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 12 month trial, 25% treatment effect (power = 0.8, a = 0.05, 2-tailed) 61 AD patients/arm MCI 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 PiB Baseline Entry Times • NL: 19, 78±5 y/o, MMSE • 20 subjects at ADNI true baseline 29±1 • 69 subjects at ADNI 12 months • MCI: 65, 75±8 y/o, MMSE • 14 subjects at ADNI 24 months 27±2 1 Yr Longitudinal Studies – 80 Subjects • AD: 19, 73±9 y/o, MMSE • NL: 17/19 (89%) 22±3 • MCI: 50/65 (77%) • AD: 13/19 (68%) 2 Yr Longitudinal Studies – 39 Subjects Total 224 PiB Scans • NL: 11 • MCI: 26 • AD: 2 3 Yr Longitudinal Studies – 2 Subjects • NL: 2 • MCI: 0 • AD: 0 Mathis, Univ Pittsburgh Baseline PiB 9/19 Normals PiB+ 47/65 MCI PiB+ 17/19 AD PiB+ Longitudinal PiB MCI Converters (1-2 years) 21/47 PiB+ 3/18 PiBMathis, Univ Pittsburgh Extent of Hypometabolism as a Predictor of MCI Conversion Timing of conversion associated with more hypometabolic voxels Foster, Univ Utah ROI Generation Identification of ROIs from voxelwise analyses in the literature Peak voxels plotted in MNI coordinates, smoothed, thresholded Post Cingulate Gyrus L Inf Temporal Gyrus L Angular Gyrus R Angular Gyrus R Inf Temporal Gyrus Jagust et al, Neurology 2009 FDG AVLT Combined = 12 fold higher risk of conversion Landau et al, Neurology 2010 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 followup 2.7 +/- 0.8 yrs Age Education Female ApoE4 carriers MMSE 75.8 +/- 4.8 yrs 15.9 +/- 3.2 yrs 39% 23% 28.9 +/- 1.1 FDG-PET (UC Berkeley) Sensitivity = 90% Specificity = 93% Alzheimer’s patients N = 35 Age = 67.2 +/- 10.4 57% Female Normal older subjects N = 39 Age = 73.1 +/- 5.8 62% Female Mean FDG ROI uptake (relative to cerebellum/vermis region) Hippocampal volumes (UCSF) Sensitivity = 94% Specificity = 95% Alzheimer’s patients N = 51 Age = 78.6 +/- 8.5 43% Female Normal older subjects N = 53 Age = 74.3 +/- 7.5 53% Female Bilateral hippocampal volume (adjusted for total intracranial volume) Normals stratified into high/low memory No association between high/low performer status and status on any of the normal/abnormal markers Median split of normals into high/low performers based on baseline performance on the Auditory Verbal Learning Test (free recall) Neither group showed significant ADAS-cog change Auditory Verbal Learning Test Statistical analyses – multivariate Low performers Baseline Parameter estimate FDG-PET imaging p-value ns Hippocampal volume 1.31 +/- 0.58 0.03 ApoE4 carrier status 0.99 +/- 0.66 0.03 ADAS-cog decline age, sex, education 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) Stable Switch Stable Switch Normals MCI Stable Switch AD Variability by Scanner HRRT 2 2 6 16 7 SD of Rate of Change Normal MCI AD The Future: ADNI2 and GO Cross-sectional and longitudinal studies of Ab 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 Susan Landau Bob Koeppe Eric Reiman Kewei Chen Chet Mathis Julie Price Norman Foster Dan Bandy Danielle Harvey Norbert Schuff Mike Weiner Acknowledgements The ADNI Executive Committee, Site Investigators, Participants National Institute on Aging/Neil Buckholtz ISAB Alzheimer’s Association