4.5 Article

Striatal and white matter predictors of estimated diagnosis for Huntington disease

期刊

BRAIN RESEARCH BULLETIN
卷 82, 期 3-4, 页码 201-207

出版社

PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.brainresbull.2010.04.003

关键词

Huntington disease; MRI; Clinical trials; Observational study

资金

  1. National Institutes for Health
  2. National Institute of Neurological Disorders and Stroke [NS40068]
  3. CHDI Foundation, Inc

向作者/读者索取更多资源

Previous MRI studies with participants prior to manifest Huntington disease have been conducted in small single-site samples. The current study reports data from a systematic multi-national study during the prodromal period of Huntington disease and examines whether various brain structures make unique predictions about the proximity to manifest disease. MRI scans were acquired from 657 participants enrolled at 1 of 32 PREDICT-HD research sites. Only prodromal Huntington disease participants (those not meeting motor criteria for diagnosis) were included and subgrouped by estimated diagnosis proximity (Near, Mid, and Far) based upon a formula incorporating age and CAG-repeat length. Results show volumes of all three subgroups differed significantly from Controls for total brain tissue, cerebral spinal fluid, white matter, cortical gray matter, thalamus, caudate, and putamen. Total striatal volume demonstrated the largest differences between Controls and all three prodromal subgroups. Cerebral white matter offered additional independent power in the prediction of estimated proximity to diagnosis. In conclusion, this large cross-sectional study shows that changes in brain volume are detectable years to decades prior to estimated motor diagnosis of Huntington disease. This suggests that a clinical trial of a putative neuroprotective agent could begin as much as 15 years prior to estimated motor diagnosis in a cohort of persons at risk for but not meeting clinical motor diagnostic criteria for Huntington disease, and that neuroimaging (striatal and white matter volumes) may be among the best predictors of diagnosis proximity. (C) 2010 Elsevier Inc. All rights reserved.

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