Dementia is a growing problem in the aging population in the U.S. and worldwide, with prevalence approaching 50% in those older than 85 years old and Alzheimer's disease (AD) is the most frequent cause. Significantly increased risk for AD dementia is associated with advanced age and carrier-status of the Apolipoprotein E4 allele (APOE-e4), while neuropathological hallmarks of AD include both intracellular pathological neurofibrils as well as extracellular accumulation of Amyloid beta (A?) plaques. However recent studies suggest other pathological factors such as cerebral iron content may also play important roles and may combine with amyloid-? to accelerate clinical deterioration. The recent development of quantitative susceptibility mapping (QSM) using MRI phase measurement offers a non-invasive measure of cerebral iron at high spatial resolution, allowing in vivo assessment of local iron concentration in brain regions vulnerable to AD pathology. This provides a great opportunity for improving our knowledge on the pathophysiological mechanism of AD related to brain iron. A large set of susceptibility MRI data, including longitudinal follow-ups, has already been acquired as part of the NIH funded BIOCARD study located at Johns Hopkins with extensive valuable measurement on cognitive, clinical, genetic, and other AD biomarker data, including up to 20 years of prior cognitive testing. The overall objective of the proposed project therefore is to analyze these data and determine the contribution of cerebral iron load to cognitive aging and cognitive impairment related to Alzheimer?s disease (AD) in elderly population. In the proposed project, our first aim is to determine whether local cerebral iron level is associated with cognitive performance in cognitively normal subjects and MCI in a cross-sectional way. We will first determine the local brain iron level using QSM analysis of the MR phase data with recently developed automated susceptibility brain multi-atlas quantification tools. We will then test the hypothesis that increased iron levels in certain vulnerable brain regions is significantly associated with lower cognitive performance accounting for other known AD risk factors such as APOE-e4 genetic status, brain atrophy and cerebral A? load. The interaction between brain iron levels and A? load measured by PET imaging will further be assessed in these regions. For the second aim we will measure the cerebral iron deposition rate using the longitudinal dataset acquired in the same cohort in brain regions where cerebral iron levels are associated with cognitive performance as found in the first aim. We will then test the hypothesis that higher cerebral iron levels and faster iron deposition rate in these AD vulnerable brain regions are associated prospectively with greater short- term cognitive decline, particularly among participants with MCI, and retrospectively with more negative long- term cognitive trajectories in the years preceding the iron measurement. If successful, we will help establish the role of brain iron accumulation in AD on cognitive decline and its relation to other AD risk factors, which in turn would help better understand the AD pathophysiology and potentially help establish new imaging markers.
The objective of this project is to determine the contribution of cerebral iron load to cognitive aging and cognitive impairment related to Alzheimer?s disease (AD) in elderly population. We will assess local cerebral iron load and its longitudinal changes using the recently developed quantitative susceptibility mapping (QSM) techniques and acquired susceptibility MRI data in the ongoing longitudinal BIOCARD study established on cognitive normal subjects and MCI. The role of brain iron level as predictor of cognitive function will be tested accounting for other known genetic and pathological AD risk factors including APOE e4, brain atrophy, CSF Amyloid beta and tau, and brain Amyloid beta load as measured by PET. Iron levels and its deposition rate will further be tested against longitudinal prospective and retrospective cognitive trajectories.