Abstract
This paper presents homogeneous clusters of patients, identified in the Alzheimer’s Disease Neuroimaging Initiative (ADNI) data population of 317 females and 342 males, described by a total of 243 biological and clinical descriptors. Clustering was performed with a novel methodology, which supports identification of patient subpopulations that are homogeneous regarding both clinical and biological descriptors. Properties of the constructed clusters clearly demonstrate the differences between female and male Alzheimer’s disease patient groups. The major difference is the existence of two male subpopulations with unexpected values of intracerebral and whole brain volumes.
Highlights
A key issue in understanding of the Alzheimer’s disease (AD) is the recognition of relations between clinical characteristics of patients and their biological properties that can be objectively measured
Some recent studies [1] suggest the existence of different AD subtypes, and it may be expected that the identification of relevant relations is potentially easier for AD subtypes than for the complete AD population
These clusters are currently considered as potentially interesting hypotheses; their future verification on independent data might lead to new scientific insights and potentially useful medical knowledge
Summary
A key issue in understanding of the Alzheimer’s disease (AD) is the recognition of relations between clinical characteristics of patients and their biological properties that can be objectively measured. As a result—besides some clusters that are in agreement with the existing domain knowledge—we have surprisingly identified some additional clusters that are hard to evaluate These clusters are currently considered as potentially interesting hypotheses; their future verification on independent data might lead to new scientific insights and potentially useful medical knowledge. The clusters are described in terms of statistical properties of patients included into each cluster, together with the complete list of identification numbers of corresponding patients. In this way, the interested reader may access additional information about specific patients from the ADNI database.
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