Enhancing tES automated models for aging brains

In the field of transcranial electrical stimulation, computational models serve to provide predictions for the direction and intensity of current flow in the brain. Recent advances in computational modeling have provided relatively automated methods for extracting person specific head models of current flow from structure MRI data (T1). However, age-related changes in structure from atrophy and other non-pathological processes provide unique challenges for automated modeling approaches. This National Institute of Mental Health funded supplement (Bikson, PI) brings together expertise in computational modeling at City College of New York (Marom Bikson, PhD), tDCS and aging expertise at the University of Florida (Adam Woods, PhD), and current imaging expertise at University of Southern California (Danny Wang, PhD) to optimize automated modeling for aging brains. This project will help extend this promising technology to clinical trials in age-related conditions and provide available tools to the aging research community for implementation.











Funding Source:

National Institute of Mental Health Supplement to R01 MH111896