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A z score (or standardized value) is measured in units of the standard deviation. It tells you how many standard deviations the value x is above (to the right of) or below (to the left of) the mean, μ. Values of x that are larger than the mean have positive z scores, and values of x that are smaller than the mean have negative z scores. If x equals the mean, then x has a zero z score. It is important to note that the mean of the z scores is zero, and the standard deviation is one.
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A z score (or standardized value) is measured in units of the standard deviation. It indicates how many standard deviations the value x is above (to the right of) or below (to the left of) the mean, μ. Values of x that are larger than the mean have positive z scores, and values of x that are smaller than the mean have negative z scores. If x equals the mean, then x has a zero z score. It is important to note that the mean of the z scores is zero, and the standard deviation is one.
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z scores are the standardized values obtained after converting a normal distribution into a standard normal distribution. A z score is measured in units of the standard deviation. The z score tells you how many standard deviations the value x is above (to the right of) or below (to the left of) the mean, μ. Values of x that are larger than the mean have positive z scores, and values of x that are smaller than the mean have negative z scores. If x equals the mean, then x has a z score of...
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The z score is one of the three measures of relative standing. It describes the location of a value in a dataset relative to the mean. z scores are obtained after the standardization of the values in a dataset. The z score for the mean is 0.
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Investigating Migraine-Like Behavior Using Light Aversion in Mice
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Proposal for a Migraine Aura Complexity Score.

Igor Petrusic1,2, Michele Viana3,4, Marko Dakovic2

  • 11 Faculty of Medicine, University of Belgrade, Belgrade, Serbia.

Cephalalgia : an International Journal of Headache
|November 22, 2018
PubMed
Summary
This summary is machine-generated.

A new Migraine Aura Complexity Score was developed to quantify aura symptoms. This score shows a positive correlation with cortical thickness, aiding in patient stratification for neuroimaging studies.

Keywords:
Migraine auracomplexitycortical thicknessdysphasicsomatosensoryvisual

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Area of Science:

  • Neurology
  • Neuroimaging
  • Medical Scoring Systems

Background:

  • Migraine aura complexity lacks a standardized assessment tool.
  • Current methods do not adequately synthesize the quantity and quality of aura symptoms.

Purpose of the Study:

  • To develop and validate a Migraine Aura Complexity Score (MACS).
  • To assess the applicability of MACS in neuroimaging research for migraine aura.

Main Methods:

  • Patient interviews to characterize migraine aura symptoms.
  • Exploratory and confirmatory analyses to establish the MACS.
  • Correlation of MACS with average cortical thickness using Surface-based Morphometric Analysis.

Main Results:

  • The study included 23 migraineurs with aura.
  • Confirmatory factor analysis supported a three-factor model for grading aura.
  • A significant positive correlation was observed between MACS and average cortical thickness in both hemispheres.

Conclusions:

  • The Migraine Aura Complexity Score is a valuable tool for assessing migraine aura.
  • MACS can facilitate patient stratification in neuroradiological studies of migraine.