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

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.