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Related Concept Videos

Coefficient of Correlation01:12

Coefficient of Correlation

The correlation coefficient, r, developed by Karl Pearson in the early 1900s, is numerical and provides a measure of strength and direction of the linear association between the independent variable x and the dependent variable y.
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What the VALUE of r tells us:
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The size of the correlation r indicates the strength of the linear...
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In a linear calibration curve, there is a value called the calibration coefficient, denoted by 'r,' which measures the strength and the direction of association between two variables. The correlation coefficient value ranges from −1 to +1. A value of +1 indicates a perfect positive linear correlation, −1 denotes a perfect negative correlation, and 0 implies no correlation between the two variables. A positive correlation value establishes that as one variable increases, the other increases, and...
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In pharmaceutical development, it's crucial to establish a predictive in vitro–in vivo correlation (IVIVC) for two or more formulations to gain a comprehensive understanding of release properties. IVIVC reduces the need for costly in vivo studies and facilitates the establishment of meaningful dissolution specifications with significant cost savings and decreased regulatory burden. Furthermore, a meaningful IVIVC should predict Cmax and AUC within 20%, aligning with FDA guidance while adhering...
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Spearman's Rank Correlation Test01:20

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Spearman's rank correlation test, also known as Spearman's rho, is a nonparametric method for assessing the strength and direction of association between two variables. This test is particularly valuable when the data distribution is unknown or when the assumption of normality does not hold. Named after the English psychologist and statistician Dr. Charles Edward Spearman, it serves as the nonparametric counterpart to Pearson's correlation coefficient.
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Related Experiment Video

Updated: Jun 26, 2026

How to Calculate and Validate Inter-brain Synchronization in a fNIRS Hyperscanning Study
05:33

How to Calculate and Validate Inter-brain Synchronization in a fNIRS Hyperscanning Study

Published on: September 8, 2021

Measuring fMRI reliability with the intra-class correlation coefficient.

Alejandro Caceres1, Deanna L Hall, Fernando O Zelaya

  • 1Centre for Neuroimaging Sciences, Institute of Psychiatry, King's College London, London, UK. alejandro.caceres@iop.kcl.ac.uk

Neuroimage
|January 27, 2009
PubMed
Summary
This summary is machine-generated.

High group activation in fMRI scans predicts reliable test-retest reliability (ICC), especially in specific brain regions. This new method enhances analysis of individual differences in brain activity across sessions.

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Last Updated: Jun 26, 2026

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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

Area of Science:

  • Neuroimaging
  • Cognitive Neuroscience
  • Biostatistics

Background:

  • The intra-class correlation coefficient (ICC) is crucial for assessing test-retest reliability in functional magnetic resonance imaging (fMRI) data.
  • Evaluating the consistency of subject-specific brain activation patterns across different scanning sessions is essential for reliable fMRI studies.

Purpose of the Study:

  • To introduce an advanced method for voxel-wise ICC analysis to enhance the measurement of fMRI test-retest reliability.
  • To investigate the relationship between group activation levels and the reliability of subject differentiation in subsequent fMRI sessions.
  • To compare the robustness of the proposed voxel-wise ICC method against existing approaches across different analytical parameters.

Main Methods:

  • Extension of voxel-wise intra-class correlation coefficient (ICC) analysis for fMRI data.
  • Assessment of reliability in regions with high group activation versus general brain or white matter voxels.
  • Analysis of voxels with high ICC but low group activation, considering stable signals and hemodynamic response function (HRF) model fit.
  • Region of interest (ROI) level comparisons of voxel-wise ICC robustness under varying smoothing and cluster sizes.
  • Application of the method to auditory and verbal working memory tasks.

Main Results:

  • Voxels exhibiting high group activation are more likely to demonstrate reliable subject differentiation in a second fMRI session.
  • Stable signals that do not conform to the HRF model can lead to high ICC values despite low group activation.
  • The proposed voxel-wise ICC calculation demonstrates superior robustness compared to previous methods when subjected to variations in smoothing and cluster size.
  • The method enables formal comparisons of reliability across different brain regions, identifying those that best distinguish individuals.

Conclusions:

  • The developed voxel-wise ICC method provides a more robust and informative approach to assessing fMRI test-retest reliability.
  • High group activation serves as a reliable indicator for subject differentiability across sessions.
  • The findings facilitate the identification of brain regions with optimal individual discriminability for various cognitive tasks.