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Related Concept Videos

Percentile01:18

Percentile

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A percentile indicates the relative standing of a data value when data are sorted into numerical order from smallest to largest. It represents the percentages of data values that are less than or equal to the pth percentile. For example, 15% of data values are less than or equal to the 15th percentile. Low percentiles always correspond to lower data values. High percentiles always correspond to higher data values.Percentiles divide ordered data into hundredths. To score in the...
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Quartile01:15

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Quartiles are numbers that separate the data into quarters. Quartiles may or may not be part of the data. To find the quartiles, first, find the median or second quartile. The first quartile, Q1, is the middle value of the lower half of the data, and the third quartile, Q3, is the middle value, or median, of the upper half of the data. To get the idea, consider the same data set:
1; 1; 2; 2; 4; 6; 6.8; 7.2; 8; 8.3; 9; 10; 10; 11.5
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Review and Preview01:10

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In statistics, several tools are used to interpret the data. Measures of central tendency represent the characteristics of the data, such as mean, median, and mode. Additionally, measures of variance like standard deviation and range are used to find the spread of data from the mean. Relative standing measures the distance between data locations. Commonly used measures of relative standings are percentile, z score, and quartiles.
Percentiles are a type of fractile that partition data into...
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Ranks01:02

Ranks

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Unlike parametric methods, nonparametric statistics are ideal for nominal and ordinal data, requiring fewer assumptions about the population's nature or distribution. This makes nonparametric methods easier to apply and interpret, as they do not depend on parameters like mean or standard deviation. One common approach in nonparametric analysis is to sort data according to a specific criterion. For instance, we might arrange weather data from hottest to coldest days in a month or rank cities...
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Whole-brain Segmentation and Change-point Analysis of Anatomical Brain MRI—Application in Premanifest Huntington's Disease
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Quantile rank maps: a new tool for understanding individual brain development.

Huaihou Chen1, Clare Kelly2, F Xavier Castellanos3

  • 1Department of Biostatistics, College of Public Health & Health Professions, College of Medicine, University of Florida, Gainesville, FL, USA.

Neuroimage
|January 14, 2015
PubMed
Summary

We developed a new neurodevelopmental brain mapping technique to compare individual brain data against age norms. This method generates brain maps showing age-dependent quantile ranks, potentially aiding in clinical screening for developmental differences.

Keywords:
Box–Cox transformationGeneralized additive models for location, scale and shapeMRIPenalized B-splinesQuantile rank mapResting-state fMRI

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Area of Science:

  • Neuroscience
  • Developmental Biology
  • Medical Imaging

Background:

  • Accurate neurodevelopmental brain mapping is crucial for understanding typical development and identifying atypical patterns.
  • Existing methods may lack the precision to capture age-specific normative variations in brain structure or function.
  • Clinical screening for neurodevelopmental disorders requires sensitive and specific tools.

Purpose of the Study:

  • To introduce a novel method for neurodevelopmental brain mapping.
  • To enable comparison of an individual's brain metrics against age-specific norms.
  • To develop tools for potential clinical screening applications.

Main Methods:

  • Estimating smoothly age-varying distributions in brain regions of interest.
  • Deriving age-dependent, region-wise quantile ranks for individuals.
  • Generating brain maps of these quantile ranks.
  • Proposing bootstrap-based confidence intervals for quantile rank estimates.
  • Introducing a recalibrated Kolmogorov-Smirnov test for group differences in age-varying distributions.

Main Results:

  • The proposed method generates individual-specific neurodevelopmental brain maps based on age norms.
  • Quantile rank maps provide a visual representation of how an individual's brain metrics compare to peers.
  • The recalibrated Kolmogorov-Smirnov test demonstrates robustness and improved detection of group differences compared to linear regression.
  • Methods were successfully applied to real-world neuroimaging datasets (NKI Rockland Sample, ABIDE).

Conclusions:

  • The novel brain mapping technique offers a promising approach for assessing neurodevelopmental trajectories.
  • Quantile rank maps have potential utility in clinical settings for early identification of developmental variations.
  • The enhanced statistical tests provide more reliable group comparisons in developmental neuroimaging studies.