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{{Infobox scientist
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===Deciphering Brain Signals===
===Deciphering Brain Signals===
Brown later focused his statistics research on developing signal processing algorithms and statistical methods for neuronal data analysis. He developed a state-space point process (SSPP) paradigm to study how neural systems maintain dynamic representations of information.<ref name=estimatingspacemodel><ref>{{cite journal|last1=Smith|first1=AC|last2=Brown|first2=EN|title=Estimating a state-space model from point process observations.|journal=Neural computation|date=May 2003|volume=15|issue=5|pages=965-91|pmid=12803953}}</ref> For the analysis of neural spiking activity and binary behavioral tasks represented as multivariate or univariate point processes (0-1 events that occur in continuous time), his research produced analogs of the [[Kalman filter]], [[Kalman_filter#Fixed-interval_smoothers|Kalman smoothing]], sequential [[Monte Carlo algorithm|Monte Carlo algorithms]], and combined state and parameter estimation algorithms commonly applied to continuous-valued time series observations.
Brown later focused his statistics research on developing signal processing algorithms and statistical methods for neuronal data analysis. He developed a state-space point process (SSPP) paradigm to study how neural systems maintain dynamic representations of information.<ref name=estimatingspacemodel>{{cite journal|last1=Smith|first1=AC|last2=Brown|first2=EN|title=Estimating a state-space model from point process observations.|journal=Neural computation|date=May 2003|volume=15|issue=5|pages=965-91|pmid=12803953}}</ref> For the analysis of neural spiking activity and binary behavioral tasks represented as multivariate or univariate point processes (0-1 events that occur in continuous time), his research produced analogs of the [[Kalman filter]], [[Kalman_filter#Fixed-interval_smoothers|Kalman smoothing]], sequential [[Monte Carlo algorithm|Monte Carlo algorithms]], and combined state and parameter estimation algorithms commonly applied to continuous-valued time series observations.


