Interactive Effects of Aging and AD on Brain Networks

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PROJECT SUMMARY Among the known risk factors for late-onset AD, age is considered the greatest. After age of 65, the risk of developing AD doubles every five years. While there is a consensus that late-onset AD mainly impacts the aging brain, the direct effects of aging on development and progression of AD have been mostly overlooked. In fact, the majority of human neuroimaging studies of AD consider age as a confounding factor when reporting the AD outcomes. Several age-related processes including inflammation, mitochondrial dysfunction, synaptic loss and vascular dysfunction may contribute to AD. These processes impact microstructural properties of gray and white matter such as neurite morphology years before they can be reliably detected using conventional MRI measures. They also impact network-level computations as brain reorganizes to compensate for these changes. A gap in knowledge is that brain regions that show most age-related changes in their microstructural organization may be more vulnerable to AD pathology. Advances in MRI techniques have provided us with the ability to probe microstructural organization of cortical and white matter such as neurite morphology in human in vivo. To bridge this gap and in response to the high-priority research topic PAR-19-070 (NOT-AG-18-051: understanding AD in the context of aging brain), we propose a multi-level study to examine microstructural (e.g., cortical neurite morphology) and connectome-level organizational properties of brain networks that are most affected in aging and may contribute to AD. We will pursue three Aims: (1) To examine microstructural properties of gray matter and white matter that are most vulnerable in aging and are most impacted by AD pathology. We will leverage Stanford ADRC PET-MR and deep phenotyping resources and will collect novel, quantitative MRI markers of brain microstructure including measures of neurite morphology and macromolecular tissue volume (MTV) in 120 older adults who have a clinical consensus diagnosis of either cognitively normal controls (HC) or mild cognitive impairment (MCI), and will be confirmed to be Aβ- or Aβ+ with PET; (2) To examine the interaction of aging and AD on organizational properties of human connectome. We will leverage ADNI neuroimaging data to achieve this goal and will validate the findings using an independent dataset, namely the Stanford ADRC dataset; (3) To characterize the trajectory of changes in organizational properties of brain networks in normal aging and during transition to AD phenotypes. Leveraging ADNI longitudinal data, we will apply connectomic analysis, accelerated longitudinal design with mixed effect modelling to model the trajectory of organizational changes of brain networks in normal aging and test the alterations of trajectories at different stages of AD. Successful completion of this study will significantly improve our understanding of AD in the context of aging and will inform development of novel therapeutics of AD targeting aging mechanisms.