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 #
3U01AG024904-05S8
Application #
8073713
Study Section
Special Emphasis Panel (ZAG1-ZIJ-4 (M6))
Program Officer
Ryan, Laurie M
Project Start
2004-09-30
Project End
2010-09-29
Budget Start
2010-06-01
Budget End
2010-09-29
Support Year
5
Fiscal Year
2010
Total Cost
$3,100,000
Indirect Cost
Name
Northern California Institute Research & Education
Department
Type
DUNS #
613338789
City
San Francisco
State
CA
Country
United States
Zip Code
94121
Goyal, Devendra; Tjandra, Donna; Migrino, Raymond Q et al. (2018) Characterizing heterogeneity in the progression of Alzheimer's disease using longitudinal clinical and neuroimaging biomarkers. Alzheimers Dement (Amst) 10:629-637
De Vito, Alyssa N; Calamia, Matthew; Weitzner, Daniel S et al. (2018) Examining differences in neuropsychiatric symptom factor trajectories in empirically derived mild cognitive impairment subtypes. Int J Geriatr Psychiatry 33:1627-1634
Young, Alexandra L; Marinescu, Razvan V; Oxtoby, Neil P et al. (2018) Uncovering the heterogeneity and temporal complexity of neurodegenerative diseases with Subtype and Stage Inference. Nat Commun 9:4273
Marino, Simeone; Xu, Jiachen; Zhao, Yi et al. (2018) Controlled feature selection and compressive big data analytics: Applications to biomedical and health studies. PLoS One 13:e0202674
Yu, Kaixin; Wang, Xuetong; Li, Qiongling et al. (2018) Individual Morphological Brain Network Construction Based on Multivariate Euclidean Distances Between Brain Regions. Front Hum Neurosci 12:204
Berezuk, Courtney; Ramirez, Joel; Black, Sandra E et al. (2018) Managing money matters: Managing finances is associated with functional independence in MCI. Int J Geriatr Psychiatry 33:517-522
Li, Xinzhong; Wang, Haiyan; Long, Jintao et al. (2018) Systematic Analysis and Biomarker Study for Alzheimer's Disease. Sci Rep 8:17394
Mahdavi, Shirin; Towhidkhah, Farzad; Alzheimer's Disease Neuroimaging Initiative (2018) Computational human head models of tDCS: Influence of brain atrophy on current density distribution. Brain Stimul 11:104-107
Huang, Sheng-Yao; Hsu, Jung-Lung; Lin, Kun-Ju et al. (2018) Characteristic patterns of inter- and intra-hemispheric metabolic connectivity in patients with stable and progressive mild cognitive impairment and Alzheimer's disease. Sci Rep 8:13807
Lee, Younghee; Han, Seonggyun; Kim, Dongwook et al. (2018) Genetic variation affecting exon skipping contributes to brain structural atrophy in Alzheimer's disease. AMIA Jt Summits Transl Sci Proc 2017:124-131

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