M. Weiner, P. Aisen, R Peterson, C. Jack, W. Jagust, J Trojanowski,
L. Shaw, A. Toga, L. Beckett, D. Harvey, C Mathis, A. Gamst. R.
Green.A Saykin, S. Potkin, J Morris, L Thal (D)
Neil Buckholz, David Lee, Holly Soares
Industry Scientific Advisory Board (ISAB)
And Site PIs, Study Coordinators and 821 subjects enrolled in 58
Sites in US and Canada
FUNDED BY NATIONAL INSTITUTE ON AGING
ADNINaturalistic study of AD progression
• 200 NORMAL 3 yrs
• 400 MCI 3 yrs
• 200 AD 2 yrs
• Visits every 6 months
• 57 sites
• Clinical, blood, LP
• Cognitive Tests
• 1.5T MRI
Some also have
• 3.0T MRI (25%)
• FDG-PET (50%)
• PiB-PET (approx 100)
All data in public database:
UCLA/LONI/ADNI: No
embargo of data
CONVERSION RATES
• MCI-AD
– 1 YR 16%, 2 YR 40%
• Control – MCI
– 1 YR 1.4%, 2 YR 3.9% (about 8 subjects)
Test Sample Size
MMSE 803
RAVLT 607
ADAS 592
CDR SOB 449
POWER OF CLINICAL/COGNITIVE TESTS
25% CHANGE 1YR STUDY (2 ARM) :
AD (155 Subjects)
POWER OF CLINICAL/COGNITIVE TESTS
25% CHANGE 1YR STUDY (2 ARM) :MCI (355 Subjects)
Test Sample Size
RAVLT 6056
ADAS 4547
MMSE 3879
CDR SOB 853
FREESURFER PARCELATION
Freesurfer Hippocampus (1.5T)
Feb-09; N. Schuff
Mean Cortical Thickness Change over 12 Months
Holland et al.
Dia
gn
os
ed
as
NC
Dia
gn
os
ed
as
AD
+2%
-2%
Lateral View Medial View
Boundary Shift Integral
X. Hua/P. Thompson, UCLA
TENSOR BASED MOPHOMERY
Baseline White Matter Hyperintensities
Normal MCI AD
•Normal and MCI had similar WMH
distributions; increased WMH burden in AD with
suggestions of anterior-posterior progression
15
1.5T MRI Comparisons - AD (n=69)
Lab Variable SS/arm
Alexander L. Hippo. Formation 334
Dale Whole Brain 207
Schuff - FS Hippocampus 201
Dale Ventricles 132
Dale Hippocampus 126
Studholme Temporal lobe % change 123
Schuff - FS Ventricles 119
Studhome CV - % change 106
Fox VBSI % change 105
Fox BSI % change 71
Thompson CV - % change 54
16
1.5T Comparisons - MCI (n=148)
Lab Variable SS/arm
Alexander L. Hippo Formation 379
Dale Ventricles 287
Schuff - FS Hippocampus 284
Schuff - FS Ventricles 255
Dale Hippocampus 252
Fox VBSI % change 247
Dale Whole Brain 222
Studholme Temporal lobe % change 201
Studholme CV - % change 177
Fox BSI % change 174
Thompson CV - % change 83
Normal Aging vs. Alzheimer’s Disease
FDG PET
Normal
AD
Example Final Meta-ROIs
Posterior Cingulate (Bilateral))
Angular Gyrus
Mid Temporal
Meta-ROIs
contrasted with
MNI-ROIs obtained
from AAL atlas
Simple Rules for Visual Classification
98% specificity for FTD in autopsy-confirmed cases; Foster et al. Brain 2007; 130: 2616-35.
Courtesy of: Norman Foster M.D., Angela Wang, Ph.D.
Statistical ROI’s of 12-Month CMRglDeclineAD
MCI
21
PET Comparisons - AD (n=36)
Lab Variable SS/arm
Foster hypometabolism1 638
Foster hypometabolism2 549
Jagust ROI-avg 412
Reiman CV-fROI 96
22
PET Comparisons - MCI (n=81)
Lab Variable SS/arm
Jagust ROI-avg 7649
Foster hypometabolism1 1876
Foster hypometabolism2 1280
Reiman CV - fROI 280
23
1.5T vs PET Comparison in AD (n=30)
Lab Modality Variable SS/arm
Foster PET hypometabolism1 593
Foster PET hypometabolism2 508
Jagust PET ROI-avg 396
Schuff - FS MRI Hippocampus 173
Schuff - FS MRI Ventricles 95
Reiman PET CV - fROI 91
Fox MRI VBSI % change 87
Thompson MRI CV - % change 53
Fox MRI BSI % change 50
24
1.5T MRI vs PET Comparison in MCI (n=69)
Lab Modality Variable SS/arm
Jagust PET ROI-avg 4605
Foster PET hypometabolism1 2176
Foster PET hypometabolism2 1629
Fox MRI VBSI % change 284
Schuff - FS MRI Ventricles 277
Reiman PET CV - fROI 249
Schuff - FS MRI Hippocampus 202
Fox MRI BSI % change 177
Thompson MRI CV - % change 73
First Conclusion
• In general atrophy, measured by MRI is a more
sensitive and robust measure of rate of change
– Hippocampus, ventricles, not that different
• With the exception of Eric Reiman’s statistically
generated ROI, PET measures have less
statistical power to detect a slowing of change
than MRI
• BUT PET may be more sensitive to detect a
treatment effect which improves function!!!
