Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

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

Innovative Clinical Trial Approach for Evaluating Digital Medical Devices Under European Fast-Track Regulatory Frameworks.

Statistics in medicine·2026

Same author

Causal mediation analysis with one or multiple mediators: A comparative study.

Psychological methods·2026

Same author

NeuroConText: Contrastive learning for neuroscience meta-analysis with rich text representation.

Imaging neuroscience (Cambridge, Mass.)·2026

Same author

Individual Brain Charting: fifth release of high-resolution fMRI data for cognitive mapping.

Scientific data·2026

Same author

Subject fingerprinting and task classification rely on distinct functional connectivity features.

Brain structure & function·2026

Same author

An Interactive Brain Atlas of Knowledge.

bioRxiv : the preprint server for biology·2025

Same journal

NanoporeDB: A Structural Resource Of Multimeric Protein Nanopores For Single-Molecule Sensing.

GigaScience·2026

Same journal

From the Brain Cell Atlas to Precision Neurology: A review of the application of AI-driven multi-omics in brain science.

GigaScience·2026

Same journal

Comparison of Deep Learning Approaches for Extreme Low-SNR Image Restoration.

GigaScience·2026

Same journal

ScopeViewer: A Browser-Based Solution for Visualizing Large Biological Images.

GigaScience·2026

Same journal

ChatMDV: Reducing Technical Barriers in Bioinformatics Analysis using Large Language Models.

GigaScience·2026

Same journal

ClusterGraph: a new tool for visualisation and compression of multidimensional data.

GigaScience·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Dec 23, 2025

Three-Dimensional Shape Modeling and Analysis of Brain Structures

Three-Dimensional Shape Modeling and Analysis of Brain Structures

Published on: November 14, 2019

How machine learning is shaping cognitive neuroimaging.

Gael Varoquaux¹, Bertrand Thirion¹

¹Parietal, INRIA, NeuroSpin, bat 145 CEA Saclay, 91191 Gif sur Yvette, France.

|November 19, 2014

Summary

This summary is machine-generated.

Data mining can unlock cognitive insights from complex brain imaging data. By asking precise questions, predictive models reveal new aspects of cognitive organization and brain function.

Keywords:

Cognition Decoding Encoding Machine learning Neuroimaging fMRI

More Related Videos

Using Fiberless, Wearable fNIRS to Monitor Brain Activity in Real-world Cognitive Tasks

Using Fiberless, Wearable fNIRS to Monitor Brain Activity in Real-world Cognitive Tasks

Published on: December 2, 2015

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

Related Experiment Videos

Last Updated: Dec 23, 2025

Three-Dimensional Shape Modeling and Analysis of Brain Structures

Three-Dimensional Shape Modeling and Analysis of Brain Structures

Published on: November 14, 2019

Using Fiberless, Wearable fNIRS to Monitor Brain Activity in Real-world Cognitive Tasks

Using Fiberless, Wearable fNIRS to Monitor Brain Activity in Real-world Cognitive Tasks

Published on: December 2, 2015

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

Area of Science:

Neuroscience
Cognitive Science
Data Science

Background:

Functional brain images offer rich, albeit noisy, data reflecting neural activity.
Understanding cognition requires analyzing these complex neuroimaging datasets.

Purpose of the Study:

To explore how data mining can be applied to functional brain images for cognitive modeling.
To review the use of predictive models in neuroimaging for uncovering cognitive mechanisms.
To provide a statistical learning perspective on current advancements and limitations.

Main Methods:

Review of studies employing predictive models on neuroimaging data.
Analysis from a statistical learning perspective.

Main Results:

Predictive models, when applied to well-posed questions, can reveal novel aspects of cognitive organization.
Data mining can effectively leverage neuroimaging data to build cognitive models.

Conclusions:

The application of data mining and predictive modeling to neuroimaging data is a promising avenue for understanding cognition.
Further research is needed to address the limitations and fully exploit the potential of these methods.