{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,7]],"date-time":"2026-10-07T17:08:32Z","timestamp":1791392912548,"version":"4.3.3"},"reference-count":33,"publisher":"Oxford University Press (OUP)","issue":"4","license":[{"start":{"date-parts":[[2017,2,9]],"date-time":"2017-02-09T00:00:00Z","timestamp":1486598400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["NS073671"],"award-info":[{"award-number":["NS073671"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["NS082062"],"award-info":[{"award-number":["NS082062"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2017,12,1]]},"abstract":"<jats:title>Summary<\/jats:title><jats:p>Precise modeling of disease progression in neurodegenerative disorders may enable early intervention before clinical manifestation of a disease, which is crucial since early intervention at the premanifest stage is expected to be more effective. Neuroimaging biomarkers are indicative of the underlying disease pathology and may be used to predict future disease occurrence at the premanifest stage. As observed in many pivotal studies, longitudinal measurements of clinical outcomes, such as motor or cognitive symptoms, often present nonlinear sigmoid shapes over time, where the inflection points of the trajectories mark a meaningful time in disease progression. Therefore, to identify neuroimaging biomarkers predicting disease progression, we propose a nonlinear mixed effects model based on a sigmoid function to predict longitudinal clinical outcomes, and associate a linear combination of neuroimaging biomarkers with subject-specific inflection points. Based on an expectation-maximization (EM) algorithm, we propose a method that can fit a nonlinear model with many potentially correlated biomarkers for random inflection points while achieving computational stability. Variable selection is introduced in the algorithm in order to identify important biomarkers of disease progression and to reduce prediction variability. We apply the proposed method to the data from the Predictors of Huntington's Disease study to select brain subcortical regional volumes predictive of the inflection points of the motor and cognitive function trajectories. Our results reveal that brain atrophy in the striatum and expansion of the ventricular system are highly predictive of the inflection points. Furthermore, these inflection points may precede clinically defined disease onset by as early as a decade and thus may be useful biomarkers as early signs of Huntington's Disease onset.<\/jats:p>","DOI":"10.1111\/biom.12663","type":"journal-article","created":{"date-parts":[[2017,2,9]],"date-time":"2017-02-09T21:43:09Z","timestamp":1486676589000},"page":"1343-1354","source":"Crossref","is-referenced-by-count":7,"title":["Penalized Nonlinear Mixed Effects Model to Identify Biomarkers That Predict Disease Progression"],"prefix":"10.1093","volume":"73","author":[{"given":"Huaihou","family":"Chen","sequence":"first","affiliation":[{"name":"Department of Biostatistics, University of Florida , Gainesville, Florida 32611 , U.S.A."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Donglin","family":"Zeng","sequence":"additional","affiliation":[{"name":"Department of Biostatistics, University of North Carolina , Chapel Hill, North Carolina 27599-7420 , U.S.A."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuanjia","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Biostatistics, Mailman School of Public Health, Columbia University , New York, New York 10032 , U.S.A."}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2017,2,9]]},"reference":[{"key":"2024011603282243800_biom12663-bib-0001","doi-asserted-by":"crossref","first-page":"1093","DOI":"10.1093\/brain\/awh451","article-title":"The 9 year cognitive decline before dementia of the Alzheimer type: A prospective population-based study","volume":"128","author":"Amieva","year":"2005","journal-title":"Brain"},{"key":"2024011603282243800_biom12663-bib-0002","doi-asserted-by":"crossref","first-page":"75","DOI":"10.3389\/fnagi.2013.00012","article-title":"Refining the diagnosis of Huntington disease: The PREDICT-HD study","volume":"5","author":"Biglan","year":"2013","journal-title":"Frontiers in Aging Neuroscience"},{"key":"2024011603282243800_biom12663-bib-0003","doi-asserted-by":"crossref","first-page":"1069","DOI":"10.1111\/j.1541-0420.2010.01391.x","article-title":"Joint variable selection for fixed and random effects in linear mixed-effects models","volume":"66","author":"Bondell","year":"2010","journal-title":"Biometrics"},{"key":"2024011603282243800_biom12663-bib-0004","doi-asserted-by":"crossref","first-page":"237","DOI":"10.1007\/s00422-010-0401-y","article-title":"What do the basal ganglia do? 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