AD (n=102) Tau Ab1-42 P-Tau181P Tau/Ab1-42 P-Tau181P/Ab1-42
Mean±SD 122±58 143±41 42±20 0.9±0.5 0.3±0.2
MCI (n=200)
Mean±SD 103±61 164±55 35±18 0.8±0.6 0.3±0.2
NC (n=114)
Mean±SD 70±30 206±55 25±15 0.4±0.3 0.1±0.1
BIOMARKERS
John Trojanowski, Les Shaw, U Penn.
p<0.0001, for each of the 5 biomarker tests for AD vs NC and for MCI vs
NC.
For AD vs MCI:p<0.005, Tau; p<0.01, Ab1-42; p<0.01, P-Tau 181P; p<0.0005,
Tau/Ab1-42; p<0.005, P-Tau 181P/Ab1-42. Mann-Whitney test
PIB Imaging:
Alzheimer’s Disease
FDG
PIB
Follow-Up of PIB-Positive ADNI MCI’s
PiB(+) 47
Converters to AD 14
PiB(-) 18
Converters to AD 3
ADNI PiB MCI’s
N = 65, 12 mo. follow-up
Follow-Up of ADNI PiB Controls
ADNI PiB Ctrl’s
N = 19, 12 mo. follow-up
PiB(+) 9
Converters to MCI 2
PiB(-) 10
Converters to MCI 0
PIB vs CSF Biomarkers: Ab
Penn Autopsy
Sample (56 AD, 52
Cog normal)
192 pg/ml
50.0
100
150
200
250
300
1 1.2 1.4 1.6 1.8 2 2.2 2.4
MCI
AD
Control
CS
F A
b 1
-42
Mean Cortical SUVR
Total N = 55 (11 Control, 34 MCI, 10 AD)
Biomarker Agreement
(kappa)
CSF Ab1-42 CSF t-tau CSF p-tau FDG
PIB 0.71 0.21 0.50 0.12
FDG 0.23 0.28 0.25
PIB gives info similar to LP
But LP gives more than amyloid
Conclusion: need to encourage LPs
Effects of CSF-Aβ on atrophy
rate in MCI L R
p-v
alu
es)
Lower baseline CSF-Aβ associated with increase cortical atrophy in
temporal lobe (bilaterally),left precuneus,right superior frontal.
Duygu Tosun
Effects of CSF-τ on Atrophy
Rate in MCI L R
p-v
alu
esb
eta
valu
es
(in
tera
ctio
n t
erm
)
Higher basline CSF-τ levels are associated with increase cortical atrophy in
the temporal lobe (bilaterally). Duygu Tosun
PREDICTING CONVERSION
FROM MCI TO AD
• There are many many predictors
• But most papers do not account for
– Age
– Baseline cognition
– APOE 4
• Thus mulitvariate analyses are critical
• What follows is preliminary
35
Predictors of Conversion from MCI to AD
(cohort with MRI)
Predictor Variable Coefficient p-value
Baseline FAQ -0.100 0.002
Baseline ADAS-Cog -0.112 0.002
Using ACHEI -0.064 0.049
Baseline MMSE 0.056 0.091
36
Predictors of Conversion from MCI to AD (cohort
with MRI and FDG-PET)
Predictor Variable Coefficient p-value
Baseline FAQ -0.090 0.024
Baseline ADAS-Cog -0.085 0.047
Baseline FDG-PET
ROI-avg0.092 0.062
37
Predictors of Conversion from MCI to AD (cohort
with MRI and CSF)
Predictor Variable Coefficient p-value
Baseline FAQ -0.124 0.017
Using ACHEI -0.094 0.057
Baseline ADAS-Cog 0.101 0.058
38
Predictors of longitudinal change in ADAS-Cog -
MCIPredictor of change/yr Univariate Model Multivariate Model*
p-value Coefficient p-value
Apoe4+ 0.005 0.57 0.24
Yrs of education 0.82 -0.004 0.96
CSF Ab <0.001 0.058 0.83
CSF tau <0.001 0.20 0.16
FDG-PET ROI-avg
(UCB)<0.001 -0.40 0.040
Hippocampus <0.001 -0.014 0.94
Ventricles <0.001 0.38 0.070
* Sample size is very small for multivariate
models (1/4 of overall sample)
39
Predictors of longitudinal change in ADAS-Cog -
AD Predictor of change/yr Univariate Model Multivariate Model*
p-value Coefficient p-value
Apoe4+ 0.30 -0.39 0.82
Yrs of education 0.15 0.05 0.79
CSF Ab 0.26 -1.39 0.12
CSF tau 0.004 0.43 0.17
FDG-PET ROI-avg
(UCB)<0.001 -2.12 0.005
Hippocampus 0.79 -0.08 0.90
Ventricles 0.95 0.43 0.47
* Sample size is very small for multivariate
models (1/4 of overall sample)
