Functional Networks of White Matter in Alzheimer's Disease and their Associations with Cognitive Decline - Alzheimer's disease (AD) is a neurodegenerative disorder that affects a patient’s memory, language, and executive function. AD affects one in nine (>10%) in the US aged 65 years and older. The specific pathogenesis and the underlying neurophysiology of AD have not been fully understood. The white matter (WM) regions of the brain, making up approximately 50% of the total brain volume, provide the communication pathways necessary for information exchange between gray matter (GM) cortical regions, and many brain disorders have been associated with WM deficiencies. Recent developments in resting-state functional magnetic resonance imaging (rfMRI) have enabled the study of the human brain’s functional connectivity and functional networks. Studies have found that there is detectable functional connectivity within WM regions and between WM and GM regions. However, these findings were based on healthy adults, and WM-rfMRI of typical aging and AD are not known. In this study, we will systematically characterize the signal properties of WM-rfMRI of typical aging, mild cognitive impairment (MCI) and AD progression. We will characterize the functional connections between the WM and GM regions and the underlying WM structural tracts. We will also investigate how WM-rfMRI is associated with phenotypic traits in MCI and AD. We will use five large neuroimaging datasets with a total of more than 5,200 subjects for the aims of this study. The rationale for this study is based on our preliminary studies that investigated WM-rfMRI of healthy adults from the Human Connectome Project dataset. We found that WM-rfMRI was associated with GM-rfMRI functional networks and that there was a significant overlap between the WM--rfMRI and the underlying WM structural tracts. In this study, we will build on our previous studies to examine WM-rfMRI properties of typical aging, MCI, and AD. Aim1: To characterize WM-rfMRI of typical aging. Aim 2: To characterize WM-rfMRI of MCI and AD; Aim 3: To investigate WM-rfMRI–phenotype associations and the predictability of phenotypes in MCI and AD. Aim 4: To develop and disseminate a WM-rfMRI in Aging, MCI, and AD toolbox. We hypothesize that the WM- rfMRI properties will have significant test-retest reliability, will be significantly different among typical aging, MCI and AD and can reliably predict clinical scores of MCI and AD. We will apply advanced analytic and machine learning approaches on WM-rfMRI for the aims of this study. To the best of our knowledge, this study will be one of the first to perform a comprehensive analysis of WM-rfMRI in typical aging, MCI, and AD. This study will contribute towards uncovering potential WM-rfMRI markers of MCI and AD, and will facilitate the use of WM- rfMRI in future aging studies. This study will also provide a strong foundation to study brain function and dysfunction as an integrated system of both WM and GM. The long-term goal of this project is to better understand the effect of WM structure and function on cognition, and to apply rfMRI for more reliable diagnosis, prognosis and treatment of AD.
