This is a competitive renewal of a 10-year project that has provided enormous insight into Alzheimer's Disease (AD), a disease that costs $113 billion/yr in the U.S. alone, with no known cure. The project develops a multidimensional, computational atlas of AD. Our 5-year plan of work provides the most powerful computational tools to track AD emerging and spreading in the living brain - years before symptoms begin. We will correlate 3 perspectives on AD in an atlas coordinate framework - serial MRI, novel PET tracers, and 3D pathology. This will provide scientists will the most sensitive approach ever created to gauge which factors affect disease progression (drug treatment, genetic risk, etc.), and how effectively treatments slow the transition to AD. First, we chart the anatomic trajectory of AD with novel analyses of serial MRI (Aims 1,2). Our tools to detect brain changes (cortical thickness mapping, tensor-based morphometry) provided the first time-lapse maps of the disease spreading in the living brain. Here we apply them to MCI subjects (who are at five-fold higher risk of converting to AD in any given year) to identify the best predictors of imminent disease onset, and to predict changes in specific cognitive domains. This will greatly advance drug trials by better identifying candidates for early treatment, who can benefit most before irreversible damage sets in. Next, we will use novel PET tracer molecules to reconstruct the dynamic sequence of AD pathology as it builds up and spreads in the living brain (Aim 3). Hailed as a breakthrough in the AD community, our newly-developed PET (positron emission tomography) tracer compound, [18F]-FDDNP, visualizes amyloid plaques and neurofibrillary tangles (NFTs) - hallmarks of AD previously only detectable at autopsy. Our sensitive surface-based 3D analytic techniques will map the spatio-temporal trajectory of plaque and tangle build-up in aging, mild cognitive impairment, and AD, comparing groups to identify brain changes that predict imminent cognitive deterioration or transition to AD; we will compare and correlate these signals with MRI measures of atrophic rates and cortical degeneration to create joint MR-PET measures of disease burden. We will pioneer 3D cryosection imaging (in 3 subjects/year) to establish how 3D reconstructed maps of tangle density and betaamyloid distribution throughout the brain correlate with imaging measures from living patients. This groundtruth data will be a resource to the AD community, revealing the cellular correlates of imaging signals whose physiological meaning is poorly understood. We will map individuals and populations, revealing group patterns of cortical thinning and plaque and tangle pathology that predict outcomes. Identifying predictors of imminent decline and disease onset, our atlas will identify candidates with emerging pathology for early drug treatment, and will store statistical data to quantify how well treatments resist AD in those at risk. We will share all images, protocols, and algorithms with our 100+ collaborating laboratories. ? ? ?
| Dennis, Emily L; Rashid, Faisal; Faskowitz, Josh et al. (2017) MAPPING AGE EFFECTS ALONG FIBER TRACTS IN YOUNG ADULTS. Proc IEEE Int Symp Biomed Imaging 2017:101-104 |
| Daianu, Madelaine; Mezher, Adam; Mendez, Mario F et al. (2016) Disrupted rich club network in behavioral variant frontotemporal dementia and early-onset Alzheimer's disease. Hum Brain Mapp 37:868-83 |
| Daianu, Madelaine; Mendez, Mario F; Baboyan, Vatche G et al. (2016) An advanced white matter tract analysis in frontotemporal dementia and early-onset Alzheimer's disease. Brain Imaging Behav 10:1038-1053 |
| Raji, Cyrus A; Merrill, David A; Eyre, Harris et al. (2016) Longitudinal Relationships between Caloric Expenditure and Gray Matter in the Cardiovascular Health Study. J Alzheimers Dis 52:719-29 |
| Ching, Christopher R K; Hua, Xue; Hibar, Derrek P et al. (2015) Does MRI scan acceleration affect power to track brain change? Neurobiol Aging 36 Suppl 1:S167-77 |
| Jahanshad, Neda; Nir, Talia M; Toga, Arthur W et al. (2015) Seemingly unrelated regression empowers detection of network failure in dementia. Neurobiol Aging 36 Suppl 1:S103-12 |
| Daianu, Madelaine; Jahanshad, Neda; Nir, Talia M et al. (2015) Rich club analysis in the Alzheimer's disease connectome reveals a relatively undisturbed structural core network. Hum Brain Mapp 36:3087-103 |
| Nir, Talia M; Jahanshad, Neda; Toga, Arthur W et al. (2015) Connectivity network measures predict volumetric atrophy in mild cognitive impairment. Neurobiol Aging 36 Suppl 1:S113-20 |
| Nir, Talia M; Villalon-Reina, Julio E; Prasad, Gautam et al. (2015) Diffusion weighted imaging-based maximum density path analysis and classification of Alzheimer's disease. Neurobiol Aging 36 Suppl 1:S132-40 |
| Madsen, Sarah K; Gutman, Boris A; Joshi, Shantanu H et al. (2015) Mapping ventricular expansion onto cortical gray matter in older adults. Neurobiol Aging 36 Suppl 1:S32-41 |
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