40
Predictors of longitudinal change in hippocampal
volume - MCI
Predictor of
change/yr
Univariate Model Multivariate Model*
p-value Coefficient p-value
Apoe4+<0.001 -36 0.0022
Yrs of education0.04 -2.7 0.12
CSF Ab<0.001 -0.66 0.91
CSF tau<0.001 -4.4 0.15
FDG-PET ROI-
avg (UCB)0.005 9.3 0.026
* Sample size is very small for multivariate
models (1/4 of overall sample)
41
Predictors of longitudinal change in hippocampal
volume - ADPredictor of
change/yr
Univariate Model Multivariate Model*
p-value Coefficient p-value
Apoe4+0.087 -29 0.18
Yrs of education0.79 -3.4 0.18
CSF Ab0.002 -1.3 0.92
CSF tau0.031 -8.7 0.046
FDG-PET ROI-
avg (UCB)0.73 10.2 0.75
* Sample size is very small for multivariate
models (1/4 of overall sample)
Second Conclusion
• Cognitive measures and APOE are predictors
• FDG PET appears to be a good predictor
– May be used to select people at high risk for
future decline and conversion
• But much more careful statistical analysis of
the ADNI data is needed
• This is not the final word, by far
DATA FROM ADNI ON
NORMAL CONTROLS
45
Predictors of longitudinal change in ADAS-Cog -
NCPredictor of change/yr Univariate Model Multivariate Model*
p-value Coefficient p-value
Apoe4+ 0.22 1.06 0.02
Yrs of education 0.19 -0.026 0.64
CSF Ab 0.82 0.10 0.63
CSF tau 0.75 0.33 0.19
FDG-PET ROI-avg
(UCB)0.076 0.05 0.77
Hippocampus 0.016 -0.25 0.27
Ventricles 0.45 0.18 0.31
* Sample size is very small for multivariate
models (1/4 of overall sample)
46
Predictors of longitudinal change in hippocampal
volume - NC
Predictor of
change/yr
Univariate Model Multivariate Model*
p-value Coefficient p-value
Apoe4+0.095 -27 0.082
Yrs of education0.36 0.85 0.68
CSF Ab0.001 6.1 0.38
CSF tau0.031 -6.6 0.40
FDG-PET ROI-
avg (UCB)0.72 5.4 0.34
* Sample size is very small for multivariate
models (1/4 of overall sample)
12-Month Hippocampal Volume Change vs. Baseline Beta Amyloid
HOW TO DESIGN A
PREVENTION STUDY USING
NORMALS WITH
BIOMARKER PREDICTORS
AND OUTCOMES
SAMPLE SIZE/ARM TO DETECT A 25%
SLOWING OF HIPPOCAMPAL VOL IN 2 YRS
A prevention trial on normals could be designed with
an interim analysis of hippo vol, and continue with
clinical/cognitive endpoints
NIA GRAND OPPORTUNITES
(GO) GRANT
• GO grant ($24 million from NIA) will
– add a cohort of 200 very mild “early” CI, (EMCI) who are
• symptomatic, with very mild memory impairments
• not “late” amnestic MCI (so called “Petersen Criteria”
• not normal controls
• LPs on 100% of these new subjects
• Follow controls/MCI an additional year
• F18 amyloid imaging on ALL existing and new ADNI/GO subjects (AV-45)
• Complete analysis of all ADNI data
18F-AV-45 Scans
Spectrum of
Pathology
AVID
SCOPE OF ADNI2
• If renewed ($69 million), ADNI2 will:
• Continue to follow more than 400 controls and
MCI from ADNI1 for 5 more years
• Enroll:
– 100 additional EMCI (supplements 200 from GO
– 150 new controls, LMCI, and AD
• MRI at 3,6, months and annually
• F18 amyloid (AV-45)/FDG baseline and Yr2
• LP on 100% of subjects at enrollment
• Genetics
HOW TO EXTEND IMPACT OF ADNI
• We need a world wide coordinated effort using similar methods with data sharing!!!!!!
• Collect longitudinal data from young
• Clinical trial AND Population based studies using MRI, PET, blood, and CSF biomarkers.
