Dynamical signatures of cellular fluctuations and oscillator stability in peripheral circadian clocks

Cell-autonomous and self-sustained molecular oscillators drive circadian behavior and physiology in mammals. From rhythms recorded in cultured fibroblasts we identified the dominant cause for amplitude reduction as desynchronization of self-sustained oscillators. Here, we propose a general framework for quantifying luminescence signals from biochemical oscillators, both in populations and individual cells. Our model combines three essential aspects of circadian clocks: the stability of the limit cycle, fluctuations, and intercellular coupling. From population recordings we can simultaneously estimate the stiffness of individual frequencies, the period dispersion, and the interaction strength. Consistent with previous work, coupling is found to be weak and insufficient to synchronize cells. Moreover, we find that frequency fluctuations remain correlated for longer than one clock cycle, which is confirmed from individual cell recordings. Using genetic models for circadian clocks, we show that this reflects the stability properties of the underlying circadian limit-cycle oscillators, and we identify biochemical parameters that influence oscillator stability in mammals. Our study thus points to stabilizing mechanisms that dampen fluctuations to maintain accurate timing in peripheral circadian oscillators

Multiple dynamical time-scales in networks with hierarchically nested modular organization

Many natural and engineered complex networks have intricate mesoscopic organization, e.g., the clustering of the constituent nodes into several communities or modules. Often, such modularity is manifested at several different hierarchical levels, where the clusters defined at one level appear as elementary entities at the next higher level. Using a simple model of a hierarchical modular network, we show that such a topological structure gives rise to characteristic time-scale separation between dynamics occurring at different levels of the hierarchy. This generalizes our earlier result for simple modular networks, where fast intra-modular and slow inter-modular processes were clearly distinguished. Investigating the process of synchronization of oscillators in a hierarchical modular network, we show the existence of as many distinct time-scales as there are hierarchical levels in the system. This suggests a possible functional role of such mesoscopic organization principle in natural systems, viz., in the dynamical separation of events occurring at different spatial scales.Comment: 10 pages, 4 figure

A Study of Brain Networks Associated with Swallowing Using Graph-Theoretical Approaches

Functional connectivity between brain regions during swallowing tasks is still not well understood. Understanding these complex interactions is of great interest from both a scientific and a clinical perspective. In this study, functional magnetic resonance imaging (fMRI) was utilized to study brain functional networks during voluntary saliva swallowing in twenty-two adult healthy subjects (all females, 23.1±1.52 years of age). To construct these functional connections, we computed mean partial correlation matrices over ninety brain regions for each participant. Two regions were determined to be functionally connected if their correlation was above a certain threshold. These correlation matrices were then analyzed using graph-theoretical approaches. In particular, we considered several network measures for the whole brain and for swallowing-related brain regions. The results have shown that significant pairwise functional connections were, mostly, either local and intra-hemispheric or symmetrically inter-hemispheric. Furthermore, we showed that all human brain functional network, although varying in some degree, had typical small-world properties as compared to regular networks and random networks. These properties allow information transfer within the network at a relatively high efficiency. Swallowing-related brain regions also had higher values for some of the network measures in comparison to when these measures were calculated for the whole brain. The current results warrant further investigation of graph-theoretical approaches as a potential tool for understanding the neural basis of dysphagia. © 2013 Luan et al

An Adaptive Complex Network Model for Brain Functional Networks

Brain functional networks are graph representations of activity in the brain, where the vertices represent anatomical regions and the edges their functional connectivity. These networks present a robust small world topological structure, characterized by highly integrated modules connected sparsely by long range links. Recent studies showed that other topological properties such as the degree distribution and the presence (or absence) of a hierarchical structure are not robust, and show different intriguing behaviors. In order to understand the basic ingredients necessary for the emergence of these complex network structures we present an adaptive complex network model for human brain functional networks. The microscopic units of the model are dynamical nodes that represent active regions of the brain, whose interaction gives rise to complex network structures. The links between the nodes are chosen following an adaptive algorithm that establishes connections between dynamical elements with similar internal states. We show that the model is able to describe topological characteristics of human brain networks obtained from functional magnetic resonance imaging studies. In particular, when the dynamical rules of the model allow for integrated processing over the entire network scale-free non-hierarchical networks with well defined communities emerge. On the other hand, when the dynamical rules restrict the information to a local neighborhood, communities cluster together into larger ones, giving rise to a hierarchical structure, with a truncated power law degree distribution

Deterministic polarization chaos from a laser diode

Fifty years after the invention of the laser diode and fourty years after the report of the butterfly effect - i.e. the unpredictability of deterministic chaos, it is said that a laser diode behaves like a damped nonlinear oscillator. Hence no chaos can be generated unless with additional forcing or parameter modulation. Here we report the first counter-example of a free-running laser diode generating chaos. The underlying physics is a nonlinear coupling between two elliptically polarized modes in a vertical-cavity surface-emitting laser. We identify chaos in experimental time-series and show theoretically the bifurcations leading to single- and double-scroll attractors with characteristics similar to Lorenz chaos. The reported polarization chaos resembles at first sight a noise-driven mode hopping but shows opposite statistical properties. Our findings open up new research areas that combine the high speed performances of microcavity lasers with controllable and integrated sources of optical chaos.Comment: 13 pages, 5 figure