Brown used the methods to: show that ensembles of neurons in the rodent hippocampus maintained a highly accurate representation of the animal’s spatial location;<ref name=statparadigmneuralspikes>{{cite journal|last1=Brown|first1=EN|last2=Frank|first2=LM|last3=Tang|first3=D|last4=Quirk|first4=MC|last5=Wilson|first5=MA|title=A statistical paradigm for neural spike train decoding applied to position prediction from ensemble firing patterns of rat hippocampal place cells.|journal=The Journal of neuroscience : the official journal of the Society for Neuroscience|date=15 September 1998|volume=18|issue=18|pages=7411-25|pmid=9736661}}</ref> track the formation of neural receptive fields on a millisecond time scale;<ref name=neuralreceptivefirelds>{{cite journal|last1=Brown|first1=EN|last2=Nguyen|first2=DP|last3=Frank|first3=LM|last4=Wilson|first4=MA|last5=Solo|first5=V|title=An analysis of neural receptive field plasticity by point process adaptive filtering.|journal=Proceedings of the National Academy of Sciences of the United States of America|date=9 October 2001|volume=98|issue=21|pages=12261-6|pmid=11593043|accessdate=10 July 2015}}</ref><ref name=hippocampusplasticity>{{cite journal|last1=Frank|first1=LM|last2=Eden|first2=UT|last3=Solo|first3=V|last4=Wilson|first4=MA|last5=Brown|first5=EN|title=Contrasting patterns of receptive field plasticity in the hippocampus and the entorhinal cortex: an adaptive filtering approach.|journal=The Journal of neuroscience : the official journal of the Society for Neuroscience|date=1 May 2002|volume=22|issue=9|pages=3817-30|pmid=11978857|accessdate=10 July 2015}}</ref><ref name=hippocampalplasticitynewenvironments>{{cite journal|last1=Frank|first1=LM|last2=Stanley|first2=GB|last3=Brown|first3=EN|title=Hippocampal plasticity across multiple days of exposure to novel environments.|journal=The Journal of neuroscience : the official journal of the Society for Neuroscience|date=1 September 2004|volume=24|issue=35|pages=7681-9|pmid=15342735|accessdate=10 July 2015}}</ref> track concurrent changes in neural activity and behavior during learning experiments;<ref name=MonkeyHippocampusLearning>{{cite journal|last1=Wirth|first1=S.|title=Single Neurons in the Monkey Hippocampus and Learning of New Associations|journal=Science|date=6 June 2003|volume=300|issue=5625|pages=1578–1581|doi=10.1126/science.1084324}}</ref> decode how groups of motor neurons represent movement information;<ref name=pointprocessneuralspiking>{{cite journal|last1=Truccolo|first1=W|last2=Eden|first2=UT|last3=Fellows|first3=MR|last4=Donoghue|first4=JP|last5=Brown|first5=EN|title=A point process framework for relating neural spiking activity to spiking history, neural ensemble, and extrinsic covariate effects.|journal=Journal of neurophysiology|date=February 2005|volume=93|issue=2|pages=1074-89|pmid=15356183|accessdate=10 July 2015}}</ref> and track burst suppression in patients under general anesthesia.<ref name=burstsuppressionalgorithms>{{cite journal|last1=Chemali|first1=J|last2=Ching|first2=S|last3=Purdon|first3=PL|last4=Solt|first4=K|last5=Brown|first5=EN|title=Burst suppression probability algorithms: state-space methods for tracking EEG burst suppression.|journal=Journal of neural engineering|date=October 2013|volume=10|issue=5|pages=056017|pmid=24018288|accessdate=10 July 2015}}</ref>
Brown used the methods to: show that ensembles of neurons in the rodent hippocampus maintained a highly accurate representation of the animal’s spatial location;<ref name=statparadigmneuralspikes>{{cite journal|last1=Brown|first1=EN|last2=Frank|first2=LM|last3=Tang|first3=D|last4=Quirk|first4=MC|last5=Wilson|first5=MA|title=A statistical paradigm for neural spike train decoding applied to position prediction from ensemble firing patterns of rat hippocampal place cells.|journal=The Journal of neuroscience : the official journal of the Society for Neuroscience|date=15 September 1998|volume=18|issue=18|pages=7411-25|pmid=9736661}}</ref> track the formation of neural receptive fields on a millisecond time scale;<ref name=neuralreceptivefirelds>{{cite journal|last1=Brown|first1=EN|last2=Nguyen|first2=DP|last3=Frank|first3=LM|last4=Wilson|first4=MA|last5=Solo|first5=V|title=An analysis of neural receptive field plasticity by point process adaptive filtering.|journal=Proceedings of the National Academy of Sciences of the United States of America|date=9 October 2001|volume=98|issue=21|pages=12261-6|pmid=11593043|accessdate=10 July 2015}}</ref><ref name=hippocampusplasticity>{{cite journal|last1=Frank|first1=LM|last2=Eden|first2=UT|last3=Solo|first3=V|last4=Wilson|first4=MA|last5=Brown|first5=EN|title=Contrasting patterns of receptive field plasticity in the hippocampus and the entorhinal cortex: an adaptive filtering approach.|journal=The Journal of neuroscience : the official journal of the Society for Neuroscience|date=1 May 2002|volume=22|issue=9|pages=3817-30|pmid=11978857|accessdate=10 July 2015}}</ref><ref name=hippocampalplasticitynewenvironments>{{cite journal|last1=Frank|first1=LM|last2=Stanley|first2=GB|last3=Brown|first3=EN|title=Hippocampal plasticity across multiple days of exposure to novel environments.|journal=The Journal of neuroscience : the official journal of the Society for Neuroscience|date=1 September 2004|volume=24|issue=35|pages=7681-9|pmid=15342735|accessdate=10 July 2015}}</ref> track concurrent changes in neural activity and behavior during learning experiments;<ref name=MonkeyHippocampusLearning>{{cite journal|last1=Wirth|first1=S.|title=Single Neurons in the Monkey Hippocampus and Learning of New Associations|journal=Science|date=6 June 2003|volume=300|issue=5625|pages=1578–1581|doi=10.1126/science.1084324}}</ref> decode how groups of motor neurons represent movement information;<ref name=pointprocessneuralspiking>{{cite journal|last1=Truccolo|first1=W|last2=Eden|first2=UT|last3=Fellows|first3=MR|last4=Donoghue|first4=JP|last5=Brown|first5=EN|title=A point process framework for relating neural spiking activity to spiking history, neural ensemble, and extrinsic covariate effects.|journal=Journal of neurophysiology|date=February 2005|volume=93|issue=2|pages=1074-89|pmid=15356183|accessdate=10 July 2015}}</ref> and track burst suppression in patients under general anesthesia.<ref name=burstsuppressionalgorithms>{{cite journal|last1=Chemali|first1=J|last2=Ching|first2=S|last3=Purdon|first3=PL|last4=Solt|first4=K|last5=Brown|first5=EN|title=Burst suppression probability algorithms: state-space methods for tracking EEG burst suppression.|journal=Journal of neural engineering|date=October 2013|volume=10|issue=5|pages=056017|pmid=24018288|accessdate=10 July 2015}}</ref>
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==References==
==References==
{{Reflist|30em}}
{{Reflist|30em}}
==External links==
*[http://imes.mit.edu/people/faculty/brown-emery/ Emery N. Brown's Profile from M.I.T.]
*[http://www.neurostat.mit.edu/home/ The Neuroscience Statistics Research Lab's Home Page]
*[http://www.ncbi.nlm.nih.gov/pubmed/?term=Emery+N.+Brown/ Emery N. Brown's Publications]

