The quantitative analysis of neurodegenerative disease: Classification, noda, constellations, and multivariate geometry

Richard A Armstrong

Research output: Contribution to journalReview article

Abstract

A  variety of methods are available for the quantitative description and analysis of neurodegenerative disease.
If neurodegenerative disease exists as a series of distinct disorders, then classificatory methods such as hierarchical
cluster analysis (HCA) and decision tree analysis (DTA) can be used to classify cases into groups more objectively.
If neurodegenerative disease consists of overlapping phenotypes, then the Braun-Blanquet ‘nodal’ system and ‘constellation
diagrams’ implicitly recognise intermediate cases and reveal their relationships to the main groupings.
By contrast, if cases are more continuously distributed without easily distinguishable disease entities, then methods
based on spatial geometry, such as a triangular system or principal components analysis (PCA), may be more appropriate
as they display cases spatially according to their similarities and differences. This review compares the different
methods and concludes that as a result of the heterogeneity and overlap commonly present plus the multiplicity
of possible descriptive variables, methods such as PCA are likely to be particularly useful in the quantitative analysis
of neurodegenerative disease. A more general application of such methods, however, has implications for studies
of disease risk factors and pathogenesis and in clinical trials.
LanguageEnglish
Pages1-13
JournalFolia Neuropathologica
Volume56
Issue number1
DOIs
Publication statusPublished - 1 May 2018

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Neurodegenerative Diseases
Principal Component Analysis
Decision Trees
Decision Support Techniques
Clinical Trials
Phenotype

Bibliographical note

Copyright: © 2018 Mossakowski Medical Research Centre Polish Academy of Sciences and the Polish Association of Neuropathologists. This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) License (http://creativecommons.org/licenses/by-nc-sa/4.0/), allowing third parties to copy and redistribute the material in any medium or format and to remix, transform, and build upon the material, provided the original work is properly cited and states its license.

Cite this

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abstract = "A  variety of methods are available for the quantitative description and analysis of neurodegenerative disease.If neurodegenerative disease exists as a series of distinct disorders, then classificatory methods such as hierarchicalcluster analysis (HCA) and decision tree analysis (DTA) can be used to classify cases into groups more objectively.If neurodegenerative disease consists of overlapping phenotypes, then the Braun-Blanquet ‘nodal’ system and ‘constellationdiagrams’ implicitly recognise intermediate cases and reveal their relationships to the main groupings.By contrast, if cases are more continuously distributed without easily distinguishable disease entities, then methodsbased on spatial geometry, such as a triangular system or principal components analysis (PCA), may be more appropriateas they display cases spatially according to their similarities and differences. This review compares the differentmethods and concludes that as a result of the heterogeneity and overlap commonly present plus the multiplicityof possible descriptive variables, methods such as PCA are likely to be particularly useful in the quantitative analysisof neurodegenerative disease. A more general application of such methods, however, has implications for studiesof disease risk factors and pathogenesis and in clinical trials.",
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The quantitative analysis of neurodegenerative disease : Classification, noda, constellations, and multivariate geometry. / Armstrong, Richard A.

In: Folia Neuropathologica, Vol. 56, No. 1, 01.05.2018, p. 1-13.

Research output: Contribution to journalReview article

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