{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,26]],"date-time":"2026-09-26T06:12:37Z","timestamp":1790403157176,"version":"4.1.0"},"reference-count":36,"publisher":"Wiley","issue":"3","license":[{"start":{"date-parts":[[2018,3,13]],"date-time":"2018-03-13T00:00:00Z","timestamp":1520899200000},"content-version":"vor","delay-in-days":12,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100000272","name":"National Institute for Health Research","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100000272","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100006662","name":"NIHR","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100006662","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100004440","name":"Wellcome Trust","doi-asserted-by":"publisher","award":["200181\/Z\/15\/Z"],"award-info":[{"award-number":["200181\/Z\/15\/Z"]}],"id":[{"id":"10.13039\/100004440","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Annals of Neurology"],"published-print":{"date-parts":[[2018,3]]},"abstract":"<jats:sec><jats:title>Objective<\/jats:title><jats:p>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\u2010life clinical diagnosis in HD.<\/jats:p><\/jats:sec><jats:sec><jats:title>Method<\/jats:title><jats:p>A multivariate machine learning approach was applied to resting\u2010state 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\u2010group comparisons between preHD and controls, and within the preHD group in relation to \u201cestimated\u201d and \u201cactual\u201d proximity to disease onset. Imaging measures were modeled individually, and combined, and permutation modeling robustly tested classification accuracy.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>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.<\/jats:p><\/jats:sec><jats:sec><jats:title>Interpretation<\/jats:title><jats:p>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\u2013543<\/jats:p><\/jats:sec>","DOI":"10.1002\/ana.25171","type":"journal-article","created":{"date-parts":[[2018,2,5]],"date-time":"2018-02-05T16:32:46Z","timestamp":1517848366000},"page":"532-543","source":"Crossref","is-referenced-by-count":40,"title":["Predicting clinical diagnosis in Huntington's disease: An imaging polymarker"],"prefix":"10.1002","volume":"83","author":[{"given":"Sarah L.","family":"Mason","sequence":"first","affiliation":[{"name":"John Van Geest Centre for Brain Repair University of Cambridge  United Kingdom"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Richard E.","family":"Daws","sequence":"additional","affiliation":[{"name":"The Computational, Cognitive &amp; Clinical Neuroimaging Laboratory (CNL), Division of Brain Sciences Imperial College London  United Kingdom"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Eyal","family":"Soreq","sequence":"additional","affiliation":[{"name":"The Computational, Cognitive &amp; 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