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1.  Impact of Adaptation Currents on Synchronization of Coupled Exponential Integrate-and-Fire Neurons 
PLoS Computational Biology  2012;8(4):e1002478.
The ability of spiking neurons to synchronize their activity in a network depends on the response behavior of these neurons as quantified by the phase response curve (PRC) and on coupling properties. The PRC characterizes the effects of transient inputs on spike timing and can be measured experimentally. Here we use the adaptive exponential integrate-and-fire (aEIF) neuron model to determine how subthreshold and spike-triggered slow adaptation currents shape the PRC. Based on that, we predict how synchrony and phase locked states of coupled neurons change in presence of synaptic delays and unequal coupling strengths. We find that increased subthreshold adaptation currents cause a transition of the PRC from only phase advances to phase advances and delays in response to excitatory perturbations. Increased spike-triggered adaptation currents on the other hand predominantly skew the PRC to the right. Both adaptation induced changes of the PRC are modulated by spike frequency, being more prominent at lower frequencies. Applying phase reduction theory, we show that subthreshold adaptation stabilizes synchrony for pairs of coupled excitatory neurons, while spike-triggered adaptation causes locking with a small phase difference, as long as synaptic heterogeneities are negligible. For inhibitory pairs synchrony is stable and robust against conduction delays, and adaptation can mediate bistability of in-phase and anti-phase locking. We further demonstrate that stable synchrony and bistable in/anti-phase locking of pairs carry over to synchronization and clustering of larger networks. The effects of adaptation in aEIF neurons on PRCs and network dynamics qualitatively reflect those of biophysical adaptation currents in detailed Hodgkin-Huxley-based neurons, which underscores the utility of the aEIF model for investigating the dynamical behavior of networks. Our results suggest neuronal spike frequency adaptation as a mechanism synchronizing low frequency oscillations in local excitatory networks, but indicate that inhibition rather than excitation generates coherent rhythms at higher frequencies.
Author Summary
Synchronization of neuronal spiking in the brain is related to cognitive functions, such as perception, attention, and memory. It is therefore important to determine which properties of neurons influence their collective behavior in a network and to understand how. A prominent feature of many cortical neurons is spike frequency adaptation, which is caused by slow transmembrane currents. We investigated how these adaptation currents affect the synchronization tendency of coupled model neurons. Using the efficient adaptive exponential integrate-and-fire (aEIF) model and a biophysically detailed neuron model for validation, we found that increased adaptation currents promote synchronization of coupled excitatory neurons at lower spike frequencies, as long as the conduction delays between the neurons are negligible. Inhibitory neurons on the other hand synchronize in presence of conduction delays, with or without adaptation currents. Our results emphasize the utility of the aEIF model for computational studies of neuronal network dynamics. We conclude that adaptation currents provide a mechanism to generate low frequency oscillations in local populations of excitatory neurons, while faster rhythms seem to be caused by inhibition rather than excitation.
doi:10.1371/journal.pcbi.1002478
PMCID: PMC3325187  PMID: 22511861
2.  The Role of Ongoing Dendritic Oscillations in Single-Neuron Dynamics 
PLoS Computational Biology  2009;5(9):e1000493.
The dendritic tree contributes significantly to the elementary computations a neuron performs while converting its synaptic inputs into action potential output. Traditionally, these computations have been characterized as both temporally and spatially localized. Under this localist account, neurons compute near-instantaneous mappings from their current input to their current output, brought about by somatic summation of dendritic contributions that are generated in functionally segregated compartments. However, recent evidence about the presence of oscillations in dendrites suggests a qualitatively different mode of operation: the instantaneous phase of such oscillations can depend on a long history of inputs, and under appropriate conditions, even dendritic oscillators that are remote may interact through synchronization. Here, we develop a mathematical framework to analyze the interactions of local dendritic oscillations and the way these interactions influence single cell computations. Combining weakly coupled oscillator methods with cable theoretic arguments, we derive phase-locking states for multiple oscillating dendritic compartments. We characterize how the phase-locking properties depend on key parameters of the oscillating dendrite: the electrotonic properties of the (active) dendritic segment, and the intrinsic properties of the dendritic oscillators. As a direct consequence, we show how input to the dendrites can modulate phase-locking behavior and hence global dendritic coherence. In turn, dendritic coherence is able to gate the integration and propagation of synaptic signals to the soma, ultimately leading to an effective control of somatic spike generation. Our results suggest that dendritic oscillations enable the dendritic tree to operate on more global temporal and spatial scales than previously thought; notably that local dendritic activity may be a mechanism for generating on-going whole-cell voltage oscillations.
Author Summary
A central issue in biology is how local processes yield global consequences. This is especially relevant for neurons since these spatially extended cells process local synaptic inputs to generate global action potential output. The dendritic tree of a neuron, which receives most of the inputs, expresses ion channels that can generate nonlinear dynamics. A prominent phenomenon resulting from such ion channels are voltage oscillations. The distribution of the active membrane channels throughout the cell is often highly non-uniform. This can turn the dendritic tree into a network of sparsely spaced local oscillators. Here we analyze whether local dendritic oscillators can produce cell-wide voltage oscillations. Our mathematical theory shows that indeed even when the dendritic oscillators are weakly coupled, they lock their phases and give global oscillations. We show how the biophysical properties of the dendrites determine the global locking and how it can be controlled by synaptic inputs. As a consequence of global locking, even individual synaptic inputs can affect the timing of action potentials. In fact, dendrites locking in synchrony can lead to sustained firing of the cell. We show that dendritic trees can be bistable, with dendrites locking in either synchrony or asynchrony, which may provide a novel mechanism for single cell-based memory.
doi:10.1371/journal.pcbi.1000493
PMCID: PMC2725317  PMID: 19730677
3.  Quantitative analysis of regulatory flexibility under changing environmental conditions 
Day length changes with the seasons in temperate latitudes, affecting the many biological rhythms that entrain to the day/night cycle: we measure these effects on the expression of Arabidopsis clock genes, using RNA and reporter gene readouts, with a new method of phase analysis.Dusk sensitivity is proposed as a simple, natural and general mathematical measure to analyse and manipulate the changing phase of a clock output relative to the change in the day/night cycle.Dusk sensitivity shows how increasing the numbers of feedback loops in the Arabidopsis clock models allows more flexible regulation, consistent with a previously-proposed, general operating principle of biological networks.The Arabidopsis clock genes show flexibility of regulation that is characteristic of a three-loop clock model, validating aspects of the model and the operating principle, but some clock output genes show greater flexibility arising from direct light regulation.
The analysis of dynamic, non-linear regulation with the aid of mechanistic models is central to Systems Biology. This study compares the predictions of mechanistic, mathematical models of the circadian clock with molecular time-series data on rhythmic gene expression in the higher plant Arabidopsis thaliana. Analysis of the models helps us to understand (explain and predict) how the clock gene circuit balances regulation by external and endogenous factors to achieve particular behaviours. Such multi-factorial regulation is ubiquitous in, and characteristic of, living systems.
The Earth's rotation causes predictable changes in the environment, notably in the availability of sunlight for photosynthesis. Many biological processes are driven by the environmental input via sensory pathways, for example, from photoreceptors. Circadian clocks provide an alternative strategy. These endogenous, 24-h rhythms can drive biological processes that anticipate the regular environmental changes, rather than merely responding. Many rhythmic processes have both light and clock control. Indeed, the clock components themselves must balance internal timing with external inputs, because circadian clocks are reset daily through light regulation of one or more clock components. This process of entrainment is complicated by the change in day length. When the times of dawn and dusk move apart in summer, and closer together in winter, does the clock track dawn, track dusk or interpolate between them?
In plants, the clock controls leaf and petal movements, the opening and closing of stomatal pores, the discharge of floral fragrances, and many metabolic activities, especially those associated with photosynthesis. Centuries of physiological studies have shown that these rhythms can behave differently. Flowering in Ipomoea nil (Pharbitis nil, Japanese morning glory) is controlled by a rhythm that tracks the time of dusk, to give a classic example. We showed that two other rhythms associated with vegetative growth track dawn in this species (Figure 5A), so the clock system allows flexible regulation.
