Disclosures/Conflicts Consulting: GE Healthcare Bayer Abbott Elan/Janssen Synarc Genentech Merck

ADNI PET Achievements Literature-defined prespecified ROIs Statistically defined ROIs Multivariate approaches to prediction of conversion/decline Cross-sectional and longitudinal PIB studies Biomarker comparisons (PIB-CSF)

Statistically Defined ROIs in AD and MCI for Longitudinal Progression AD MCI 12 month trial, 25% treatment effect (power = 0.8,  = 0.05, 2-tailed) 61 AD patients/arm 217 MCI patients/arm Chen et al, Neuroimage 2010

26 MCI patients with a higher HCI 71 MCI patients with a lower HCI 21 MCI patients with a smaller hippo vol 76 MCI patients with a larger hippo vol 20 MCI patients with both a higher HCI & smaller hippo vol 38 MCI patients with neither a higher HCI or smaller hippo vol Chen et al, submitted

Enrollment in ADNI PiB Studies to June 2010 ( All Data Are Available On The LONI Website) Baseline – 103 Subjects at 14 PET Sites NL: 19, 78±5 y/o, MMSE 29±1 MCI: 65, 75±8 y/o, MMSE 27±2 AD: 19, 73±9 y/o, MMSE 22±3 1 Yr Longitudinal Studies – 80 Subjects NL: 17/19 (89%) MCI: 50/65 (77%) AD: 13/19 (68%) PiB Baseline Entry Times 20 subjects at ADNI true baseline 69 subjects at ADNI 12 months 14 subjects at ADNI 24 months 3 Yr Longitudinal Studies – 2 Subjects NL: 2 MCI: 0 AD: 0 2 Yr Longitudinal Studies – 39 Subjects NL: 11 MCI: 26 AD: 2 Total 224 PiB Scans Mathis, Univ Pittsburgh

Baseline PiB Longitudinal PiB 9/19 Normals PiB+ 47/65 MCI PiB+ 17/19 AD PiB+ MCI Converters (1-2 years) 21/47 PiB+ 3/18 PiB- Mathis, Univ Pittsburgh

Extent of Hypometabolism as a Predictor of MCI Conversion Timing of conversion associated with more hypometabolic voxels Foster, Univ Utah

L Angular Gyrus R Angular Gyrus R Inf Temporal Gyrus L Inf Temporal Gyrus Post Cingulate Gyrus ROI Generation Identification of ROIs from voxelwise analyses in the literature Peak voxels plotted in MNI coordinates, smoothed, thresholded

Jagust et al, Neurology 2009

Landau et al, Neurology 2010 FDG AVLT Combined = 12 fold higher risk of conversion

Prediction of Cognitive Decline in Normal ADNI Participants Define normal/abnormal cutoffs using external samples Classification of each subject as normal/abnormal on each marker Determine whether normal/abnormal status predicts cognitive change

Participants 92 cognitively normal ADNI participants (FDG-PET, structural MRI, and ApoE genotyping) Mean followup2.7 +/- 0.8 yrs Age /- 4.8 yrs Education /- 3.2 yrs Female 39% ApoE4 carriers23% MMSE /- 1.1

FDG-PET (UC Berkeley) Alzheimer’s patients N = 35 Age = / % Female Normal older subjects N = 39 Age = / % Female Mean FDG ROI uptake (relative to cerebellum/vermis region) Sensitivity = 90% Specificity = 93%

Hippocampal volumes (UCSF) Alzheimer’s patients N = 51 Age = / % Female Normal older subjects N = 53 Age = / % Female Bilateral hippocampal volume (adjusted for total intracranial volume) Sensitivity = 94% Specificity = 95%

Normals stratified into high/low memory No association between high/low performer status and status on any of the normal/abnormal markers Neither group showed significant ADAS-cog change Median split of normals into high/low performers based on baseline performance on the Auditory Verbal Learning Test (free recall) Auditory Verbal Learning Test

FDG-PET imaging Baseline Hippocampal volume age, sex, education ApoE4 carrier status Parameter estimate p-value / ns / ADAS-cog decline Statistical analyses – multivariate Low performers Abnormal hipp volume and ApoE4 carriers  2.3 pts/yr decline relative to normal

Defining the Technical Sources of Variability in ADNI PET Data What is the effect of changing scanners in a longitudinal study? How variable are longitudinal measurements on different scanners? How does instrument variation compare to site variation? What is the effect of processing on variation?

Effects of Scanner Switch in a Longitudinal Study Rate of FDG Change (in ROI) NormalsMCIAD Stable Switch Stable

Variability by Scanner NormalMCIAD SD of Rate of Change HRRT

The Future: ADNI2 and GO Cross-sectional and longitudinal studies of A  deposition with AV-45 Comparison with other biomarkers in prediction/multivariate approaches Comparison with other biomarkers as outcomes Replication of statistical ROI approach using identical ROI Further investigate sources of variability

Acknowledgements Susan Landau Bob Koeppe Eric Reiman Kewei Chen Chet Mathis The ADNI Executive Committee, Site Investigators, Participants National Institute on Aging/Neil Buckholtz ISAB Alzheimer’s Association Julie Price Norman Foster Dan Bandy Danielle Harvey Norbert Schuff Mike Weiner