Alzheimer's disease (AD) is a degenerative brain disorder and the most common cause of dementia, affecting more than 5 million Americans and 30 million people worldwide. In this dissertation, I describe two studies investigating imaging and non-imaging biomarkers for the diagnosis of AD. Two groups of patients were studied: individuals with a clinical diagnosis of AD and individuals with mild cognitive impairment (MCI), an intermediate state between normal cognitive aging and AD. Patients with MCI have an increased risk of developing AD-type dementia. Although many MCI patients develop dementia, other individuals with MCI stay cognitively stable or even regain normal cognitive status. In the first study, diffusion tensor imaging (DTI) was used to study the effects of AD and MCI on the integrity of limbic white matter pathways. Declines in the integrity of the fornix and the descending cingulum were detectable in both AD and MCI patients. Decreased integrity of the descending cingulum was associated with decreased glucose metabolism in the posterior cingulate cortex (PCC), the earliest detectable sign of AD on positron emission tomography scans. These findings suggest that the integrity of limbic white matter pathways, as measured by DTI, may serve as a useful biomarker for the diagnosis of AD. These findings also support the "disconnection hypothesis" as a mechanism for the PCC hypometabolism observed in patients with incipient AD. In the second study, we used statistical pattern classification methods and data from the Alzheimer's Disease Neuroimaging Initiative to develop a prognostic model of dementia for patients with MCI. More than 750 variables spanning clinical, magnetic resonance imaging (MRI), and plasma proteomic data were considered as potential predictors of progression from MCI to dementia. A model based on a small number of clinical and MRI predictors was found to have good predictive performance. I describe the characteristics of the model, including its advantages and limitations. The prognostic model of dementia developed here provides a non-invasive, cost-effective approach that can be used to improve the selection of MCI patients in clinical trials and identify high-risk MCI patients for early anti-AD treatment.
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