The relatively small number of components involved in the circadian clockwork makes it an ideal candidate for mathematical modelling. Molecular genetic studies in a variety of model eukaryotes have shown that the circadian rhythm is generated by a network of 6–20 genes. These genes form feedback loops generating a rhythm in mRNA production. A single negative feedback loop in which a gene encodes a protein that, after several hours, turns off transcription is capable of generating a circadian rhythm, in principle. A single light input can entrain the clock to ‘local time', synchronised with a light–dark cycle. However, real circadian clocks have proven to be more complicated than this, with multiple light inputs and interlocked feedback loops.
We have previously argued from mathematical analysis that multi-loop networks increase the flexibility of regulation (Rand et al, 2004) and have shown that appropriately deployed flexibility can confer functional robustness (Akman et al, 2010). Here we test whether that flexibility can be demonstrated in vivo, in the model plant, A. thaliana. The Arabidopsis clock mechanism comprises a feedback loop in which two partially redundant, myb transcription factors, LATE ELONGATED HYPOCOTYL (LHY) and CIRCADIAN CLOCK ASSOCIATED 1 (CCA1), repress the expression of their activator, TIMING OF CAB EXPRESSION 1 (TOC1). We previously modelled this single-loop circuit and showed that it was not capable of recreating important data (Locke et al, 2005a). An extended, two-loop model was developed to match observed behaviours, incorporating a hypothetical gene Y, for which the best identified candidate was the GIGANTEA gene (GI) (Locke et al, 2005b). Two further models incorporated the TOC1 homologues PSEUDO-RESPONSE REGULATOR (PRR) 9 and PRR7 (Locke et al, 2006; Zeilinger et al, 2006). In these circuits, a morning oscillator (LHY/CCA1–PRR9/7) is coupled to an evening oscillator (Y/GI–TOC1) via the original LHY/CCA1–TOC1 loop.
These clock models, like those for all other organisms, were developed using data from simple conditions of constant light, darkness or 12-h light–12-h dark cycles. We therefore tested how the clock genes in Arabidopsis responded to light–dark cycles with different photoperiods, from 3 h light to 18 h light per 24-h cycle (Edinburgh, 56° North latitude, has 17.5 h light in midsummer). The time-series assays of mRNA and in vivo reporter gene images showed a range of peak times for different genes, depending on the photoperiod (Figure 5C). A new data analysis method, mFourfit, was introduced to measure the peak times, in the Biological Rhythms Analysis Software Suite (BRASS v3.0). None of the genes showed the dusk-tracking behaviour characteristic of the Ipomoea flowering rhythm. The one-, two- and three-loop models were analysed to understand the observed patterns. A new mathematical measure, dusk sensitivity, was introduced to measure the change in timing of a model component versus a change in the time of dusk. The one- and two-loop models tracked dawn and dusk, respectively, under all conditions. Only the three-loop model (Figure 5B) had the flexibility required to match the photoperiod-dependent changes that we found in vivo, and in particular the unexpected, V-shaped pattern in the peak time of TOC1 expression. This pattern of regulation depends on the structure and light inputs to the model's evening oscillator, so the in vivo data supported this aspect of the model. LHY and CCA1 gene expression under short photoperiods showed greater dusk sensitivity, in the interval 2–6 h before dawn, than the three-loop model predicted, so these data will help to constrain future models.
The approach described here could act as a template for experimental biologists seeking to understand biological regulation using dynamic, experimental perturbations and time-series data. Simulation of mathematical models (despite known imperfections) can provide contrasting hypotheses that guide understanding. The system's detailed behaviour is complex, so a natural and general measure such as dusk sensitivity is helpful to focus on one property of the system. We used the measure to compare models, and to predict how this property could be manipulated. To enable additional analysis of this system, we provide the time-series data and experimental metadata online.
The circadian clock controls 24-h rhythms in many biological processes, allowing appropriate timing of biological rhythms relative to dawn and dusk. Known clock circuits include multiple, interlocked feedback loops. Theory suggested that multiple loops contribute the flexibility for molecular rhythms to track multiple phases of the external cycle. Clear dawn- and dusk-tracking rhythms illustrate the flexibility of timing in Ipomoea nil. Molecular clock components in Arabidopsis thaliana showed complex, photoperiod-dependent regulation, which was analysed by comparison with three contrasting models. A simple, quantitative measure, Dusk Sensitivity, was introduced to compare the behaviour of clock models with varying loop complexity. Evening-expressed clock genes showed photoperiod-dependent dusk sensitivity, as predicted by the three-loop model, whereas the one- and two-loop models tracked dawn and dusk, respectively. Output genes for starch degradation achieved dusk-tracking expression through light regulation, rather than a dusk-tracking rhythm. Model analysis predicted which biochemical processes could be manipulated to extend dusk tracking. Our results reveal how an operating principle of biological regulators applies specifically to the plant circadian clock.
doi:10.1038/msb.2010.81
PMCID: PMC3010117  PMID: 21045818
Arabidopsis thaliana; biological clocks; dynamical systems; gene regulatory networks; mathematical models; photoperiodism
4.  Data assimilation constrains new connections and components in a complex, eukaryotic circadian clock model 
Integrating molecular time-series data resulted in a more robust model of the plant clock, which predicts that a wave of inhibitory PRR proteins controls the morning genes LHY and CCA1.PRR5 is experimentally validated as a late-acting component of this wave.The family of sequentially expressed PRR proteins allows flexible entrainment of the clock, whereas a single protein could not, suggesting that the duplication of clock genes might confer this generic, functional advantage.The observed post-translational regulation of the evening protein TOC1 by interaction with ZTL and GI remains consistent with an indirect activation of TOC1 mRNA expression by GI, which was previously postulated from modelling.
Circadian rhythms are present in most eukaryotic organisms including plants. The core genes of the circadian clock are very important for plant physiology as they drive the rhythmic expression of around 30% of Arabidopsis genes (Edwards et al, 2006; Michael et al, 2008). The clock is normally entrained by daily environmental changes in light and temperature. Oscillations also persist under constant environmental conditions in a laboratory. The clock gene circuit in Arabidopsis is based on multiple interlocked feedback loops, which are typical of circadian genetic networks in other organisms (Dunlap and Loros, 2004; Bell-Pedersen et al, 2005). Mechanistic, mathematical models are increasingly useful in analysing and understanding how the observed molecular components give rise to the rhythmic behaviour of this dynamic, non-linear system.
Our previous model of Arabidopsis circadian clock (Locke et al, 2006) presented the core, three-loop structure of the clock, which comprised morning and evening oscillators and coupling between them (Figure 1). The morning loop included the dawn-expressed LATE ELONGATED HYPOCOTYL (LHY) and CIRCADIAN CLOCK ASSOCIATED 1 (CCA1) genes, which negatively regulate their expression through activation of the inhibitor proteins, PSEUDO-RESPONSE REGULATOR 9 (PRR9) and PRR7. These were described by a single, combined model component, PRR9/7. The evening loop included the dusk-expressed gene TIMING OF CAB EXPRESSION 1 (TOC1), which negatively regulates itself through inhibition of a hypothetical activator, gene Y. The evening-expressed gene GIGANTEA (GI) contributes to Y function. The morning and evening loops were connected through inhibition of the evening genes by LHY/CCA1 and activation of LHY/CCA1 expression by a hypothetical evening gene X. Here, we extend the previous model of circadian gene expression (Locke et al, 2006) based on recently published data (Figure 1). The new model retains the good match of our previous model to the large volume of molecular time-series data, and improves the behaviour of the model clock system under a range of light conditions and in a wider range of mutants.
The morning loop was extended by adding a hypothetical clock component, the night inhibitor (NI), which acts together with PRR9 and PRR7 to keep the expression of LHY and CCA1 at low levels over a broad interval spanning dusk. This regulation is important to set the phase of LHY/CCA1 expression at dawn. Data from the literature suggested that the PRR5 gene was a candidate for NI, leading us to predict that the sequentially expressed PRR9, PRR7 and PRR5 proteins together formed a wave of inhibitors of LHY and CCA1. This hypothesis was tested under discriminating light conditions, in which the light interval is replaced with the dawn and dusk pulses of light to form a ‘skeleton photoperiod'. Combining this protocol with mutation of the PRR7 and/or PRR5 genes, our new experimental results validated the model predictions and confirmed that PRR5 contributes to the function that we modelled as NI. During revision of this paper, that result received further experimental support (Nakamichi et al, 2010).