{{Persondata
| NAME = Brown, Emery
| ALTERNATIVE NAMES =
| SHORT DESCRIPTION = Professor, neuroscentist
| DATE OF BIRTH =
| PLACE OF BIRTH =
| DATE OF DEATH =
| PLACE OF DEATH =
}}
{{DEFAULTSORT:Brown, Emery}}
[[Category:Year of birth missing (living people)]]
[[Category:Living people]]
[[Category:Harvard Medical School faculty]]
[[Category:Massachusetts Institute of Technology faculty]]
[[Category:Fellows of the American Academy of Arts and Sciences]]
[[Category:Harvard Medical School alumni]]
[[Category:Harvard University alumni]]

Revision as of 20:44, 10 September 2015

Emery N. Brown
File:Emery N. Brown.jpg
Alma materHarvard University
Known forSystems Neuroscience
Computational Neuroscience
Mechanisms of Anesthesia
Neural Signal Processing
AwardsNational Institutes of Health Director's Pioneer Award
NIH Director’s Transformative Research Award
Fellow of the American Academy of Arts and Science
Member of the National Academy of Sciences
Member of the National Academy of Medicine
Member of the National Academy of Engineering
Guggenheim Fellowship
American Society of Anesthesiologists Excellence in Research Award
Scientific career
FieldsNeuroscience
Systems Neuroscience
Statistics
Anesthesiology
Computational Neuroscience
Bioengineering
InstitutionsHarvard Medical School
Massachusetts Institute of Technology
Massachusetts General Hospital

Emery Neal Brown, M.D., Ph.D. is an American statistician, neuroscientist and anesthesiologist. He is the Warren M. Zapol Professor of Anesthesia at Harvard Medical School and at Massachusetts General Hospital (MGH), and a practicing anesthesiologist at MGH. At MIT he is the Edward Hood Taplin Professor of Medical Engineering and professor of computational neuroscience the Associate Director of the Institute for Medical Engineering and Science, and the Director of the Harvard-MIT Health Sciences and Technology Program. Brown is one of only 19 individuals who has been elected to all three branches of the National Academies of Sciences, Engineering, and Medicine, Brown is also the first African American and first anesthesiologist to be elected to all three National Academies.[1][2][3][4][5]


