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

7.4K
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...
7.4K

You might also read

Related Articles

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

Sort by
Same author

Dynamic reconfiguration of subunits from the hippocampal-amygdala complex indicate patterns of psychosis vulnerability in 22q11.2 deletion syndrome.

Scientific reports·2026
Same author

Structural brain alterations in chronic primary pain: a multimodal MRI study.

NeuroImage. Clinical·2026
Same author

Shared and Individual Resting-State MEG Network Signatures of Tinnitus Revealed by Holistic Graph Learning.

IEEE open journal of engineering in medicine and biology·2026
Same author

Trial-by-trial fMRI-neurofeedback dissociates fusiform and occipital contributions to face detection and recognition.

Nature communications·2026
Same author

The microstructure-weighted human connectome: network properties and structure-function correlations across spatial scales.

bioRxiv : the preprint server for biology·2026
Same author

The Nature of a Writing System Shapes the Cognitive and Neural Mechanisms for Reading Acquisition.

Neurobiology of language (Cambridge, Mass.)·2026

Related Experiment Video

Updated: Sep 30, 2025

Topographical Estimation of Visual Population Receptive Fields by fMRI
06:02

Topographical Estimation of Visual Population Receptive Fields by fMRI

Published on: February 3, 2015

9.4K

Real-time and Recursive Estimators for Functional MRI Quality Assessment.

Nikita Davydov1,2,3, Lucas Peek4, Tibor Auer5

  • 1Aligned Research Group, Los Gatos, USA.

Neuroinformatics
|March 17, 2022
PubMed
Summary

This study introduces a new real-time quality assessment (rtQA) for functional MRI (fMRI) to automatically detect artifacts. This method enhances data reliability for neuroimaging research and clinical use.

Keywords:
Functional MRINeurofeedback paradigmsOpenNFTReal-time quality assessmentRecursiveRestTaskrtspm Python library

More Related Videos

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

11.8K
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

13.0K

Related Experiment Videos

Last Updated: Sep 30, 2025

Topographical Estimation of Visual Population Receptive Fields by fMRI
06:02

Topographical Estimation of Visual Population Receptive Fields by fMRI

Published on: February 3, 2015

9.4K
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

11.8K
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

13.0K

Area of Science:

  • Neuroimaging
  • Biomedical Engineering
  • Data Science

Background:

  • Real-time quality assessment (rtQA) is crucial for functional magnetic resonance imaging (fMRI) to ensure data integrity.
  • Technical and physiological noise significantly degrade blood oxygen level-dependent (BOLD) sensitivity, leading to fMRI artifacts.
  • Subjective visual inspection of fMRI data during acquisition is insufficient for detecting subtle distortions.

Purpose of the Study:

  • To develop and validate a comprehensive, automated real-time quality assessment (rtQA) system for fMRI data.
  • To enable rapid identification and mitigation of fMRI artifacts during data acquisition.
  • To improve the robustness and reliability of fMRI studies and neurofeedback applications.

Main Methods:

  • Applied real-time and recursive methods to assess whole-brain fMRI volumes and time-series.
  • Estimated recursive temporal signal-to-noise ratio (rtSNR) and contrast-to-noise ratio (rtCNR).
  • Calculated real-time head motion parameters, framewise displacement (FD), micro-displacement (MD), and derivative of root mean squared variance over voxels (DVARS).
  • Utilized a modified Kalman filter to detect spikes and filtered noise in target regions and networks.
  • Implemented incremental general linear modeling (GLM) to assess nuisance regressor contributions.

Main Results:

  • Demonstrated the proposed rtQA system in real-time fMRI neurofeedback and resting-state simulations, including scenarios with excessive head motion.
  • The rtQA system was successfully implemented as an extension of the OpenNFT software and a unified Python library.
  • Flexible estimation and visualization of rtQA metrics facilitated efficient data quality control.

Conclusions:

  • The developed automated rtQA system provides efficient and reliable assessment of fMRI data quality in real-time.
  • This tool supports informed decisions regarding experiment interruption or restart, enhancing fMRI acquisition robustness.
  • Increased confidence in neural estimates is achieved through improved fMRI data quality and artifact detection.