Model simulations revealed the functional importance of the inhibitor wave in entraining the clock to the light/dark cycle. Separating PRR9 from the other inhibitors in the model showed how the strong light activation observed for this gene contributes to more rapid entrainment. The observed, post-translation regulation of all three inhibitor proteins by light (Farre and Kay, 2007; Ito et al, 2007; Kiba et al, 2007) was also included in the model. Light-regulated degradation provides a molecular mechanism to explain the later phase of LHY and CCA1 expression under long photoperiods compared with short photoperiods, in line with experimental observations.
The connection between evening and morning loops was revised by including the inhibition of the morning gene PRR9 by the evening component TOC1, based on the data on TOC1-overexpressing plants (Makino et al, 2002; Ito et al, 2005). This inhibition causes a delay of PRR9 expression relative to LHY/CCA1, which allows LHY/CCA1 to reach a higher expression level at dawn. Our simulations showed that a partial mutant that lacks this inhibition of PRR9 by TOC1 is sufficient to cause the higher level of PRR9 and the short circadian period observed in toc1 mutant plants.
The evening loop was extended by introducing the observed, post-translational regulation of the TOC1 protein by the F-box protein ZEITLUPE (ZTL) and stabilization of ZTL by its interaction with GI in the presence of light (Kim et al, 2007). GI's function in the clock model has thus been revised according to the data: GI promotes an inhibition of TOC1 protein function through positive regulation of ZTL. This results, together with negative regulation of Y by TOC1, in indirect activation of TOC1 mRNA expression by GI, which agrees with our earlier experimental data (Locke et al, 2006). Simulations showed that the post-translational regulation of TOC1 by ZTL and GI results in the observed long period of the ztl mutant and fast dampening of rhythms in the lhy/cca1/gi triple mutant.
This is the first mathematical model that incorporates the observed post-translational regulation into the genetic network of the Arabidopsis clock. In addition to specific, mechanistic insights, the model shows a generic advantage from the duplication of clock genes and their expression at different phases. Such clock gene duplications are observed in eukaryotes with larger genomes, such as the mouse. Analogous, functional duplication can be achieved by differential regulation of a single clock gene in distinct cells, as in Drosophila.
Circadian clocks generate 24-h rhythms that are entrained by the day/night cycle. Clock circuits include several light inputs and interlocked feedback loops, with complex dynamics. Multiple biological components can contribute to each part of the circuit in higher organisms. Mechanistic models with morning, evening and central feedback loops have provided a heuristic framework for the clock in plants, but were based on transcriptional control. Here, we model observed, post-transcriptional and post-translational regulation and constrain many parameter values based on experimental data. The model's feedback circuit is revised and now includes PSEUDO-RESPONSE REGULATOR 7 (PRR7) and ZEITLUPE. The revised model matches data in varying environments and mutants, and gains robustness to parameter variation. Our results suggest that the activation of important morning-expressed genes follows their release from a night inhibitor (NI). Experiments inspired by the new model support the predicted NI function and show that the PRR5 gene contributes to the NI. The multiple PRR genes of Arabidopsis uncouple events in the late night from light-driven responses in the day, increasing the flexibility of rhythmic regulation.
doi:10.1038/msb.2010.69
PMCID: PMC2964123  PMID: 20865009
Arabidopsis thaliana; biological clocks; circadian rhythms; mathematical model; systems biology
5.  Identification of highly synchronized subnetworks from gene expression data 
BMC Bioinformatics  2013;14(Suppl 9):S5.
Background
There has been a growing interest in identifying context-specific active protein-protein interaction (PPI) subnetworks through integration of PPI and time course gene expression data. However the interaction dynamics during the biological process under study has not been sufficiently considered previously.
Methods
Here we propose a topology-phase locking (TopoPL) based scoring metric for identifying active PPI subnetworks from time series expression data. First the temporal coordination in gene expression changes is evaluated through phase locking analysis; The results are subsequently integrated with PPI to define an activity score for each PPI subnetwork, based on individual member expression, as well topological characteristics of the PPI network and of the expression temporal coordination network; Lastly, the subnetworks with the top scores in the whole PPI network are identified through simulated annealing search.
Results
Application of TopoPL to simulated data and to the yeast cell cycle data showed that it can more sensitively identify biologically meaningful subnetworks than the method that only utilizes the static PPI topology, or the additive scoring method. Using TopoPL we identified a core subnetwork with 49 genes important to yeast cell cycle. Interestingly, this core contains a protein complex known to be related to arrangement of ribosome subunits that exhibit extremely high gene expression synchronization.
Conclusions
Inclusion of interaction dynamics is important to the identification of relevant gene networks.
doi:10.1186/1471-2105-14-S9-S5
PMCID: PMC3698028  PMID: 23901792
6.  Input-Dependent Frequency Modulation of Cortical Gamma Oscillations Shapes Spatial Synchronization and Enables Phase Coding 
PLoS Computational Biology  2015;11(2):e1004072.
Fine-scale temporal organization of cortical activity in the gamma range (∼25–80Hz) may play a significant role in information processing, for example by neural grouping (‘binding’) and phase coding. Recent experimental studies have shown that the precise frequency of gamma oscillations varies with input drive (e.g. visual contrast) and that it can differ among nearby cortical locations. This has challenged theories assuming widespread gamma synchronization at a fixed common frequency. In the present study, we investigated which principles govern gamma synchronization in the presence of input-dependent frequency modulations and whether they are detrimental for meaningful input-dependent gamma-mediated temporal organization. To this aim, we constructed a biophysically realistic excitatory-inhibitory network able to express different oscillation frequencies at nearby spatial locations. Similarly to cortical networks, the model was topographically organized with spatially local connectivity and spatially-varying input drive. We analyzed gamma synchronization with respect to phase-locking, phase-relations and frequency differences, and quantified the stimulus-related information represented by gamma phase and frequency. By stepwise simplification of our models, we found that the gamma-mediated temporal organization could be reduced to basic synchronization principles of weakly coupled oscillators, where input drive determines the intrinsic (natural) frequency of oscillators. The gamma phase-locking, the precise phase relation and the emergent (measurable) frequencies were determined by two principal factors: the detuning (intrinsic frequency difference, i.e. local input difference) and the coupling strength. In addition to frequency coding, gamma phase contained complementary stimulus information. Crucially, the phase code reflected input differences, but not the absolute input level. This property of relative input-to-phase conversion, contrasting with latency codes or slower oscillation phase codes, may resolve conflicting experimental observations on gamma phase coding. Our modeling results offer clear testable experimental predictions. We conclude that input-dependency of gamma frequencies could be essential rather than detrimental for meaningful gamma-mediated temporal organization of cortical activity.
Author Summary
Almost 350 years ago the physicist and polymath Christiaan Huygens first observed the synchronization between two pendulum clocks attached to a common support. Since then synchronization has been recognized as a universal phenomenon from astronomy to biology. The phase-locking (synchrony) and the phase-relation between the two pendulums are determined by two principal forces: the synchronization force exerted over the connection and the tendency to desynchronize due to frequency (speed) differences. We propose that gamma synchronization (25–80Hz) among oscillating cortical neurons in the brain can be understood according to the same principles—like a field of many connected pendula—with the critical addition that input changes the frequency of gamma oscillations, as shown by recent experimental studies. It has been assumed that input-dependent changes in oscillation frequency are detrimental for a meaningful role of gamma synchronization in neural processing. To the contrary, our theoretical analysis demonstrates that because input can change the frequency of the oscillation, phase-locking and phase-relations among neurons relate systematically to input. By analogy, it is because a local push to a pendulum will change its frequency, that resulting changes in phase-locking and phase-relation among the pendula can be used to derive the external force applied.
doi:10.1371/journal.pcbi.1004072
PMCID: PMC4334551  PMID: 25679780
7.  Potential Mechanisms for Imperfect Synchronization in Parkinsonian Basal Ganglia 
PLoS ONE  2012;7(12):e51530.
