Helen S. Mayberg
Icahn School of Medicine at Mount Sinai
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Primary Section: 28, Systems Neuroscience Membership Type:
Member
(elected 2022)
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Biosketch
Helen Mayberg is a neurologist recognized for her neuroimaging studies of brain circuits in depression and their translation to the development of deep brain stimulation as a novel therapeutic for treatment resistant patients. Born and raised in Southern California, she received a BA in Psychobiology from UCLA and a MD from the University of Southern California, then trained in Neurology at Columbia's Neurological Institute in New York and did a research fellowship in nuclear medicine at Johns Hopkins. She had early academic appointments at Johns Hopkins and the University of Texas Health Sciences Center in San Antonio, held the inaugural Sandra Rotman Chair in Neuropsychiatry at the University of Toronto, the first Dorothy C. Fuqua Chair in Psychiatric Imaging and Therapeutics at Emory University and is now the Mount Sinai Professor of Neurotherapeutics at the Icahn School of Medicine where she is founding Director of the Nash Family Center for Advanced Circuit Therapeutics. She is a member of the both the National Academy of Sciences and the National Academy of Medicine as well as the National Academy of Inventors and American Academy of Arts and Sciences.
Research Interests
Helen Mayberg's lab takes a transdisciplinary neural systems approach to the study of major depression and its recovery. Studies integrate multimodal imaging, quantitative behavioral and psychophysiological metrics, and computer vision and machine learning techniques within experimental clinical trial protocols to define brain mechanisms mediating antidepressant treatments. The core mission is to develop imaging biomarkers and algorithms that will discriminate patient subgroups and optimize treatment selection in the management of individual patients across all stages of illness. The lab also serves as base camp for ongoing testing and refinement of deep brain stimulation for treatment resistant depression utilizing a range of mechanism-of-action and predictive biomarker strategies including collaborations involving animal models. Technological advances with implanted devices now enables intracranial monitoring during chronic treatment, providing new tools for treatment optimization and mechanistic perspectives on the trajectory and sustainability of DBS effects. Complementary strategies using computer vision and machine learning approaches are being developed to detect more subtle changes in core depression-relevant behaviors, developed using high-throughput video analyses of face expression, vocal output and body movements.
