides released from gluten-free climate-smart cowpea-based pasta during gastric and intestinal in vitro digestion.

Food chemistry·2026
Same author

In vitro protein digestibility of gluten-free climate-smart cowpea-based pasta.

Food research international (Ottawa, Ont.)·2025
Same author

Demonstration of active neutron interrogation of special nuclear materials using a high-intensity short-pulse-laser-driven neutron source.

Scientific reports·2025
Same author

The Neuroimaging Data Model Linear Regression Tool (nidm_linreg): PyNIDM Project.

F1000Research·2024
Same author

Automatic rating of incomplete hippocampal inversions evaluated across multiple cohorts.

ArXiv·2024

Related Experiment Video

Updated: Jun 16, 2026

Dynamic Inter-subject Functional Connectivity Reveals Moment-to-Moment Brain Network Configurations Driven by Continuous or Communication Paradigms
08:36

Dynamic Inter-subject Functional Connectivity Reveals Moment-to-Moment Brain Network Configurations Driven by Continuous or Communication Paradigms

Published on: March 21, 2019

A group model for stable multi-subject ICA on fMRI datasets.

G Varoquaux1, S Sadaghiani, P Pinel

  • 1Parietal project team, INRIA, Saclay-Ile-de-France, Saclay, France. gael.varoquaux@normalesup.org

Neuroimage
|February 16, 2010
PubMed
Summary

We developed Canonical Independent Component Analysis (CanICA) to model subject variability in functional Magnetic Resonance Imaging (fMRI) data. This method enhances the reproducibility of group-level functional brain network analysis for disease markers.

More Related Videos

Simultaneous Data Collection of fMRI and fNIRS Measurements Using a Whole-Head Optode Array and Short-Distance Channels
08:19

Simultaneous Data Collection of fMRI and fNIRS Measurements Using a Whole-Head Optode Array and Short-Distance Channels

Published on: October 20, 2023

Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time
07:12

Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time

Published on: July 1, 2014

Related Experiment Videos

Last Updated: Jun 16, 2026

Dynamic Inter-subject Functional Connectivity Reveals Moment-to-Moment Brain Network Configurations Driven by Continuous or Communication Paradigms
08:36

Dynamic Inter-subject Functional Connectivity Reveals Moment-to-Moment Brain Network Configurations Driven by Continuous or Communication Paradigms

Published on: March 21, 2019

Simultaneous Data Collection of fMRI and fNIRS Measurements Using a Whole-Head Optode Array and Short-Distance Channels
08:19

Simultaneous Data Collection of fMRI and fNIRS Measurements Using a Whole-Head Optode Array and Short-Distance Channels

Published on: October 20, 2023

Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time
07:12

Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time

Published on: July 1, 2014

Area of Science:

  • Neuroimaging
  • Data Analysis
  • Computational Neuroscience

Background:

  • Spatial Independent Component Analysis (ICA) is a key method for analyzing functional Magnetic Resonance Imaging (fMRI) data, extracting functional brain networks.
  • Existing ICA methods face challenges in modeling subject variability for reliable group comparisons, particularly for identifying disease markers.
  • There is a need for robust methods to model and estimate group-level patterns from multi-subject fMRI data.

Purpose of the Study:

  • To propose a novel hierarchical model for analyzing multi-subject fMRI data within an ICA framework.
  • To introduce an estimation procedure, Canonical ICA (CanICA), to address subject variability in group fMRI studies.
  • To enhance the reliability and reproducibility of functional network analysis for inter-group comparisons.

Main Methods:

  • Developed CanICA, a hierarchical model integrating probabilistic dimension reduction, canonical correlation analysis, and ICA.
  • Implemented a cross-validation procedure to assess the stability of group-level ICA patterns.
  • Compared CanICA against state-of-the-art multi-subject fMRI ICA methods.

Main Results:

  • CanICA demonstrated superior reproducibility of extracted features at the group level compared to existing methods.
  • The method was validated on two distinct datasets (resting-state and functional localizer) from healthy controls.
  • Identified more stable and reproducible functional brain networks across subjects.

Conclusions:

  • CanICA provides a robust framework for modeling subject variability in multi-subject fMRI data.
  • The proposed method improves the reliability of functional network identification for group studies and potential clinical applications.
  • CanICA enhances the potential for paradigm-free population comparisons using fMRI-derived biomarkers.