Understanding spatial and temporal patterning of astrocyte calcium transients via interactions between network transport and extracellular diffusion.

Astrocytes form interconnected networks in the brain and communicate via calcium signaling. We investigate how modes of coupling between astrocytes influence the spatio-temporal patterns of calcium signaling within astrocyte networks and specifically how these network interactions promote coordination within this group of cells. To investigate these complex phenomena, we study reduced cultured networks of astrocytes and neurons. We image the spatial temporal patterns of astrocyte calcium activity and quantify how perturbing the coupling between astrocytes influences astrocyte activity patterns. To gain insight into the pattern formation observed in these cultured networks, we compare the experimentally observed calcium activity patterns to the patterns produced by a reduced computational model, where we represent astrocytes as simple units that integrate input through two mechanisms: gap junction coupling (network transport) and chemical release (extracellular diffusion). We examine the activity patterns in the simulated astrocyte network and their dependence upon these two coupling mechanisms. We find that gap junctions and extracellular chemical release interact in astrocyte networks to modulate the spatiotemporal patterns of their calcium dynamics. We show agreement between the computational and experimental findings, which suggests that the complex global patterns can be understood as a result of simple local coupling mechanisms.

Pattern segmentation with activity dependent natural frequency shift and sub-threshold resonance.

Understanding the mechanisms underlying distributed pattern formation in brain networks and its content driven dynamical segmentation is an area of intense study. We investigate a theoretical mechanism for selective activation of diverse neural populations that is based on dynamically shifting cellular resonances in functionally or structurally coupled networks. We specifically show that sub-threshold neuronal depolarization from synaptic coupling or external input can shift neurons into and out of resonance with specific bands of existing extracellular oscillations, and this can act as a dynamic readout mechanism during information storage and retrieval. We find that this mechanism is robust and suggest it as a general coding strategy that can be applied to any network with oscillatory nodes.

Novel genital alphapapillomaviruses in baboons (Papio hamadryas anubis) with cervical dysplasia.

Genital Alphapapillomavirus (αPV) infections are one of the most common sexually transmitted human infections worldwide. Women infected with the highly oncogenic genital human papillomavirus (HPV) types 16 and 18 are at high risk for development of cervical cancer. Related oncogenic αPVs exist in rhesus and cynomolgus macaques. Here the authors identified 3 novel genital αPV types (PhPV1, PhPV2, PhPV3) by PCR in cervical samples from 6 of 15 (40%) wild-caught female Kenyan olive baboons (Papio hamadryas anubis). Eleven baboons had koilocytes in the cervix and vagina. Three baboons had dysplastic proliferative changes consistent with cervical squamous intraepithelial neoplasia (CIN). In 2 baboons with PCR-confirmed PhPV1, 1 had moderate (CIN2, n = 1) and 1 had low-grade (CIN1, n = 1) dysplasia. In 2 baboons with PCR-confirmed PhPV2, 1 had low-grade (CIN1, n = 1) dysplasia and the other had only koilocytes. Two baboons with PCR-confirmed PhPV3 had koilocytes only. PhPV1 and PhPV2 were closely related to oncogenic macaque and human αPVs. These findings suggest that αPV-infected baboons may be useful animal models for the pathogenesis, treatment, and prophylaxis of genital αPV neoplasia. Additionally, this discovery suggests that genital αPVs with oncogenic potential may infect a wider spectrum of non-human primate species than previously thought.

Functional clustering in hippocampal cultures: relating network structure and dynamics.

