Emery N. Brown, MD, PhD

Degrees

• PhD in Statistics, Harvard University, 1988
• MD, Harvard Medical School, 1987
• AM in Statistics, Harvard University, 1984
• BA, Harvard College, 1978

Selected Awards/Societies

• 2008
  Fellow of the IEEE
• 2007
  Elected Member of the Institute of Medicine of the National Academies
• 2007
  NIH Director's Pioneer Award
• 2007
  Fellow of the American Association for the Advancement of Science
• 2006
  Fellow of the American Statistical Association
• 2006
  Fellow of the American Institute for Medical and Biological Engineering
• 2002
  Elected Member of the Association of University Anesthesiologists

Research Interests

Neural Signal Processing Algorithms

Recent technological and experimental advances in the capabilities to record signals from neural systems have led to an unprecedented increase in the types and volume of data collected in neuroscience experiments and hence, in the need for appropriate techniques to analyze them. Therefore, using combinations of likelihood, Bayesian, state-space, time-series and point process approaches, a primary focus of the research in my laboratory is the development of statistical methods and signal-processing algorithms for neuroscience data analysis.

We have used our methods to:

• characterize how hippocampal neurons represent spatial information in their ensemble firing patterns.
• analyze formation of spatial receptive fields in the hippocampus during learning of novel environments.
• relate changes in hippocampal neural activity to changes in performance during procedural learning.
• improve signal extraction from fMR imaging time-series.
• characterize the spiking properties of neurons in primary motor cortex.
• localize dynamically sources of neural activity in the brain from EEG and MEG recordings made during cognitive, motor and somatosensory tasks.
• measure the period of the circadian pacemaker (human biological clock) and its sensitivity to light.
• characterize the dynamics of human heart beats in physiological and pathological states.

Understanding General Anesthesia
General anesthesia is a neurophysiological state in which a patient is rendered unconscious, insensitive to pain, amnestic, and immobile, while being maintained physiologically stable. General anesthesia has been administered in the U.S. for nearly 160 years and currently, more than 50,000 people receive anesthesia daily in this country for surgery alone. Still, the mechanism by which an anesthetic drug induces general anesthesia remains a medical mystery. A new research direction in my laboratory is to use a systems neuroscience approach to study how the state of general anesthesia is induced and maintained. To do so, we are using fMRI, EEG, neurophysiological recordings, microdialysis methods and mathematical modeling in interdisciplinary collaborations with investigators in HST, the Department of Brain and Cognitive Sciences at MIT, Massachusetts General Hospital and Boston University. The long-term goal of this research is to establish a neurophysiological definition of anesthesia, safer, site-specific anesthetic drugs and to develop better neurophysiologically-based methods for measuring depth of anesthesia.

Reference Publications

• Ergun A, Barbieri R, Eden UT, Wilson MA, Brown EN. Construction of point process adaptive filter algorithms for neural systems using sequential Monte Carlo methods. IEEE Transactions on Biomedical Engineering, 2007, 54(3): 419-428.
• Smith AC, Wirth A, Suzuki WA, Brown EN. Bayesian analysis of interleaved learning and response bias in behavioral experiments. Journal of Neurophysiology, 2007, 97(3): 2516-2524.
• Diniz Behn C, Brown EN, Scammell TE, Kopell NJ. A mathematical model of network dynamics governing mouse sleep-wake behavior. Journal of Neurophysiology, 2007, 97(6): 3828-3840.
• Srinivasan L, Eden UT, Mitter SK, Brown EN. General purpose filter design for neural prosthetic devices. Journal of Neurophysiology, 2007 (Published online May 23, 2007).