– Longitudinal followup
– Autopsy confirmation
• Provides mechanism, diagnostic criteria, design of prevention trials, ultimately subjects for prevention trials
• Expensive but necessary
IMPACT OF ADNI
• ADNI will provide new information
concerning the pathophysiology of AD
• Help develop early detection methods
– Identification of risk
• Develop improved treatment trials
– Predictors and outcomes
• Lead to the treatment and prevention of AD
These slides and much more at
ADNI-INFO.ORG
All data at
www.loni.ucla.edu/ADNI/
WORK IN OUR LAB
• MRI of neurodegenerative diseases: normal
aging, AD, FTD, epilepsy, HIV, PTSD,
TBI, clinical trials
• Structural, perfusion, and diffusion tensor
MRI: technique development/applications
• Multimodality approach
• Explore value of high field MRI 3-7 T
– Hippocampal subfields
HIGH RESOLUTION T2
Histology of Hippocampus
entorhinal cortex
subiculum
CA1-CA2 transition zone
CA1
CA3/4 and dentate
anterior
posterior
1
2
3
4
5
HIPPOCAMPAL SUBFIELDS:
SUSANNE MUELLER MD
0
50
100
150
200
250
300
350
ER
C_3
th
ER
C_4
th
ER
C_5
th
ER
C_6
th
ER
C_7
th+
SU
B_3
th
SU
B_4
th
SU
B_5
th
SU
B_6
th
SU
B_7
th+
CA
1_3t
h
CA
1_4t
h
CA
1_5t
h
CA
1_6t
h
CA
1_7t
h+
CA
2_3t
h
CA
2_4t
h
CA
2_5t
h
CA
2_6t
h
CA
2_7t
h+
Oth
er_3
th
Oth
er_4
th
Oth
er_5
th
Oth
er_6
th
Oth
er_7
th+
0
5
10
15
20
25
EFFECTS OF NORMAL AGING ON
SUBFIELDS CA-1
0
100
200
300
400
500
600
700
0 20 40 60 80 100
Age
CA
1
p = 0.0001
AGING: Effects on Hippocampal Subfields
APO E4 and Hippocampal Subfields
Mean Volumes and SD of Total Hippocampus and Subfields in cmm3 in Healthy Controls
Table 1. Subfield volumes Normalized to ICV of Controls, MCI and AD
Control MCI AD
ERC 119.3 (30.2) 120.5 (35.0) $ * 90.0 (30.8)
Subiculum 124.9 (23.5) 118.8 (18.5) $ * 97.5 (25.7)
CA1 201.0 (29.6) 186.2 (30.9) $166.0 (33.2)
CA1&CA2Transition 12.9 (3.4) $ 9.5 (2.2) $ 9.2 (2.2)
CA3&Denate Gyrus 138.4 (25.7) 142.1 (30.8) 135.6 (32.0)
Hippocampus proper 352.3 (50.6) 337.7 (56.2) $ 310.8 (60.4)
$ p<0.05 compared to Controls
* p<0.05 compared to MCI
ICV = intracranial volume
EFFECTS OF AD AND MCI ON SUBFIELDS
Regional Patterns of FA changes in
Patients vs. Control group
bvFTD
SD
PNFA
r.Unc
a.CC
a.Cg
l.Unc
l.Unc
l.pHP
l.AF
l.AF
l.AF r.AF
r.Unc
l.AF
Patterns of GM loss
(Seeley, 2009)
Group comparisons (II): Tract-based
(II) Tract-based study of FA reduction for specific
tracts in bvFTD, SD and PNFA vs. CN
FA ~ Dx + Age + sex
Significant level: P = 0.05 (ANOVA)
Unc
CST
a.CC
a.Cg
pHP AF
Longitudinal DTI study in ALS
Yu Zhang, MD
Longitudinal changes (left, right)
* Right CST showed a significant FA changes by paired-samples T test.
0.3
0.4
0.5
0.6
0.7
1 2 3 4 5
Left CST Right CST
TP1 —— TP2 TP1 —— TP2
P = 0.47 P = 0.01
Specific Aim
Test diagnostic value of hypoperfusion on Alzheimer’s Disease
after accounting for cortical atrophy
Hypoperfusion
Cortical atrophy
Alzheimer’s
disease
diagnosis
Multimodality Image Processing
of ASL-MRI and sMRI 23 AD patients
• 65 ± 10 yrs, range
51-84 yrs
• 8 women (36%)
• MMSE 21 ± 8
39 healthy elderly
• 66 ± 8 yrs, range
51-82 yrs
• 19 women (70%)
• MMSE 29 ± 1
Multimodality processing:
1. Co-registration of ASL to T2
2. Nonlinear geometric distortion correction
3. Partial volume effect correction wrt T1 tissue segmentation
4. Normalize with SMC-CBF
Mappings of Cortical CBF and
Cortical Atrophy on Cortical
Surface: A Sample AD Patients
Cortical CBF:
average CBF along
the path
Cortical thickness:
length of the path
Laplace’s eqn
Surface-based warp to
a reference cortical
surface space
Reduction in the Effect of Hypoperfusion on AD
Diagnosis after Accounting for Atrophy
Significance map
(p<0.05)
Diagnosis ~ CBF
Diagnosis ~ Thickness
Diagnosis ~ CBF + Thickness
Diagnosis ~ CBF + Thickness
Test for Indirect EffectsIndirect effect of the
hypoperfusion on the AD
diagnosis via the cortical atrophy
Indirect effect of the cortical
atrophy on the AD diagnosis via
the hypoperfusion
7T
3T (Verio)
Courtesy of LL. Wald et al., MGH NMR-Center, Boston, USA
7TTSE, 11 echoes, 7 min exam, 20cm FOV, 512x512 (0.4mm x 0.4mm), 3mm SL
white matter SNR = 65
gray matter SNR = 76
7T* vs. 3T
*Works in Progress. The
information about this product is
3T
white matter SNR = 26
gray matter SNR = 34
MANY THANKS TO
• NIA: Drs Hodes and Buckholz
• Industry Scientific Advisory Board
• Foundation for NIH