In this work we investigate the relationship between gross anatomic structural network properties, neuronal dynamics and the resultant functional structure in dissociated rat hippocampal cultures. Specifically, we studied cultures as they developed under two conditions: the first supporting glial cell growth (high glial group), and the second one inhibiting it (low glial group). We then compared structural network properties and the spatio-temporal activity patterns of the neurons. Differences in dynamics between the two groups could be linked to the impact of the glial network on the neuronal network as the cultures developed. We also implemented a recently developed algorithm called the functional clustering algorithm (FCA) to obtain the resulting functional network structure. We show that this new algorithm is useful for capturing changes in functional network structure as the networks evolve over time. The FCA detects changes in functional structure that are consistent with expected dynamical differences due to the impact of the glial network. Cultures in the high glial group show an increase in global synchronization as the cultures age, while those in the low glial group remain locally synchronized. We additionally use the FCA to quantify the amount of synchronization present in the cultures and show that the total level of synchronization in the high glial group is stronger than in the low glial group. These results indicate an interdependence between the glial and neuronal networks present in dissociated cultures.

Functional clustering algorithm for the analysis of dynamic network data.

We formulate a technique for the detection of functional clusters in discrete event data. The advantage of this algorithm is that no prior knowledge of the number of functional groups is needed, as our procedure progressively combines data traces and derives the optimal clustering cutoff in a simple and intuitive manner through the use of surrogate data sets. In order to demonstrate the power of this algorithm to detect changes in network dynamics and connectivity, we apply it to both simulated neural spike train data and real neural data obtained from the mouse hippocampus during exploration and slow-wave sleep. Using the simulated data, we show that our algorithm performs better than existing methods. In the experimental data, we observe state-dependent clustering patterns consistent with known neurophysiological processes involved in memory consolidation.

Internetwork and intranetwork communications during bursting dynamics: applications to seizure prediction.

We use a simple dynamical model of two interacting networks of integrate-and-fire neurons to explain a seemingly paradoxical result observed in epileptic patients indicating that the level of phase synchrony declines below normal levels during the state preceding seizures (preictal state). We model the transition from the seizure free interval (interictal state) to the seizure (ictal state) as a slow increase in the mean depolarization of neurons in a network corresponding to the epileptic focus. We show that the transition from the interictal to preictal and then to the ictal state may be divided into separate dynamical regimes: the formation of slow oscillatory activity due to resonance between the two interacting networks observed during the interictal period, structureless activity during the preictal period when the two networks have different properties, and bursting dynamics driven by the network corresponding to the epileptic focus. Based on this result, we hypothesize that the beginning of the preictal period marks the beginning of the transition of the epileptic network from normal activity toward seizing.

Fractal methods to analyze ion channel kinetics.

We describe the traditional nonfractal and the new fractal methods used to analyze the currents through ion channels in the cell membrane. We discuss the hidden assumptions used in these methods and how those assumptions lead to different interpretations of the same experimental data. The nonfractal methods assumed that channel proteins have a small number of discrete states separated by fixed energy barriers. The goal was to determine the parameters of the kinetic diagram, which are the number of states, the pathways between them, and the kinetic rate constants of those pathways. The discovery that these data have fractal characteristics suggested that fractal approaches might provide more appropriate tools to analyze and interpret these data. The fractal methods determine the characteristics of the data over a broad range of time scales and how those characteristics depend on the time scale at which they are measured. This is done by using a multiscale method to accurately determine the probability density function over many time scales and by determining how the effective kinetic rate constant, the probability of switching states, depends on the effective time scale at which it is measured. These fractal methods have led to new information about the physical properties of channel proteins in terms of the number of conformational substates, the distribution of energy barriers between those states, and how those energy barriers change with time. The new methods developed from the fractal paradigm shifted the analysis of channel data from determining the parameters of a kinetic diagram to determining the physical properties of channel proteins in terms of the distribution of energy barriers and/or their time dependence.

Qualitative and quantitative analysis of monocyte transendothelial migration by confocal microscopy and three-dimensional image reconstruction.