Neural activity in the brain of parkinsonian patients is characterized by the intermittently synchronized oscillatory dynamics. This imperfect synchronization, observed in the beta frequency band, is believed to be related to the hypokinetic motor symptoms of the disorder. Our study explores potential mechanisms behind this intermittent synchrony. We study the response of a bursting pallidal neuron to different patterns of synaptic input from subthalamic nucleus (STN) neuron. We show how external globus pallidus (GPe) neuron is sensitive to the phase of the input from the STN cell and can exhibit intermittent phase-locking with the input in the beta band. The temporal properties of this intermittent phase-locking show similarities to the intermittent synchronization observed in experiments. We also study the synchronization of GPe cells to synaptic input from the STN cell with dependence on the dopamine-modulated parameters. Earlier studies showed how the strengthening of dopamine-modulated coupling may lead to transitions from non-synchronized to partially synchronized dynamics, typical in Parkinson's disease. However, dopamine also affects the cellular properties of neurons. We show how the changes in firing patterns of STN neuron due to the lack of dopamine may lead to transition from a lower to a higher coherent state, roughly matching the synchrony levels observed in basal ganglia in normal and parkinsonian states. The intermittent nature of the neural beta band synchrony in Parkinson's disease is achieved in the model due to the interplay of the timing of STN input to pallidum and pallidal neuronal dynamics, resulting in sensitivity of pallidal output to the phase of the arriving STN input. Thus the mechanism considered here (the change in firing pattern of subthalamic neurons through the dopamine-induced change of membrane properties) may be one of the potential mechanisms responsible for the generation of the intermittent synchronization observed in Parkinson's disease.
doi:10.1371/journal.pone.0051530
PMCID: PMC3526636  PMID: 23284707
8.  Broadband Criticality of Human Brain Network Synchronization 
PLoS Computational Biology  2009;5(3):e1000314.
Self-organized criticality is an attractive model for human brain dynamics, but there has been little direct evidence for its existence in large-scale systems measured by neuroimaging. In general, critical systems are associated with fractal or power law scaling, long-range correlations in space and time, and rapid reconfiguration in response to external inputs. Here, we consider two measures of phase synchronization: the phase-lock interval, or duration of coupling between a pair of (neurophysiological) processes, and the lability of global synchronization of a (brain functional) network. Using computational simulations of two mechanistically distinct systems displaying complex dynamics, the Ising model and the Kuramoto model, we show that both synchronization metrics have power law probability distributions specifically when these systems are in a critical state. We then demonstrate power law scaling of both pairwise and global synchronization metrics in functional MRI and magnetoencephalographic data recorded from normal volunteers under resting conditions. These results strongly suggest that human brain functional systems exist in an endogenous state of dynamical criticality, characterized by a greater than random probability of both prolonged periods of phase-locking and occurrence of large rapid changes in the state of global synchronization, analogous to the neuronal “avalanches” previously described in cellular systems. Moreover, evidence for critical dynamics was identified consistently in neurophysiological systems operating at frequency intervals ranging from 0.05–0.11 to 62.5–125 Hz, confirming that criticality is a property of human brain functional network organization at all frequency intervals in the brain's physiological bandwidth.
Author Summary
Systems in a critical state are poised on the cusp of a transition between ordered and random behavior. At this point, they demonstrate complex patterning of fluctuations at all scales of space and time. Criticality is an attractive model for brain dynamics because it optimizes information transfer, storage capacity, and sensitivity to external stimuli in computational models. However, to date there has been little direct experimental evidence for critical dynamics of human brain networks. Here, we considered two measures of functional coupling or phase synchronization between components of a dynamic system: the phase lock interval or duration of synchronization between a specific pair of time series or processes in the system and the lability of global synchronization among all pairs of processes. We confirmed that both synchronization metrics demonstrated scale invariant behaviors in two computational models of critical dynamics as well as in human brain functional systems oscillating at low frequencies (<0.5 Hz, measured using functional MRI) and at higher frequencies (1–125 Hz, measured using magnetoencephalography). We conclude that human brain functional networks demonstrate critical dynamics in all frequency intervals, a phenomenon we have described as broadband criticality.
doi:10.1371/journal.pcbi.1000314
PMCID: PMC2647739  PMID: 19300473
9.  Intermittent neural synchronization in Parkinson’s disease 
Nonlinear dynamics  2011;68(3):329-346.
Motor symptoms of Parkinson’s disease are related to the excessive synchronized oscillatory activity in the beta frequency band (around 20Hz) in the basal ganglia and other parts of the brain. This review explores the dynamics and potential mechanisms of these oscillations employing ideas and methods from nonlinear dynamics. We present extensive experimental documentation of the relevance of synchronized oscillations to motor behavior in Parkinson’s disease, and we discuss the intermittent character of this synchronization. The reader is introduced to novel time-series analysis techniques aimed at the detection of the fine temporal structure of intermittent phase locking observed in the brains of parkinsonian patients. Modeling studies of brain networks are reviewed, which may describe the observed intermittent synchrony, and we discuss what these studies reveal about brain dynamics in Parkinson’s disease. The parkinsonian brain appears to exist on the boundary between phase-locked and nonsynchronous dynamics. Such a situation may be beneficial in the healthy state, as it may allow for easy formation and dissociation of transient patterns of synchronous activity which are required for normal motor behavior. Dopaminergic degeneration in Parkinson’s disease may shift the brain networks closer to this boundary, which would still permit some motor behavior while accounting for the associated motor deficits. Understanding the mechanisms of the intermittent synchrony in Parkinson’s disease is also important for biomedical engineering since efficient control strategies for suppression of pathological synchrony through deep brain stimulation require knowledge of the dynamics of the processes subjected to control.
doi:10.1007/s11071-011-0223-z
PMCID: PMC3347643  PMID: 22582010
Intermittency; phase locking; phase synchronization; basal ganglia; subthalamic nucleus; neuronal modeling
10.  Specific Entrainment of Mitral Cells during Gamma Oscillation in the Rat Olfactory Bulb 
PLoS Computational Biology  2009;5(10):e1000551.
Local field potential (LFP) oscillations are often accompanied by synchronization of activity within a widespread cerebral area. Thus, the LFP and neuronal coherence appear to be the result of a common mechanism that underlies neuronal assembly formation. We used the olfactory bulb as a model to investigate: (1) the extent to which unitary dynamics and LFP oscillations can be correlated and (2) the precision with which a model of the hypothesized underlying mechanisms can accurately explain the experimental data. For this purpose, we analyzed simultaneous recordings of mitral cell (MC) activity and LFPs in anesthetized and freely breathing rats in response to odorant stimulation. Spike trains were found to be phase-locked to the gamma oscillation at specific firing rates and to form odor-specific temporal patterns. The use of a conductance-based MC model driven by an approximately balanced excitatory-inhibitory input conductance and a relatively small inhibitory conductance that oscillated at the gamma frequency allowed us to provide one explanation of the experimental data via a mode-locking mechanism. This work sheds light on the way network and intrinsic MC properties participate in the locking of MCs to the gamma oscillation in a realistic physiological context and may result in a particular time-locked assembly. Finally, we discuss how a self-synchronization process with such entrainment properties can explain, under experimental conditions: (1) why the gamma bursts emerge transiently with a maximal amplitude position relative to the stimulus time course; (2) why the oscillations are prominent at a specific gamma frequency; and (3) why the oscillation amplitude depends on specific stimulus properties. We also discuss information processing and functional consequences derived from this mechanism.
Author Summary
Olfactory function relies on a chain of neural relays that extends from the periphery to the central nervous system and implies neural activity with various timescales. A central question in neuroscience is how information is encoded by the neural activity. In the mammalian olfactory bulb, local neural activity oscillations in the 40–80 Hz range (gamma) may influence the timing of individual neuron activities such that olfactory information may be encoded in this way. In this study, we first characterize in vivo the detailed activity of individual neurons relative to the oscillation and find that, depending on their state, neurons can exhibit periodic activity patterns. We also find, at least qualitatively, a relation between this activity and a particular odor. This is reminiscent of general physical phenomena—the entrainment by an oscillation—and to verify this hypothesis, in a second phase, we build a biologically realistic model mimicking these in vivo conditions. Our model confirms quantitatively this hypothesis and reveals that entrainment is maximal in the gamma range. Taken together, our results suggest that the neuronal activity may be specifically formatted in time during the gamma oscillation in such a way that it could, at this stage, encode the odor.
doi:10.1371/journal.pcbi.1000551
PMCID: PMC2760751  PMID: 19876377
11.  Embedding mRNA Stability in Correlation Analysis of Time-Series Gene Expression Data 
PLoS Computational Biology  2008;4(8):e1000141.
