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

Nonstationary noise estimation in functional MRI.

C J Long1, E N Brown, C Triantafyllou

  • 1MGH/MIT/HMS Martinos Center for Biomedical Imaging, Charlestown, MA 02129, USA. cjl@nmr.mgh.harvard.edu

Neuroimage
|September 1, 2005
PubMed
Summary

This study introduces two novel time-varying methods to analyze nonstationary noise in functional MRI (fMRI) data, improving brain function analysis by moving beyond fixed temporal assumptions. These techniques enhance noise characterization and improve detection power in critical brain regions.

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

Open-source device for high sensitivity magnetic particle spectroscopy, relaxometry, and hysteresis loop tracing.

The Review of scientific instruments·2024
Same author

Clinical Evaluation of Scout Accelerated Motion Estimation and Reduction Technique for 3D MR Imaging in the Inpatient and Emergency Department Settings.

AJNR. American journal of neuroradiology·2023
Same author

Why are pediatric urologists unable to predict renal deterioration using urodynamics? A focused narrative review of the shortcomings of the literature.

Journal of pediatric urology·2022
Same author

Accelerated resolution therapy and a thematic approach to military experiences in US Special Operations Veterans.

BMJ military health·2021
Same author

Author Correction: Local negative permittivity and topological phase transition in polar skyrmions.

Nature materials·2021
Same author

Corrigendum to 'Role of electroencephalogram oscillations and the spectrogram in monitoring anaesthesia' [BJA Education 20 (2020) 166-172].

BJA education·2021

Area of Science:

  • Neuroimaging
  • Signal Processing
  • Statistical Modeling

Background:

  • Accurate noise characterization is crucial for functional MRI (fMRI) analysis.
  • Conventional fMRI noise models often assume stationarity, potentially obscuring dynamic brain function information.
  • Time-varying noise structures in fMRI data require advanced analytical approaches.

Purpose of the Study:

  • To present two novel time-varying procedures for examining nonstationary noise in fMRI data.
  • To offer alternatives to fixed temporal assumptions in fMRI noise modeling.
  • To investigate the impact of nonstationary noise analysis on brain function characterization.

Main Methods:

  • Developed a locally parametric AutoRegressive (AR) plus drift model with time-evolving parameters to approximate nonstationary noise.

Related Experiment Videos

  • Employed Stein's Unbiased Risk Estimator (SURE) criterion for optimal bandwidth selection in the AR model.
  • Utilized a nonparametric Functional Data Analysis (FDA) method for nonstationary covariance estimation in fMRI noise.

    • Main Results:

    • Demonstrated both methods on simulated and resting-state fMRI data, revealing the presence of nonstationary noise.
    • Showed that incorporating time variation in AR parameters reduced residual structure across various magnetic field strengths (1.5, 3, and 7 T).
    • The FDA noise model, integrated into an activation mapping procedure, improved detection power in medial temporal regions during a face recognition task compared to SPM.

    Conclusions:

    • Time-varying noise models offer a more comprehensive approach to fMRI data analysis than stationary models.
    • The proposed locally parametric AR and FDA methods effectively capture nonstationary noise structures.
    • Nonstationary noise modeling, particularly with FDA, can enhance sensitivity and detection power in specific task-related brain regions.