Eric Landsness, MD, PhD (Speaker/Moderator)
Washington University in St. Louis
Dr. Eric C. Landsness is an Assistant Professor of Neurology at Washington University School of Medicine. Dr. Landsness received his BS in Electrical Engineering (2003) from the University of Washington-Seattle. He then entered the Medical Scientist Training Program (MSTP) at the University of Wisconsin-Madison. His PhD thesis in the lab of Dr. Giulio Tononi focused on the underlying mechanisms of sleep, plasticity-dependent learning, and their impact on brain disease which resulted in 13 publications. This was followed by a Neurology residency and Sleep fellowship at Barnes-Jewish Hospital/Washington University School of Medicine from 2013-2018.
In 2018, Dr. Landsness started as an Instructor at Washington University under the mentorship of Jin-Moo Lee. In two short years he has received funding from the American Sleep Medicine Foundation, the American Heart Association and the NIH. He has also received a number of honors including a S. Wier Mitchell Award from the American Academy of Neurology for the best manuscript. Dr. Landsness has mentored 2 undergraduate students since starting on faculty at Washington University, has a leadership role in the American Neurological Association and runs the @WashUNeurology twitter and his personal @EricLandsness account.
Dr. Landsness’ research focuses on improving the understanding of the connection between sleep and stroke. Specifically, there is significant overlap between plasticity-dependent mechanisms for stroke recovery and sleep-dependent plasticity. His research goals are to contribute to the understanding of how sleep modulates stroke recovery and to the discovery of new sleep-focused treatments and interventions to improve outcomes in patients with neurological disease. Some of his recent accomplishments include demonstrating the use of a genetically encoded calcium indicator to image, with simultaneous high spatial and temporal resolution, the slow waves of anesthesia and sleep. He has also shown that following stroke sleep slow waves in the perilesional area predict recovery. For this Emerging Scholars presentation at ANA2020 he will be describing how non-invasive mesoscale calcium imaging reliably classifies sleep states.