2 Yr Longitudinal Studies
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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
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