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

Generalized likelihood ratio detection for fMRI using complex data.

F Y Nan1, R D Nowak

  • 1Department of Electrical Engineering, Michigan State University, E. Lansing, 48824, USA. nanfangy@egr.msu.edu

IEEE Transactions on Medical Imaging
|June 29, 1999
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

Adaptive weighted highpass filters using multiscale analysis.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2008
Same author

Wavelet-based Rician noise removal for magnetic resonance imaging.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2008
Same author

Wavelet-domain filtering for photon imaging systems.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2008
Same author

Wavelet-based image estimation: an empirical Bayes approach using Jeffrey's noninformative prior.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2008
Same author

Minimax optimal level-set estimation.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2007
Same author

Linearly constrained minimum variance source imaging using cortical bases.

Neurology & clinical neurophysiology : NCN·2005
Same journal

Physiology-guided Self-supervised Learning for Simultaneous Dual-Tracer PET Separation.

IEEE transactions on medical imaging·2026
Same journal

Informed-Exploration Reinforcement Learning for Automated Virtual Coronary Intervention Planning.

IEEE transactions on medical imaging·2026
Same journal

4D Reconstruction of Fetal Left Ventricle from Echocardiography via 2.5D Radial Segmentation and Graph-Fourier Reconstruction.

IEEE transactions on medical imaging·2026
Same journal

Generalised Medical Phrase Grounding.

IEEE transactions on medical imaging·2026
Same journal

EndoLRMGS: Combining Large Reconstruction Modelling and Gaussian Splatting for Complete Endoscopic Scene Reconstruction.

IEEE transactions on medical imaging·2026
Same journal

A Neural-Analytical Fusion Scatter Correction Method for Multi-Source CT Using Equivalent High-Order Scatter.

IEEE transactions on medical imaging·2026
See all related articles

A new generalized likelihood ratio test (GLRT) detector improves functional magnetic resonance imaging (fMRI) analysis by utilizing phase information. This method outperforms existing magnitude and complex correlation tests, especially at lower signal intensities.

Area of Science:

  • Neuroimaging
  • Biophysics

Background:

  • Functional magnetic resonance imaging (fMRI) typically uses magnitude-based statistical tests.
  • Complex correlation (CC) tests leverage signal phase but neglect baseline phase information.

Purpose of the Study:

  • To introduce a novel generalized likelihood ratio test (GLRT) detector for fMRI.
  • To enhance fMRI analysis by incorporating both response and baseline phase information.

Main Methods:

  • Theoretical analysis of the GLRT detector.
  • Monte Carlo simulations to evaluate performance.
  • Comparison with standard magnitude tests and CC tests.

Main Results:

  • The GLRT detector outperforms standard magnitude and CC tests at low signal intensities.

Related Experiment Videos

  • At high signal intensities, GLRT matches standard tests and surpasses CC tests.
  • The GLRT effectively utilizes common phase information in fMRI signals and baseline data.

    • Conclusions:

    • The proposed GLRT detector offers superior performance in fMRI analysis compared to existing methods.
    • GLRT provides a more comprehensive approach by integrating phase information from both signal and baseline.
    • This advancement has the potential to improve the sensitivity and accuracy of fMRI studies.