• ADNI Core leaders and Core members
• ADNI Site PIs
• Our volunteer subjects
ADNI Industry Scientific Advisory Board
PIB/PET Supplement : Alzheimer’s Association and GE Healthcare
Cerebrospinal Fluid Extension: Alzheimer’s Association, AstraZeneca, Cure Alzheimer’s Fund, Merck, Pfizer and an anonymous foundation
Genome-Wide Genotyping :Gene Network Sciences, Merck, Pfizer and an
anonymous foundation
Genome-Wide Genotyping Genetic Analysis: NIBIB, Merck, Pfizer and an
anonymous foundation
New members Abbott, Genentech, Roche, Bayer
Site PI Study Coordinator
Oregon Health and Science University Jeffrey Kaye, MD Sara Dolen, BS, BA
USC Lon Schneider Mauricio Becerra
UCSD James Brewer, MD, PhD Helen Vanderswag, RN
U Mich Judith Heidebrink, MD Joanne Lord, BA, CCRC, LPN
Mayo Clinic, Rochester Ronald Petersen, MD, PhD Kris Johnson, RN
Baylor College of Medicine Rachelle Doody, MD, PhD Munir Chowdhury, MBBS, MS, CCRC
Columbia Yaakov Stern, PhD Linda Sanders, BA
Washington University, St. Louis John Morris, MD Stacy Schneider, MSN
U Alabama, Birmingham Daniel Marson, JD, PhD Denise Ledlow, RN
Mount Sinai School of Medicine Hillel Grossman, MD Tessa Lundquist, BA
Rush University Medical Center Leyla deToledo-Morrell, PhD Patricia Samuels
Wien Center Ranjan Duara, MD Peggy Roberts, CRC
Johns Hopkins University Marilyn Albert, PhD Maria Zerrate, MD
New York University Medical Center Henry Rusinek, MD Lidia Glodsik-Sobanska, MD, PhD
Duke University Medical Center P. Murali Doraiswamy, MBBS, MD Marilyn Aiello, BSc
U Penn Steven Arnold, MD Binh Ha, BA
U Kentucky Charles Smith, MD Barbara Martin
U Pitt Steven DeKosky, MD MaryAnn Oakley, MA
U Rochester Medical Center M. Saleem Ismail, MD Connie Brand, RN
UC Irvine Ruth Mulnard, RN, DNSc Catherine McAdams-Ortiz, RN, MSN
U Texas, Southwestern MC Richard King, MD Kristin Martin-Cook, MS
Emory University Allan Levey, MD, PhD Janet Cellar, RN, MSN
U Kansas Jeffrey Burns, MD Pat Laubinger, MPA, BSNq
UCLA George Bartzokis, MD Deidre ONeill, BS
Site PI Study Coordinator
Mayo Clinic, Jacksonville Neill Graff-Radford, MD Heather Johnson, MLS, CCRP
Indiana University Martin Farlow, MD Scott Herring, RN
Yale School of Medicine Christopher van Dyck, MD Amanda Benincasa, BS
McGill University/Jewish Memory Clinic Howard Chertkow, MD Chris Hosein, Med
Sunnybrook Health Sciences, Ontario Sandra Black, MD Joanne Lawrence
U.B.C. Clinic for AD & Related, B.C. Howard Feldman, MD Benita Mudge BSc
Cognitive Neurology - St. Joseph’s, Ontario Andrew Kertesz, MD Darlyne Morlog
U Nevada School of Medicine, Las Vegas Charles Bernick, MD Michelle Sholar, BA
Northwestern University John (Chuang-Kuo) Wu, MD, PhD Stephanie Epstein-Fields, MA
Medical University of South Carolina Jacobo Mintzer, MD Arthur Williams
Premiere Research Institute Carl Sadowsky, MD Teresa Villena
UCSF Howard Rosen, MD Kari Haws
Georgetown University Paul Aisen, MD Carolyn Ward, MSPH
Brigham and Women’s Hospital Reisa Sperling, MD MMSc Meghan Frey, MA
Stanford University Jerome Yesavage, MD Viktoriya Samarina, BA
Sun Health/Arizona Consortium Marwan Sabbagh, MD Sherye Sirrel, MS
Boston University Robert Green, MD, MPH David Essaff
Howard University Thomas Obisesan, MD, MPH Saba Wolday
Case Western Reserve University Alan Lerner, MD Leon Hudson
UC Davis – Sacramento Charles DeCarli, MD Katherine Vieira, RN,NP
Neurological Care of CNY Smita Kittur, MD Atitya Siddiqui, MBBS
Dent Neurologic Institute Vernice Bates, MD RoseAnn Oakes
Parkwood Hospital Michael Borrie, MD Sarah Best, BSc
University of Wisconsin Sterling Johnson, PhD Sandra Harding
Site PI Study Coordinator
UC Irvine – BIC Steven Potkin, MD Loni Lee, BA
Banner Alzheimer’s Institute Pierre Tariot, MD Stephanie Reeder
Ohio State University Douglas Scharre, MD Jennifer Icenhour
Albany Medical College Earl Zimmerman, MD John Cowan
Thomas Jefferson University Marjorie Marenberg, MD, PhD Eileen Maloney, PhD
Hartford Hospital, Olin Neuro Research Center Godfrey Pearlson, MD Melissa Andrews, BA
Dartmouth-Hitchcock Medical Center Andrew Saykin, PsyD Jessica Englert, PhD
Wake Forest University Health Sciences Jeff Williamson, MD, MHS Leslie Gordineer
Rhode Island Hospital Brian Ott, MD Esther Oden
Butler Hospital Memory and Aging Program Stephen Salloway, MD Martha Elaine Dunlap, BA
ADCS/ADNI CLINICAL COREPaul Aisen, M.D.
