Fluorodeoxyglucose positron emission tomography of mild cognitive impairment with clinical follow-up at 3 years

José V. Pardo, Joel T. Lee, Michael A. Kuskowski, Kristin R. Munch, John V. Carlis, Sohail A. Sheikh, Christa Surerus, Scott M. Lewis, J. Riley McCarten, Howard Fink, Susan McPherson, Hemant H. Shah, Susan Rottunda, Maurice W. Dysken

Research output: Contribution to journalArticlepeer-review

15 Scopus citations


Background: Alzheimer's disease (AD) is the most common dementing illness. Development of effective treatments directed at AD requires an early diagnosis. Mild cognitive impairment (MCI) often heralds AD. Thus, characterizing MCI is fundamental to the early diagnosis of AD. Methods: 19 MCI patients referred from a memory loss clinic and 27 healthy subjects, all followed up for 3 years. Metabolism scans (MCI minus controls) were compared voxel-wise after anatomic normalization and were examined both visually and with a computerized classifier. Results: Agreement between raters as to whether the individual scans were normal or abnormal was high. Agreement between raters of the eventual clinical diagnosis and baseline metabolic pattern was poor. A computerized classifier was unsuccessful at classifying MCI from normal; however, its performance improved when using only prototypic AD-like MCI scans, indicating the classifier worked well when shared patterns existed in the data. Outcomes on follow-up were nine of 19 AD, five of 19 remained MCI, and five of 19 developed dementias other than AD. Both MCI cases of early Lewy body dementia (LBD) showed an AD-like metabolic pattern. Conclusions: Visual inspection proved reliable in determining normal from abnormal scans, but it proved unreliable at predicting diagnosis on follow-up. Computerized classification of MCI by using an AD-like metabolic template (such as derived from the averaged MCI images) showed potential to identify patients who will develop AD. However, the metabolic pattern in early LBD did not differ from that in AD.

Original languageEnglish (US)
Pages (from-to)326-333
Number of pages8
JournalAlzheimer's and Dementia
Issue number4
StatePublished - Jul 2010

Bibliographical note

Funding Information:
This work was supported by NIA RO1 AG120852 and the Alzheimer's Association . We thank Dr Satoshi Minoshima (University of Washington, Seattle, WA) for enabling the use of Neurostat. We thank our volunteers for participating in this research.


  • Aging
  • Alzheimer, Frontotemporal dementia
  • Brain metabolism
  • Classification
  • Diagnosis
  • Lewy body
  • MCI
  • PET
  • Support vector machine


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