Biography

Brown grew up in Ocala, Florida, where he attended Fessenden Elementary and Middle Schools, Osceola Junior High School and North Marion High School. He graduated from Phillips Exeter Academy, in Exeter, N.H. in 1974 after spending the second semester of his senior year at Exeter in the School Year Abroad Program studying Spanish in Barcelona, Spain.[6] In 1978, he received his B.A. (magna cum laude) in applied mathematics from Harvard College.[2][6] Following graduation, Brown received an International Rotary Foundation Fellowship to study mathematics at the Institut Fourier des Mathèmatiques Pures in Grenoble, France.[6]

Upon returning from Grenoble, he entered the Harvard Medical School M.D. Ph.D. Program. He received his M.A in 1984 in statistics and his Ph.D. in statistics in 1988 from Harvard University and his M.D. (magna cum laude) in 1987 from Harvard Medical School.[2]

Brown completed his internship in internal medicine in 1989 at the Brigham and Women’s Hospital, a research fellowship in endocrinology at the Brigham and Women’s Hospital in 1992 and his residency in anesthesiology at MGH in 1992. In 1992, Brown joined the staff in the Department of Anesthesia at MGH and the faculty at Harvard Medical School. In 2005 he joined the faculty at MIT.[6]

Currently, Brown is the Warren M. Zapol Professor of Anesthesia at Harvard Medical School, the Edward Hood Taplin Professor of Medical Engineering at M.I.T.'s Institute for Medical Engineering and Science, a Professor of Computational Neuroscience at the Massachusetts Institute of Technology.[2] In addition to his professorial positions, Brown serves as the Director of the Neuroscience Statistics Research Laboratory at the Massachusetts Institute of Technology, the co-director of the Harvard-MIT Division of Health Sciences and Technology and an associate director of M.I.T.'s Institute for Medical Engineering & Science.[2] Brown also works as an anesthesiologist at MGH.[3]

Scientific Career

Brown has published widely on topics in Computational Neuroscience and Anesthesiology.[7] Brown is the principal investigator of the Neuroscience Statistics Research Laboratory at MGH and MIT, where he currently conducts his research.[8]

Measuring Time on the Human Biological Clock

Brown developed statistical methods to characterize the properties of the human circadian system (biological clock) from core temperature data recorded under the constant routine and free-running and forced desynchrony protocols. Through the early part of his career, Brown collaborated with circadian researchers to apply his methods to answer fundamental research questions in circadian physiology. Brown’s statistical methods were critical for: estimating accurately the period and internal time on human circadian clocks from continuous core temperature measurement;[9][10] showing that bright lights could be used to shift the phase of the human circadian clock;[11] properly timed administration of light and dark periods could be used to realign the internal clocks of shift workers with external time;[12] and that, contrary to beliefs at the time, the period of the human biological clock, like that of other animals, was closer to 24 hours rather than 25 hours.[13]

Deciphering Brain Signals

Brown later focused his statistics research on developing signal processing algorithms and statistical methods for neuronal data analysis. He developed a state-space point process (SSPP) paradigm to study how neural systems maintain dynamic representations of information.[14] For the analysis of neural spiking activity and binary behavioral tasks represented as multivariate or univariate point processes (0-1 events that occur in continuous time), his research produced analogs of the Kalman filter, Kalman smoothing, sequential Monte Carlo algorithms, and combined state and parameter estimation algorithms commonly applied to continuous-valued time series observations.

