Using Artificial Intelligence to Predict Cognitive Training Response in Amnestic Mild Cognitive Impairment

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Project Summary/Abstract: This K23 Clinical Trial project will provide Dr. Gullett, an Assistant Professor at the University of Florida, the direct mentored-training needed to address important questions related to intervention response in an amnestic mild cognitive impairment (aMCI) diagnosed population at risk for Alzheimer’s disease. As a neuropsychologist, Dr. Gullett has gained clinical experience in the assessment of neurodegenerative diseases including Alzheimer’s disease and its precursor, MCI, as well as research experience using structural neuroimaging to investigate various clinical disorders. The support provided by the K23 mechanism through the NIA will provide Dr. Gullett with the protected mentored-training needed to build on his current skills and become an expert in clinical neuroscience, machine learning, and behavioral interventions for mild cognitive impairment and Alzheimer’s disease. Career development and training plan: Dr. Gullett’s training plan consists of foundational formal coursework in 1) clinical trials, 2) MCI and Alzheimer’s disease effects, and 3) biostatistics and machine learning investigative techniques. These foundations will be directly applied through mentorship by experts in the fields of behavioral cognitive interventions, neuroimaging, and machine learning, as well as a proposed in-person workshop in functional neuroimaging analysis. This mentored-training plan will provide Dr. Gullett with the expertise to not only carry out the proposed project, but to become a unique and invaluable resource for future collaborative efforts applying neuroscience-based machine learning tools to investigate personalized interventions for Alzheimer’s disease. Research plan: The proposed project will provide the clinical trials training needed for Dr. Gullett to establish the effectiveness of a planned take-home, 12-week cognitive training program in patients with amnestic mild cognitive impairment (N=75; Aim 1). The expert mentorship team proposed has decades of experience in behavioral clinical trials interventions, which will provide the applicant with design and methodology guidance, as well as the recruitment infrastructure and resources needed to successfully carry out the proposed project. Further, this project will provide training in multi-modal neuroimaging-based machine learning to determine the baseline neural, cognitive, and functional factors that distinguish aMCI patients who respond to treatment from those who do not (Aim 2). This innovative approach will ultimately allow the applicant to investigate which of a myriad of features aMCI patients possess at a baseline assessment are the most salient predictors of their ability to improve from a well-validated cognitive training intervention. A project such as this will enable Dr. Gullett to develop a unique skillset to facilitate an R01-level academic career tasked with providing individual aMCI patients personalized interventions based on their own unique neurobiological and cognitive features.