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 Video

Updated: Jun 18, 2026

Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging
17:06

Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging

Published on: November 8, 2012

Topological FDR for neuroimaging.

J Chumbley1, K Worsley, G Flandin

  • 1Wellcome Trust Centre for Neuroimaging, London, UK. jrchum@gmail.com

Neuroimage
|December 1, 2009
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

Caching mechanisms for habit formation in Active Inference.

Neurocomputing·2020
Same author

On the psychology and economics of antisocial personality.

Proceedings of the National Academy of Sciences of the United States of America·2019
Same author

Distinct Top-down and Bottom-up Brain Connectivity During Visual Perception and Imagery.

Scientific reports·2017
Same author

Reconstructing anatomy from electro-physiological data.

NeuroImage·2017
Same author

Sensor space group analysis for fNIRS data.

Journal of neuroscience methods·2016
Same author

BDNF Val66Met polymorphism influence on striatal blood-level-dependent response to monetary feedback depends on valence and agency.

Neuroscience·2014

We introduce a new topological false discovery rate (FDR) method for statistical parametric mapping (SPM) to detect continuous signals. This approach enhances sensitivity for identifying signal peaks compared to traditional methods.

Area of Science:

  • Neuroimaging and Statistical Analysis
  • Statistical Parametric Mapping (SPM)
  • Brain Signal Detection

Background:

  • Current statistical methods in neuroimaging often struggle with continuous signals and unbounded spatial support.
  • Existing approaches like family-wise error (FWE) control on local maxima have limitations in sensitivity.
  • The need for more sensitive and accurate methods for detecting brain activation patterns is critical.

Purpose of the Study:

  • To describe and validate a novel topological false discovery rate (FDR) procedure for statistical parametric mapping.
  • To compare the performance of this topological FDR procedure against conventional FWE control and other FDR methods (cluster-wise, voxel-wise).
  • To assess the sensitivity and spatial accuracy of the proposed method in detecting signal peaks.

Main Methods:

More Related Videos

Probing the Brain in Autism Using fMRI and Diffusion Tensor Imaging
12:21

Probing the Brain in Autism Using fMRI and Diffusion Tensor Imaging

Published on: September 12, 2011

Related Experiment Videos

Last Updated: Jun 18, 2026

Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging
17:06

Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging

Published on: November 8, 2012

Probing the Brain in Autism Using fMRI and Diffusion Tensor Imaging
12:21

Probing the Brain in Autism Using fMRI and Diffusion Tensor Imaging

Published on: September 12, 2011

  • Developed a topological FDR procedure inferring on signal's topological features (local maxima/peaks) above a threshold.
  • Utilized random field theory to assign p-values to maxima within a statistical parametric map (SPM).
  • Employed the Benjamini and Hochberg (BH) procedure for adaptive thresholding to control FDR.
  • Conducted simulations to compare topological FDR with FWE control, cluster-wise FDR, and voxel-wise FDR regarding discovery counts and spatial accuracy (Euclidean distance).

Main Results:

  • The topological FDR control of maxima/peaks demonstrated greater sensitivity than FWE control of peaks, with a negligible increase in false positives.
  • Voxel-wise FDR control was found to be significantly less accurate than topological FWE or FDR methods.
  • The proposed topological FDR procedure offers a sensitive complement to traditional FWE control for analyzing continuous neuroimaging signals.

Conclusions:

  • Topological FDR control provides a more sensitive approach for detecting signal peaks in statistical parametric mapping compared to FWE control.
  • The proposed method offers improved spatial accuracy over voxel-wise FDR, making it suitable for continuous signals with unbounded support.
  • This technique is particularly valuable for analyzing complex neuroimaging data, as illustrated by its application in an fMRI study of visual attention.