Alzheimer?s disease (AD) is the most common form of dementia, with the number of affected Americans expected to reach 13.4 million by the year 2050. While it is well known that AD leads to progressive neuronal death, the exact mechanism of AD remains elusive. Currently, a definitive diagnosis can only be reached by autopsy or brain biopsy, and the neurodegenerative processes in two AD patients can follow very different courses. Further, treatment options for AD remain limited, let alone cure. For this reason, non-invasive neuroimaging has been extensively investigated in the hope that it may provide more sensitive markers for screening and early detection of AD. Yet, despite the amount of resources devoted to AD imaging research, CSF Tau and A?42 continue to outperform any non-invasive imaging markers. Multimodal connectomics, including functional and structural connectome (derived from fMRI and diffusion MRI respectively), has the potential to gain system-level structure- function insights into the mechanisms of AD and thus offers a novel platform for developing new diagnostic strategies. Despite a number of interesting connectome findings in recent years, few of these connectome results have been replicated independently or proven clinically relevant, which can be partially explained by the sensitivity to parameter settings during preprocessing and connectome construction, such as the choice of brain parcellation and the type of fMRI time series correlations (full versus partial) or tractography (deterministic or probabilistic). Moreover, conventional connectome approaches usually focus on scalar summary statistics (e.g., nodal or edge-wise measures) using linear statistical techniques, which fit at each node (or edge) independent of other nodes (or edges) and thus discard important informative graph structure. Instead, this proposal will develop a multi-view connectome framework that homogenizes multiple instances of stable and reproducible high-level connectome properties across modalities and across spatiotemporal scales. This framework will be applied and cross-validated using two independent AD cohorts (Alzheimer's Disease Neuroimaging Initiative or ADNI and Wisconsin Alzheimer?s Disease Research Center cohort or Wisconsin ADRC). The identified connectome features can serve as the potential non-invasive markers for guiding the AD diagnosis.
Alzheimer?s disease (AD) is the most common form of dementia, with the number of affected Americans expected to reach 13.4 million by the year 2050. In this proposal we will develop a novel multi-view connectome framework that will analyzes instances or ?views? of stable high-level connectome properties across modalities and across spatiotemporal scales. We will apply this framework to two large Alzheimer?s imaging cohorts, leading to the discovery of novel sensitive imaging markers for the early detection of AD.
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