Key words: Alzheimer's Disease, connectomics, functional MRI, diffusion tensor imaging, early detection, biomarkers AD is the only major cause of mortality in the world without an effective disease modifying treatment. This is of urgent concern in the USA where the number of people living with AD is projected to reach 13.4 million by 2050. Developing biomarkers for early detection is of critical importance as researchers believe the failure of clinical trials is in part attributed to testing of potential therapeutic agents too late in the disease. Beyond early detection, there is a need for development of dynamic biomarkers of response to treatment. Increasing evidence for the AD dysconnection hypothesis, which posits that AD neurodegeneration spreads along damaged connections via propagation of dysfunctional signaling, suggests that connectivity biomarkers have potential to serve as biomarkers of early detection and dynamic disease tracking. The emerging field of brain connectomics, applying principles of complex systems theory to model the whole brain as a network structurally and functionally, has provided an ideal framework with which to test the AD dysconnection hypothesis. Connectomics studies of AD have identified changes in several properties of network topology and communication at various stages of AD and also have identified associations between these properties and biomarkers of neurodegeneration and neurocognitive decline. However, understanding of AD progression from a network perspective has not been thoroughly characterized across the network communication spectrum and structural-functional connectivity relationships remain largely unexplored. Studies thus far have assumed shortest path routing as the exclusive method for communication in brain networks, despite evidence showing the brain not to communicate exclusively via this model. Finally, connectivity biomarkers do not currently possess the discriminative power needed for clinical use. This proposal seeks to address these issues. First, we propose a novel group decomposition approach to increase disease related signal to background in structural and functional connectivity. Second, we will construct a network morphospace encompassing network measures of connectivity that are sensitive to AD progression. Third, we will investigate the relationship between connectivity biomarkers and longitudinal changes in amyloid deposition, neurodegeneration, and neurocognitive decline. In this way, we will comprehensively test the dysconnection hypothesis and make progress in exploiting the clinical potential of connectivity biomarkers. The candidate has a PhD in biomedical engineering focusing in neuroimaging. The proposed research and training plan seek to add translational and clinical knowledge and to further her quantitative foundation. Dr. Liana Apostolova, a physician-scientist whose specialty is in geriatric neurology and imaging-genetic biomarkers of dementia, is the primary sponsor along with additional co-sponsors and consultants who add relevant expertise. The proposal will facilitate the candidate's goal of becoming an independent researcher.
Researchers believe that the failure of clinical trials for treatment of AD may be substantially attributed to testing of therapeutic agents too late in the disease and the lack of biomarkers that can demonstrate functional effect of treatment, highlighting the critical need for development of biomarkers for early detection and dynamic disease tracking. Increasing evidence for the AD dysconnection hypothesis, which posits that AD spreads along damaged connections via propagation of dysfunctional signaling, suggests that connectivity biomarkers have potential to fill these needs. Therefore, our goal is to comprehensively test the dysconnection hypothesis by (1) developing methods to improve the discriminative power of connectivity biomarkers, (2) comprehensively investigating the sensitivity of network measures to detecting AD related changes across the disease spectrum, and (3) investigating the relationship between connectivity biomarkers and canonical biomarkers of amyloid deposition, neurodegeneration, and neurocognitive decline.