{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T16:41:26Z","timestamp":1790268086609,"version":"4.1.0"},"reference-count":48,"publisher":"Wiley","issue":"5","license":[{"start":{"date-parts":[[2018,4,2]],"date-time":"2018-04-02T00:00:00Z","timestamp":1522627200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100005725","name":"CHDI Foundation","doi-asserted-by":"publisher","award":["A\u20109856"],"award-info":[{"award-number":["A\u20109856"]}],"id":[{"id":"10.13039\/100005725","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Ann Clin Transl Neurol"],"published-print":{"date-parts":[[2018,5]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Objective<\/jats:title><jats:p>Determining the sequence in which Huntington's disease biomarkers become abnormal can provide important insights into the disease progression and a quantitative tool for patient stratification. Here, we construct and present a uniquely fine\u2010grained model of temporal progression of Huntington's disease from premanifest through to manifest stages.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>We employ a probabilistic event\u2010based model to determine the sequence of appearance of atrophy in brain volumes, learned from structural <jats:styled-content style=\"fixed-case\">MRI<\/jats:styled-content> in the Track\u2010<jats:styled-content style=\"fixed-case\">HD<\/jats:styled-content> study, as well as to estimate the uncertainty in the ordering. We use longitudinal and phenotypic data to demonstrate the utility of the patient staging system that the resulting model provides.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>The model recovers the following order of detectable changes in brain region volumes: putamen, caudate, pallidum, insula white matter, nonventricular cerebrospinal fluid, amygdala, optic chiasm, third ventricle, posterior insula, and basal forebrain. This ordering is mostly preserved even under cross\u2010validation of the uncertainty in the event sequence. Longitudinal analysis performed using 6\u00a0years of follow\u2010up data from baseline confirms efficacy of the model, as subjects consistently move to later stages with time, and significant correlations are observed between the estimated stages and nonimaging phenotypic markers.<\/jats:p><\/jats:sec><jats:sec><jats:title>Interpretation<\/jats:title><jats:p>We used a data\u2010driven method to provide new insight into Huntington's disease progression as well as new power to stage and predict conversion. Our results highlight the potential of disease progression models, such as the event\u2010based model, to provide new insight into Huntington's disease progression and to support fine\u2010grained patient stratification for future precision medicine in Huntington's disease.<\/jats:p><\/jats:sec>","DOI":"10.1002\/acn3.558","type":"journal-article","created":{"date-parts":[[2018,4,2]],"date-time":"2018-04-02T14:11:20Z","timestamp":1522678280000},"page":"570-582","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":59,"title":["An image\u2010based model of brain volume biomarker changes in Huntington's disease"],"prefix":"10.1002","volume":"5","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4885-6241","authenticated-orcid":false,"given":"Peter A.","family":"Wijeratne","sequence":"first","affiliation":[{"name":"Department of Computer Science Centre for Medical Image Computing University College London Gower Street London WC1E 6BT 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