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Related Concept Videos

Variability: Analysis01:11

Variability: Analysis

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...
Biostatistics: Overview01:20

Biostatistics: Overview

Biostatistics plays a crucial role in understanding and analyzing data in healthcare and biology. Biostatisticians conduct experiments, gather evidence, and draw meaningful conclusions using statistical methods and techniques. Different variables form the foundation of biostatistical analysis, allowing researchers to understand and interpret data effectively. These variables are classified into different types, each serving a specific purpose in statistical analysis.
Discrete variables are...
What is Variation?01:14

What is Variation?

Apart from the measures of central tendency, distribution, outliers, and the changing characteristics of data with time, an important characteristic of any data set is its variation or spread. In some data sets, the data values are concentrated closely near the mean; in others, the data values are more widely spread out from the mean.
The range, standard deviation, standard error, and variance are the different measures of variation.
Range: The range is the difference between its maximum and...
Variation01:19

Variation

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.
When independent and dependent variables are plotted on a scatter plot, the slope of a line is a value that describes the rate of change between the two...
Variance01:15

Variance

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 data.
Regression Toward the Mean01:52

Regression Toward the Mean

Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when researchers try to extrapolate results...

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The importance of being variable.

Douglas D Garrett1, Natasa Kovacevic, Anthony R McIntosh

  • 1Rotman Research Institute, Baycrest, Toronto, Ontario M6A 2E1, Canada. d.garrett@utoronto.ca

The Journal of Neuroscience : the Official Journal of the Society for Neuroscience
|March 25, 2011
PubMed
Summary
This summary is machine-generated.

Brain signal variability, not average activation, better reflects human aging. Younger, faster adults show higher brain variability, suggesting current brain measures may overlook key aging and cognition insights.

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Area of Science:

  • Neuroscience
  • Cognitive Neuroscience
  • Aging Research

Background:

  • Blood oxygen level-dependent (BOLD) signal variability is a potential biomarker for human aging.
  • Previous research suggests older brains are less variable than younger brains.
  • The relationship between BOLD variability and cognitive task performance remains unclear.

Purpose of the Study:

  • To investigate the relationship between BOLD signal variability, age, and cognitive performance.
  • To compare BOLD variability with traditional mean activation measures in aging research.
  • To determine if BOLD variability offers unique insights into brain function across the lifespan.

Main Methods:

  • Examined BOLD signal variability in healthy younger (20-30 years) and older (56-85 years) adults.
  • Assessed performance on three cognitive tasks: perceptual matching, attentional cueing, and delayed match-to-sample.
  • Analyzed reaction time speed and consistency alongside BOLD variability.

Main Results:

  • Younger, faster, and more consistent performers demonstrated significantly higher brain variability.
  • Older, slower, and less consistent adults exhibited lower brain variability.
  • BOLD variability and mean activation measures showed largely orthogonal spatial patterns, often with opposite effect directions.

Conclusions:

  • BOLD signal variability is a powerful index of cognitive function and aging, distinct from mean activation.
  • Relying solely on mean-based brain measures may lead to an underappreciation of aging, cognition, and brain function relationships.
  • BOLD variability provides a more nuanced understanding of brain function across the adult lifespan.