Division of Sleep Medicine @ Harvard Medical School
Faculty Profile
Emery N. Brown, MD, PhD
Massachusetts General Hospital Professor of Anaesthesia, Harvard Medical School
Anesthetist, Department of Anesthesia, Massachusetts General Hospital
Administrative Title(s)
Director, Neuroscience Statistics Research Laboratory, Department of Anesthesia and Critical Care, Massachusetts General Hospital
Other Affiliation(s)
Professor of Computational Neuroscience
Professor of Health Sciences and Technology, Massachusetts Institute of Technology
Professor of Health Sciences and Technology, Massachusetts Institute of Technology
Research Unit(s)
Neuroscience Statistics Research Laboratory, Department of Anesthesia and Critical Care, Massachusetts General Hospital
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:
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 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.
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 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.
Trainees
Masters Students
2003-2006 Esosa O. Amayo
2004-2005 Neil U. Desai
Ph.D. Students
1992-1993 Patricia Meehan
1998-2005 Patrick Purdon
2000-2005 Uri Eden
2002-2006 Cecilia Behn
2004-2006 Lakshminarayan Srinivasan
Current Ph.D. Students
2001- Sujith Vijayan
2002- Michelle McCarthy
2005- Dennis Dean
2006- Anna Dreyer
Post-Doctoral Fellows
1997-1999 Ao Yuan
1998-2000 Riccardo Barbieri
1998-2000 Harry P. Luithardt
1999-2004 Anne C. Smith
2000-2003 Loren M. Frank
2000-2003 Catherine Mullaly
2001-2006 Christopher Long
2003-2004 Joao Scalon
2005-2006 Uri Eden
2005-2006 Todd Coleman
Current Post-Doctoral Fellows
2003- Supratim Saha
2003- Murat Okatan
2004- Gabriela Czanner
2004- Robert Haslinger
2005- Simona Temereanca
2006- Srideva Sarma
2003-2006 Esosa O. Amayo
2004-2005 Neil U. Desai
Ph.D. Students
1992-1993 Patricia Meehan
1998-2005 Patrick Purdon
2000-2005 Uri Eden
2002-2006 Cecilia Behn
2004-2006 Lakshminarayan Srinivasan
Current Ph.D. Students
2001- Sujith Vijayan
2002- Michelle McCarthy
2005- Dennis Dean
2006- Anna Dreyer
Post-Doctoral Fellows
1997-1999 Ao Yuan
1998-2000 Riccardo Barbieri
1998-2000 Harry P. Luithardt
1999-2004 Anne C. Smith
2000-2003 Loren M. Frank
2000-2003 Catherine Mullaly
2001-2006 Christopher Long
2003-2004 Joao Scalon
2005-2006 Uri Eden
2005-2006 Todd Coleman
Current Post-Doctoral Fellows
2003- Supratim Saha
2003- Murat Okatan
2004- Gabriela Czanner
2004- Robert Haslinger
2005- Simona Temereanca
2006- Srideva Sarma
Research Funding
NIMH R01MH59733; PI: Statistical Analysis of Hippocampal Information Encoding. To develop statistical models to study information encoding in the rat hippocampus.
NIBIB R01 EB00522; PI: Statistical Modeling of Functional MRI Signals. To develop statistical methods for the analysis of fMRI data.
NIDA R01 DA015644; PI: Dynamic Signal Processing Analyses of Neural Plasticity. To develop dynamic signal processing algorithms to characterize and track neural plasticity in the representation of biological signals in the rat and monkey hippocampus and other structures in the medial temporal lobe during memory
NIMH R01MH59733; PI: Statistical Analysis of Hippocampal Information Encoding. To develop dynamic algorithms to characterize learning from binary response data.
NIBIB R01 EB00522; PI: Statistical Modeling of Functional MRI Signals. To develop statistical methods for the analysis of fMRI data.
NIDA R01 DA015644; PI: Dynamic Signal Processing Analyses of Neural Plasticity. To develop dynamic signal processing algorithms to characterize and track neural plasticity in the representation of biological signals in the rat and monkey hippocampus and other structures in the medial temporal lobe during memory
NIMH R01MH59733; PI: Statistical Analysis of Hippocampal Information Encoding. To develop dynamic algorithms to characterize learning from binary response data.