Ron Petersen,
M.D.,Ph.D.
Clinical Monitors Alan Pamoleras
Gina Camilo, M.D.
Janet Kastelan
Karen Croot
Kris Brugger
Mario Schittini, M.D.
Pam Saunders, Ph.D.
Viviana Messick
Rebecca Jones, Ph.D.
ADNI Team Anthony Gamst, Ph.D.
Mike Donohue Ph.D.
Ralzta Vozdolska,
Ph.D.
Devon Gessert
Sarah Walter
Monica Chu
Admin. Jacqueline Bochenek
Deborah Tobias
Jeremy Pizzola
Nancy Bastian
Debbie Stice
Sylvia Plummer
Susan Grunde
David Lamb
Linda Mellor
Rumiko Morris
Regulatory Kristin Woods
Elizabeth Shaffer
Recruitment Jeffree Itrich
Peggy Chambers
Meetings Elizabeth Shaffer
Biostat Gustavo Jimenez
Hong-Mei Qui
Publications1) Mueller SG, Weiner MW, Thal LJ, Petersen RC, Jack CR, Jagust W, Trojanowski JQ, Toga AW, Beckett L.: Ways toward an early diagnosis in Alzheimer’s disease: The Alzheimer’s Disease Neuroimaging Initiative (ADNI), Alzheimer’s & Dementia 1:55-66, 2005.
2) Mueller SG, Weiner MW, Thal LJ, Petersen RC, Jack C, Jagust W, Trojanowski JQ, Toga AW, Beckett L. The Alzheimer’s Disease Neuroimaging Initiative. Neuroimaging Clin N Am, 15(4):869-77, 2005.
3) Fukuyama H. Neuroimaging in mild cognitive impairment. Rinsho Shinkeigaku, 46(11):791-4, 2006.
4) Iwatsubo T. Beta-and gamma-secretases. Rinsho Shinkeigaku, 46(11):925-6, 2006.
5) Leow AD, Klunder AD, Jack CR Jr, Toga AW, Dale AM, Bernstein MA, Britson PJ, Gunter JL, Ward CP, Whitwell JL, Borowski BJ, Fleisher AS, Fox NC, Harvey D, Kornak J, Schuff N, Studholme C, Alexander GE, Weiner MW, Thompson PM; ADNI Preparatory Phase Study.: Longitudinal stability of MRI for mapping brain change using tensor-based morphometry. Neuroimage, 31(2):627-40, 2006.
6) Mueller SG, Weiner MW, Thal LJ, Petersen RC, Jack C, Jagust W, Trojanowski JQ, Toga AW, Beckett LA. Ways toward an early diagnosis in Alzheimer’s disease: The Alzheimer’s Disease Neuroimaging Initiative. Cognition and Dementia, 5(4):56-62, 2006.
7) Arai H. Alzheimer’s Disease Neuroimaging Initiative and mild cognitive impairment. RinshoShinkeigaku, 47(11):905-7, 2007.
8) Fletcher PT, Powell S, Foster NL, Joshi SC. Quantifying metabolic asymmetry modulo structure in Alzheimer’s disease. Inf Process Med Imaging, 20:446-57, 2007.
9) Ihara Y. Overview on Alzheimer’s disease. Rinsho Shinkeigaku, 47(11):902-4, 2007.
10) Murayam S, Saito Y. Neuropathology of mild cognitive impairment Alzheimer’s disease. Rinsho Shinkeigaku, 47(11): 912-4, 2007.
11) Fan Y, Batmanghelich N, Clark CM, Davatzikos C, the Alzheimer’s Disease Neuroimaging Initiative. Spatial Patterns of Brain Atrophy in MCI Patients, Identified via High-Dimensional Pattern Classification, Predict Subsequent Cognitive Decline. NeurImage, 39(4): 1731-1743, 2008.
19) Frisoni GB, Henneman WJP, Weiner MW, Scheltens P, Vellas B, Reynish E, Hudecova J, Hampel H, Burger K, Blennow K, Waldemar G, Johannsen P, Wahlund L-O, Zito G, Rossini PM, Winblad B, Barkhof F, Alzheimer’s Disease Neuroimaging Initiative. The pilot European Alzheimer’s Disease Neuroimaging Initiative of the European Alzheimer’s Disease Consortium. Alzheimer’s & Dementia, 4(4): 255-64, 2008.
20) Morra JH, Tu Z, Apostolova LG, Green AE, Avedissian C, Madsen SK, Parikshak N, Hua X, Toga AW, Jack CR Jr, Weiner MW, Thompson PM, the Alzheimer’s Disease Neuroimaging Initiative. Validation of a fully automated 3D hippocampal segmentation method using subjects with Alzheimer’s disease mild cognitive impairment , and elderly controls. Neuroimage, 43(1): 59-68, 2008.
21) Hua X, Leow AD, Parikshak N, Lee S, Chiang MC, Toga AW, Jack CR Jr, Weiner MW, Thompson PM, the Alzheimer’s Disease Neuroimaging Initiative. Tensor-based morphometry as a neuroimaging biomarker for Alzheimer’s disease: An MRI study of 676 AD, MCI, and normal subjects. NeuroImage, 43(3):458-69, 2008.