Baseline Religion Involvement Predicts Subsequent Salivary Cortisol Levels Among Male But not Female Black Youth

Background: Compared to Whites, Blacks are exposed to higher levels of chronic stress in the United States. As a result, major Black-White differences exist in the baseline and response of cortisol. Yet, the potential association between baseline religiosity and subsequent cortisol levels of Blacks are not known. Objectives: In the current study we aimed to determine the association between baseline religious behaviors and daytime salivary cortisol level among male and female Black youth. Materials and Methods: With a longitudinal design, data came from wave 1 (1994) and wave 6 (2000) of a cohort from an urban area in the Midwest of the United States. The study followed 227 Black adolescents (109 males and 118 females) for six years. Socio-demographics and religious behaviors (frequency of participation in religious activities) were measured at baseline. Base morning cortisol level at wave 6 was the outcome. We fitted a linear regression model to test the association between baseline religiosity at wave 1 and cortisol level at wave 6, while baseline age, socio-economics, and psychological symptoms were controlled. Results: In the pooled sample, frequency of participation in religious activities at baseline was negatively associated with mean cortisol level at follow up (r = -0.29, P > 0.01) among all, males (r = -0.38, P > 0.01), but not females (r = -.20, P > 0.05). Frequency of participation in religious activities remained a significant predictor of subsequent cortisol level (b = -0.283, 95% CI = -.107 - -0.022) while the effect of age, socioeconomics, and psychological symptoms were controlled. We could only find such an association among male Black youth (b = -0.368, 95% CI = -0.148 - -0.024) but not female Black youth (b = -0.229, 95% CI = -.113 - 0.011). Conclusions: Religiosity has been used as a coping mechanism among Blacks. Religiosity may also be related to stress regulation among Black youth. Future studies need to test complex associations between race, sex, religiosity, chronic stress, coping, and function of hypothalamo-pituitary-adrenal (HPA). It is not known whether male Black youth who are and those who are not religious differently cope with stress associated with daily discrimination and living in disadvantaged neighborhoods

Reproductive Phase Locking of Mosquito Populations in Response to Rainfall Frequency

The frequency of moderate to heavy rainfall events is projected to change in response to global warming. Here we show that these hydrologic changes may have a profound effect on mosquito population dynamics and rates of mosquito-borne disease transmission. We develop a simple model, which treats the mosquito reproductive cycle as a phase oscillator that responds to rainfall frequency forcing. This model reproduces observed mosquito population dynamics and indicates that mosquito-borne disease transmission can be sensitive to rainfall frequency. These findings indicate that changes to the hydrologic cycle, in particular the frequency of moderate to heavy rainfall events, could have a profound effect on the transmission rates of some mosquito-borne diseases

Emergence of a Small-World Functional Network in Cultured Neurons

The functional networks of cultured neurons exhibit complex network properties similar to those found in vivo. Starting from random seeding, cultures undergo significant reorganization during the initial period in vitro, yet despite providing an ideal platform for observing developmental changes in neuronal connectivity, little is known about how a complex functional network evolves from isolated neurons. In the present study, evolution of functional connectivity was estimated from correlations of spontaneous activity. Network properties were quantified using complex measures from graph theory and used to compare cultures at different stages of development during the first 5 weeks in vitro. Networks obtained from young cultures (14 days in vitro) exhibited a random topology, which evolved to a small-world topology during maturation. The topology change was accompanied by an increased presence of highly connected areas (hubs) and network efficiency increased with age. The small-world topology balances integration of network areas with segregation of specialized processing units. The emergence of such network structure in cultured neurons, despite a lack of external input, points to complex intrinsic biological mechanisms. Moreover, the functional network of cultures at mature ages is efficient and highly suited to complex processing tasks

Functional Brain Network Modularity Captures Inter- and Intra-Individual Variation in Working Memory Capacity

Cognitive abilities, such as working memory, differ among people; however, individuals also vary in their own day-to-day cognitive performance. One potential source of cognitive variability may be fluctuations in the functional organization of neural systems. The degree to which the organization of these functional networks is optimized may relate to the effective cognitive functioning of the individual. Here we specifically examine how changes in the organization of large-scale networks measured via resting state functional connectivity MRI and graph theory track changes in working memory capacity.Twenty-two participants performed a test of working memory capacity and then underwent resting-state fMRI. Seventeen subjects repeated the protocol three weeks later. We applied graph theoretic techniques to measure network organization on 34 brain regions of interest (ROI). Network modularity, which measures the level of integration and segregation across sub-networks, and small-worldness, which measures global network connection efficiency, both predicted individual differences in memory capacity; however, only modularity predicted intra-individual variation across the two sessions. Partial correlations controlling for the component of working memory that was stable across sessions revealed that modularity was almost entirely associated with the variability of working memory at each session. Analyses of specific sub-networks and individual circuits were unable to consistently account for working memory capacity variability.The results suggest that the intrinsic functional organization of an a priori defined cognitive control network measured at rest provides substantial information about actual cognitive performance. The association of network modularity to the variability in an individual's working memory capacity suggests that the organization of this network into high connectivity within modules and sparse connections between modules may reflect effective signaling across brain regions, perhaps through the modulation of signal or the suppression of the propagation of noise