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

Adaptive Gaussian graph-spectral filtering for scale-specific connectivity inference.

NeuroImage·2026
Same author

Transcranial acoustoelectric imaging (tABI) of seizure activity in human head model with neuronavigation.

Journal of neural engineering·2026
Same author

Plasma inflammatory markers and brain white matter microstructure in late middle-aged and older adults.

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

Reduced cortical brain perfusion following COVID-19 infection: impact of COVID-19 severity and relation to memory performance.

Frontiers in human neuroscience·2026
Same author

Analysis of Quantitative Susceptibility Mapping Data for Multi-Site and Multi-Modal Brain Imaging Studies: For Measuring Brain Iron and Its Changes with Age.

Gerontology·2026
Same author

Heterogeneity of treatment effects in transcranial direct current stimulation for knee osteoarthritis pain and symptoms.

Pain reports·2026

Related Experiment Video

Updated: Apr 18, 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

12.5K

A unified machine learning method for task-related and resting state fMRI data analysis.

Xiaomu Song, Nan-kuei Chen

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |January 9, 2015
    PubMed
    Summary

    This study introduces a novel machine learning approach for analyzing functional magnetic resonance imaging (fMRI) data. The method reliably maps brain function by treating it as an outlier detection process, adapting to variations in fMRI signals.

    More Related Videos

    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

    12.8K
    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

    18.7K

    Related Experiment Videos

    Last Updated: Apr 18, 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

    12.5K
    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

    12.8K
    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

    18.7K

    Area of Science:

    • Neuroimaging
    • Machine Learning
    • Data Analysis

    Background:

    • Functional magnetic resonance imaging (fMRI) is crucial for brain function localization.
    • Current fMRI analysis relies on fixed thresholds, which struggle with signal non-stationarity and subject variability.
    • This limitation hinders reliable mapping of brain activity and connectivity.

    Purpose of the Study:

    • To develop a unified machine learning method for analyzing both task-related and resting-state fMRI data.
    • To overcome the limitations of fixed thresholds in fMRI analysis.
    • To provide a robust and adaptable approach for mapping brain function.

    Main Methods:

    • A machine learning framework is proposed, formulating brain function mapping as an outlier detection problem.
    • Support vector machines (SVMs) are employed for initial mapping and refinement.
    • The method avoids fixed thresholds, allowing adaptation to fMRI non-stationarity.

    Main Results:

    • The proposed method demonstrated reliable mapping of brain function in experimental fMRI data.
    • The approach successfully adapted to intra- and inter-subject variations inherent in fMRI.
    • The technique proved effective for both task-based and resting-state analyses.

    Conclusions:

    • The novel machine learning method offers a reliable and adaptable solution for fMRI data analysis.
    • This approach overcomes the limitations of traditional fixed-threshold techniques.
    • The method is broadly applicable to diverse quantitative fMRI studies.