Predictive analytics for cognitive decline and Alzheimer’s disease

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Project Summary/Abstract Alzheimer’s disease (AD), the most common cause of dementia in the elderly, is a major global healthcare burden. However, there is still no effective disease modifying therapy for AD and clinical trials with the aim of preventing or stabilizing cognitive impairment have largely failed. Decision making in both clinical practice and research is highly dependent on practical predictive tools, which can effectively predict cognitive or functional outcomes in individuals. Such models could be potentially used in clinical research to boost the power of trials by enrollment of participants who are most likely to show disease progression during the trial’s timeframe. Alternatively, these models could be used to identifying individuals who would benefit from primary or secondary prevention once there are effective treatments for AD. In this project, we aim to provide a framework for practical prediction of cognitive decline with aging and prodromal AD, by applying a novel ML framework to multiple dimensions of data (demographics, genetic risk scores, neuropsychological measures, structural MRI, and amyloid imaging). Our ultimate goal is to arrive at a new “Machine Learning predictive framework for aging and AD” (ML4AD), comprised of dimensions each of which each will add incremental value to the predictive models, hence increasing the performance of predictive models while keeping the costs and burden of research at a minimum. The candidate for this Mentored Patient-Oriented Career Development Award (K23), Dr. Ali Ezzati, is a Neurologist whose career goal is to develop predictive tools to help research and clinical decision making in cognitive aging and dementia. The proposed research will leverage the rich clinical and biomarker dataset available from several ongoing international studies, but will also provide a unique avenue of investigation for the candidate. The candidate's career development will benefit from close mentorship and scientific guidance of outstanding investigators in aging/AD neurobiology (Dr. Lipton), machine learning and computational neuroscience (Dr. Davatzikos), and biostatistics (Dr. Hall). The findings from this study will inform future secondary prevention trials, in which sensitive indicators of early AD will be necessary to identify high-risk subjects and track early clinical decline. This work will serve as the foundation to move forward in independent research focusing on development of predictive tools in AD and related neurodegenerative disorders. Key words: Alzheimer’s Disease, Dementia, Mild Cognitive Impairment, Cognitive neurology, Artificial Intelligence, Machine Learning, Predictive Analytics, Longitudinal Cohort, Big Data