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Related Concept Videos

Correlations02:20

Correlations

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Correlation means that there is a relationship between two or more variables (such as ice cream consumption and crime), but this relationship does not necessarily imply cause and effect. When two variables are correlated, it simply means that as one variable changes, so does the other. We can measure correlation by calculating a statistic known as a correlation coefficient. A correlation coefficient is a number from -1 to +1 that indicates the strength and direction of the relationship between...
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Correlation and Causation01:27

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Statistical tests can calculate whether there is a relationship, or correlation, between independent and dependent variables. An indirect relationship of the variables signifies a correlation, while a direct relationship shows causation. If it is determined that no connection exists between the variables, then the correlation is a coincidence.
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Correlation01:09

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In statistics, two variables are said to be correlated if the values of one variable are associated with the other variable. Depending on the relationship between two variables, correlation can be of three types– positive correlation, negative correlation, and zero correlation.
Two variables, for example, a and b, are said to be positively correlated if both variables move in the same direction. In other words, a positive correlation exists between two variables, a and b, if:
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Correlation and Regression00:53

Correlation and Regression

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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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Coefficient of Correlation01:12

Coefficient of Correlation

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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.
If you suspect a linear relationship between x and y, then r can measure how strong the linear relationship is.
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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

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Acquisition and Semi-Automated Analysis of Respiratory Muscle Surface Electromyography
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Muscle Biopsy and Electromyography Correlation.

Elie Naddaf1, Margherita Milone1, Michelle L Mauermann1

  • 1Department of Neurology, Mayo Clinic, Rochester, MN, United States.

Frontiers in Neurology
|October 26, 2018
PubMed
Summary

This study reveals specific electromyography (EMG) patterns correlating with muscle biopsy findings in myopathies. Understanding these correlations enhances the interpretation of diagnostic tests for neuromuscular diseases.

Keywords:
electrodiagnostic testingelectromyographyfibrillation potentialsmotor unit potentialsmuscle biopsymuscle histopathology

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Area of Science:

  • Neurology
  • Pathology
  • Electrophysiology

Background:

  • The relationship between electromyography (EMG) and muscle biopsy results in myopathies is not fully understood.
  • Previous research primarily focused on differentiating myopathic from neuropathic conditions.

Purpose of the Study:

  • To explore the correlations between individual electromyographic and histopathologic findings in myopathies.
  • To improve the interpretation of electrodiagnostic testing in myopathic patients.

Main Methods:

  • 100 patients with both muscle biopsy and EMG on identical muscles were analyzed.
  • A grading system (0-4) was used for 16 histopathologic findings and EMG parameters.
  • Kendall's tau was employed for non-parametric ordinal correlation analysis.

Main Results:

  • Fibrillation potentials correlated with various fiber abnormalities, inflammation, and connective tissue changes.
  • Motor unit potential characteristics (duration, phases, turns) showed correlations with specific histopathologic findings.
  • Rapid recruitment was associated with regenerating fibers, inflammation, and increased connective tissue.

Conclusions:

  • This study establishes clear correlations between specific EMG and histopathologic findings in myopathies.
  • These findings enhance the interpretation of electrodiagnostic tests for neuromuscular disorders.
  • The results provide a foundation for further research correlating clinical, EMG, and histopathologic data.