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Related Experiment Video

Updated: Jul 31, 2025

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

Spatial-extent inference for testing variance components in reliability and heritability studies.

Ruyi Pan^1,2, Erin W Dickie^2,3, Colin Hawco^2,3

¹Department of Statistical Sciences, University of Toronto, Toronto, ON, M5G 1Z5, Canada.

Biorxiv : the Preprint Server for Biology

|May 3, 2023

Summary

This summary is machine-generated.

We developed CLEAN-V, a powerful statistical test for variance components in neuroimaging. This method enhances the detection of heritability and reliability, outperforming existing approaches with improved statistical power.

Keywords:

clusterwise inference heritability spatial autocorrelation task-fMRI test-retest reliability variance component

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Area of Science:

Neuroimaging
Statistical Genetics
Brain Imaging Analysis

Background:

Clusterwise inference is common in neuroimaging for sensitivity but limited to General Linear Model (GLM) for mean parameters.
Statistical methods for variance components, crucial for heritability and reliability, are underdeveloped and often lack power due to computational challenges.

Conclusions:

CLEAN-V offers a computationally efficient and practically useful tool for analyzing variance components in neuroimaging.
The method significantly enhances the power to detect heritability and reliability, outperforming current techniques.