We hypothesize that Alzheimer's disease (AD) has a preclinical stage in which elevated levels of brain amyloid protein and accumulation of beta-amyloid deposits foreshadow the gradual onset of neuronal dysfunction, cell loss and dementia. While the exact role of amyloid in the initiation of brain damage is still unclear, clarifying the exact timing of amyloid deposition that precede AD would be extremely helpful in understanding the biological origins of AD and in designing appropriate interventions. Brain imaging provides a window into many of the hypothesized biochemical, functional and anatomic changes in AD. With Positron Emission Tomography (PET) using [11C]PIB it is possible to estimate the density of beta-amyloid plaques by imaging the PIB binding sites. With PET using [18F]FDG it is possible to estimate neuronal function from measures of metabolic activity. Finally, with magnetic resonance imaging (MRI) volume loss over time can be quantified in regional and global brain measures. It is our premise that by examining the temporal and spatial interrelationships between these three measures important insights will be gained in the pathophysiology of AD. The value of these imaging biomarkers are further enhanced by combining the data with CSF biomarkers and clinical and psychometric data. Towards these goals, the Imaging Core will provide two key support activities to the DIAN effort:
Aim 1 : Oversee the collection of all image data. This data includes MR scans for morphometrics, PET FDG scans for metabolism and PET PIB scans for imaging beta-amyloid plaques.
Aim 2 : Perform image processing and analysis to extract biologically relevant measures from the image data set. These measures include whole brain volume and cortical and subcortical regional measures of gray matter volume, relative glucose metabolism and PIB-derived estimates of beta-amyloid plaque deposition.
|Ringman, John M; Monsell, Sarah; Ng, Denise W et al. (2016) Neuropathology of Autosomal Dominant Alzheimer Disease in the National Alzheimer Coordinating Center Database. J Neuropathol Exp Neurol 75:284-90|
|Su, Yi; Blazey, Tyler M; Owen, Christopher J et al. (2016) Quantitative Amyloid Imaging in Autosomal Dominant Alzheimer's Disease: Results from the DIAN Study Group. PLoS One 11:e0152082|
|Jin, Yan; Su, Yi; Zhou, Xiao-Hua et al. (2016) Heterogeneous multimodal biomarkers analysis for Alzheimer's disease via Bayesian network. EURASIP J Bioinform Syst Biol 2016:12|
|Chatterjee, Pratishtha; Lim, Wei L F; Shui, Guanghou et al. (2016) Plasma Phospholipid and Sphingolipid Alterations in Presenilin1 Mutation Carriers: A Pilot Study. J Alzheimers Dis 50:887-94|
|Su, Yi; Vlassenko, Andrei G; Couture, Lars E et al. (2016) Quantitative hemodynamic PET imaging using image-derived arterial input function and a PET/MR hybrid scanner. J Cereb Blood Flow Metab :|
|Chen, Ling; Sun, Jianguo; Xiong, Chengjie (2016) A multiple imputation approach to the analysis of clustered interval-censored failure time data with the additive hazards model. Comput Stat Data Anal 103:242-249|
|Lee, Seonjoo; Viqar, Fawad; Zimmerman, Molly E et al. (2016) White matter hyperintensities are a core feature of Alzheimer's disease: Evidence from the dominantly inherited Alzheimer network. Ann Neurol 79:929-39|
|Bateman, Randall J; Benzinger, Tammie L; Berry, Scott et al. (2016) The DIAN-TU Next Generation Alzheimer's prevention trial: Adaptive design and disease progression model. Alzheimers Dement :|
|Su, Yi; Blazey, Tyler M; Owen, Christopher J et al. (2016) Correction: Quantitative Amyloid Imaging in Autosomal Dominant Alzheimer's Disease: Results from the DIAN Study Group. PLoS One 11:e0163669|
|Babulal, Ganesh M; Ghoshal, Nupur; Head, Denise et al. (2016) Mood Changes in Cognitively Normal Older Adults are Linked to Alzheimer Disease Biomarker Levels. Am J Geriatr Psychiatry 24:1095-1104|
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