Congratulations to Dr. Ruogu Fang (PI), Dr. Adam Woods (PI) and Dr. Samuel Wu (Co-I) on their recent NIH NIA (National Institute on Aging) R01 grant titled, Mechanisms, response heterogeneity and dosing from MRI-derived electric field models in tDCS augmented cognitive training: a secondary data analysis of the ACT study, for $2.9 million.
Dr. Fang is the Director of Smart Medical Informatics Learning and Evaluation (SMILE) Lab in the Biomedical Engineering Department. Dr. Fang is also a tenure-track assistant professor in the Department of Biomedical Engineering, with affiliation to Electrical and Computer Engineering, Computer and Information Science and Engineering, and Radiology. Dr. Woods is an associate professor and Associate Director of the Center for Cognitive Aging and Memory Clinical Translational Research (CAM) in the McKnight Brain Institute at UF. Dr. Wu is a professor and associate chair in the Department of Biostatistics at the University of Florida.
Age-related cognitive decline has become a significant public health concern. As the population ages, the number of older adults experiencing cognitive and functional disturbances from Alzheimer’s and other age-related neurodegenerative diseases has increased. There is currently a paucity of effective interventions to prevent or treat cognitive decline or to enhance brain function in the elderly. Such interventions will be necessary to reduce the projected prevalence of cognitive impairment and Alzheimer’s disease over the coming decades. Cognitive training (CT) has shown promise in this domain. The NIA Alzheimer’s Disease Initiative funded Phase III Augmenting Cognitive Training in Older Adults (ACT) trial demonstrated that transcranial direct current stimulation (tDCS) paired with cognitive training could achieve this goal.
All prior trials of tDCS have applied a fixed dosing strategy in attempts to enhance CT. However, previous research demonstrates that individual variability in head and brain anatomy (e.g., degree of atrophy, skull thickness, etc.) significantly alters the spread and intensity of direct electrical current delivered to the brain from person to person.
This project will leverage existing multimodal neuroimaging and behavioral outcomes data from the ACT trial to 1) elucidate mechanism of action underlying response to tDCS treatment with CT, 2) address heterogeneity of response in tDCS augmented CT by determining how individual variation in the dose of electrical current delivered to the brain interacts with individual brain anatomical and lesion characteristics; and 3) refine the intervention strategy of tDCS paired with CT by evaluating methods for precision delivery targeted dosing characteristics to facilitate tDCS augmented outcomes. We will employ state of the art MRI-derived computational modeling and machine learning (ML) to 1) create precision individualized models of electrical current in the brain from tDCS for all 360 participants in ACT, 2) determine the characteristics of electrical current associated with trial outcomes, and 3) evaluate a deployable method for calculating precision dosing of tDCS parameters for optimizing trial outcomes in older adults.
Leveraging a robust and comprehensive behavioral and multimodal neuroimaging data set for ACT with advanced computational methods, the proposed study will provide critical information for mechanism, heterogeneity of treatment response and a pathway to refined precision dosing approaches for remediating age-related cognitive decline and altering the trajectory of older adults toward Alzheimer’s disease.
- Dr. Ruogu Fang, Assistant Professor, Department of Biomedical Engineering, University of Florida, Director, Smart Medical Informatics Learning and Evaluation (SMILE) Lab
- Dr. Adam Woods, Associate Professor, Associate Director, Center for Cognitive Aging and Memory Clinical Translational Research (CAM), McKnight Brain Institute, Department of Clinical and Health Psychology, College of Public Health and Health Professions, University of Florida
- Dr. Samuel Wu, Professor, Associate Chair, Department of Biostatistics, University of Florida