The Alzheimer's Disease Neuroimaging Initiative (ADNI) is a large multi-site study in which serial clinical, biological, neuropsychological and neuroimaging data are being collected from 200 healthy controls, 400 individuals with mild cognitive impairment (MCI) and 200 patients with mild Alzheimer's disease (AD). This proposed ancillary study aims to analyze all ADNI structural and metabolic neuroimaging data to characterize morphometric and metabolic changes in early AD, with the goal of determining optimal predictor variables for identifying individuals at risk for developing progressive AD-related neurodegeneration. The variables thus identified could serve as surrogate endpoints in Phase 2 and 3 clinical treatment trials. To achieve these goals, MRI, PET, and cognitive data will be downloaded from the publicly accessible ADNI database. Methods based on FreeSurfer software will be used to perform automated volumetric segmentation, cortical surface reconstruction and cerebral parcellation on all baseline structural MRIs to obtain measures of regional cortical thickness and subcortical volumes. Automated, longitudinal, within-subject change analyses will be performed on serial MRIs to determine region-specific structural change trajectories related to disease progression. Semi-automated procedures for quantifying metabolic activity within MRI-derived anatomically- defined regions of interest will be applied to PET data from the baseline session to determine effect size of disease-related differences in metabolic activity in all cortical and subcortical structures, before and after correcting for morphometric differences. Within-subject change in metabolic activity over time, before and after correcting for morphometric changes, will be computed to determine region-specific trajectories of disease-related metabolic changes. Multivariate classification analyses will be applied to MRI measures to determine sensitivity and specificity for discriminating controls from subjects with MCI, and for discriminating MCI subjects who convert to AD from those who remain stable in diagnosis. PET and cognitive measures will be added to determine whether they improve classification accuracy. Multivariate analyses will be performed on structural measures obtained from control and MCI subjects during the test sessions of the first study year to determine the optimal set of measures for predicting risk of conversion to AD. Metabolic and cognitive measures will be assessed to determine whether they improve predictive ability. All derived data values and processed image volumes from this study will be made publicly available through the Biomedical Informatics Research Network. This study will significantly enhance understanding of brain changes that occur in early AD;identify candidate neuroimaging biomarkers for use in clinical trials;facilitate the research of other AD investigators;and provide normative data for use in investigation of other aging-related disorders.

Public Health Relevance

The results of this project will provide important new information about the changes in brain structure and metabolism that occur in the earliest stages of Alzheimer's Disease (AD), and relate these measures to change in cognitive performance. This knowledge may improve our ability to predict who is most likely to develop AD and will provide researchers with objective measures that can be used to assess the ability of new treatments to prevent or delay the neurodegeneration associated with AD. Additionally, the high-throughput neuroimaging analysis methods developed here could be used in future studies for detecting and monitoring brain changes that occur in other neurological disorders.

Agency
National Institute of Health (NIH)
Institute
National Institute on Aging (NIA)
Type
Research Project (R01)
Project #
5R01AG031224-05
Application #
8287592
Study Section
Special Emphasis Panel (ZRG1-CND-E (90))
Program Officer
Hsiao, John
Project Start
2008-08-15
Project End
2013-06-30
Budget Start
2012-07-01
Budget End
2013-06-30
Support Year
5
Fiscal Year
2012
Total Cost
$372,621
Indirect Cost
$130,109
Name
University of California San Diego
Department
Neurosciences
Type
Schools of Medicine
DUNS #
804355790
City
La Jolla
State
CA
Country
United States
Zip Code
92093
Wang, Yunpeng; Bos, Steffan D; Harbo, Hanne F et al. (2016) Genetic overlap between multiple sclerosis and several cardiovascular disease risk factors. Mult Scler 22:1783-1793
LeBlanc, Marissa; Zuber, Verena; Andreassen, Bettina Kulle et al. (2016) Identifying Novel Gene Variants in Coronary Artery Disease and Shared Genes With Several Cardiovascular Risk Factors. Circ Res 118:83-94
Desikan, R S; Schork, A J; Wang, Y et al. (2015) Genetic overlap between Alzheimer's disease and Parkinson's disease at the MAPT locus. Mol Psychiatry 20:1588-95
Desikan, Rahul S; Schork, Andrew J; Wang, Yunpeng et al. (2015) Polygenic Overlap Between C-Reactive Protein, Plasma Lipids, and Alzheimer Disease. Circulation 131:2061-9
Chang, Yu-Ling; Fennema-Notestine, Christine; Holland, Dominic et al. (2014) APOE interacts with age to modify rate of decline in cognitive and brain changes in Alzheimer's disease. Alzheimers Dement 10:336-48
White, Nathan S; McDonald, Carrie; McDonald, Carrie R et al. (2014) Diffusion-weighted imaging in cancer: physical foundations and applications of restriction spectrum imaging. Cancer Res 74:4638-52
Andreassen, Ole A; Zuber, Verena; Thompson, Wesley K et al. (2014) Shared common variants in prostate cancer and blood lipids. Int J Epidemiol 43:1205-14
Biffi, Alessandro; Sabuncu, Mert R; Desikan, Rahul S et al. (2014) Genetic variation of oxidative phosphorylation genes in stroke and Alzheimer's disease. Neurobiol Aging 35:1956.e1-8
Desikan, Rahul S; Thompson, Wesley K; Holland, Dominic et al. (2014) The role of clusterin in amyloid-β-associated neurodegeneration. JAMA Neurol 71:180-7
Grant, Igor; Franklin Jr, Donald R; Deutsch, Reena et al. (2014) Asymptomatic HIV-associated neurocognitive impairment increases risk for symptomatic decline. Neurology 82:2055-62

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