Emery N. Brown

Bio: Emery N. Brown is an academic researcher from Massachusetts Institute of Technology. The author has contributed to research in topics: Burst suppression & Spike train. The author has an hindex of 89, co-authored 571 publications receiving 32588 citations. Previous affiliations of Emery N. Brown include Boston University & United States Department of Veterans Affairs.

Stability, Precision, and Near-24-Hour Period of the Human Circadian Pacemaker

Abstract: Regulation of circadian period in humans was thought to differ from that of other species, with the period of the activity rhythm reported to range from 13 to 65 hours (median 25.2 hours) and the period of the body temperature rhythm reported to average 25 hours in adulthood, and to shorten with age. However, those observations were based on studies of humans exposed to light levels sufficient to confound circadian period estimation. Precise estimation of the periods of the endogenous circadian rhythms of melatonin, core body temperature, and cortisol in healthy young and older individuals living in carefully controlled lighting conditions has now revealed that the intrinsic period of the human circadian pacemaker averages 24.18 hours in both age groups, with a tight distribution consistent with other species. These findings have important implications for understanding the pathophysiology of disrupted sleep in older people.

Sensitivity of the human circadian pacemaker to nocturnal light: melatonin phase resetting and suppression

Abstract: Ocular exposure to early morning room light can significantly advance the timing of the human circadian pacemaker. The resetting response to such light has a non-linear relationship to illuminance. The dose-response relationship of the human circadian pacemaker to late evening light of dim to moderate intensity has not been well established. Twenty-three healthy young male and female volunteers took part in a 9 day protocol in which a single experimental light exposure6.5 h in duration was given in the early biological night. The effects of the light exposure on the endogenous circadian phase of the melatonin rhythm and the acute effects of the light exposure on plasma melatonin concentration were calculated. We demonstrate that humans are highly responsive to the phase-delaying effects of light during the early biological night and that both the phase resetting response to light and the acute suppressive effects of light on plasma melatonin follow a logistic dose-response curve, as do many circadian responses to light in mammals. Contrary to expectations, we found that half of the maximal phase-delaying response achieved in response to a single episode of evening bright light ( approximately 9000 lux (lx)) can be obtained with just over 1 % of this light (dim room light of approximately 100 lx). The same held true for the acute suppressive effects of light on plasma melatonin concentrations. This indicates that even small changes in ordinary light exposure during the late evening hours can significantly affect both plasma melatonin concentrations and the entrained phase of the human circadian pacemaker.

A point process framework for relating neural spiking activity to spiking history, neural ensemble, and extrinsic covariate effects.

Abstract: Multiple factors simultaneously affect the spiking activity of individual neurons. Determining the effects and relative importance of these factors is a challenging problem in neurophysiology. We propose a statistical framework based on the point process likelihood function to relate a neuron's spiking probability to three typical covariates: the neuron's own spiking history, concurrent ensemble activity, and extrinsic covariates such as stimuli or behavior. The framework uses parametric models of the conditional intensity function to define a neuron's spiking probability in terms of the covariates. The discrete time likelihood function for point processes is used to carry out model fitting and model analysis. We show that, by modeling the logarithm of the conditional intensity function as a linear combination of functions of the covariates, the discrete time point process likelihood function is readily analyzed in the generalized linear model (GLM) framework. We illustrate our approach for both GLM and non-GLM likelihood functions using simulated data and multivariate single-unit activity data simultaneously recorded from the motor cortex of a monkey performing a visuomotor pursuit-tracking task. The point process framework provides a flexible, computationally efficient approach for maximum likelihood estimation, goodness-of-fit assessment, residual analysis, model selection, and neural decoding. The framework thus allows for the formulation and analysis of point process models of neural spiking activity that readily capture the simultaneous effects of multiple covariates and enables the assessment of their relative importance.

General anesthesia, sleep, and coma

Abstract: This review discusses the clinical and neurophysiological features of general anesthesia and their relationships to sleep and coma, focusing on the neural mechanisms of unconsciousness induced by selected intravenous anesthetic drugs.

Multiple neural spike train data analysis: state-of-the-art and future challenges.

Abstract: Multiple electrodes are now a standard tool in neuroscience research that make it possible to study the simultaneous activity of several neurons in a given brain region or across different regions. The data from multi-electrode studies present important analysis challenges that must be resolved for optimal use of these neurophysiological measurements to answer questions about how the brain works. Here we review statistical methods for the analysis of multiple neural spike-train data and discuss future challenges for methodology research.

疟原虫var基因转换速率变化导致抗原变异[英]／Paul H, Robert P, Christodoulou Z, et al//Proc Natl Acad Sci U S A

Abstract: 抗原变异可使得多种致病微生物易于逃避宿主免疫应答。表达在感染红细胞表面的恶性疟原虫红细胞表面蛋白1（PfPMP1）与感染红细胞、内皮细胞、树突状细胞以及胎盘的单个或多个受体作用，在黏附及免疫逃避中起关键的作用。每个单倍体基因组var基因家族编码约60种成员，通过启动转录不同的var基因变异体为抗原变异提供了分子基础。

Pattern Recognition and Machine Learning

Abstract: Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Principles of Neural Science

Abstract: I have developed "tennis elbow" from lugging this book around the past four weeks, but it is worth the pain, the effort, and the aspirin. It is also worth the (relatively speaking) bargain price. Including appendixes, this book contains 894 pages of text. The entire panorama of the neural sciences is surveyed and examined, and it is comprehensive in its scope, from genomes to social behaviors. The editors explicitly state that the book is designed as "an introductory text for students of biology, behavior, and medicine," but it is hard to imagine any audience, interested in any fragment of neuroscience at any level of sophistication, that would not enjoy this book. The editors have done a masterful job of weaving together the biologic, the behavioral, and the clinical sciences into a single tapestry in which everyone from the molecular biologist to the practicing psychiatrist can find and appreciate his or