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

Group Joint ICA (gjICA): A Method for Multimodal Fusion of Concurrent EEG and fMRI Data.

Human brain mapping·2026
Same author

Injury Severity Influences Long-Term Cognitive Control in Pediatric "Mild" Traumatic Brain Injury.

Human brain mapping·2026
Same author

Prediction of venous thromboembolism after metabolic and bariatric surgery using machine learning approach: a MBSAQIP study.

Surgical endoscopy·2026
Same author

Oscillating hypercapnia induces neural abundant protein efflux and potential depletion in health and chronic traumatic brain injury.

Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism·2026
Same author

Mechanisms and barriers for understanding neural abundant protein efflux following traumatic brain injury.

Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism·2026
Same author

Shared Neurocardiac Pathways Linking Atrial Fibrillation and Depression: A UK Biobank Analysis.

medRxiv : the preprint server for health sciences·2026

Related Experiment Video

Updated: Apr 4, 2026

Real-Time fMRI Brain Mapping in Animals
04:05

Real-Time fMRI Brain Mapping in Animals

Published on: September 24, 2020

4.2K

Real-time fMRI processing with physiological noise correction - Comparison with off-line analysis.

Masaya Misaki1, Nafise Barzigar2, Vadim Zotev1

  • 1Laureate Institute for Brain Research, Tulsa, OK, USA.

Journal of Neuroscience Methods
|September 8, 2015
PubMed
Summary

A new real-time fMRI processing system on a PC achieves rapid whole-brain analysis. This system enables advanced noise correction, but real-time GLM requires careful sample size consideration.

Keywords:
Functional connectivityImage processingNeurofeedbackPhysiological noise correctionReal-time fMRI processing

More Related Videos

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

1.9K
Author Spotlight: Methodologies and Advancements of Chronic Pain Management Research
08:33

Author Spotlight: Methodologies and Advancements of Chronic Pain Management Research

Published on: January 5, 2024

1.9K

Related Experiment Videos

Last Updated: Apr 4, 2026

Real-Time fMRI Brain Mapping in Animals
04:05

Real-Time fMRI Brain Mapping in Animals

Published on: September 24, 2020

4.2K
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

1.9K
Author Spotlight: Methodologies and Advancements of Chronic Pain Management Research
08:33

Author Spotlight: Methodologies and Advancements of Chronic Pain Management Research

Published on: January 5, 2024

1.9K

Area of Science:

  • Neuroimaging
  • Computational Neuroscience
  • Biomedical Engineering

Background:

  • Real-time functional magnetic resonance imaging (rtfMRI) applications are expanding.
  • Existing rtfMRI systems face limitations in real-time data processing speed compared to offline analysis.

Purpose of the Study:

  • To demonstrate the feasibility of intensive, whole-brain fMRI data processing in real-time using a personal computer (PC) with a dedicated graphics processing unit (GPU).
  • To implement and evaluate novel real-time physiological noise correction methods.

Main Methods:

  • Developed a real-time fMRI processing (rtfMRIp) system on a PC with GPU acceleration.
  • Implemented slice-timing correction, motion correction, spatial smoothing, signal scaling, and general linear model (GLM) analysis.
  • Integrated real-time RETROICOR and respiration volume per time (RVT) for physiological noise correction.

Main Results:

  • Achieved whole-brain fMRI analysis ( >100,000 voxels, >250 volumes) in under 300ms, significantly faster than volume acquisition time.
  • Demonstrated comparable results for reduced slice-timing correction in real-time versus offline analysis.
  • Observed overfitting in real-time GLM analysis with a small number of sampled volumes.

Conclusions:

  • Comprehensive real-time fMRI data processing is achievable on a standard PC.
  • The number of samples is a critical factor for accurate real-time GLM analysis in rtfMRI.
  • Real-time physiological noise correction (RETROICOR, RVT) was successfully implemented without recursive algorithms.