22) Becker RE, Greig NH. Alzheimer’s disease drug development: old problems require new priorities. CNS Neurol Disord Drug Targets, 7(6):499-511, 2008.
23) Walhovd KB, Fjell AM, Dale AM, McEvoy LK, Brewer J, Karow DS, Salmon DP, Fennema-Notestine C; the Alzheimer’s Disease Neuroimaging Initiative. Multi-modal imaging predicts memory performance in normal aging and cognitive decline. Neurobiol Aging, Oct 5 [Epub ahead of print], 2008.
24) Mormino EC, Kluth JT, Madison CM, Rabinovici GD, Baker SL, Miller BL, Koeppe RA, Mathis CA, Weiner MW, Jagust WJ, and the Alzheimer’s Disease Neuroimaging Initiative. Episodic memory loss is related to hippocampal-mediated beta-amyloid deposition in elderly subjects. Brain Nov 28 [Epub ahead of print], 2008.
25) Morra JH, Tu Z, Apostolova LG, Green AE, Avedissian C, Madsen SK, Parikshak N, Toga AW, Jack CR Jr, Schuff N, Weiner MW, Thompson PM, The Alzheimer’s Disease Neuroimaging Initiative. Automated mapping of hippocampal atrophy in 1-year repeat MRI data from 490 subjects with Alzheimer’s disease, mild cognitive impairment, and elderly controls. Neuroimage Nov 8 [Epub ahead of print], 2008.
26) Shaw LM, Vanderstichele H, Knapik-Czajka M, Clark CM, Aisen PS, Petersen RC, Blennow
34) Fennema-Notestine C, Hagler DJ Jr, McEvoy LK, Fleisher AS, Wu EH, Karow DS, Dale AM; the Alzheimer's Disease Neuroimaging Initiative. Structural MRI Biomarkers for Preclinical Alzheimer’s Disease: Initial Results from the ADNI Cohort. Human Brain Mapping, March 2009.
35) Chou YY, Leporé N, Avedissian C, Madsen SK, Parikshak N, Hua X, Shaw LM, Trojanowski JQ, Weiner MW, Toga AW, Thompson PM; The Alzheimer's Disease Neuroimaging Initiative. Mapping Ventricular Expansion, Clinical Measures and CSF Biomarkers in Alzheimer’s Disease and Mild Cognitive Impairment Using Multi-Atlas Fluid Image Alignment. NeuroImage, 2009 Feb 21.
36) Kovacevic, S et. al: Fully-automated measures of medial temporal lobe volume predict clinical decline in MCI patients. Alzheimer's Disease and Associated Disorders, 2008, in press.
37) Reiman, E et. al.: Categorical and Correlational Analyses of Baseline Fluorodeoxyglucose Positron Emission Tomography Images From the Alzheimer's Disease Neuroimaging Initiative (ADNI). NeuroImage, in press.
38) Landau SM, Harvey D, Madison CM, Koeppe RA, Reiman EM, Foster NL, Weiner MW, Jagust WJ, the Alzheimer’s Disease Neuroimaging Initiative. Associations between cognitive, functional, and FDG-PET measures of decline in AD and MCI. Neurobiology of Aging, In Press, 2009.
39) Potkin SG, Guffanti G, Lakatos A, Turner JA, Kruggel F, Fallon JH, Saykin AJ, Orro A, Lupoli S, Salvi E, Weiner M, Macciardi F. Hippocampal atrophy as a quantitative trait in a genome-wide association study identifying novel susceptibility genes for Alzheimer’s disease. PLoS ONE, 4(8):e6501-15, 2009.
40) Clark, C.M., Davatzikos, C., Borthakur, A., Newberg, A., Leight, S., Lee, V.M.-Y., Trojanowski, J.Q. Biomarkers for early detection of Alzheimer pathology. NeuroSignals, 16:11-18, 2008.
41) Haschke, M., Zhang, Y.L., Kahle, C., Klawitter, J., Korecka, M., Shaw, L.M., Christians, U. HPLC-atmospheric pressure chemical ionization MS/MS for quantification of 15-F2t-isoprostane in human urine and plasma. Clinical Chemestry, 53:489-497, 2007.
42) Shaw, L.M., Korecka, M., Clark, C.M., Lee, V.M..-Y., and Trojanowski, J.Q. Biomarkers of neurodegenertaion for diagnosis and monitoring therapeutics. Nat. Rev. Drug Discovery, 6:295-
Abstracts1) Weiner MW, Thal L, Jack C, Jagust W, Toga A, Beckett L, Peterson R: Alzheimer’s disease neuroimaging initiative, Alzheimer’s Disease and Parkinson’s Diseases: Insights, Progress and Perspectives 7th International Conference, Sorrento, Italy March 9-13, 2005.
2) Weiner MW, Thal L, Petersen R, Jagust W, Trojanowski J, Toga A, Beckett L, Jack C.: Alzheimer’s disease neuroimaging initiative. Poster from 2nd Congress of the International Society for Vascular Behavioural and Cognitive Disorders, Florence, Italy, June 8-12, 2005.
