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

Noise reduction in BOLD-based fMRI using component analysis.

Christopher G Thomas1, Richard A Harshman, Ravi S Menon

  • 1Department of Medical Biophysics, University of Western Ontario, London, Ontario, Canada.

Neuroimage
|November 5, 2002
PubMed
Summary
This summary is machine-generated.

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

Fast Online 3D SPACE and FLAIR Imaging at 7T Using Multiple Subject-Specific Parallel Transmission Pulses Based on Subpopulation Universal Pulses.

Magnetic resonance in medicine·2026
Same author

Mapping functional homologies between human and marmoset brain networks using movie-driven ultra-high field fMRI.

Communications biology·2026
Same author

Basic Science and Pathogenesis.

Alzheimer's & dementia : the journal of the Alzheimer's Association·2025
Same author

Periventricular gradients in axonal and myelin microstructure are present in people with multiple sclerosis having low and high disability.

Multiple sclerosis (Houndmills, Basingstoke, England)·2025
Same author

Biomarkers.

Alzheimer's & dementia : the journal of the Alzheimer's Association·2025
Same author

Biomarkers.

Alzheimer's & dementia : the journal of the Alzheimer's Association·2025
Same journal

Spatial frequency channels implement a mental ruler in spatial vision.

NeuroImage·2026
Same journal

Exploring the Link Between Intravoxel Incoherent Motion Measured Brain Diffusivity During Wakefulness and Sleep Macrostructure in the Elderly.

NeuroImage·2026
Same journal

Closed-loop adaptation of transcranial magnetic stimulation intensity with electroencephalography feedback.

NeuroImage·2026
Same journal

Volumetric postmortem MRI of the medial temporal lobe in Alzheimer's disease and related disorders: methodological advances and implications for in vivo biomarker development.

NeuroImage·2026
Same journal

Neural responses to equity and inequity when receiving vicarious rewards for self and charity during adolescence.

NeuroImage·2026
Same journal

Cognitive Strategy-based neuromodulation optimizes neural communication to improve working memory.

NeuroImage·2026
See all related articles

Independent Component Analysis (ICA) and Principle Component Analysis (PCA) reduce noise in fMRI data. ICA excels at removing structured noise, while PCA is better for random noise, enhancing BOLD signal detection.

Area of Science:

  • Neuroimaging
  • Signal Processing

Background:

  • Functional Magnetic Resonance Imaging (fMRI) is susceptible to structured and random noise.
  • Noise can obscure the Blood-Oxygen-Level-Dependent (BOLD) signal, impacting analysis accuracy.

Purpose of the Study:

  • To investigate the efficacy of Principle Component Analysis (PCA) and Independent Component Analysis (ICA) for fMRI noise reduction.
  • To compare the performance of PCA and ICA in isolating and removing different types of noise.
  • To evaluate the impact of noise reduction on BOLD contrast sensitivity.

Main Methods:

  • Decomposition of fMRI time series using PCA and ICA.
  • Identification of noise components via an unsupervised algorithm examining Fourier decomposition.
  • Removal of identified noise components and reconstruction of time series data.

Related Experiment Videos

  • Calculation of BOLD contrast sensitivity (CS(BOLD)) for all voxels.

    • Main Results:

    • Noise reduction increased CS(BOLD) in activated voxels due to decreased image-to-image variability.
    • ICA demonstrated superior performance in isolating and removing structured noise.
    • PCA proved more effective in isolating and removing random noise.
    • Significant differences were observed in how PCA and ICA handled structured versus random noise.

    Conclusions:

    • Component analysis techniques, specifically PCA and ICA, offer a viable framework for fMRI noise reduction.
    • The choice between PCA and ICA depends on the predominant noise type (structured vs. random).
    • Effective noise reduction enhances the sensitivity of fMRI analyses for detecting BOLD signal changes.