Disclosures/Conflicts Consulting: GE Healthcare Bayer Abbott Elan/Janssen Synarc Genentech Merck.
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Transcript of Disclosures/Conflicts Consulting: GE Healthcare Bayer Abbott Elan/Janssen Synarc Genentech Merck.
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Disclosures/Conflicts
Consulting:
GE Healthcare
Bayer
Abbott
Elan/Janssen
Synarc
Genentech
Merck
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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)
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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
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26 MCI patients with a higher HCI71 MCI patients with a lower HCI
21 MCI patients with a smaller hippo vol76 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
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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
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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
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Extent of Hypometabolism as a Predictor of MCI Conversion
Timing of conversion
associated with more
hypometabolic voxels
Foster, Univ Utah
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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
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Jagust et al, Neurology 2009
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Landau et al, Neurology 2010
FDG AVLT
Combined = 12 fold higher risk of conversion
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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
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Participants
92 cognitively normal ADNI participants (FDG-PET, structural MRI, and ApoE genotyping)
Mean followup 2.7 +/- 0.8 yrs
Age 75.8 +/- 4.8 yrsEducation 15.9 +/- 3.2 yrsFemale 39%ApoE4 carriers 23%MMSE 28.9 +/- 1.1
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FDG-PET (UC Berkeley)
Alzheimer’s patients
N = 35Age = 67.2 +/- 10.4
57% Female
Normal older subjects
N = 39Age = 73.1 +/- 5.8
62% Female
Mean FDG ROI uptake (relative to cerebellum/vermis region)
Sensitivity = 90%Specificity = 93%
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Hippocampal volumes (UCSF)
Alzheimer’s patients
N = 51Age = 78.6 +/- 8.5
43% Female
Normal older subjects
N = 53Age = 74.3 +/- 7.5
53% Female
Bilateral hippocampal volume (adjusted for total intracranial volume)
Sensitivity = 94%Specificity = 95%
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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
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FDG-PET imaging
Baseline
Hippocampal volume
age, sex, education
ApoE4 carrier status
Parameter estimate p-value
1.31 +/- 0.58
ns
0.03
0.99 +/- 0.66 0.03
ADAS-cog decline
Statistical analyses – multivariateLow performers
Abnormal hipp volume and ApoE4 carriers 2.3 pts/yr decline relative to normal
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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?
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Effects of Scanner Switch in a Longitudinal Study
Rate of FDG Change (in ROI)
Normals MCI AD
Stable Switch Switch SwitchStable Stable
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Variability by Scanner
Normal MCI AD
SD of Rate of Change
HRRT
616
7
2
2
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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
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AcknowledgementsSusan LandauBob KoeppeEric ReimanKewei ChenChet Mathis
The ADNI Executive Committee, Site Investigators, ParticipantsNational Institute on Aging/Neil Buckholtz
ISAB Alzheimer’s Association
Julie PriceNorman FosterDan BandyDanielle HarveyNorbert SchuffMike Weiner