january 2010 by michael w. weiner at salpetriere in paris

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


Jagust ROI-avg 412

Reiman CV-fROI 96

22

PET Comparisons - MCI (n=81)

Lab Variable SS/arm

Jagust ROI-avg 7649



Reiman CV - fROI 280

23

1.5T vs PET Comparison in AD (n=30)

Lab Modality Variable SS/arm

Foster PET hypometabolism1 593


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



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)



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)



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


AD Predictor of change/yr Univariate Model Multivariate Model*


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



40

Predictors of longitudinal change in hippocampal

volume - MCI

Predictor of

change/yr

Univariate Model Multivariate Model*


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



41


volume - ADPredictor of

change/yr



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



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


NCPredictor of change/yr Univariate Model Multivariate Model*


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



46


volume - NC

Predictor of

change/yr



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



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


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


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/

january 2010 by michael w. weiner at salpetriere in paris

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