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Predicting clinical diagnosis in Huntington's disease: An imaging polymarker.

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Mason, Sarah L 
Daws, Richard E 
Soreq, Eyal 
Johnson, Eileanoir B 
Scahill, Rachael I 


OBJECTIVE: Huntington's disease (HD) gene carriers can be identified before clinical diagnosis; however, statistical models for predicting when overt motor symptoms will manifest are too imprecise to be useful at the level of the individual. Perfecting this prediction is integral to the search for disease modifying therapies. This study aimed to identify an imaging marker capable of reliably predicting real-life clinical diagnosis in HD. METHOD: A multivariate machine learning approach was applied to resting-state and structural magnetic resonance imaging scans from 19 premanifest HD gene carriers (preHD, 8 of whom developed clinical disease in the 5 years postscanning) and 21 healthy controls. A classification model was developed using cross-group comparisons between preHD and controls, and within the preHD group in relation to "estimated" and "actual" proximity to disease onset. Imaging measures were modeled individually, and combined, and permutation modeling robustly tested classification accuracy. RESULTS: Classification performance for preHDs versus controls was greatest when all measures were combined. The resulting polymarker predicted converters with high accuracy, including those who were not expected to manifest in that time scale based on the currently adopted statistical models. INTERPRETATION: We propose that a holistic multivariate machine learning treatment of brain abnormalities in the premanifest phase can be used to accurately identify those patients within 5 years of developing motor features of HD, with implications for prognostication and preclinical trials. Ann Neurol 2018;83:532-543.



Adult, Aged, Cohort Studies, Female, Humans, Huntington Disease, Magnetic Resonance Imaging, Male, Middle Aged, Predictive Value of Tests

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Ann Neurol

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SLM is funded by a National Institute for Health Research (NIHR) Translational Research Collaboration for Rare Diseases fellowship. This research has been funded/supported by the National Institute for Health Research Rare Diseases Translational Research Collaboration (NIHR RD-TRC). The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR or the Department of Health. RAB is funded by the NIHR Cambridge Biomedical Research Centre and the Cambridge University NHS Foundation Trust. RED is employed on an EC Marie-Curie CIG, awarded to AH, SJT, EJ and RS receive funding from a Wellcome Collaborative Award (200181/Z/15/Z)