Brown used the methods to: show that ensembles of neurons in the rodent hippocampus maintained a highly accurate representation of the animal’s spatial location;[15] track the formation of neural receptive fields on a millisecond time scale;[16][17][18] track concurrent changes in neural activity and behavior during learning experiments;[19] decode how groups of motor neurons represent movement information;[20] and track burst suppression in patients under general anesthesia.[21]

Brown applied the state-space paradigm to: analyze learning in behavioral neuroscience experiments;[22][23][24] study the relationship between learning and changes in hippocampal function in humans;[25] assess the efficacy of deep brain stimulation in enhancing behavior performance in humans and non-human primates;[26] and define precisely changes in levels of consciousness under propofol-induced general anesthesia.[27]

With Partha Mitra, Brown co-founded and co-directed the Neuroinformatics Summer Course at the Marine Biological Laboratory in Woods Hole, MA from 2002-2006. He co-directs with Robert Kass the biannual Statistical Analysis of Neural Data Conference at the Carnegie Mellon University Center for the Neural Basis of Cognition[28][29]. He co-authored a textbook in neuroscience data analysis with Robert Kass and Uri Eden.[30]

The Nature of General Anesthesia

Unraveling the mystery of general anesthesia is another major question facing modern medicine.[4] In 2004, Brown began a systems neuroscience research program to study the mechanisms of anesthetic action by forming and leading an interdisciplinary collaboration comprised of anesthesiologists, neuroscientists, a statistician, a neurosurgeon, neurologists, bioengineers and a mathematician at MGH, MIT and Boston University.[31] In 2007 he received an NIH Director’s Pioneer Award to support this research making him, the first anesthesiologist and the first statistician to receive this award.[32] His anesthesiology research has made fundamental theoretical and experimental contributions to understanding the neurophysiology of general anesthesia. In two seminal papers,[33][34] Brown provided the first systems neuroscience analysis of how anesthetics act at specific receptors in specific neural circuits to produce commonly observed altered arousal states. This analysis provided an essential missing link between the substantial body of research on the molecular pharmacology of anesthetic action and the behavioral responses commonly seen in anesthetized patients. Brown also shows that, contrary to common dogma general anesthesia is not sleep, but rather a reversible coma.[33]

Brown’s research group has provided detailed insights into how anesthetics produce unconsciousness. The brain is not shut off under general anesthesia. Instead, anesthetics induce highly structured oscillations between key brain regions. These oscillations, which are readily visible in standard electroencephalogram (EEG) recordings, alter arousal by impairing normal communication between regions. This is analogous to what happens when an epilepsy patient loses consciousness with the appearance of the regular, hypersynchronous oscillations of a seizure. Anesthetic-induced oscillations are also akin to what happens when a hum in a phone line makes it impossible to sustain a normal conversation.[33][34]

Brown has performed many studies on the properties of propofol-induced anesthesia in particular. He found that propofol-induced unconsciousness is mediated simultaneously by two different oscillatory processes. The first is strong coherent alpha oscillations (8 to 10 cycles per second) between the cortex and the thalamus (26-28) and the second are strong incoherent cortical slow-wave oscillations (<1 cycle per second).[35][36][37] The alpha oscillations impair communication between the thalamus and cortex. The slow-waves restrict to narrow time intervals the times at which cortical neurons can discharge, thus making it difficult to sustain communication within the cortex.[36] Furthermore, each anesthetic has a different EEG signature reflecting different neural circuit mechanisms action. These signatures change with age and the anesthetic dose.[38][39] A practical implication of this finding is that the EEG can be used in real time to monitor accurately the anesthetic state of patients. Brown’s group has developed an online teaching program to train anesthesiologists on this monitoring approach.[40]

Brown and colleagues are establishing a new paradigm for waking patients up following general anesthesia. They have shown that the anesthetic state can be rapidly reversed by administering methylphenidate (Ritalin)[41] or activation of dopaminergic systems.[42] This suggests a new, feasible way to actively restore cognitive function in patients after anesthesia and sedation. They have received FDA approval to undertake a clinical trial to test this idea in humans (NCT 02051452).[43][44][45] They have also shown that burst-suppression, a state of profound brain inactivation seen in deep general anesthesia, hypothermia, coma and developmental brain disorders, can be simply explained by a unifying neural-metabolic model.[46] Brown’s group have also shown that burst suppression can be precisely controlled to maintain a therapeutic, medically-induced coma. This research uses a closed-loop control system based on his SSPP paradigm.[47][48] This could have important implications for treating patients, such as Gabrielle Giffords, Michael Schumaker, Malala Yousafzai and Joan Rivers, who sustain brain injuries or have intracranial hypertension and require a medically-induced coma to facilitate brain recovery.

