4.6 Article

The Cortical Signature of Alzheimer's Disease: Regionally Specific Cortical Thinning Relates to Symptom Severity in Very Mild to Mild AD Dementia and is Detectable in Asymptomatic Amyloid-Positive Individuals

期刊

CEREBRAL CORTEX
卷 19, 期 3, 页码 497-510

出版社

OXFORD UNIV PRESS INC
DOI: 10.1093/cercor/bhn113

关键词

Alzheimer's disease; cerebral cortex; magnetic resonance imaging; medial temporal lobe; parietal cortex

资金

  1. National Institute on Aging [K23-AG22509, P50AG05134, P50-AG05681, P01-AG03991, R01-AG29411, R21-AG29840]
  2. National Institute of Neurological Disorders and Stroke [R01-NS042861]
  3. National Center for Research Resources [P41-RR14075, U24-RR021382]
  4. Alzheimer's Association
  5. Howard Hughes Medical Institute
  6. Mental Illness and Neuroscience Discovery Institute

向作者/读者索取更多资源

Alzheimer's disease (AD) is associated with neurodegeneration in vulnerable limbic and heteromodal regions of the cerebral cortex, detectable in vivo using magnetic resonance imaging. It is not clear whether abnormalities of cortical anatomy in AD can be reliably measured across different subject samples, how closely they track symptoms, and whether they are detectable prior to symptoms. An exploratory map of cortical thinning in mild AD was used to define regions of interest that were applied in a hypothesis-driven fashion to other subject samples. Results demonstrate a reliably quantifiable in vivo signature of abnormal cortical anatomy in AD, which parallels known regional vulnerability to AD neuropathology. Thinning in vulnerable cortical regions relates to symptom severity even in the earliest stages of clinical symptoms. Furthermore, subtle thinning is present in asymptomatic older controls with brain amyloid binding as detected with amyloid imaging. The reliability and clinical validity of AD-related cortical thinning suggests potential utility as an imaging biomarker. This disease signature approach to cortical morphometry, in which disease effects are mapped across the cortical mantle and then used to define ROIs for hypothesis-driven analyses, may provide a powerful methodological framework for studies of neuropsychiatric diseases.

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