Teaching
HST188J (9.914 MIT) Introduction to Statistics for Neuroscience Research. This is a new semester graduate course on statistics taught for the first time in spring of 2006.
Course in Neuroinformatics Marine Biology Laboratory Woods Hole, MA.
Course in Neuroinformatics Marine Biology Laboratory Woods Hole, MA.
Selected 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 Mar;54(3):419-28.
Srinivasan L, Eden UT, Willsky AS, Brown EN. A state-space analysis for reconstruction of goal-directed movements using neural signals.
Neural Computation, 2006Oct;18(10):2465-94.
Barbieri R, Brown EN. Analysis of heart beat dynamics by point process adaptive filtering.
IEEE Transactions on Biomedical Engineering, 2006, 53(1): 4-12.
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.
Barbieri R, Matten EC, Alabi, A. Brown EN. A point process model of human heart beat intervals: new definitions of heart rate and heart rate variability.
American Journal of Physiology: Heart and Circulatory Physiology (published on line Sept. 16, 2004), 2005, 288:H424-H435.
Truccolo W, Eden UT, Fellow M, Donoghue JD, Brown EN. A point process framework for relating neural spiking activity to spiking history, neural ensemble and covariate effects.
Journal of Neurophysiology (published online Sept. 8, 2004), 2005, 93:1074-1089.
Frank LM, Stanley GB, Brown EN. Hippocampal plasticity across multiple days of exposure to novel environments.
Journal of Neuroscience, 2004, 24 (35):7681-89.
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.
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.
Eden UT, Frank LM, Barbieri R, Solo V, Brown EN, Dynamic analyses of neural encoding by point process adaptive filtering.
Neural Computation, 2004, 16(5): 971-998.
Barbieri R, Frank LM, Nguyen DP, Quirk MC, Solo V, Wilson MA, Brown EN. Dynamic analyses of information encoding by neural ensembles.
Neural Computation, 2004, 16 (2): 277-307.
Klerman EB, Adler GK, Jin M, Maliszewski AM, Brown EN. A statistical model of diurnal variation in human growth hormone.
American Journal of Physiology, 2003, E1118-26.
Wirth S, Yanike M. Frank LM, Smith AC, Brown EN, Suzuki WA. Single neurons in the monkey hippocampus and learning of new associations.
Science, 2003, 300: 1578-81.
Smith AC, Brown EN. Estimating a state-space model from point process observations.
Neural Computation, 2003, 15: 965-91.
Frank LM, Eden UT, Solo V, Wilson MA, Brown EN. Contrasting patterns of receptive field plasticity in the hippocampus and the entorhinal cortex: an adaptive filtering approach.
Journal of Neuroscience, 2002, 22:3817-30.
Brown EN, Barbieri R, Ventura V, Kass RE, Frank LM. The time-rescaling theorem and its application to neural spike train data analysis.
Neural Computation, 2002, 14(2):325-46.
Brown EN, Nguyen DP, Frank LM, Wilson MA, Solo V. An analysis of neural receptive field plasticity by point process adaptive filtering.
Proceedings of the National Academy of Sciences, 2001, 98:12261-66.
Brown EN, Meehan PM, Dempster AP. A stochastic differential equation model of diurnal
cortisol patterns.
American Journal of Physiology (Endocrinology and Metabolism), 2001, 280:E450-61.
Purdon PL, Solo V, Weisskoff RM, Brown EN. Locally regularized spatio-temporal modeling and model comparison for functional MRI.
NeuroImage, 2001. Oct;14(4):912-23.
Brown EN, Choe Y, Luithardt H, Czeisler CA. A statistical model of the human core-temperature circadian rhythm.
American Journal of Physiology, 2000, 279:E669-E83.
Czeisler CA, Duffy JF, Shanahan TL, Brown EN, Mitchell JF, Rimmer DW, Ronda JM, Silva E, Allan JS, Emens JS, Dijk DJ, Kronauer RE. Age-independent stability, precision, and near 24 hour period of the human circadian pacemaker.
Science, 1999, 284:2177-81.
Brown EN, Frank LM, Tang D, Quirk MC, Wilson MA. A statistical paradigm for neural spike train decoding applied to position prediction from ensemble firing patterns of rat hippocampal place cells.
Journal of Neuroscience, 1998, 18:7411-25.
Brown EN, Choe Y, Shanahan TL, Czeisler CA. A mathematical model of diurnal variation in plasma melatonin levels.