Brown's anesthesiology research has been featured on National Public Radio[49], in Scientific American[50], the MIT Technology Review,[4] the New York Times[51] and in TEDMED[52] 2014.

National Committee Service

Brown has served on numerous national panels and advisory committees. Most recently he served on the NIH BRAIN Initiative Working Group.[53] His current committee service includes being a member of the Burroughs-Wellcome Fund Board of Directors,[54] the NSF Mathematical and Physical Sciences Advisory Committee,[55] the NIH Council of Councils,[56] the Board of Trustees of the International Anesthesia Research Society,[57] the Scientific Advisory Committee of CURE Epilepsy[58] and the Governing Council of the American Academy of Arts and Sciences.[59]

Awards and Honors

Brown was the recipient of a Robert Wood Johnson Minority Medical Faculty Development Fellowship,[60] an NSF Minority Career Development Fellowship, National Institute of Mental Health Independent Scientist Award,[61] America’s Leading Doctor Award from Black Enterprise Magazine,[62] the Jerome Sacks Award from the National Institute of Statistical Sciences for Outstanding Cross Disciplinary Research,[2] an NIH Director’s Pioneer Award,[2] an NIH Director’s Transformative Research Award,[2] Guggenheim Fellowship,[2] And the American Society of Anesthesiologists Award for Excellence in Research.[2]

Brown is a fellow of the American Institute for Medical and Biological Engineering, the American Statistical Association, the IEEE, the American Association for the Advancement of Sciences, and the American Academy of Arts and Sciences. Brown is a member of all three branches of the National Academies, which are the National Academy of Medicine, the National Academy of Sciences and the National Academy of Engineering.[61][2] He is the first African American and the first anesthesiologist elected to all three branches.[63]