Current methods for the identification of putatively co-regulated genes directly from gene expression time profiles are based on the similarity of the time profile. Such association metrics, despite their central role in gene network inference and machine learning, have largely ignored the impact of dynamics or variation in mRNA stability. Here we introduce a simple, but powerful, new similarity metric called lead-lag R2 that successfully accounts for the properties of gene dynamics, including varying mRNA degradation and delays. Using yeast cell-cycle time-series gene expression data, we demonstrate that the predictive power of lead-lag R2 for the identification of co-regulated genes is significantly higher than that of standard similarity measures, thus allowing the selection of a large number of entirely new putatively co-regulated genes. Furthermore, the lead-lag metric can also be used to uncover the relationship between gene expression time-series and the dynamics of formation of multiple protein complexes. Remarkably, we found a high lead-lag R2 value among genes coding for a transient complex.
Author Summary
Microarrays provide snapshots of the transcriptional state of the cell at some point in time. Multiple snapshots can be taken sequentially in time, thus providing insight into the dynamics of change. Since genome-wide expression data report on the abundance of mRNA, not on the underlying activity of genes, we developed a novel method to relate the expression pattern of genes, detected in a time-series experiment, using a similarity measure that incorporates mRNA decay and called lead-lag R2. We used the lead-lag R2 similarity measure to predict the presence of common transcription factors between gene pairs using an integrated dataset consisting of 13 yeast cell-cycles. The method was benchmarked against six well-established similarity measures and obtained the best true positive rate result, around 95%. We believe that the lead-lag analysis can be successfully used also to predict the presence of a common mechanism able to modulate the degradation rate of specific transcripts. Finally, we envisage the possibility to extend our analysis to different experimental conditions and organisms, thus providing a simple off-the-shelf computational tool to support the understanding of the transcriptional and post-transcriptional regulation layer and its role in many diseases, such as cancer.
doi:10.1371/journal.pcbi.1000141
PMCID: PMC2453326  PMID: 18670596
12.  Feedback between p21 and reactive oxygen production is necessary for cell senescence 
The sustained activation of CDKN1A (p21/Waf1/Cip1) by a DNA damage response induces mitochondrial dysfunction and reactive oxygen species (ROS) production via signalling through CDKN1A-GADD45A-MAPK14- GRB2-TGFBR2-TGFbeta in senescing primary human and mouse cells in vitro and in vivo.Enhanced ROS production in senescing cells generates additional DNA damage. Although this damage is repairable and transient, it elevates the average levels of DNA damage response permanently, thus forming a positive feedback loop.This loop is necessary and sufficient to maintain the stability of growth arrest until a ‘point of no return' is reached during establishment of senescence.
The phenomenon of cellular ‘senescence'—the permanent arrest of division in normally proliferating mammalian cells such as fibroblasts—is thought to be a central component of the ageing process. Senescence contributes both to age-related loss of tissue homeostasis, as the loss of division capacity leads to impaired cell renewal, and also to protect against cancer, because it acts to block the uncontrolled proliferation of cells that may give rise to a malignant tumour. Replicative senescence is triggered by uncapped telomeres or by ‘unrepairable' non-telomeric DNA damage. Both lesions initiate the same canonical DNA damage response (DDR) (d'Adda di Fagagna, 2008). This response is characterized by activation of sensor kinases (ATM/ATR, DNA-PK), formation of DNA damage foci containing activated H2A.X (γH2A.X) and ultimately induction of cell cycle arrest through activation of checkpoint proteins, notably p53 (TP53) and the CDK inhibitor p21 (CDKN1A). This signalling pathway continues to contribute actively to the stability of the G0 arrest in fully senescent cells long after induction of senescence (d'Adda di Fagagna et al, 2003). However, senescence is more complex than mere CDKI-mediated growth arrest. Senescent cells alter their expression of literally hundreds of genes (Shelton et al, 1999), prominent among these being pro-inflammatory secretory genes (Coppe et al, 2008) and marker genes for a retrograde response induced by mitochondrial dysfunction (Passos et al, 2007a).
There is a growing evidence that multiple mechanisms interact to underpin ageing at the cellular level (Kirkwood, 2005; Passos et al, 2007b) necessitating a systems biology approach if the complex mechanisms of ageing are to be understood (Kirkwood, 2008). With respect to cell senescence, the two major unanswered questions are (i) How does a DNA lesion that can be repaired, at least in principle, induce and maintain irreversible growth arrest? and (ii) How does a growth arrest trigger a completely different cellular phenotype as soon as it becomes irreversible?
To understand those questions, we performed a kinetic analysis of the establishment phase of senescence initiated by DNA damage or telomere dysfunction, focussing on pathways downstream of the classical DDR. Using an approach that combined (i) in-silico interactome analysis, (ii) functional target gene inhibition, (iii) stochastic modelling, and (iv) live cell microscopy, we identified a positive feedback loop between DDR and mitochondrial production of reactive oxygen species (ROS) as necessary and sufficient for long-term maintenance of growth arrest. Using pathway log likelihood scores calculated by a quantitative in-silico interactome analysis to guide siRNA and small molecule inhibition experiments, and using results of sequential and combined inhibition experiments to refine the predictions from the interactome analysis, we found that DDR triggered mitochondrial dysfunction leading to enhanced ROS activation through a linear signal transduction through TP53, CDKN1A, GADD45A, p38 (MAPK14), GRB2, TGFBR2 and TGFβ(Figure 2D). We hypothesized that these ROS stochastically generate novel DNA damage in the nucleus, thus forming a positive feedback loop contributing to the long-term maintenance of DDR (Figure 3A). First confirmation came from static inhibitor experiments as before, showing that nuclear DNA damage foci frequencies in senescent cells were reduced if feedback signalling was suppressed. To formally establish the existence of a feedback loop and its relevance for senescence, we used live cell microscopy in combination with quantitative modelling.
We transformed the conceptual model shown in Figure 3A into a stochastic mechanistic model of the DDR feedback loop by extending the previously published model of the TP53/Mdm2 circuit (Proctor and Gray, 2008) to include reactions for synthesis/activation and degradation/deactivation/repair of CDKN1A, GADD45, MAPK14, ROS and DNA damage. The model replicated very precisely the kinetic behaviour of activated TP53, CDKN1A, ROS and DNA damage foci after initiation of senescence by irradiation. Having established its concordance with the experimental data, the model was then used to predict the effects of intervening in the feedback loop. The model predicted that any intervention reducing ROS levels by about half would decrease average DNA damage foci frequencies from six to four foci/nucleus within about 15 h. It further predicted that this would be sufficient to reduce CDKN1A to basal levels continuously for at least 6 h in about 20% of the treated cells, thus allowing a significant fraction of cells to escape from growth arrest and to resume proliferation. This should happen even if the intervention into the feedback loop was started at a late time point (e.g. 6 days) after induction of senescence.
To analyse DNA damage foci dynamics we used a reporter construct (AcGFP–53BP1c) that quantitatively reports single DNA damage foci kinetics in time-resolved live cell microscopy (Nelson et al, 2009). Foci frequency measurements quantitatively confirmed the prediction from the stochastic model. More importantly, we found that many individual foci in both telomere- and stress-dependent senescence had short lifespans with half-lives below 15 h. Feedback loop inhibition reduced only the frequencies of short-lived DNA damage foci in accordance with the hypothesis that ROS production contributed to DDR by constant replenishment of short-lived DNA damage foci.
Finally, we inhibited signalling through the loop at different time points after induction of senescence by ionizing radiation and measured ROS levels, DNA damage foci frequencies and proliferation markers. Treatments with the MAPK14 inhibitor SB203580 or the free radical scavenger PBN were used to block the loop. The results quantitatively confirmed the model prediction and indicated that the feedback loop between DDR and ROS production was both necessary and sufficient to maintain cell cycle arrest for at least 6–10 days after induction of senescence. Interestingly, the loop was still active at later time points and in deep senescence, but proliferation arrest was then stabilized by additional factor(s). This indicated that certain features of the senescent phenotype-like ROS production that might be responsible for the negative impact of senescent cells into their tissue environment can be successfully inhibited even in deep senescence. This may prove relevant for novel therapeutic studies aiming to modulate intracellular ROS levels in both aging and cancer.
