The overarching research goal of this Mentored Research Scientist Development Award is to enable improved estimation and quantitative analysis of tau neurofibrillary tangle (NFT) distributions in vivo using [18F]T807 PET. The candidate is a junior faculty member at Harvard Medical School and Massachusetts General Hospital with expertise in signal processing, image analysis, and quantitative molecular imaging who intends to specialize in the imaging of neurodegenerative and aging-related disorders. The training goal of this award is to provide the candidate with instruction in neuroimaging, neuroscience, and neurology, which will enable her to extend her existing skillset to Alzheimer's disease (AD) imaging and pursue independent research in this area. The candidate proposes to perform kinetic modeling and network analysis on dynamic PET data obtained using a novel PET tracer known as [18F]T807 that binds to NFTs, which are a hallmark of AD. The importance of in vivo tau quantitation in AD is supported by autopsy data which indicate that the NFT burden is strongly correlated with neurodegeneration and cognitive deficits. Despite the availability of novel compounds for imaging NFTs, quantitation still remains challenging due to the resolution limits of PET, which are typically much higher than the dimensions of the subcortical structures where NFTs initially appear. This project will address the major challenges in tau quantitation. Accurate tau quantitation would enable early diagnosis of AD, allow accurate monitoring of disease progression, and be a vehicle for the development of disease-modifying therapies (e.g. anti-amyloid and anti-tau treatments). Specifically this application seeks to: (1) perform MR-guided resolution recovery of PET images of tau, (2) perform MR-guided denoising of high-resolution dynamic [18F]T807 images and compute Logan distribution volume ratio images in order to generate tau connectivity networks, (3) study the spatiotemporal characteristics of tau networks and the relationship between tau networks and structural networks obtained from MR tractography. The third and final goal will enable us to test our hypothesis that cell- to-cell propagation of tau has strong links to the structural networ in the brain. The aforementioned steps will be applied to a cohort of subjects comprised of both elderly cognitively normal and impaired individuals. To achieve her research and career goals, the candidate will pursue a rigorous career development plan which will include formal coursework in neuroimaging, neuroscience, and neurology, attendance of seminars covering state-of-the-art topics in neuroimaging and AD, and participation in domestic and international conferences on medical imaging and AD. Throughout the award period, she will receive advice and guidance from a mentoring team comprised of renowned experts in the fields of quantitative molecular imaging, neuroscience, and neurology. Both individual meetings with the mentors and collective meetings with the full mentoring team will be held periodically to monitor the research and career development progress of the candidate.
The objective of this career development award is to develop methods for accurately measuring the distribution of abnormal tau protein tangles in the brain, a hallmark of Alzheimer's disease (AD). Novel computational techniques will be developed to utilize high-resolution magnetic resonance imaging (MRI) for improving the resolution of positron emission tomography (PET) images of tau tangles and to determine the relationship between network properties of tau distribution and the structural network of the brain, as revealed by diffusion tensor imaging (DTI). Our imaging techniques will open a new avenue for early detection and monitoring of AD and will deepen our understanding of the mechanisms underlying tau propagation in AD.
| Yang, Jiarui; Hu, Chenhui; Guo, Ning et al. (2017) Partial volume correction for PET quantification and its impact on brain network in Alzheimer's disease. Sci Rep 7:13035 |
| Groll, A; Kim, K; Bhatia, H et al. (2017) Hybrid Pixel-Waveform (HPWF) Enabled CdTe Detectors for Small Animal Gamma-Ray Imaging Applications. IEEE Trans Radiat Plasma Med Sci 1:3-14 |
| Ying, Jun; Dutta, Joyita; Guo, Ning et al. (2016) Classification of Exacerbation Frequency in the COPDGene Cohort Using Deep Learning with Deep Belief Networks. IEEE J Biomed Health Inform : |