References

  1. ^ "Dr. Emery Brown Elected to National Academy of Engineering". www.asahq.org. American Society of Anesthesiologists. Retrieved 7 August 2015.
  2. ^ a b c d e f g h i j k "Emery Brown". M.I.T. Retrieved 10 October 2013.
  3. ^ a b "Emery N. Brown". Massachusetts General Hospital.
  4. ^ a b c Humphries, Courtney. "The Mystery Behind Anesthesia". www.technologyreview.com. MIT Technology Review. Retrieved 5 January 2015.
  5. ^ Brown, Emery. "Redesigning Anesthesia". nih.gov. Nation Institutes of Health. Retrieved 6 January 2015.
  6. ^ a b c d "Science Leaders" (PDF). www.stlamerican.com. The St. Louis of America. Retrieved 6 August 2015.
  7. ^ "Emery Brown Publications". www.ncbi.nlm.nih.gov/pubmed. Pubmed. Retrieved 10 July 2015.
  8. ^ "Neurostats Lab Members". www.neurostat.mit.edu. Neurostats Lab at MIT. Retrieved 10 July 2015.
  9. ^ Brown, EN; Czeisler, CA (1992). "The statistical analysis of circadian phase and amplitude in constant-routine core-temperature data". Journal of biological rhythms. 7 (3): 177–202. PMID 1421473.
  10. ^ Brown, EN; Choe, Y; Luithardt, H; Czeisler, CA (September 2000). "A statistical model of the human core-temperature circadian rhythm". American journal of physiology. Endocrinology and metabolism. 279 (3): E669-83. PMID 10950837.
  11. ^ Czeisler, CA; Kronauer, RE; Allan, JS; Duffy, JF; Jewett, ME; Brown, EN; Ronda, JM (16 June 1989). "Bright light induction of strong (type 0) resetting of the human circadian pacemaker". Science (New York, N.Y.). 244 (4910): 1328–33. PMID 2734611.
  12. ^ Czeisler, CA; Johnson, MP; Duffy, JF; Brown, EN; Ronda, JM; Kronauer, RE (3 May 1990). "Exposure to bright light and darkness to treat physiologic maladaptation to night work". The New England journal of medicine. 322 (18): 1253–9. PMID 2325721.
  13. ^ Czeisler, CA; Duffy, JF; Shanahan, TL; Brown, EN; Mitchell, JF; Rimmer, DW; Ronda, JM; Silva, EJ; Allan, JS; Emens, JS; Dijk, DJ; Kronauer, RE (25 June 1999). "Stability, precision, and near-24-hour period of the human circadian pacemaker". Science (New York, N.Y.). 284 (5423): 2177–81. PMID 10381883. Retrieved 7 January 2015.
  14. ^ Smith, AC; Brown, EN (May 2003). "Estimating a state-space model from point process observations". Neural computation. 15 (5): 965–91. PMID 12803953.
  15. ^ Brown, EN; Frank, LM; Tang, D; Quirk, MC; Wilson, MA (15 September 1998). "A statistical paradigm for neural spike train decoding applied to position prediction from ensemble firing patterns of rat hippocampal place cells". The Journal of neuroscience : the official journal of the Society for Neuroscience. 18 (18): 7411–25. PMID 9736661.
  16. ^ Brown, EN; Nguyen, DP; Frank, LM; Wilson, MA; Solo, V (9 October 2001). "An analysis of neural receptive field plasticity by point process adaptive filtering". Proceedings of the National Academy of Sciences of the United States of America. 98 (21): 12261–6. PMID 11593043. {{cite journal}}: |access-date= requires |url= (help)
  17. ^ Frank, LM; Eden, UT; Solo, V; Wilson, MA; Brown, EN (1 May 2002). "Contrasting patterns of receptive field plasticity in the hippocampus and the entorhinal cortex: an adaptive filtering approach". The Journal of neuroscience : the official journal of the Society for Neuroscience. 22 (9): 3817–30. PMID 11978857. {{cite journal}}: |access-date= requires |url= (help)
  18. ^ Frank, LM; Stanley, GB; Brown, EN (1 September 2004). "Hippocampal plasticity across multiple days of exposure to novel environments". The Journal of neuroscience : the official journal of the Society for Neuroscience. 24 (35): 7681–9. PMID 15342735. {{cite journal}}: |access-date= requires |url= (help)
  19. ^ Wirth, S. (6 June 2003). "Single Neurons in the Monkey Hippocampus and Learning of New Associations". Science. 300 (5625): 1578–1581. doi:10.1126/science.1084324.
  20. ^ Truccolo, W; Eden, UT; Fellows, MR; Donoghue, JP; Brown, EN (February 2005). "A point process framework for relating neural spiking activity to spiking history, neural ensemble, and extrinsic covariate effects". Journal of neurophysiology. 93 (2): 1074–89. PMID 15356183. {{cite journal}}: |access-date= requires |url= (help)