Cellular senescence—the permanent arrest of cycling in normally proliferating cells such as fibroblasts—contributes both to age-related loss of mammalian tissue homeostasis and acts as a tumour suppressor mechanism. The pathways leading to establishment of senescence are proving to be more complex than was previously envisaged. Combining in-silico interactome analysis and functional target gene inhibition, stochastic modelling and live cell microscopy, we show here that there exists a dynamic feedback loop that is triggered by a DNA damage response (DDR) and, which after a delay of several days, locks the cell into an actively maintained state of ‘deep' cellular senescence. The essential feature of the loop is that long-term activation of the checkpoint gene CDKN1A (p21) induces mitochondrial dysfunction and production of reactive oxygen species (ROS) through serial signalling through GADD45-MAPK14(p38MAPK)-GRB2-TGFBR2-TGFβ. These ROS in turn replenish short-lived DNA damage foci and maintain an ongoing DDR. We show that this loop is both necessary and sufficient for the stability of growth arrest during the establishment of the senescent phenotype.
doi:10.1038/msb.2010.5
PMCID: PMC2835567  PMID: 20160708
aging; cell senescence; DNA damage foci; mitochondria; reactive oxygen
13.  Phase-Locked Signals Elucidate Circuit Architecture of an Oscillatory Pathway 
PLoS Computational Biology  2010;6(12):e1001040.
This paper introduces the concept of phase-locking analysis of oscillatory cellular signaling systems to elucidate biochemical circuit architecture. Phase-locking is a physical phenomenon that refers to a response mode in which system output is synchronized to a periodic stimulus; in some instances, the number of responses can be fewer than the number of inputs, indicative of skipped beats. While the observation of phase-locking alone is largely independent of detailed mechanism, we find that the properties of phase-locking are useful for discriminating circuit architectures because they reflect not only the activation but also the recovery characteristics of biochemical circuits. Here, this principle is demonstrated for analysis of a G-protein coupled receptor system, the M3 muscarinic receptor-calcium signaling pathway, using microfluidic-mediated periodic chemical stimulation of the M3 receptor with carbachol and real-time imaging of resulting calcium transients. Using this approach we uncovered the potential importance of basal IP3 production, a finding that has important implications on calcium response fidelity to periodic stimulation. Based upon our analysis, we also negated the notion that the Gq-PLC interaction is switch-like, which has a strong influence upon how extracellular signals are filtered and interpreted downstream. Phase-locking analysis is a new and useful tool for model revision and mechanism elucidation; the method complements conventional genetic and chemical tools for analysis of cellular signaling circuitry and should be broadly applicable to other oscillatory pathways.
Author Summary
Key to robust discernment of cell circuit architecture is to have as many distinct response features as possible for comparison and evaluation. One under-appreciated characteristic of oscillatory circuits is that under periodic stimulation, these systems will exhibit responses synchronized to this stimulatory input, a phenomenon termed phase-locking. We demonstrate that phase-locked response characteristics vary noticeably depending on circuit activation and recovery properties; these response characteristics thereby provide a unique set of criteria for oscillatory circuit architecture analysis. The concept is validated through experiments on an oscillatory calcium pathway in mammalian cells; the experimental setup allowed us to explore, for the first time, the properties of chemically induced phase-locking of intracellular signals. Observations of this phenomenon were then used to test the predictions of several existing mathematical models of calcium signaling. Most of the models we evaluated were unable to match all our experimental observations, suggesting that current models are missing mechanistic elements in the context of calcium signaling for the cell type and receptor/stimulant tested. The observations of phase-locking further led us to identify one simple mechanistic modification that would account for all the experimental observations. The techniques and methodology presented should be broadly applicable to a variety of biological oscillators.
doi:10.1371/journal.pcbi.1001040
PMCID: PMC3009597  PMID: 21203481
14.  Entorhinal stellate cells show preferred spike phase-locking to theta inputs that is enhanced by correlations in synaptic activity 
In active networks, excitatory and inhibitory synaptic inputs generate membrane voltage fluctuations that drive spike activity in a probabilistic manner. Despite this, some cells in vivo show a strong propensity to precisely lock to the local field potential and maintain a specific spike-phase relationship relative to other cells. In recordings from rat medial entorhinal cortical stellate cells, we measured spike phase-locking in response to sinusoidal “test” inputs in the presence of different forms of background membrane voltage fluctuations, generated via dynamic clamp. We find that stellate cells show strong and robust spike phase-locking to theta (4–12 Hz) inputs. This response occurs under a wide variety of background membrane voltage fluctuation conditions that include a substantial increase in overall membrane conductance. Furthermore, the IH current present in stellate cells is critical to the enhanced spike phase-locking response at theta. Finally, we show that correlations between inhibitory and excitatory conductance fluctuations, which can arise through feed-back and feed-forward inhibition, can substantially enhance the spike phase-locking response. The enhancement in locking is a result of a selective reduction in the size of low frequency membrane voltage fluctuations due to cancelation of inhibitory and excitatory current fluctuations with correlations. Hence, our results demonstrate that stellate cells have a strong preference for spike phase-locking to theta band inputs and that the absolute magnitude of locking to theta can be modulated by the properties of background membrane voltage fluctuations.
doi:10.1523/JNEUROSCI.3892-12.2013
PMCID: PMC3680114  PMID: 23554484
synaptic correlations; high conductance; theta; IH; voltage fluctuations; balanced excitation and inhibition; spike phase-locking
15.  Programmed fluctuations in sense/antisense transcript ratios drive sexual differentiation in S. pombe 
Strand-specific RNA sequencing of S. pombe reveals a highly structured programme of ncRNA expression at over 600 loci. Functional investigations show that this extensive ncRNA landscape controls the complex programme of sexual differentiation in S. pombe.
The model eukaryote S. pombe features substantial numbers of ncRNAs many of which are antisense regulatory transcripts (ARTs), ncRNAs expressed on the opposing strand to coding sequences.Individual ARTs are generated during the mitotic cycle, or at discrete stages of sexual differentiation to downregulate the levels of proteins that drive and coordinate sexual differentiation.Antisense transcription occurring from events such as bidirectional transcription is not simply artefactual ‘chatter', it performs a critical role in regulating gene expression.
Regulation of the RNA profile is a principal control driving sexual differentiation in the fission yeast Schizosaccharomyces pombe. Before transcription, RNAi-mediated formation of heterochromatin is used to suppress expression, while post-transcription, regulation is achieved via the active stabilisation or destruction of transcripts, and through at least two distinct types of splicing control (Mata et al, 2002; Shimoseki and Shimoda, 2001; Averbeck et al, 2005; Mata and Bähler, 2006; Xue-Franzen et al, 2006; Moldon et al, 2008; Djupedal et al, 2009; Amorim et al, 2010; Grewal, 2010; Cremona et al, 2011).
Around 94% of the S. pombe genome is transcribed (Wilhelm et al, 2008). While many of these transcripts encode proteins (Wood et al, 2002; Bitton et al, 2011), the majority have no known function. We used a strand-specific protocol to sequence total RNA extracts taken from vegetatively growing cells, and at different points during a time course of sexual differentiation. The resulting data redefined existing gene coordinates and identified additional transcribed loci. The frequency of reads at each of these was used to monitor transcript abundance.
Transcript levels at 6599 loci changed in at least one sample (G-statistic; False Discovery Rate <5%). 4231 (72.3%), of which 4011 map to protein-coding genes, while 809 loci were antisense to a known gene. Comparisons between haploid and diploid strains identified changes in transcript levels at over 1000 loci.
At 354 loci, greater antisense abundance was observed relative to sense, in at least one sample (putative antisense regulatory transcripts—ARTs). Since antisense mechanisms are known to modulate sense transcript expression through a variety of inhibitory mechanisms (Faghihi and Wahlestedt, 2009), we postulated that the waves of antisense expression activated at different stages during meiosis might be regulating protein expression.
To ask whether transcription factors that drive sense-transcript levels influenced ART production, we performed RNA-seq of a pat1.114 diploid meiosis in the absence of the transcription factors Atf21 and Atf31 (responsible for late meiotic transcription; Mata et al, 2002). Transcript levels at 185 ncRNA loci showed significant changes in the knockout backgrounds. Although meiotic progression is largely unaffected by removal of Atf21 and Atf31, viability of the resulting spores was significantly diminished, indicating that Atf21- and Atf31-mediated events are critical to efficient sexual differentiation.
