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

Resampling methods for improved wavelet-based multiple hypothesis testing of parametric maps in functional MRI.

Levent Sendur1, John Suckling, Brandon Whitcher

  • 1Brain Mapping Unit, University of Cambridge, Addenbrooke's Hospital, Cambridge CB2 0QQ, UK.

Neuroimage
|July 27, 2007
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

Evaluation of a New Paracingulate Sulcus Identification and Measurement Protocol.

Human brain mapping·2026
Same author

Efficacy of traditional Chinese exercises on cognitive function in older adults: a systematic review and meta-analysis of randomised controlled trials.

Age and ageing·2026
Same author

Association-sensory spatiotemporal hierarchy and functional gradient-regularised recurrent neural network with implications for schizophrenia.

NPJ systems biology and applications·2026
Same author

Understanding the long-term neurological effects of SARS-CoV-2 infection.

Nature reviews. Neurology·2026
Same author

Prenatal Volume in the Bilateral Superior Temporal Gyrus Associates With Children's Expressive Vocabulary at 24-36 Months.

Developmental science·2026
Same author

Depression, but not anxiety, is associated with epigenetic age accelerations among Asian older adults.

Molecular psychiatry·2026
Same journal

Segmentation of the parasagittal dura mater on multi-center 3D-FLAIR MRI.

NeuroImage·2026
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
See all related articles

This study introduces a novel wavelet-based method for denoising and hypothesis testing in functional magnetic resonance imaging (fMRI) data. The new approach improves signal detection by accounting for realistic noise variance in fMRI parametric maps.

Area of Science:

  • Neuroimaging
  • Signal Processing
  • Statistical Analysis

Background:

  • Functional magnetic resonance imaging (fMRI) data analysis often uses wavelet transforms for hypothesis testing.
  • Previous methods assumed uniform noise variance across wavelet transform levels, which is unrealistic for fMRI data.
  • fMRI parametric maps exhibit a 1/f-type spatial covariance, with higher variance at lower spatial frequencies.

Purpose of the Study:

  • To develop a more realistic wavelet-based denoising and multiple hypothesis testing algorithm for fMRI data.
  • To address the limitations of assuming equal noise variance across wavelet transform levels.
  • To improve the detection of experimentally induced signals in fMRI studies.

Main Methods:

  • Resampling fMRI time series data in the wavelet domain using a 1D discrete wavelet transform (DWT).

Related Experiment Videos

  • Decomposing permuted parametric maps using a 2D DWT to estimate level-specific variances (wavelet variance spectrum).
  • Employing a Bayesian bivariate shrinkage operator with resampling-based variance estimates to denoise 2D wavelet coefficients.
  • Performing multiple hypothesis testing on denoised maps in the spatial domain using thresholds from independent permuted maps.

    • Main Results:

    • The proposed resampling-based algorithm provides more accurate estimates of noise variance in wavelet coefficients.
    • The method demonstrates good Type I error control in multiple hypothesis testing.
    • Empirical results show enhanced detection of experimentally engendered signals in auditory-linguistic fMRI data.

    Conclusions:

    • The developed wavelet-based denoising and hypothesis testing method offers a more realistic and effective approach for fMRI data analysis.
    • Accounting for the true spatial covariance structure of fMRI data improves statistical power and reliability.
    • This algorithm enhances the ability to identify brain activity related to specific cognitive tasks, such as auditory-linguistic processing.