The major goals of this Alzheimer's Disease Neuroimaging Initiative (ADNI) are to: 1) Develop improved methods, which will lead to uniform standards for acquiring longitudinal, multi-site Magnetic Resonance Imaging (MRI) and Positron Emission Tomography (PET) data on patients with Alzheimer's disease (AD), mild cognitive impairment (MCI), and elderly controls. 2) Acquire a generally accessible data repository, which describes longitudinal changes in brain structure and metabolism. In parallel, acquire clinical, cognitive and biomarker data for validation of imaging surrogates. 3) Determine those methods which provide maximum power to determine treatment effects in trials involving these patient groups. A team of investigators with considerable experience in AD clinical trials, MRI, PET, biomarkers and informatics has been assembled. Study design is in response to the Request For Applications (RFA). The first six months of the project will be devoted to establishing uniform MRI and PET acquisition techniques at all of the 40-45 participating sites, followed by initiation of subject recruitment. Improved methods for MRI and PET quantification will be assessed and implemented if useful. All subjects will have clinical/cognitive assessments and 1.5 T structural MRI every 6 months for 2-3 years. Approximately 50% of subjects will also have 18fluorodeoxyglucose (FDG) PET scans at the same time intervals and 25% of subjects (who do not also have PET) will have MRI at 3 Tesla. AD subjects (n=200) will be studied at 0, 6, 12, 18, and 24 months. MCI subjects at high risk for conversion to AD (n= 400) will be studied at 0, 6, 12, 18, 24, 30, and 36 months. Age matched controls (n=200) will be studied at 0, 6, 12, and 24 months. All MRI and PET scans will be rapidly assessed for quality by the MRI and PET components of the Neuroimaging Center so that subjects may be rescanned if necessary. All clinical data will be collected, monitored, and stored by the Clinical Center at the AD Cooperative Studies program at the University of California San Diego (UCSD). The University of Pennsylvania (UPenn) will collect biomarker samples. All raw and processed image data will be archived at The Laboratory of Neuroimaging (LONI) at the University of California Los Angeles (UCLA). Pilot studies will evaluate different image processing methods to measure brain regions of interest. All data will be monitored and analyzed by project statisticians, and data base queries will be performed on request. All clinical, cognitive, imaging, and biomarker databases will be linked and all raw, processed, and statistically analyzed data will be fully and rapidly accessible to the public through the Internet. The results of this study will be extremely useful for design of future AD and MCI trials.

Agency
National Institute of Health (NIH)
Institute
National Institute on Aging (NIA)
Type
Research Project--Cooperative Agreements (U01)
Project #
5U01AG024904-04
Application #
7277662
Study Section
Special Emphasis Panel (ZAG1-ZIJ-4 (M6))
Program Officer
Buckholtz, Neil
Project Start
2004-09-30
Project End
2009-08-31
Budget Start
2007-09-01
Budget End
2008-08-31
Support Year
4
Fiscal Year
2007
Total Cost
$8,245,161
Indirect Cost
Name
Northern California Institute Research & Education
Department
Type
DUNS #
613338789
City
San Francisco
State
CA
Country
United States
Zip Code
94121
Yan, Qi; Nho, Kwangsik; Del-Aguila, Jorge L et al. (2018) Genome-wide association study of brain amyloid deposition as measured by Pittsburgh Compound-B (PiB)-PET imaging. Mol Psychiatry :
Mecca, Adam P; Michalak, Hannah R; McDonald, Julia W et al. (2018) Sleep Disturbance and the Risk of Cognitive Decline or Clinical Conversion in the ADNI Cohort. Dement Geriatr Cogn Disord 45:232-242
Weise, Christopher M; Chen, Kewei; Chen, Yinghua et al. (2018) Left lateralized cerebral glucose metabolism declines in amyloid-? positive persons with mild cognitive impairment. Neuroimage Clin 20:286-296
Li, Kan; O'Brien, Richard; Lutz, Michael et al. (2018) A prognostic model of Alzheimer's disease relying on multiple longitudinal measures and time-to-event data. Alzheimers Dement 14:644-651
Gómez-Sancho, Marta; Tohka, Jussi; Gómez-Verdejo, Vanessa et al. (2018) Comparison of feature representations in MRI-based MCI-to-AD conversion prediction. Magn Reson Imaging 50:84-95
Pardo, José V; Lee, Joel T; Alzheimer’s Disease Neuroimaging Initiative* (2018) Atypical Localization and Dissociation between Glucose Uptake and Amyloid Deposition in Cognitively Normal APOE*E4 Homozygotic Elders Compared with Patients with Late-Onset Alzheimer's Disease. eNeuro 5:
Bonham, Luke W; Geier, Ethan G; Steele, Natasha Z R et al. (2018) Insulin-Like Growth Factor Binding Protein 2 Is Associated With Biomarkers of Alzheimer's Disease Pathology and Shows Differential Expression in Transgenic Mice. Front Neurosci 12:476
Reich, Brian J; Guinness, Joseph; Vandekar, Simon N et al. (2018) Fully Bayesian spectral methods for imaging data. Biometrics 74:645-652
Ortiz, Andres; Lozano, F; Gorriz, Juan M et al. (2018) Discriminative Sparse Features for Alzheimer's Disease Diagnosis Using Multimodal Image Data. Curr Alzheimer Res 15:67-79
Oltra-Cucarella, Javier; Sánchez-SanSegundo, Miriam; Lipnicki, Darren M et al. (2018) Using Base Rate of Low Scores to Identify Progression from Amnestic Mild Cognitive Impairment to Alzheimer's Disease. J Am Geriatr Soc 66:1360-1366

Showing the most recent 10 out of 1666 publications