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

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

Neonatal and maternal outcomes following transfer of vitrified embryos compared with slow-frozen and fresh embryos: a retrospective study in singletons.

BMC pregnancy and childbirth·2026
Same author

Is the whole more than the sum of its parts? Considering global and local features of the connectome improves prediction of individuals and phenotypes.

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

Using connectome-based predictive models to reveal the systems standardized tests and clinical symptoms are reflecting.

Nature communications·2026
Same author

Optimizing functional connectivity scanning conditions for predicting autistic traits.

Nature. Mental health·2026
Same author

Feature overlap in transdiagnostic connectome-based models of sustained attention and autism symptoms.

medRxiv : the preprint server for health sciences·2026
Same author

Single cell multiomics revealed fibrotic trajectories of endometrial cells and interaction with the pro-fibrotic macrophages in intrauterine adhesion.

Genome medicine·2026

Related Experiment Video

Updated: Jul 5, 2026

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

Low-dimensional embedding of fMRI datasets.

Xilin Shen1, François G Meyer

  • 1Department of Electrical Engineering, University of Colorado at Boulder, USA.

Neuroimage
|May 3, 2008
PubMed
Summary
This summary is machine-generated.

We developed a new method to analyze functional magnetic resonance imaging (fMRI) data by embedding it in a low-dimensional space. This approach helps identify brain activity patterns, such as visual and auditory areas, during natural stimuli.

More Related Videos

High-resolution Functional Magnetic Resonance Imaging Methods for Human Midbrain
10:06

High-resolution Functional Magnetic Resonance Imaging Methods for Human Midbrain

Published on: May 10, 2012

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: Jul 5, 2026

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

High-resolution Functional Magnetic Resonance Imaging Methods for Human Midbrain
10:06

High-resolution Functional Magnetic Resonance Imaging Methods for Human Midbrain

Published on: May 10, 2012

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
  • Computational Neuroscience
  • Data Science

Background:

  • Functional magnetic resonance imaging (fMRI) generates complex, high-dimensional data.
  • Analyzing functional connectivity and identifying brain activation patterns remains challenging.
  • Existing methods may not optimally capture local functional coupling in fMRI data.

Purpose of the Study:

  • To introduce a novel low-dimensional embedding method for fMRI datasets.
  • To preserve local functional coupling between fMRI time series.
  • To provide a coordinate system for detecting activated voxels and analyzing brain activity.

Main Methods:

  • Constructing a graph of functionally connected voxels.
  • Utilizing commute time, derived from graph transition matrix eigenvectors, for functional distance measurement.
  • Embedding the fMRI dataset into a low-dimensional space using these eigenvectors.
  • Clustering the embedded data to reveal coherent structures.

Main Results:

  • The proposed method effectively embeds fMRI data in a low-dimensional space.
  • Clustering reveals interpretable coherent structures within the data.
  • The method successfully detected independent visual (V1/V2, V5/MT), auditory, and language areas in EBC competition datasets.
  • Comparison with linear and nonlinear techniques demonstrated the method's efficacy on synthetic and in vivo data.

Conclusions:

  • The novel embedding method offers an effective approach for analyzing fMRI data.
  • It facilitates the identification of brain regions and functional networks, particularly during naturalistic stimuli.
  • This technique holds promise for exploring complex fMRI datasets and understanding brain function.