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 Experiment Videos

Detecting and adjusting for artifacts in fMRI time series data.

Jörn Diedrichsen1, Reza Shadmehr

  • 1Department of Biomedical Engineering, Laboratory for Computational Motor Control, Johns Hopkins University School of Medicine, Baltimore, 720 Rutland Ave, 416 Traylor Building, MD 21205-2195, USA. jdiedric@bme.jhu.edu

Neuroimage
|June 25, 2005
PubMed
Summary

This study introduces a new method to handle noise in functional MRI (fMRI) data, improving the detection of brain activity. The approach adjusts for artifacts, enhancing sensitivity in fMRI analysis.

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

Individualized parcellation reveals functional boundaries in human prefrontal cortex.

bioRxiv : the preprint server for biology·2026
Same author

Precision Functional Parcellation of the Human Cortex via Rest-Task fMRI Fusion.

bioRxiv : the preprint server for biology·2026
Same author

Online planning of sequential actions.

Trends in cognitive sciences·2026
Same author

Reading ability in both deaf and hearing adults is linked to neural representations of abstract phonology derived from visual speech.

Proceedings of the National Academy of Sciences of the United States of America·2026
Same author

Cerebellar growth is associated with domain-specific cerebral maturation and socio-linguistic behavior.

Nature communications·2026
Same author

Sequence preparation is not always associated with a reaction time cost.

Journal of neurophysiology·2026

Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Signal Processing

Background:

  • Functional MRI (fMRI) data analysis relies on assumptions of stationary variance, often unmet in practice.
  • Sporadic artifacts like movement introduce non-stationary noise, impacting fMRI time series.
  • Existing methods may not adequately address global, spatially distributed noise patterns.

Purpose of the Study:

  • To develop and validate a novel method for detecting and correcting noise and artifacts in fMRI time series.
  • To improve the sensitivity of fMRI analysis by accounting for non-stationary noise.
  • To provide a robust approach for fMRI studies, especially those involving populations prone to artifacts.

Main Methods:

  • Derived a restricted maximum likelihood (ReML) algorithm to estimate noise variance per image.

Related Experiment Videos

  • Employed weighted least squares to estimate linear model parameters using estimated variance.
  • Applied the method to fMRI block design experiments to assess noise characteristics.

    • Main Results:

    • Demonstrated significant variation in noise estimates across fMRI images.
    • Showed the method effectively detects and weights artifact-affected images.
    • Confirmed noise process is global and multiplicative, not additive.
    • Observed a significant increase in sensitivity for detecting activation regions.

    Conclusions:

    • The new ReML-based method effectively addresses non-stationary noise in fMRI data.
    • This approach enhances the reliability and sensitivity of fMRI analysis.
    • The method is particularly beneficial for studies with special populations prone to artifacts.