Cheryl Grady

Dr. Grady received her graduate training in experimental psychology at Boston University. She then went to the Laboratory of Neuroscience at the National Institute on Aging in Bethesda, Maryland, as a research psychologist and Chief of the PET Unit, where she stayed until 1996. In 1996 she moved to Toronto to take up her current position at the Rotman Research Institute at Baycrest. Dr. Grady currently is a senior scientist at the Rotman Research Institute, and was the Assistant Director of the Institute from 2004 to 2010. She is a Professor in the departments of Psychiatry and Psychology at the University of Toronto, and holds a Tier 1 Canada Research Chair in Neurocognitive Aging. In 2001 she was awarded the Justine and Yves Sergent Award for Women in Neuroscience, and in 2010 was the recipient of the Donald Stuss Award for Research Excellence. Her work has been consistently funded by the Canadian Institutes of Health Research, and she currently holds a CIHR Foundation Grant awarded in 2015. Dr. Grady is an external advisor for several research groups in the US, is an Associate Editor for Human Brain Mapping, and has served as Scientific Officer for the Behavioural Sciences C (BSC) review committee for CIHR.

Research Synopsis

Dr. Grady’s research uses functional and structural neuroimaging techniques to study cognitive aging. The work aims to determine how the functional connectivity of various brain networks mediates cognition and how this connectivity is modified by age. Her recent work has shown that age differences in connectivity of the fronto-parietal control network influence connectivity in other networks, and that stronger inter-network connectivity of this network during encoding is beneficial for memory performance in older adults. Other work has shown lifespan differences in the relation between brain network connectivity and cognitive control, and that these differences are relevant for adaptive function in everyday life. Additional research projects involve assessing brain signal variability in relation to aging and cognitive performance, and using predictive modeling to identify patterns of brain activity linked to successful vs. unsuccessful memory retrieval in young and older adults.