If changes to relative antisense/sense transcript levels during a particular phase of sexual differentiation were to regulate protein expression, then the continued presence of the antisense at points in the differentiation programme where it would normally be absent should abolish protein function during this phase. We tested this hypothesis at four loci representing the three means of antisense production: convergent gene expression, improper termination and nascent transcription from an independent locus. Induction of the natural antisense transcripts that opposed spo4+, spo6+ and dis1+ (Figures 3 and 7) in trans from a heterologous locus phenocopied a loss of function of the target protein. ART overexpression decreased Dis1 protein levels. Antisense transcription opposing spk1+ originated from improper termination of the sense ups1+ transcript on the opposite strand (Figure 3B, left locus). Expression of either the natural full-length ups1+ transcript or a truncated version, restricted to the portion of ups1+ overlapping spk1+ (Figure 3, orange transcripts) in trans from a heterologous locus phenocopied the spk1.Δ differentiation deficiency. Convergent transcription from a neighbouring gene on the opposing strand is, therefore, an effective mechanism to generate RNAi-mediated (below) silencing in fission yeast. Further analysis of the data revealed, for many loci, substantial changes in UTR length over the course of meiosis, suggesting that UTR dynamics may have an active role in regulating gene expression by controlling the transcriptional overlap between convergent adjacent gene pairs.
The RNAi machinery (Grewal, 2010) was required for antisense suppression at each of the dis1, spk1, spo4 and spo6 loci, as antisense to each locus had no impact in ago1.Δ, dcr1.Δ and rdp1.Δ backgrounds. We conclude that RNAi control has a key role in maintaining the fidelity of sexual differentiation in fission yeast. The histone H3 methyl transferase Clr4 was required for antisense control from a heterologous locus.
Thus, a significant portion of the impact of ncRNA upon sexual differentiation arises from antisense gene silencing. Importantly, in contrast to the extensively characterised ability of the RNAi machinery to operate in cis at a target locus in S. pombe (Grewal, 2010), each case of gene silencing generated here could be achieved in trans by expression of the antisense transcript from a single heterologous locus elsewhere in the genome.
Integration of an antibiotic marker gene immediately downstream of the dis1+ locus instigated antisense control in an orientation-dependent manner. PCR-based gene tagging approaches are widely used to fuse the coding sequences of epitope or protein tags to a gene of interest. Not only do these tagging approaches disrupt normal 3′UTR controls, but the insertion of a heterologous marker gene immediately downstream of an ORF can clearly have a significant impact upon transcriptional control of the resulting fusion protein. Thus, PCR tagging approaches can no longer be viewed as benign manipulations of a locus that only result in the production of a tagged protein product.
Repression of Dis1 function by gene deletion or antisense control revealed a key role this conserved microtubule regulator in driving the horsetail nuclear migrations that promote recombination during meiotic prophase.
Non-coding transcripts have often been viewed as simple ‘chatter', maintained solely because evolutionary pressures have not been strong enough to force their elimination from the system. Our data show that phenomena such as improper termination and bidirectional transcription are not simply interesting artifacts arising from the complexities of transcription or genome history, but have a critical role in regulating gene expression in the current genome. Given the widespread use of RNAi, it is reasonable to anticipate that future analyses will establish ARTs to have equal importance in other organisms, including vertebrates.
These data highlight the need to modify our concept of a gene from that of a spatially distinct locus. This view is becoming increasingly untenable. Not only are the 5′ and 3′ ends of many genes indistinct, but that this lack of a hard and fast boundary is actively used by cells to control the transcription of adjacent and overlapping loci, and thus to regulate critical events in the life of a cell.
Strand-specific RNA sequencing of S. pombe revealed a highly structured programme of ncRNA expression at over 600 loci. Waves of antisense transcription accompanied sexual differentiation. A substantial proportion of ncRNA arose from mechanisms previously considered to be largely artefactual, including improper 3′ termination and bidirectional transcription. Constitutive induction of the entire spk1+, spo4+, dis1+ and spo6+ antisense transcripts from an integrated, ectopic, locus disrupted their respective meiotic functions. This ability of antisense transcripts to disrupt gene function when expressed in trans suggests that cis production at native loci during sexual differentiation may also control gene function. Consistently, insertion of a marker gene adjacent to the dis1+ antisense start site mimicked ectopic antisense expression in reducing the levels of this microtubule regulator and abolishing the microtubule-dependent ‘horsetail' stage of meiosis. Antisense production had no impact at any of these loci when the RNA interference (RNAi) machinery was removed. Thus, far from being simply ‘genome chatter', this extensive ncRNA landscape constitutes a fundamental component in the controls that drive the complex programme of sexual differentiation in S. pombe.
doi:10.1038/msb.2011.90
PMCID: PMC3738847  PMID: 22186733
antisense; meiosis; ncRNA; S. pombe; siRNA
16.  The neuromagnetic response to spoken sentences: Co-modulation of theta band amplitude and phase 
NeuroImage  2012;60(4):2118-2127.
Speech elicits a phase-locked response in the auditory cortex that is dominated by theta (3–7 Hz) frequencies when observed via magnetoencephalography (MEG). This phase-locked response is potentially explained as new phase-locked activity superimposed on the ongoing theta oscillation or, alternatively, as phase-resetting of the ongoing oscillation. The conventional method used to distinguish between the two hypotheses is the comparison of post- to prestimulus amplitude for the phase-locked frequency across a set of trials. In theory, increased amplitude indicates the presence of additive activity, while unchanged amplitude points to phase-resetting. However, this interpretation may not be valid if the amplitude of ongoing background activity also changes following the stimulus. In this study, we employ a new approach that circumvents this problem. Specifically, we utilize a fine-grained time–frequency analysis of MEG channel data to examine the co-modulation of amplitude change and phase coherence in the post-stimulus theta-band response. If the phase-locked response is attributable solely to phase-resetting of the ongoing theta oscillation, then amplitude and phase coherence should be uncorrelated. In contrast, additive activity should produce a positive correlation. We find significant positive correlation not only during the onset response but also throughout the response period. In fact, transient increases in phase coherence are accompanied by transient increases in amplitude in accordance with a “signal plus background” model of the evoked response. The results support the hypothesis that the theta-band phase-locked response to attended speech observed using MEG is dominated by additive phase-locked activity.
doi:10.1016/j.neuroimage.2012.02.028
PMCID: PMC3593735  PMID: 22374481
Auditory; Speech; MEG; Evoked; Phase; Amplitude
17.  Connectivity in the Yeast Cell Cycle Transcription Network: Inferences from Neural Networks 
PLoS Computational Biology  2006;2(12):e169.
A current challenge is to develop computational approaches to infer gene network regulatory relationships based on multiple types of large-scale functional genomic data. We find that single-layer feed-forward artificial neural network (ANN) models can effectively discover gene network structure by integrating global in vivo protein:DNA interaction data (ChIP/Array) with genome-wide microarray RNA data. We test this on the yeast cell cycle transcription network, which is composed of several hundred genes with phase-specific RNA outputs. These ANNs were robust to noise in data and to a variety of perturbations. They reliably identified and ranked 10 of 12 known major cell cycle factors at the top of a set of 204, based on a sum-of-squared weights metric. Comparative analysis of motif occurrences among multiple yeast species independently confirmed relationships inferred from ANN weights analysis. ANN models can capitalize on properties of biological gene networks that other kinds of models do not. ANNs naturally take advantage of patterns of absence, as well as presence, of factor binding associated with specific expression output; they are easily subjected to in silico “mutation” to uncover biological redundancies; and they can use the full range of factor binding values. A prominent feature of cell cycle ANNs suggested an analogous property might exist in the biological network. This postulated that “network-local discrimination” occurs when regulatory connections (here between MBF and target genes) are explicitly disfavored in one network module (G2), relative to others and to the class of genes outside the mitotic network. If correct, this predicts that MBF motifs will be significantly depleted from the discriminated class and that the discrimination will persist through evolution. Analysis of distantly related Schizosaccharomyces pombe confirmed this, suggesting that network-local discrimination is real and complements well-known enrichment of MBF sites in G1 class genes.
