Automated Web-Based Behavioral Diagnostics of Cognitive Impairment

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DESCRIPTION (provided by applicant): Alzheimer's disease affects an estimated 5.3 million Americans currently, and this number is expected to grow significantly in the coming decade. A critical goal of Alzheimer's disease research is to improve current methods of diagnosis so that patients can be identified sooner and, therefore, obtain greater advantage from available therapies. The major goal of this project is to develop and validate an automated, web-based method for early diagnosis of cognitive decline and memory loss. We will build on current research that has demonstrated a clear link between performance on the Visual Paired- Comparison Task (VPC) and diagnosis of MCI, and we will extend the impact of this finding by developing a suite of software tools and analysis methods that will improve the accuracy and extend the accessibility of cognitive diagnostics with a web-based VPC task that can be widely deployed. Although the VPC task is promising as a diagnostic aid, clinical application is severely limited by the need to use an eye tracker to precisely monitor subjects' eye movements. Unfortunately, eye trackers are expensive, require trained personnel, and are not widely available. However, our preliminary findings show that it is possible to produce picture examination behavior that is similar to eye-movements, using modified versions of the VPC task that could be administered by anyone, on any computer with an internet connection. In addition, powerful machine learning techniques from computer science can help accurately analyze and diagnose the resulting behavior. An important contribution from this work will be the possibility of predicting oncoming cognitive decline in MCI patients sooner than is now possible, that is, at a time when the nervous system is less compromised and more likely to benefit from therapeutic intervention. In support of this objective, we propose three aims: 1) Develop and investigate appropriate representation of eye movement characteristics and corresponding machine learning-based classification techniques for effective identification of the patient status, 2) Develop and validate a web-based version of the VPC task, and 3) explore the feasibility of our task as an automatic screening instrument for the general elderly population. Successful completion of these aims has the potential to dramatically alter the current practice of clinical translational research as well as the current methods used for diagnosing cognitive deficits. This would enable thousands and potentially millions of patients and potential research subjects to take the test as part of their routine checkup, requiring nothing more than a computer with an internet connection.