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Emery Brown, Ph.D., M.D.

Associate Professor of Anesthesia, Health Science, and Technology
Massachusetts General Hospital
Dept. of Anaesthesia and CC, Clinics 3
55 Fruit St.
Boston, MA 02114
Telephone: 617-726-8786
Fax: 617-726-8410
Email: brown@neurostat.mgh.harvard.edu
Predocs: 5 Postdocs: 5 Completed PhD's: 4

  

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.

General anesthesia is a neurophysiological state in which a patient is rendered unconscious, insensitive to pain, amnestic, and immobile, while being maintained physiologically stable. The mechanism by which an anesthetic drug induces general anesthesia remains unknown. A new research direction in my laboratory is use of functional neuroimaging to study how the state of general anesthesia is induced and maintained in humans.

 

Selected References:

  • Brown EN, Mitra PP, Kass RE. Multiple neural spike train data analysis: state-of-the-art and future challenges. Nature Neuroscience , 2004; 7(5): 456-61.
  • Smith AC, Frank LM, Wirth S, Yanike M, Hu D, Kubota Y, Graybiel AM, Suzuki WA, Brown EN. Dynamic analysis of learning in behavioral experiments. Journal of Neuroscience , 2004, 24(2): 447-461.
  • Okatan M, Wilson MA, Brown EN. Analyzing functional connectivity using a network likelihood model of ensemble neural spiking activity. Neural Computation , 2005, 9:1927-61.
  • Srinivasan L, Eden UT Willsky AS, Brown EN. A state-space analysis for reconstruction of goal-directed movements using neural signals, Neural Computation, 2006, In Press.
  • Ergun A, Barbieri R, Eden UT, Wilson MA, Brown EN. Construction of point process adaptive filter algorithms for neural systems using sequential Monte Carlomethods. IEEE Transactions on Biomedical Engineering, 2006, In Press