Jove
Visualize
Contact Us

Related Concept Videos

Cluster Sampling Method01:20

Cluster Sampling Method

11.0K
Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
11.0K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Effect sizes in human functional neuroimaging.

Research square·2026
Same author

A quasi-stationary distribution bound for fault analysis in gene regulatory networks.

Journal of the Royal Society, Interface·2026
Same author

The Hidden Landscape of Missed Effects in Human Functional Neuroimaging.

bioRxiv : the preprint server for biology·2026
Same author

Feature selection leads to divergent neurobiological interpretations of brain-based machine learning biomarkers.

Nature human behaviour·2026
Same author

A low-variance subspace underlies individual differences in resting state fMRI.

bioRxiv : the preprint server for biology·2026
Same author

Connectome caricatures remove large-amplitude coactivation patterns in resting-state fMRI to emphasize individual differences.

Nature neuroscience·2025
Same journal

Layered social competition coordinates reproductive hierarchy formation in ants.

bioRxiv : the preprint server for biology·2026
Same journal

Combination epigenetic-targeted therapy increases the immunogenicity of poorly immunogenic sarcomas.

bioRxiv : the preprint server for biology·2026
Same journal

Loss of LanC-like proteins delays post-injury regeneration of aging skeletal muscles.

bioRxiv : the preprint server for biology·2026
Same journal

Integrative Transfer Network: Deep Transfer Learning Across Populations and Prediction Targets.

bioRxiv : the preprint server for biology·2026
Same journal

Confidence-supported label-free metabolic imaging with FPhaS phase autofluorescence microscopy.

bioRxiv : the preprint server for biology·2026
Same journal

Sequence-encoded autoinhibition couples mRNA decapping activity to phase separation.

bioRxiv : the preprint server for biology·2026
See all related articles
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: Apr 28, 2026

Dynamic Inter-subject Functional Connectivity Reveals Moment-to-Moment Brain Network Configurations Driven by Continuous or Communication Paradigms
08:36

Dynamic Inter-subject Functional Connectivity Reveals Moment-to-Moment Brain Network Configurations Driven by Continuous or Communication Paradigms

Published on: March 21, 2019

8.3K

The Incremental Cluster Threshold-Free Cluster Enhancement Algorithm for Functional Connectivity Analysis.

Fabricio Cravo1,2, Raimundo Rodriguez3,4, Alfonso Nieto-Castanon5,6,7

  • 1Department of Psychology, Northeastern University, Boston, MA, USA.

Biorxiv : the Preprint Server for Biology
|April 27, 2026
PubMed
Summary
This summary is machine-generated.

Incremental Cluster TFCE (IC-TFCE) significantly speeds up neuroimaging analysis by avoiding redundant calculations. This new method makes complex statistical inference feasible for large-scale brain data, improving computational efficiency.

More Related Videos

Modeling the Functional Network for Spatial Navigation in the Human Brain
05:55

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

1.7K
Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time
07:12

Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time

Published on: July 1, 2014

12.3K

Related Experiment Videos

Last Updated: Apr 28, 2026

Dynamic Inter-subject Functional Connectivity Reveals Moment-to-Moment Brain Network Configurations Driven by Continuous or Communication Paradigms
08:36

Dynamic Inter-subject Functional Connectivity Reveals Moment-to-Moment Brain Network Configurations Driven by Continuous or Communication Paradigms

Published on: March 21, 2019

8.3K
Modeling the Functional Network for Spatial Navigation in the Human Brain
05:55

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

1.7K
Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time
07:12

Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time

Published on: July 1, 2014

12.3K

Area of Science:

  • Neuroimaging
  • Computational Neuroscience
  • Statistical Inference

Background:

  • Threshold-free cluster enhancement (TFCE) is a widely used neuroimaging statistical method.
  • Current TFCE implementations are computationally intensive, limiting their application with increasing data complexity and sample sizes.
  • The quadratic growth of functional connectivity (FC) edges with regions of interest (ROIs) makes standard TFCE computationally infeasible for fine parcellations.

Purpose of the Study:

  • To develop a computationally efficient algorithm for TFCE that produces numerically equivalent results to standard TFCE.
  • To decouple the runtime of TFCE from discretization precision.
  • To enable TFCE analysis on large-scale neuroimaging datasets with fine parcellations.

Main Methods:

  • Introduced Incremental Cluster TFCE (IC-TFCE), an algorithm that builds clusters incrementally, avoiding recomputation at each threshold step.
  • Stored TFCE results on a region of interest (ROI)-based structure instead of an FC edge structure for enhanced speed.
  • Developed and validated a novel graph transformation for applying IC-TFCE to voxel data.

Main Results:

  • IC-TFCE achieves a 3-93x speedup for FC TFCE, depending on the precision parameter $dh$.
  • The algorithm makes TFCE analyses with over 1000 ROIs computationally tractable.
  • Mathematical proofs and numerical comparisons validate the correctness of IC-TFCE.
  • An empirical power analysis guided parameter selection for practitioners.

Conclusions:

  • IC-TFCE offers a computationally efficient and scalable alternative to standard TFCE for neuroimaging statistical inference.
  • This method significantly enhances the feasibility of TFCE for large-scale, high-resolution neuroimaging studies.
  • IC-TFCE facilitates more robust statistical analyses and aids in parameter selection for researchers.