Directory

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
• de-noise two photon in vivo imaging data

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 more than 160 years and currently, more than 100,000 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. My laboratory is using 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 BCS, the MIT/Harvard Division of Health Science and Technology, 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.

Dr. Brown served on the National Institutes of Health (NIH) BRAIN Initiative Working Group, and is currently a member of the NIH Council of Councils, the National Science Foundation Mathematics and Physical Sciences Advisory Committee, the Board of Directors of the Burroughs-Wellcome Fund, the Board of Trustees of the International Anesthesia Research Society and the Scientific Advisory Committee of CURE Epilepsy. Dr. Brown is the recipient of an NIH Director’s Pioneer Award, an NIH Director’s Transformative Research Award and the 2011 Jerome Sacks Award for Outstanding Cross Disciplinary Research from the National Institute of Statistical Science. He is a Fellow of the American Institute for Medical and Biological Engineering, the American Statistical Association, the American Association for the Advancement of Science, the IEEE and the American Academy of Arts Sciences. Dr. Brown is a member of the Institute of Medicine, the National Academy of Sciences and the National Academy of Engineering.