3) Weiner MW, Thal LJ, Petersen RC, Jack Jr. CR, Jagust W, Trojanowski JQ, Beckett LA. Imaging biomarkers to monitor treatment effects for Alzheimer’s Disease trials: The Alzheimer’s Disease Imaging Initiative. Alzheimer’s Association 10th International Conference on Alzheimer’s Disease and Related Disorders. Madrid, Spain. 2(3 Suppl 1): S311 (P2-254). July 15-20, 2006.
4) Weiner MW, Thal L, Petersen R, Jack C, Jagust W, Trojanowski J, Shaw L, Toga A, Beckett L, Stables L, Mueller S, Lorenzen P, Schuff N. MRI of Alzheimer’s and Parkinson’s: The Alzheimer’s Disease Neuroimaging Initiative (ADNI-Info.Org). Neurodegenerative Dis, 4(Suppl 1):276, 832, 2007.
5) Gunter JL, Bernstein MA, Britson PJ, Felmlee JP, Schuff N, Weiner M, Jack CR.: MRI system tracking and correction using the ADNI phantom. Alzheimer’s & Dementia, 3(3 Suppl 2):S109 P-038. Second Alzheimer’s Association International Conference on Prevention of Dementia, Washington, DC. June 9-12, 2007.
6) Fletcher PT, Wang AY, Tasdizen T, Chen K, Jagust WJ, Koeppe RA, Reiman EM, Weiner MW, Minoshima S, Foster NL.: Variability of Normal Cerebral Glucose Metabolism from the Alzheimer’s Disease Neuroimaging Initiative (ADNI): Implication for Clinical Trials. Annals of Neurology, 62(Suppl 11):S52-3. American Neurological Association 132nd Annual Meeting, Washington, DC. October 7-10, 2007.
7) Chen K, Reiman EM, Alexander GE, Lee W, Reschke C, Smilovici O, Bandy D, Weiner MW, Koeppe RA, Jagust WJ.: Six-month cerebral metabolic declines in Alzheimer’s Disease, amnestic mild cognitive impairment and elderly normal control groups: Preliminary findings from the Alzheimer’s Disease Neuroimaging Initiative. Alzheimer’s & Dementia, 3(3 Suppl 2):S174 O1-04-05. Second Alzheimer’s Association International Conference on Prevention of Dementia,
19) Gunter JL, Borowski B, Bernstein M, Ward C, Britson P, Felmlee J, Schuff N, Weiner M, Jack C. Systematics of the Alzheimer’s Disease Neuroimaging Initiative (ADNI) Phantom. P2-011, Page T369, 11th International Conference on Alzheimer’s Disease and Related Disorders, Chicago, IL, 2008.
20) Gunter JL, Borowski B, Britson P, Bernstein M, Ward C, Felmlee J, Schuff N, Weiner M, Jack C. Alzheimer’s Disease Neuroimaging Initiative (ADNI) Phantom and Scanner Longitudinal Performance. P2-012, Page T370, 11th International Conference on Alzheimer’s Disease and Related Disorders, Chicago, IL, 2008.
21) Lee W, Langbaum JBS, Chen K, Recshke C, Bandy D, Alexander GE, Foster NL, Weiner MW, Koeppe RA, Jagust WJ, Reiman EM. Categorical and Correlational Analyses of Baseline Fluorodeoxyglucose Positron Emission Tomography Images from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). IC-P1-036, Page T23 & P2-037, Page T379, 11th International Conference on Alzheimer’s Disease and Related Disorders, Chicago, IL, 2008.
22) Petersen RC, Aisen P, Beckett L, Donohue M, Weng Q, Salmon D, Weiner M. Alzheimer’s Disease Neuroimaging Initiative (ADNI): Baseline Characteristics. P3-040, Page T528, 11th International Conference on Alzheimer’s Disease and Related Disorders, Chicago, IL, 2008.
23) Gunter JL, Borowski B, Britson P, Bernstein M, Ward C, Felmlee J, Schuff N, Weiner M, Jack CR, the Alzheimer’s Disease Neuroimaging Initiative. ADNI Phantom & Scanner Longitudinal Performance. IC-P3-181, Page T80, 11th International Conference on Alzheimer’s Disease and Related Disorders, Chicago, IL, 2008.
24) Schuff N, Woerner N, Boreta L, Kornfield T, Jack Jr. CR, Weiner MW. Rate of Hippocampal Atrophy in the Alzheimer’s Disease Neuroimaging Initiative (ADNI): Effects of APOE4 and Value of Additional MRI Scans. IC-P3-213, Page T91, 11th International Conference on Alzheimer’s Disease and Related Disorders, Chicago, IL, 2008.
25) Chen K, Reschke C, Lee W, Bandy D, Foster NL, Weiner MW, Koeppe RA, Jagust WJ, Reiman EM. The Pattern of Cerebral Hypometablism in Amnestic Mild Cognitive Impairment and Its Relationship to Subsequent Conversion to Probable Alzheimer’s Disease: Preliminary Findings from the Alzheimer’s Disease Neuroimaging Initiative. IC-P2-086, Page T42, 11th International Conference on Alzheimer’s Disease and Related Disorders, Chicago, IL, 2008.
26) Reiman EM, Chen K, Ayutyanont N, Lee W, Bandy D, Reschke C, Alexander GE, Weiner
These slides and much more at
ADNI-INFO.ORG
All data at
www.loni.ucla.edu/ADNI/