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Related Concept Videos

Microsoft Excel: Pearson's Correlation01:18

Microsoft Excel: Pearson's Correlation

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Microsoft Excel is a powerful tool for statistical analysis, including calculating Pearson's correlation coefficient, which measures the strength and direction of a linear relationship between two continuous variables. Pearson's correlation coefficient, often denoted as "r," ranges from -1 to 1. A value close to 1 indicates a strong positive correlation, meaning as one variable increases, the other does too. A value close to -1 indicates a strong negative correlation, implying...
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In statistics, correlation describes the degree of association between two variables. In the subfield of linear regression, correlation is mathematically expressed by the correlation coefficient, which describes the strength and direction of the relationship between two variables. The coefficient is symbolically represented by 'r' and ranges from -1 to +1. A positive value indicates a positive correlation where the two variables move in the same direction. A negative value suggests a...
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Is Pearson's correlation coefficient enough for functional connectivity in fMRI?

Hecheng Jin1, Julian S B Ramirez1, Kyoungseob Byeon1

  • 1Child Mind Institute, New York, NY, United States.

Imaging Neuroscience (Cambridge, Mass.)
|December 11, 2025
PubMed
Summary
This summary is machine-generated.

Multiscale Graph Correlation (MGC) and Pearson's r offer similar functional connectivity (FC) insights, but MGC better detects nonlinear brain dynamics, especially under anesthesia, though Pearson's r is more reliable and computationally efficient.

Keywords:
Multiscale Graph CorrelationPearson’s correlation coefficientfMRIfunctional connectivitynonlinear dependencies

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Area of Science:

  • Neuroscience
  • Data Analysis
  • Brain Imaging

Background:

  • Functional connectivity (FC) quantifies brain region interactions using statistical dependencies.
  • Pearson's correlation coefficient (Pearson's r) captures linear relationships, potentially missing nonlinear dynamics.
  • Multiscale Graph Correlation (MGC) assesses both linear and nonlinear dependencies across multiple scales.

Purpose of the Study:

  • To systematically compare Pearson's r and MGC for measuring functional connectivity.
  • To evaluate reliability, sensitivity to data quantity, and ability to detect experimental condition changes and brain-behavior associations.
  • To explore the utility of MGC in identifying nonlinear interactions and optimal scales in brain networks.

Main Methods:

  • Comparison of Pearson's r and MGC on fMRI datasets.
  • Assessment of reliability, data quantity sensitivity, and performance in distinct experimental conditions (anesthesia).
  • Evaluation of brain-behavior association detection capabilities.

Main Results:

  • Pearson's r and MGC showed similar spatial patterns and alignment for within-network FC, with global optimal scales.
  • Local optimal scales between networks suggested nonlinear FC dependencies, particularly detected by MGC.
  • Pearson's r demonstrated higher overall reliability, while both methods improved with more data.
  • MGC revealed state-dependent optimal scale variability under anesthesia, indicating sensitivity to altered brain states.
  • MGC required more computational resources and did not outperform Pearson's r in brain-behavior association tasks.

Conclusions:

  • Pearson's r is sufficient for standard FC analysis due to its reliability and efficiency.
  • MGC provides nuanced insights into nonlinear brain dynamics and state changes, valuable for specific research questions.
  • Researchers should weigh MGC's benefits against its computational cost and complexity for FC quantification.