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

Sparse Variational Student-t Processes for Heavy-Tailed Modeling.

IEEE transactions on neural networks and learning systems·2026
Same author

Tabular diffusion counterfactual explanations.

Frontiers in artificial intelligence·2026
Same author

Alpha Modulation of Spiking Activity Across Multiple Brain Regions in Mice Performing a Tactile Selective Detection Task.

The European journal of neuroscience·2025
Same author

Superficial Fluctuations in Functional Near-Infrared Spectroscopy during Concurrent Transcranial Magnetic Stimulation.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025
Same author

TMS-induced modulation of brain networks and its associations to rTMS treatment for depression: a concurrent fMRI-EEG-TMS study.

Brain stimulation·2025
Same author

Brain2Model Transfer: Training sensory and decision models with human neural activity as a teacher.

ArXiv·2025

Related Experiment Video

Updated: Aug 29, 2025

Network Analysis of the Default Mode Network Using Functional Connectivity MRI in Temporal Lobe Epilepsy
12:09

Network Analysis of the Default Mode Network Using Functional Connectivity MRI in Temporal Lobe Epilepsy

Published on: August 5, 2014

18.1K

Deep Metric Representation Learning for Clinical Resting State fMRI.

Arunesh Mittal, John Paisley, Paul Sajda

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |September 10, 2022
    PubMed
    Summary

    Deep metric learning effectively refines resting-state fMRI (rs-fMRI) data representations. This approach enhances performance on various downstream neuroimaging tasks, optimizing large datasets.

    More Related Videos

    Developing Neuroimaging Phenotypes of the Default Mode Network in PTSD: Integrating the Resting State, Working Memory, and Structural Connectivity
    10:43

    Developing Neuroimaging Phenotypes of the Default Mode Network in PTSD: Integrating the Resting State, Working Memory, and Structural Connectivity

    Published on: July 1, 2014

    15.3K
    Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
    14:27

    Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

    Published on: June 26, 2013

    15.8K

    Related Experiment Videos

    Last Updated: Aug 29, 2025

    Network Analysis of the Default Mode Network Using Functional Connectivity MRI in Temporal Lobe Epilepsy
    12:09

    Network Analysis of the Default Mode Network Using Functional Connectivity MRI in Temporal Lobe Epilepsy

    Published on: August 5, 2014

    18.1K
    Developing Neuroimaging Phenotypes of the Default Mode Network in PTSD: Integrating the Resting State, Working Memory, and Structural Connectivity
    10:43

    Developing Neuroimaging Phenotypes of the Default Mode Network in PTSD: Integrating the Resting State, Working Memory, and Structural Connectivity

    Published on: July 1, 2014

    15.3K
    Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
    14:27

    Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

    Published on: June 26, 2013

    15.8K

    Area of Science:

    • Neuroimaging
    • Machine Learning
    • Data Science

    Background:

    • Resting-state fMRI (rs-fMRI) datasets are growing rapidly.
    • Deep learning methods offer new opportunities for neuroimaging analysis.

    Purpose of the Study:

    • To apply deep metric learning for creating effective embeddings of rs-fMRI data.
    • To develop an efficient training method for this deep metric learning model.

    Main Methods:

    • Utilized deep metric learning to generate data embeddings.
    • Developed an efficient training methodology.
    • Compared the proposed method against existing models.

    Main Results:

    • Deep metric learning serves as a valuable refinement step for fMRI data.
    • Learned representations significantly improve downstream task performance.
    • The proposed training method is efficient.

    Conclusions:

    • Deep metric learning is a powerful technique for enhancing rs-fMRI data representation.
    • This approach holds significant potential for improving various neuroimaging analysis tasks.
    • The efficient training method makes this approach practical for large datasets.