  21. ^ Chemali, J; Ching, S; Purdon, PL; Solt, K; Brown, EN (October 2013). "Burst suppression probability algorithms: state-space methods for tracking EEG burst suppression". Journal of neural engineering. 10 (5): 056017. PMID 24018288. {{cite journal}}: |access-date= requires |url= (help)
  22. ^ Smith, AC; Frank, LM; Wirth, S; Yanike, M; Hu, D; Kubota, Y; Graybiel, AM; Suzuki, WA; Brown, EN (14 January 2004). "Dynamic analysis of learning in behavioral experiments". The Journal of neuroscience : the official journal of the Society for Neuroscience. 24 (2): 447–61. PMID 14724243.
  23. ^ Smith, AC; Stefani, MR; Moghaddam, B; Brown, EN (March 2005). "Analysis and design of behavioral experiments to characterize population learning". Journal of neurophysiology. 93 (3): 1776–92. PMID 15456798. {{cite journal}}: |access-date= requires |url= (help)
  24. ^ Smith, AC; Wirth, S; Suzuki, WA; Brown, EN (March 2007). "Bayesian analysis of interleaved learning and response bias in behavioral experiments". Journal of neurophysiology. 97 (3): 2516–24. PMID 17182907. {{cite journal}}: |access-date= requires |url= (help)
  25. ^ Law, JR; Flanery, MA; Wirth, S; Yanike, M; Smith, AC; Frank, LM; Suzuki, WA; Brown, EN; Stark, CE (15 June 2005). "Functional magnetic resonance imaging activity during the gradual acquisition and expression of paired-associate memory". The Journal of neuroscience : the official journal of the Society for Neuroscience. 25 (24): 5720–9. PMID 15958738. {{cite journal}}: |access-date= requires |url= (help)
  26. ^ Smith, AC; Shah, SA; Hudson, AE; Purpura, KP; Victor, JD; Brown, EN; Schiff, ND (15 October 2009). "A Bayesian statistical analysis of behavioral facilitation associated with deep brain stimulation". Journal of neuroscience methods. 183 (2): 267–76. PMID 19576932. {{cite journal}}: |access-date= requires |url= (help)
  27. ^ Wong, KF; Smith, AC; Pierce, ET; Harrell, PG; Walsh, JL; Salazar-Gómez, AF; Tavares, CL; Purdon, PL; Brown, EN (30 April 2014). "Statistical modeling of behavioral dynamics during propofol-induced loss of consciousness". Journal of neuroscience methods. 227: 65–74. PMID 24530701. {{cite journal}}: |access-date= requires |url= (help)
  28. ^ "Sand7 Home Page". cmu.edu. Carnegie Mellon University. Retrieved 2 August 2015.
  29. ^ "Center for the Neural Basis of Cognition". cmu.edu. Carnegie Mellon University. Retrieved 2 August 2015.
  30. ^ "Analysis of Neural Data". Springer.com. Springer Series in Statistics. Retrieved 2 August 2015.
  31. ^ "Lab Members". Neuroscience Statistics Laboratory. MIT. Retrieved 2 August 2015.
  32. ^ "2007 Pioneer Award Recipients". nih.gov. National Institutes of Health. Retrieved 2 August 2015.
  33. ^ a b c Brown, EN; Lydic, R; Schiff, ND (30 December 2010). "General anesthesia, sleep, and coma". The New England journal of medicine. 363 (27): 2638–50. PMID 21190458. {{cite journal}}: |access-date= requires |url= (help)
  34. ^ a b Brown, EN; Purdon, PL; Van Dort, CJ (2011). "General anesthesia and altered states of arousal: a systems neuroscience analysis". Annual review of neuroscience. 34: 601–28. PMID 21513454. Retrieved 2 August 2015.
  35. ^ Cimenser, A; Purdon, PL; Pierce, ET; Walsh, JL; Salazar-Gomez, AF; Harrell, PG; Tavares-Stoeckel, C; Habeeb, K; Brown, EN (24 May 2011). "Tracking brain states under general anesthesia by using global coherence analysis". Proceedings of the National Academy of Sciences of the United States of America. 108 (21): 8832–7. PMID 21555565. {{cite journal}}: |access-date= requires |url= (help)
  36. ^ a b Lewis, LD; Weiner, VS; Mukamel, EA; Donoghue, JA; Eskandar, EN; Madsen, JR; Anderson, WS; Hochberg, LR; Cash, SS; Brown, EN; Purdon, PL (4 December 2012). "Rapid fragmentation of neuronal networks at the onset of propofol-induced unconsciousness". Proceedings of the National Academy of Sciences of the United States of America. 109 (49): E3377-86. PMID 23129622. {{cite journal}}: |access-date= requires |url= (help)
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