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

Brain Imaging01:14

Brain Imaging

Brain imaging technologies provide critical insights into both the structure and function of the human brain, enabling medical professionals and researchers to diagnose, study, and treat neurological disorders or psychiatric disorders more effectively.
These technologies include computerized axial tomography (CAT or CT scans), positron-emission tomography (PET scans),  magnetic resonance imaging (MRI),  functional magnetic resonance imaging (fMRI), and Transcranial Magnetic Stimulation (TMS).

You might also read

Related Articles

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

Sort by
Same author

MindGrab: A spectrally-motivated architecture for accessible deep learning in neuroimaging.

NeuroImage·2026
Same author

Deep learning interpretability in neuroimaging: A comprehensive survey and methodological recommendations.

Imaging neuroscience (Cambridge, Mass.)·2026
Same author

Combining fast and slow fMRI sampling rates can enhance predictive power in resting-state data.

NeuroImage·2025
Same author

Interpretable Disorder Signatures: Probing Neural Latent Spaces for Schizophrenia, Alzheimer's, and Autism Stratification.

Brain sciences·2025
Same author

st-DenseViT: A Weakly Supervised Spatiotemporal Vision Transformer for Dense Prediction of Dynamic Brain Networks.

Human brain mapping·2025
Same author

Using an ordinary differential equation model to separate rest and task signals in fMRI.

Nature communications·2025

Related Experiment Video

Updated: Jul 7, 2026

Concurrent EEG and Functional MRI Recording and Integration Analysis for Dynamic Cortical Activity Imaging
11:28

Concurrent EEG and Functional MRI Recording and Integration Analysis for Dynamic Cortical Activity Imaging

Published on: June 30, 2018

Bayesian brain source imaging based on combined MEG/EEG and fMRI using MCMC.

Sung C Jun1, John S George, Woohan Kim

  • 1Los Alamos National Laboratory, Los Alamos, NM 87545, USA. scjun@gist.ac.kr

Neuroimage
|March 4, 2008
PubMed
Summary

This study introduces Bayesian inference to integrate brain imaging data, such as magnetoencephalography (MEG) and functional magnetic resonance imaging (fMRI), for improved brain disorder diagnostics. The method combines data probabilistically, enhancing diagnostic accuracy.

More Related Videos

Functional Mapping with Simultaneous MEG and EEG
06:04

Functional Mapping with Simultaneous MEG and EEG

Published on: June 14, 2010

Brain Source Imaging in Preclinical Rat Models of Focal Epilepsy using High-Resolution EEG Recordings
08:20

Brain Source Imaging in Preclinical Rat Models of Focal Epilepsy using High-Resolution EEG Recordings

Published on: June 6, 2015

Related Experiment Videos

Last Updated: Jul 7, 2026

Concurrent EEG and Functional MRI Recording and Integration Analysis for Dynamic Cortical Activity Imaging
11:28

Concurrent EEG and Functional MRI Recording and Integration Analysis for Dynamic Cortical Activity Imaging

Published on: June 30, 2018

Functional Mapping with Simultaneous MEG and EEG
06:04

Functional Mapping with Simultaneous MEG and EEG

Published on: June 14, 2010

Brain Source Imaging in Preclinical Rat Models of Focal Epilepsy using High-Resolution EEG Recordings
08:20

Brain Source Imaging in Preclinical Rat Models of Focal Epilepsy using High-Resolution EEG Recordings

Published on: June 6, 2015

Area of Science:

  • Neuroscience
  • Medical Imaging
  • Computational Biology

Background:

  • Brain imaging techniques like MEG and fMRI have unique strengths and weaknesses.
  • Integrating multiple modalities can overcome individual limitations.
  • Bayesian inference offers a probabilistic framework for data fusion.

Purpose of the Study:

  • To introduce Bayesian inference methods for integrating diverse brain imaging data.
  • To formulate a Bayesian integration of MEG and fMRI data.
  • To enhance diagnostic tools for brain disorders through multimodal data fusion.

Main Methods:

  • Utilized Bayesian inference with Markov Chain Monte Carlo (MCMC) techniques.
  • Incorporated functional magnetic resonance imaging (fMRI) data into a spatial prior for magnetoencephalography (MEG) data.
  • Developed a probabilistic framework for multimodal brain imaging data integration.

Main Results:

  • Demonstrated the feasibility of Bayesian integration for MEG and fMRI data.
  • Verified the method's usefulness with both simulated and empirical datasets.
  • Showcased the potential for improved understanding of brain function and disorders.

Conclusions:

  • Bayesian inference provides a robust method for integrating multimodal brain imaging data.
  • This approach enhances the complementary strengths of different imaging techniques.
  • The developed framework holds promise for advancing diagnostic tools in neurology.