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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.
Correlation versus Causation
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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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What is Behavior?00:54

What is Behavior?

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Behaviors are actions that an organism engages in—they can be related to finding food, reproducing, defending against threats, and many other possible actions. Behaviors include activities related to the environment around the animal—such as migration—as well as social interactions within a species or population. Many behaviors involve motor output—that is, muscle movements—while others involve less visible actions, such as learning.
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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

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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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Correlating Behavioral Responses to fMRI Signals from Human Prefrontal Cortex: Examining Cognitive Processes Using Task Analysis
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Item-Wise Interindividual Brain-Behavior Correlation in Task Neuroimaging Analysis.

Xinlin Zhou1,2,3, Mengyi Li1,2,3, Hantao Zhou4

  • 1State Key Laboratory of Cognitive Neuroscience and Learning, IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China.

Frontiers in Neuroscience
|November 22, 2018
PubMed
Summary
This summary is machine-generated.

This study introduces a novel item-wise correlation method for brain-behavior research, significantly improving statistical power and reliability over traditional mean-wise approaches in neuroimaging. This enhances the discovery of brain-behavior associations.

Keywords:
brain-behavior correlationcognitive neurosciencefunctional magnetic resonance imagingitem-wise interindividual brain-behavior correlationmathematical problem-solving

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Area of Science:

  • Cognitive Neuroscience
  • Neuroimaging Analysis

Background:

  • Brain-behavior correlations are crucial in cognitive neuroscience.
  • Traditional mean-wise correlation methods often suffer from low statistical power.
  • Existing approaches may miss subtle but significant brain-behavior relationships.

Purpose of the Study:

  • To introduce and validate a new item-wise interindividual brain-behavior correlation procedure.
  • To enhance statistical power in detecting brain-behavior associations in neuroimaging studies.
  • To improve the reliability and stability of correlation analyses.

Main Methods:

  • Developed and implemented an item-wise interindividual correlation procedure.
  • Compared item-wise and mean-wise correlation in computational simulations.
  • Validated the method using functional magnetic resonance imaging (fMRI) data from a mathematical problem-solving task.
  • Performed cross-validation using odd and even trial orders.

Main Results:

  • Item-wise correlation demonstrated higher t-values and identified more significant voxels compared to mean-wise correlation, especially at signal-to-noise ratios >= 6%.
  • Significant brain-behavior correlations were detected using the item-wise method at a corrected p < 0.05 threshold.
  • Cross-validation revealed superior stability for item-wise correlation (r = 0.918) versus mean-wise correlation (r = 0.686).

Conclusions:

  • The proposed item-wise correlation procedure effectively enhances statistical power and reliability in task-based neuroimaging.
  • This method offers a more sensitive approach to uncovering brain-behavior relationships.
  • Item-wise correlation represents a valuable advancement for cognitive neuroscience research.