A novel method for qualitative and quantitative analysis of monocyte transendothelial migration is described. By labeling monocytes and endothelial cells with different fluorophores, and utilizing confocal microscopy and three-dimensional image reconstruction, transmigrating monocytes were resolved and quantified within a subendothelial collagen gel. Comparison of monocyte migration across endothelial monolayers derived from human brain microvessels versus umbilical veins revealed diapedesis across brain endothelium to be significantly delayed. Inclusion of astrocytes within the subendothelial collagen gel resulted in the formation of an array of astrocytic processes that simulated the glia limitans surrounding brain microvessels in situ, thus yielding a more physiologic paradigm of the blood-brain barrier. By virtue of its unique capacity to provide information on the total number of migrating cells, this analytic approach overcomes significant caveats associated with sampling only aspects of the migration process. The potential adaptability of this method to computer-assisted analysis further enhances its prospective use in high-throughput screening.

Distributed and partially separate pools of neurons are correlated with two different components of the gill-withdrawal reflex in Aplysia.

We compared the spike activity of individual neurons in the Aplysia abdominal ganglion with the movement of the gill during the gill-withdrawal reflex. We discriminated four populations that collectively encompass approximately half of the active neurons in the ganglion: (1) second-order sensory neurons that respond to the onset and offset of stimulation of the gill and are active before the movement starts; (2) neurons whose activity is correlated with the position of the gill and typically have a tonic output during gill withdrawal; (3) neurons whose activity is correlated with the velocity of the movement and typically fire in a phasic manner; and (4) neurons whose activity is correlated with both position and velocity. A reliable prediction of the position of the gill is achieved only with the combined output of 15-20 neurons, whereas a reliable prediction of the velocity depends on the combined output of 40 or more cells.

Imaging membrane potential with voltage-sensitive dyes.

Membrane potential can be measured optically using a variety of molecular probes. These measurements can be useful in studying function at the level of an individual cell, for determining how groups of neurons generate a behavior, and for studying the correlated behavior of populations of neurons. Examples of the three kinds of measurements are presented. The signals obtained from these measurements are generally small. Methodological considerations necessary to optimize the resulting signal-to-noise ratio are discussed.

Odors elicit three different oscillations in the turtle olfactory bulb.

We measured the spatiotemporal aspects of the odor-induced population response in the turtle olfactory bulb using a voltage-sensitive dye, RH414, and a 464-element photodiode array. In contrast with previous studies of population activity using local field potential recordings, we distinguished four signals in the response. The one called DC covered almost the entire area of the olfactory bulb; in addition, three oscillations, named rostral, middle, and caudal according to their locations, occurred over broad regions of the bulb. In a typical odor-induced response, the DC signal appeared almost immediately after the start of the stimulus, followed by the middle oscillation, the rostral oscillation, and last, the caudal oscillation. The initial frequencies of the three oscillations were 14.1, 13.0, and 6.6 Hz, respectively. When the rostral and caudal oscillations occurred together, their frequencies differed by a factor of 1.99 +/- 0.01. The following evidence suggests that the four signals are functionally independent: (1) in different animals some signals could be easily detected whereas others were undetectable; (2) the four signals had different latencies and frequencies; (3) the signals occurred in different locations and propagated in different directions; (4) the signals responded differently to changes in odor concentration; (5) the signals had different shapes; and (6) the rostral and caudal signals added in a simple, linear manner in regions where the location of the two signals overlapped. However, the finding that the frequency of the rostral oscillation is precisely two times that of the caudal oscillation suggests a significant relationship between the two. The location of the caudal oscillation in the bulb changed from cycle to cycle, implying that different groups of neurons are active in different cycles. This result is consistent with the earlier findings in the olfactory system of the locust (). Our results suggest an additional complexity of parallel processing of olfactory input by multiple functional population domains.

Local noise in neural networks models with self-control.

We modify neural networks models of the Hopfield type so that they can recognize the degree of novelty of the input stimuli on a local level. The networks control themselves the quality of recognition and can also recognize locally the bits of information in the input patterns which do not agree with known patterns, i.e. stored memories. This task is achieved by introducing local variations of the noise level beta in the network. Noise level in a given location depends on the flip frequency of the neurons close to that location.