Voxel-based analysis of amyloid positron emission tomography probe [C]BF-227 uptake in mild cognitive impairment and alzheimer's disease

He Shao, Nobuyuki Okamura, Kentaro Sugi, Shozo Furumoto, Katsutoshi Furukawa, Manabu Tashiro, Ren Iwata, Hiroshi Matsuda, Yukitsuka Kudo, Hiroyuki Arai, Hiroshi Fukuda, Kazuhiko Yanai

Research output: Contribution to journalArticle

12 Citations (Scopus)

Abstract

Aim: To determine early brain changes in the distribution of an amyloid positron emission tomography (PET) probe, 11C-labeled BF-227 or [11C]BF-227, in order to accurately predict the progression of mild cognitive impairment (MCI) to Alzheimer's disease (AD). Patients and Methods: Amyloid plaque burden was evaluated using [11C]BF-227 PET in AD, MCI and aged normal controls. A voxel-based analysis of [11C]BF-227 PET images was performed to characterize the culprit brain lesion in patients with MCI who were destined to progress to AD, referred to as MCI converters (MCI-C). In addition, binding characteristics of BF-227 to amyloid deposits were examined using postmortem AD brain samples. Results: Voxel-based statistical analyses of the BF-227 PET images clearly demonstrated an abnormal distribution of BF-227 mainly in the posterior association area in MCI-C and patients with AD. BF-227 uptake in the lateral temporal cortex was consistently observed in almost all MCI-C and patients with AD, and it distinguished MCI-C from MCI nonconverters. BF-227 binding strongly correlated with dense amyloid-β protein plaque density, but not with diffuse plaque density in the frontal cortex. Conclusion: BF-227 uptake in the lateral temporal cortex is a reliable indicator that can be used for predicting prognosis in patients with MCI.

Original languageEnglish
Pages (from-to)101-111
Number of pages11
JournalDementia and geriatric cognitive disorders
Volume30
Issue number2
DOIs
Publication statusPublished - 2010 Sep

Keywords

  • Alzheimer's disease
  • Amyloid
  • Mild cognitive impairment
  • Positron emission tomography

ASJC Scopus subject areas

  • Geriatrics and Gerontology
  • Cognitive Neuroscience
  • Psychiatry and Mental health

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