Showing papers by "Emery N. Brown published in 2001"

A Comparison of the Firing Properties of Putative Excitatory and Inhibitory Neurons From CA1 and the Entorhinal Cortex

Abstract: The superficial layers of the entorhinal cortex (EC) provide the majority of the neocortical input to the hippocampus, and the deep layers of the EC receive the majority of neocortically bound hippocampal outputs. To characterize information transmission through the hippocampal and EC circuitry, we recorded simultaneously from neurons in the superficial EC, the CA1 region of hippocampus, and the deep EC while rodents ran for food reward in two environments. Spike waveform analysis allowed us to classify units as fast-spiking (FS) putative inhibitory cells or putative excitatory (PE) cells. PE and FS units' firing were often strongly correlated at short time scales, suggesting the presence a monosynaptic connection from the PE to FS units. EC PE units, unlike those found in CA1, showed little or no tendency to fire in bursts. We also found that the firing of FS and PE units from all regions was modulated by the approximately 8 Hz theta rhythm, although the firing of deep EC FS units tended to be less strongly modulated than that of the other types of units. When we examined the spatial specificity of FS units, we determined that FS units in all three regions showed low specificity. At the same time, retrospective coding, in which firing rates were related to past position, was present in FS units from all three regions and deep EC FS units often fired in a "path equivalent" manner in that they were active in physically different, but behaviorally related positions both within and across environments. Our results suggest that while the firing of FS units from CA1 and the EC show similarly low levels of position specificity, FS units from each region differ from one another in that they mirrored the associated PE units in terms of their tendency to show more complex positional firing properties like retrospective coding and path equivalence.

An analysis of neural receptive field plasticity by point process adaptive filtering

Abstract: Neural receptive fields are plastic: with experience, neurons in many brain regions change their spiking responses to relevant stimuli. Analysis of receptive field plasticity from experimental measurements is crucial for understanding how neural systems adapt their representations of relevant biological information. Current analysis methods using histogram estimates of spike rate functions in nonoverlapping temporal windows do not track the evolution of receptive field plasticity on a fine time scale. Adaptive signal processing is an established engineering paradigm for estimating time-varying system parameters from experimental measurements. We present an adaptive filter algorithm for tracking neural receptive field plasticity based on point process models of spike train activity. We derive an instantaneous steepest descent algorithm by using as the criterion function the instantaneous log likelihood of a point process spike train model. We apply the point process adaptive filter algorithm in a study of spatial (place) receptive field properties of simulated and actual spike train data from rat CA1 hippocampal neurons. A stability analysis of the algorithm is sketched in the Appendix. The adaptive algorithm can update the place field parameter estimates on a millisecond time scale. It reliably tracked the migration, changes in scale, and changes in maximum firing rate characteristic of hippocampal place fields in a rat running on a linear track. Point process adaptive filtering offers an analytic method for studying the dynamics of neural receptive fields.

Circadian rhythms of women with fibromyalgia

Abstract: Fibromyalgia syndrome is a chronic and debilitating disorder characterized by widespread nonarticular musculoskeletal pain whose etiology is unknown. Many of the symptoms of this syndrome, including difficulty sleeping, fatigue, malaise, myalgias, gastrointestinal complaints, and decreased cognitive function, are similar to those observed in individuals whose circadian pacemaker is abnormally aligned with their sleep-wake schedule or with local environmental time. Abnormalities in melatonin and cortisol, two hormones whose secretion is strongly influenced by the circadian pacemaker, have been reported in women with fibromyalgia. We studied the circadian rhythms of 10 women with fibromyalgia and 12 control healthy women. The protocol controlled factors known to affect markers of the circadian system, including light levels, posture, sleep-wake state, meals, and activity. The timing of the events in the protocol were calculated relative to the habitual sleep-wake schedule of each individual subject. Under these conditions, we found no significant difference between the women with fibromyalgia and control women in the circadian amplitude or phase of rhythms of melatonin, cortisol, and core body temperature. The average circadian phases expressed in hours posthabitual bedtime for women with and without fibromyalgia were 3:43 +/- 0:19 and 3:46 +/- 0:13, respectively, for melatonin; 10:13 +/- 0:23 and 10:32 +/- 0:20, respectively for cortisol; and 5:19 +/- 0:19 and 4:57 +/- 0:33, respectively, for core body temperature phases. Both groups of women had similar circadian rhythms in self-reported alertness. Although pain and stiffness were significantly increased in women with fibromyalgia compared with healthy women, there were no circadian rhythms in either parameter. We suggest that abnormalities in circadian rhythmicity are not a primary cause of fibromyalgia or its symptoms.

A stochastic differential equation model of diurnal cortisol patterns

Abstract: Circadian modulation of episodic bursts is recognized as the normal physiological pattern of diurnal variation in plasma cortisol levels. The primary physiological factors underlying these diurnal patterns are the ultradian timing of secretory events, circadian modulation of the amplitude of secretory events, infusion of the hormone from the adrenal gland into the plasma, and clearance of the hormone from the plasma by the liver. Each measured plasma cortisol level has an error arising from the cortisol immunoassay. We demonstrate that all of these three physiological principles can be succinctly summarized in a single stochastic differential equation plus measurement error model and show that physiologically consistent ranges of the model parameters can be determined from published reports. We summarize the model parameters in terms of the multivariate Gaussian probability density and establish the plausibility of the model with a series of simulation studies. Our framework makes possible a sensitivity analysis in which all model parameters are allowed to vary simultaneously. The model offers an approach for simultaneously representing cortisol's ultradian, circadian, and kinetic properties. Our modeling paradigm provides a framework for simulation studies and data analysis that should be readily adaptable to the analysis of other endocrine hormone systems.

A signal estimation approach to functional MRI

Abstract: In the last half decade, fast methods of magnetic resonance imaging have led to the possibility, for the first time, of noninvasive dynamic brain imaging. This has led to an explosion of work in the Neurosciences. From a signal processing viewpoint the problems are those of nonlinear spatio-temporal system identification. Here, the authors develop new methods of identification using novel spatial regularization. They also develop a new model comparison technique and use that to compare their method with existing techniques on some experimental data.

Spatio-temporal signal processing for multisubject functional MRI studies

Abstract: We consider signal estimation for functional MRI studies on multiple subjects. There are two major issues; alignment or registration of images across subjects, and using the multisubject information to capture covariance information; we discuss only the latter. Capturing this covariance information properly can lead to great improvements in statistical efficiency beyond what simple averaging can offer as well as compact description of group features.