Supplementary Material for: Performance of FDG PET for Detection of Alzheimer’s Disease in Two Independent Multicentre Samples (NEST-DD and ADNI)

  • Cathleen Haense (Contributor)
  • Karl Herholz (Contributor)
  • William J. Jagust (Contributor)
  • Wolf Dieter Heiss (Contributor)



<i>Aim:</i> We investigated the performance of FDG PET using an automated procedure for discrimination between Alzheimer’s disease (AD) and controls, and studied the influence of demographic and technical factors. <i>Methods:</i> FDG PET data were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) [102 controls (76.0 ± 4.9 years) and 89 AD patients (75.7 ± 7.6 years, MMSE 23.5 ± 2.1) and the Network for Standardisation of Dementia Diagnosis (NEST-DD) [36 controls (62.2 ± 5.0 years) and 237 AD patients (70.8 ± 8.3 years, MMSE 20.9 ± 4.4). The procedure created t-maps of abnormal voxels. The sum of t-values in predefined areas that are typically affected by AD (AD t-sum) provided a measure of scan abnormality associated with a preset threshold for discrimination between patients and controls. <i>Results:</i> AD patients had much higher AD t-sum scores compared to controls (p < 0.01), which were significantly related to dementia severity (ADNI: r = –0.62, p < 0.01; NEST-DD: r = –0.59, p < 0.01). Early-onset AD patients had significantly higher AD t-sum scores than late-onset AD patients (p < 0.01). Differences between databases were mainly due to different age distributions. The predefined AD t-sum threshold yielded a sensitivity and specificity of 83 and 78% in ADNI and 78 and 94% in NEST-DD, respectively. <i>Conclusion:</i> The automated FDG PET analysis procedure provided good discrimination power, and was most accurate for early-onset AD.
Date made available25 Sept 2009

Research Beacons, Institutes and Platforms

  • Manchester Institute for Collaborative Research on Ageing

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