Roy Perlis, M.D., M.Sc.


Director
Center for Quantitative Health, Massachusetts General Hospital
Professor of Psychiatry
Harvard Medical School
Associate Member
Broad Institute
MD Harvard Medical School 1997
antidepressive agents; artificial intelligence; bioinformatics; bipolar disorder; citalopram; depression; evil robots; fluoxetine; gender equity; genetics; informatics; machine learning; major depressive disorder; pinball; serotonin uptake inhibitors; suicide; treatment-resistant depression

Roy Perlis, MD, MSc is the Director of the Center for Quantitative Health at Massachusetts General Hospital, and Associate Chief for Research in the Department of Psychiatry. He is Professor of Psychiatry at Harvard Medical School and Associate Editor (Neuroscience) at JAMA's new open-access journal, JAMA Network - Open. He graduated from Brown University, Harvard Medical School and Harvard School of Public Health, and completed his residency, chief residency, and clinical/research fellowship at MGH before joining the faculty. 

Dr. Perlis's research is focused on identifying predictors of treatment response in brain diseases, and using these biomarkers to develop novel treatments. He directs two complementary laboratory efforts, one focused on patient-derived cellular models and one applying machine learning to large clinical databases. These two programs converge in the MGH NeuroBank, one of the largest cellular biobanks in the world for the study of neurodevelopmental and neurodegenerative disorders. The NeuroBank spans more than 400 cell lines associated with detailed clinical phenotypic assessment and links to electronic health records. 

Dr. Perlis's laboratory has made a number of key contributions to understanding the biological basis of psychiatric disease. In work published in Nature Neuroscience, his team described abnormalities in synaptic pruning using neurons and microglia from individuals with schizophrenia, laying the groundwork for high-throughput screens to identify interventions with the potential to treat and potentially prevent schizophrenia and related disorders. In 2016 he co-led the team that identified the first genetic variation associated with major depressive disorder, published in Nature Genetics. His team was also the first to apply machine learning to predict antidepressant response and the first to complete genome-wide association studies of suicide and lithium response. In total, Dr. Perlis has authored more than 250 articles reporting original research, in journals including Nature Genetics, Nature Neuroscience, JAMA, NEJM, the British Medical Journal, and the American Journal of Psychiatry. His research has been supported by awards from NIMH, NHGRI, NHLBI, NICHD, NCCIH, and NSF, among others. In 2010 Dr. Perlis was awarded the Depression and Bipolar Support Alliance's Klerman Award; he now serves as a scientific advisor to the DBSA.