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Cluster Sampling Method

Cluster Sampling Method

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...

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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

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

Published on: March 21, 2019

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

Fabricio Cravo^1,2, Raimundo Rodriguez^3,4, Alfonso Nieto-Castanon^5,6,7

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

Biorxiv : the Preprint Server for Biology

|April 27, 2026

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.

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Last Updated: Apr 28, 2026

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Published on: March 21, 2019

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Published on: October 13, 2023

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

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Published on: July 1, 2014

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.