Helen Mayberg

Helen Mayberg
Emory University
Atlanta, Georgia, United States

Session: G. Brain disorders I

Will talk about: The evolving role for imaging in optimizing treatment for depression

Bio sketch:

Helen Mayberg received a BA in psychobiology from University of California, Los Angeles and an MD from University of Southern California.  She is a Board Certified Neurologist, trained at the Neurological Institute of NY at Columbia University and a post-doctoral fellowship in Nuclear Medicine at the Johns Hopkins Medical Institutions.

Dr Mayberg heads a multidisciplinary depression research program dedicated to the study of brain circuits in depression and the effects of various antidepressant treatments measured using a variety of functional and structural imaging tools.  The primary focus of the lab to development imaging and physiological based algorithms that will discriminate depressed patient subgroups and optimize treatment selection at all stages of the illness.  Imaging findings provided the foundation for development and testing of deep brain stimulation (DBS) of the subcallosal cingulate region (Area 25), a novel intervention for patients with treatment resistant depression.

She was recently named as one of Emory University’s “Game Changers” in recognition of her pioneering DBS research which has been heralded as a one of the first hypothesis-driven treatment strategies for a major mental illness.  Clinical trials are now ongoing in both North America and Europe.  At Emory, DBS studies aim to refine, optimize and extend the potential of this treatment strategy with experiments designed to characterize and define mechanisms mediating DBS response and to develop biomarkers that will improve patient selection, enhance precision of surgical targeting and optimize stimulation parameters.

Talk abstract:

Despite the effectiveness of antidepressant treatment for many people, there is no reliable method to match an individual depressed patient to their best option.  Needed are clinically viable algorithms that both select the best treatment and avoid ineffective ones, while also identifying patients that require alternatives to standard first-line interventions. Towards this goal, various imaging strategies have been used to explore pretreatment scan patterns predictive of differential response to different treatments.  With this approach, putative biosignatures have been identified using FDG PET and complementary resting-state fMRI imaging that stratify patients into two distinct subtypes predictive of likely remission to escitalopram or cognitive behavioral therapy as well as distinguish those patients who will fail combined treatment. Studies of known treatment resistant patients provide further evidence of additional depression subtypes with implications for treatment selection at all stages of illness.  Extending these studies, patient-specific network maps are currently being used to refine and optimize surgical targeting for deep brain stimulation.  Going forward, comparisons of the different imaging modalities as well as development of non-imaging surrogates will be necessary to determine the most accurate and accessible method for clinical use in individual patients.  In the meantime, these and related findings lay foundation for continued characterization of brain-based depression subtypes towards an evidenced based, precision medicine approach to the treatment of mood disorders.