American Journal of Physiology, (Endocrinology and Metabolism 35) 1997, 272:E506-E16.
IEEE Transactions on Biomedical Engineering, 2007 Mar;54(3):419-28.
Srinivasan L, Eden UT, Willsky AS, Brown EN. A state-space analysis for reconstruction of goal-directed movements using neural signals.
Neural Computation, 2006Oct;18(10):2465-94.
Barbieri R, Brown EN. Analysis of heart beat dynamics by point process adaptive filtering.
IEEE Transactions on Biomedical Engineering, 2006, 53(1): 4-12.
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.
Barbieri R, Matten EC, Alabi, A. Brown EN. A point process model of human heart beat intervals: new definitions of heart rate and heart rate variability.
American Journal of Physiology: Heart and Circulatory Physiology (published on line Sept. 16, 2004), 2005, 288:H424-H435.
Truccolo W, Eden UT, Fellow M, Donoghue JD, Brown EN. A point process framework for relating neural spiking activity to spiking history, neural ensemble and covariate effects.
Journal of Neurophysiology (published online Sept. 8, 2004), 2005, 93:1074-1089.
Frank LM, Stanley GB, Brown EN. Hippocampal plasticity across multiple days of exposure to novel environments.
Journal of Neuroscience, 2004, 24 (35):7681-89.
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.
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.
Eden UT, Frank LM, Barbieri R, Solo V, Brown EN, Dynamic analyses of neural encoding by point process adaptive filtering.
Neural Computation, 2004, 16(5): 971-998.
Barbieri R, Frank LM, Nguyen DP, Quirk MC, Solo V, Wilson MA, Brown EN. Dynamic analyses of information encoding by neural ensembles.
Neural Computation, 2004, 16 (2): 277-307.
Klerman EB, Adler GK, Jin M, Maliszewski AM, Brown EN. A statistical model of diurnal variation in human growth hormone.
American Journal of Physiology, 2003, E1118-26.
Wirth S, Yanike M. Frank LM, Smith AC, Brown EN, Suzuki WA. Single neurons in the monkey hippocampus and learning of new associations.
Science, 2003, 300: 1578-81.
Smith AC, Brown EN. Estimating a state-space model from point process observations.
Neural Computation, 2003, 15: 965-91.
Frank LM, Eden UT, Solo V, Wilson MA, Brown EN. Contrasting patterns of receptive field plasticity in the hippocampus and the entorhinal cortex: an adaptive filtering approach.
Journal of Neuroscience, 2002, 22:3817-30.
Brown EN, Barbieri R, Ventura V, Kass RE, Frank LM. The time-rescaling theorem and its application to neural spike train data analysis.
Neural Computation, 2002, 14(2):325-46.
Brown EN, Nguyen DP, Frank LM, Wilson MA, Solo V. An analysis of neural receptive field plasticity by point process adaptive filtering.
Proceedings of the National Academy of Sciences, 2001, 98:12261-66.
Brown EN, Meehan PM, Dempster AP. A stochastic differential equation model of diurnal
cortisol patterns.
American Journal of Physiology (Endocrinology and Metabolism), 2001, 280:E450-61.
Purdon PL, Solo V, Weisskoff RM, Brown EN. Locally regularized spatio-temporal modeling and model comparison for functional MRI.
NeuroImage, 2001. Oct;14(4):912-23.
Brown EN, Choe Y, Luithardt H, Czeisler CA. A statistical model of the human core-temperature circadian rhythm.
American Journal of Physiology, 2000, 279:E669-E83.
Czeisler CA, Duffy JF, Shanahan TL, Brown EN, Mitchell JF, Rimmer DW, Ronda JM, Silva E, Allan JS, Emens JS, Dijk DJ, Kronauer RE. Age-independent stability, precision, and near 24 hour period of the human circadian pacemaker.
Science, 1999, 284:2177-81.
Brown EN, Frank LM, Tang D, Quirk MC, Wilson MA. A statistical paradigm for neural spike train decoding applied to position prediction from ensemble firing patterns of rat hippocampal place cells.
Journal of Neuroscience, 1998, 18:7411-25.
Brown EN, Choe Y, Shanahan TL, Czeisler CA. A mathematical model of diurnal variation in plasma melatonin levels.
American Journal of Physiology, (Endocrinology and Metabolism 35) 1997, 272:E506-E16.
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