Abstract
Weight-loss is an integral part of Huntington’s disease (HD) that can start before the onset of motor symptoms. Investigating the underlying pathological processes may help in the understanding of this devastating disease as well as contribute to its management. However, the complex behavior and associations of multiple biological factors is impractical to be interpreted by the conventional statistics or human experts. For the first time, we combine a clinical dataset, expert knowledge and machine intelligence to model the multi-dimensional associations between the potentially relevant factors and weight-loss activity in HD, specifically at the premanifest stage. The HD dataset is standardized and transformed into required knowledge base with the help of clinical HD experts, which is then processed by the class rule mining and self-organising maps to identify the significant associations. Statistical results and experts’ report indicate a strong association between severe weight-loss in HD at the premanifest stage and measures of certain cognitive, psychiatric functional ability factors. These results suggest that the mechanism underlying weight-loss in HD is, at least partly related to dysfunction of certain areas of the brain, a finding that may have not been apparent otherwise. These associations will aid the understanding of the pathophysiology of the disease and its progression and may in turn help in HD treatment trials.
Highlights
Huntington’s disease (HD) is a devastating hereditary neurodegenerative disorder that results in cognitive and neuropsychiatric abnormalities years before the motor issues start to appear, on which the clinical diagnosis is based [1,2,3]
The Class Association Rules (CARs) algorithm is used with the parametric configurations and rule filtration explained in Section 2.3 while considering the listed factors as antecedents and target wtCat as consequent for both pMan-HD and fCont-HD
Weight-loss in Huntington’s disease at early stages measures to analyse the complex associations and dependencies between combined multiple factors at discrete level that can be understandable by humans. It has been established based on large scale studies such as PREDICT-HD and TRACK-HD [10,11] that HD’s non-motor factors start years before the onset of motor symptoms that defines the diagnosis of HD
Summary
Huntington’s disease (HD) is a devastating hereditary neurodegenerative disorder that results in cognitive and neuropsychiatric abnormalities years before the motor issues start to appear, on which the clinical diagnosis is based [1,2,3]. This is called the premanifest stage of the disease. Various neuroimaging studies indicated that in the premanifest-HD stage, patients show redundant brain area recruitments. Functional magnetic resonance imaging (fMRI) studies demonstrated abnormalities in cognitive domains in premanifest subjects when compared with the healthy controls in certain areas such as response inhibition [4], verbal memory [5], reward processing [6] and spatial working memory [7].
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