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: Jun 27, 2026

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

Machine learning classifiers and fMRI: a tutorial overview.

Francisco Pereira1, Tom Mitchell, Matthew Botvinick

  • 1Princeton Neuroscience Institute/Psychology Department, Princeton University, Princeton, NJ 08540, USA. fpereira@princeton.edu

Neuroimage
|December 17, 2008
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

Magnetic Resonance Imaging01:24

Magnetic Resonance Imaging

Magnetic resonance imaging (MRI) is a noninvasive medical imaging technique based on a phenomenon of nuclear physics discovered in the 1930s, in which matter exposed to magnetic fields and radio waves was found to emit radio signals. In 1970, a physician and researcher named Raymond Damadian noticed that malignant (cancerous) tissue gave off different signals than normal body tissue. He applied for a patent for the first MRI scanning device in clinical use by the early 1980s. The early MRI...

You might also read

Related Articles

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

Sort by
Same author

In-scanner thoughts contribute to resting-state functional connectivity.

Nature communications·2026
Same author

Developing a standard definition for sequences of concern.

Frontiers in bioengineering and biotechnology·2026
Same author

Brain age prediction in generalized anxiety disorder using a convolutional neural network.

Translational psychiatry·2026
Same author

A multiverse approach to heat-evoked skin conductance analysis: evaluating the influence of analytic pipeline on associations between skin conductance and pain.

Pain·2026
Same author

Nonparametric Causal Inference for Optogenetics: Sequential Excursion Effects for Dynamic Regimes.

Journal of the American Statistical Association·2026
Same author

Functional reorganization of motor cortex connectivity during learning.

bioRxiv : the preprint server for biology·2026
Same journal

Category-selective neural decreases in the human ventral occipito-temporal cortex as defined with intracranial recordings.

NeuroImage·2026
Same journal

EEG-Based Brain Fingerprints Elicited by Focal Transcranial Magnetic Stimulation of the Primary Motor Cortex.

NeuroImage·2026
Same journal

The Association between Brain Oscillatory Activity and Immediate Memory under Different Magnetoencephalography Paradigms: A population-based Study.

NeuroImage·2026
Same journal

Brain response to awe experiences in virtual reality: an integrated linear and nonlinear EEG analysis.

NeuroImage·2026
Same journal

Convergent imaging and genetic signatures of gray matter atrophy in Parkinson's disease.

NeuroImage·2026
Same journal

What actually matters in multi-compartment EEG head models: A controlled FEM study of parcellation granularity, skull layering, mesh quality, noise, and inverse solver.

NeuroImage·2026
See all related articles

Machine learning classifiers decode brain activity from functional magnetic resonance imaging (fMRI) data to reveal information about mental states and behaviors. This approach helps determine if information exists, where it is located, and how it is encoded.

Area of Science:

  • Neuroscience
  • Machine Learning
  • Data Analysis

Background:

  • Interpreting complex, multivariate brain imaging data is challenging.
  • Machine learning (ML) is increasingly used to analyze functional magnetic resonance imaging (fMRI) data.
  • ML classifiers can decode stimuli, mental states, and behaviors from fMRI data.

Purpose of the Study:

  • To provide a tutorial overview of using ML classifiers for fMRI data analysis.
  • To guide researchers in making key choices for statistically significant results.
  • To demonstrate the application of ML beyond simple pattern discrimination.

Main Methods:

  • Review of ML classifier choices for fMRI data.
  • Illustration with a case study.
  • Explanation of statistically significant result derivation.

Related Experiment Videos

Last Updated: Jun 27, 2026

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

Main Results:

  • Classifiers can determine if information about a variable exists in fMRI data (pattern discrimination).
  • Classifiers can identify the location of information within the brain (pattern localization).
  • Classifiers can characterize how information is encoded in fMRI data.

Conclusions:

  • ML classifiers offer a powerful framework for analyzing fMRI data.
  • This approach extends beyond detecting information to localizing and characterizing it.
  • The tutorial provides practical guidance for researchers in the field.