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Related Concept Videos

Heritability01:06

Heritability

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Heritability is a statistical concept that measures the degree to which genetic differences among individuals contribute to trait variations within a population. It is a fundamental idea in genetics, often prone to misinterpretation. Heritability is expressed as a percentage, reflecting the proportion of variation in a specific trait across a population that can be linked to genetic differences. However, it's important to understand that heritability does not determine how "genetic"...
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Variability: Analysis01:11

Variability: Analysis

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Measures of variability are statistical metrics that reveal the dispersion pattern within a dataset. They are pivotal in biostatistics, providing insights into the heterogeneity within health and biological data. Variability signifies the degree to which data points diverge from one another, helping researchers understand the potential range of values and associated uncertainty within the data.
The range is a simple measure of variability, indicating the difference between the highest and...
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Variance01:15

Variance

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 The deviations show how spread out the data are about the mean. A positive deviation occurs when the data value exceeds the mean, whereas a negative deviation occurs when the data value is less than the mean. If the deviations are added, the sum is always zero. So one cannot simply add the deviations to get the data spread. By squaring the deviations, the numbers are made positive; thus, their sum will also be positive.
The standard deviation measures the spread in the same units as the...
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Variation01:19

Variation

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An important characteristic of any set of data is the variation in the data. In some data sets, the data values are concentrated closely near the mean; in other data sets, the data values are more widely spread out from the mean. The most common measure of variation, or spread, is the standard deviation, which is the square root of variance.
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Empirical Method to Interpret Standard Deviation01:09

Empirical Method to Interpret Standard Deviation

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The empirical rule, also known as the three-sigma rule, allows a statistician to interpret the standard deviation in a normally distributed dataset. The rule states that 68% of the data lies within one standard deviation from the mean, 95% lies within two standard deviations from the mean, and 99.7% lies within three standard deviations from the mean. Additionally, this rule is also called the 68-95-99.7 rule.
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Estimating Population Standard Deviation01:26

Estimating Population Standard Deviation

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When the population standard deviation is unknown and the sample size is large, the sample standard deviation s is commonly used as a point estimate of σ. However, it can sometimes under or overestimate the population standard deviation. To overcome this drawback, confidence intervals are determined to estimate population parameters and eliminate any calculation bias accurately. However, this only applies to random samples from normally distributed populations. Knowing the sample mean and...
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Updated: Sep 11, 2025

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
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Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

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

Ruyi Pan1,2, Erin W Dickie2,3, Colin Hawco2,3

  • 1Department of Statistical Sciences, University of Toronto, Toronto, Canada.

Imaging Neuroscience (Cambridge, Mass.)
|August 13, 2025
PubMed
Summary
This summary is machine-generated.

We introduce CLEAN-V, a novel statistical method for testing variance components in neuroimaging. This powerful and efficient approach enhances the detection of heritability and reliability, outperforming existing methods.

Keywords:
clusterwise inferenceheritabilityspatial autocorrelationtask-fMRItest-retest reliabilityvariance component

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Basics of Multivariate Analysis in Neuroimaging Data
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Area of Science:

  • Neuroimaging
  • Statistical genetics
  • Brain imaging analysis

Background:

  • Existing neuroimaging methods for variance component testing, crucial for heritability and reliability estimation, are limited by the General Linear Model (GLM) and suffer from low statistical power.
  • Methodological and computational challenges hinder the development of powerful statistical tests for variance components in neuroimaging data.

Purpose of the Study:

  • To develop a fast and powerful statistical test for variance components in neuroimaging data.
  • To address the limitations of existing methods in detecting narrow-sense heritability and test-retest reliability.
  • To improve statistical power in neuroimaging analyses of genetic and reliability components.

Main Methods:

  • Proposed CLEAN-V (CLEAN for testing Variance components), a novel statistical test for variance components.
  • Modeled global spatial dependence structure of imaging data.
  • Employed data-adaptive pooling of neighborhood information for locally powerful statistics.
  • Utilized permutations for family-wise error rate (FWER) control in multiple comparisons.

Main Results:

  • CLEAN-V demonstrated superior performance in detecting test-retest reliability and narrow-sense heritability compared to existing methods.
  • Achieved significantly improved statistical power in analyses of Human Connectome Project task-fMRI data and simulations.
  • Detected areas of significance aligned with functional magnetic resonance imaging (fMRI) activation maps.
  • Showcased computational efficiency, indicating practical utility.

Conclusions:

  • CLEAN-V offers a significant advancement in statistical testing for variance components in neuroimaging.
  • The method provides enhanced power for detecting heritability and reliability, crucial for understanding brain function and individual differences.
  • CLEAN-V's computational efficiency and availability as an R package facilitate its widespread application in neuroimaging research.