Investigating a Multimodal Approach to Clinical Diagnosis of Mild Cognitive Impairment and Alzheimer’s Disease
An estimated 5.8 million Americans suffer from dementia due to Alzheimer’s disease (AD), with that number projected to grow to 13.8 million by mid-century (Alzheimer’s Association, 2019). Mild cognitive impairment (MCI) describes the stage between normal cognitive decline that comes with aging and a dementia diagnosis (Peterson, 1999). Due to a lack of a cure or particularly effective treatment, a major goal of treatment is to focus on improving quality of life (Budson & Solomon, 2016). An early and accurate diagnosis can address this goal in a variety of ways. Despite the high prevalence and immense amount of research in MCI and AD, there is still no individual assessment measure that can definitively diagnose either. A multimodal approach must be implemented by clinicians and investigated by researchers to ensure early and accurate diagnosis. This study used multivariate logistic regression to analyze how two neuropsychological screening tests, two brain structures’ volumes, and an eye-tracking outcome all contributed to the diagnostic process. The two screening tests were the only unique contributors to the predictive model, and there was only slight evidence to suggest that the multimodal approach using these measures improved accuracy of diagnosis.
Alzheimer, MCI, Multimoda, Clinical Psychology, Geriatrics, Gerontology
Networked Digital Library of Theses and Dissertations