Synopsis
A current challenge is to develop computational approaches to infer gene network regulatory relationships by integrating multiple types of large-scale functional genomic data. This paper shows that simple artificial neural networks (ANNs) employed in a new way do this very well. The ANN models are well-suited to capitalize on natural properties of gene networks in ways that many previous methods do not. Resulting gene network connections inferred between transcription factors and RNA output patterns are robust to noise in large-scale input datasets and to differences in RNA clustering class inputs. This was shown by using the yeast cell cycle gene network as a test case. The cycle has multiple classes of oscillatory RNAs, and Hart, Mjolsness, and Wold show that the ANNs identify key connections that associate genes from each cell cycle phase group with known and candidate regulators. Comparative analysis of network connectivity across multiple genomes showed strong conservation of basic factor-to-output relationships, although at the greatest evolutionary distances the specific target genes have mainly changed identity.
doi:10.1371/journal.pcbi.0020169
PMCID: PMC1761652  PMID: 17194216
18.  Spike Phase Locking in CA1 Pyramidal Neurons depends on Background Conductance and Firing Rate 
Oscillatory activity in neuronal networks correlates with different behavioral states throughout the nervous system, and the frequency-response characteristics of individual neurons are believed to be critical for network oscillations. Recent in vivo studies suggest that neurons experience periods of high membrane conductance, and that action potentials are often driven by membrane-potential fluctuations in the living animal. To investigate the frequency-response characteristics of CA1 pyramidal neurons in the presence of high conductance and voltage fluctuations, we performed dynamic-clamp experiments in rat hippocampal brain slices. We drove neurons with noisy stimuli that included a sinusoidal component ranging, in different trials, from 0.1 to 500 Hz. In subsequent data analysis, we determined action potential phase-locking profiles with respect to background conductance, average firing rate, and frequency of the sinusoidal component. We found that background conductance and firing rate qualitatively change the phase-locking profiles of CA1 pyramidal neurons vs. frequency. In particular, higher average spiking rates promoted band-pass profiles, and the high-conductance state promoted phase-locking at frequencies well above what would be predicted from changes in the membrane time constant. Mechanistically, spike-rate adaptation and frequency resonance in the spike-generating mechanism are implicated in shaping the different phase-locking profiles. Our results demonstrate that CA1 pyramidal cells can actively change their synchronization properties in response to global changes in activity associated with different behavioral states.
doi:10.1523/JNEUROSCI.0842-12.2012
PMCID: PMC3506380  PMID: 23055508
19.  Asynchronous response of coupled pacemaker neurons 
Physical review letters  2009;102(6):068102.
We study a network model of two conductance-based pacemaker neurons of differing natural frequency, coupled with either mutual excitation or inhibition, and receiving shared random inhibitory synaptic input. The networks may phase-lock spike-to-spike for strong mutual coupling. But the shared input can desynchronize the locked spike-pairs by selectively eliminating the lagging spike or modulating its timing with respect to the leading spike depending on their separation time window. Such loss of synchrony is also found in a large network of sparsely coupled heterogeneous spiking neurons receiving shared input.
PMCID: PMC2679421  PMID: 19257636
20.  Maximal variability of phase synchrony in cortical networks with neuronal avalanches 
The Journal of Neuroscience  2012;32(3):1061-1072.
Ongoing interactions among cortical neurons often manifest as network-level synchrony. Understanding the spatiotemporal dynamics of such spontaneous synchrony is important because it may 1) influence network response to input, 2) shape activity-dependent microcircuit structure, and 3) reveal fundamental network properties, such as an imbalance of excitation (E) and inhibition (I). Here we delineate the spatiotemporal character of spontaneous synchrony in rat cortex slice cultures and a computational model over a range of different E-I conditions including disfacilitated (antagonized AMPA, NMDA receptors), unperturbed, and disinhibited (antagonized GABAA receptors). Local field potential was recorded with multi-electrode arrays during spontaneous burst activity. Synchrony among neuronal groups was quantified based on phase-locking among recording sites. As network excitability was increased from low to high, we discovered three phenomena at an intermediate excitability level: 1) onset of synchrony, 2) maximized variability of synchrony, and 3) neuronal avalanches. Our computational model predicted that these three features occur when the network operates near a unique balanced E-I condition called ‘criticality’. These results were invariant to changes in the measurement spatial extent, spatial resolution, and frequency bands. Our findings indicate that moderate average synchrony, which is required to avoid pathology, occurs over a limited range of E-I conditions and emerges together with maximally variable synchrony. If variable synchrony is detrimental to cortical function, this is a cost paid for moderate average synchrony. However, if variable synchrony is beneficial, then by operating near criticality the cortex may doubly benefit from moderate mean and maximized variability of synchrony.
doi:10.1523/JNEUROSCI.2771-11.2012
PMCID: PMC3319677  PMID: 22262904
multi-site electrode recording; synchrony; phase synchrony; entropy; neuronal avalanches; neural excitability; critical phenomena
21.  Multiple linear regression to estimate time-frequency electrophysiological responses in single trials 
Neuroimage  2015;111:442-453.
Transient sensory, motor or cognitive event elicit not only phase-locked event-related potentials (ERPs) in the ongoing electroencephalogram (EEG), but also induce non-phase-locked modulations of ongoing EEG oscillations. These modulations can be detected when single-trial waveforms are analysed in the time-frequency domain, and consist in stimulus-induced decreases (event-related desynchronization, ERD) or increases (event-related synchronization, ERS) of synchrony in the activity of the underlying neuronal populations. ERD and ERS reflect changes in the parameters that control oscillations in neuronal networks and, depending on the frequency at which they occur, represent neuronal mechanisms involved in cortical activation, inhibition and binding. ERD and ERS are commonly estimated by averaging the time-frequency decomposition of single trials. However, their trial-to-trial variability that can reflect physiologically-important information is lost by across-trial averaging. Here, we aim to (1) develop novel approaches to explore single-trial parameters (including latency, frequency and magnitude) of ERP/ERD/ERS; (2) disclose the relationship between estimated single-trial parameters and other experimental factors (e.g., perceived intensity). We found that (1) stimulus-elicited ERP/ERD/ERS can be correctly separated using principal component analysis (PCA) decomposition with Varimax rotation on the single-trial time-frequency distributions; (2) time-frequency multiple linear regression with dispersion term (TF-MLRd) enhances the signal-to-noise ratio of ERP/ERD/ERS in single trials, and provides an unbiased estimation of their latency, frequency, and magnitude at single-trial level; (3) these estimates can be meaningfully correlated with each other and with other experimental factors at single-trial level (e.g., perceived stimulus intensity and ERP magnitude). The methods described in this article allow exploring fully non-phase-locked stimulus-induced cortical oscillations, obtaining single-trial estimate of response latency, frequency, and magnitude. This permits within-subject statistical comparisons, correlation with pre-stimulus features, and integration of simultaneously-recorded EEG and fMRI.
Highlights
•ERP/ERD/ERS are reliably isolated using PCA + Varimax rotation on single-trial TFDs.•TF-MLRd enhances the SNR of ERP/ERD/ERS in single trials.•TF-MLRd provides an unbiased estimation of single-trial parameters of ERP/ERD/ERS.•Availability of single-trial estimates permits within-subject statistical comparison.
doi:10.1016/j.neuroimage.2015.01.062
PMCID: PMC4401443  PMID: 25665966
Time-frequency analysis; Single-trial analysis; Event-related desynchronization (ERD); Event-related synchronization (ERS); Multiple linear regression; Neuronal oscillations
22.  Targeted interactomics reveals a complex core cell cycle machinery in Arabidopsis thaliana 
A protein interactome focused towards cell proliferation was mapped comprising 857 interactions among 393 proteins, leading to many new insights in plant cell cycle regulation.A comprehensive view on heterodimeric cyclin-dependent kinase (CDK)/cyclin complexes in plants is obtained, in relation with their regulators.Over 100 new candidate cell cycle proteins were predicted.
The basic underlying mechanisms that govern the cell cycle are conserved among all eukaryotes. Peculiar for plants, however, is that their genome contains a collection of cell cycle regulatory genes that is intriguingly large (Vandepoele et al, 2002; Menges et al, 2005) compared to other eukaryotes. Arabidopsis thaliana (Arabidopsis) encodes 71 genes in five regulatory classes versus only 15 in yeast and 23 in human.
Despite the discovery of numerous cell cycle genes, little is known about the protein complex machinery that steers plant cell division. Therefore, we applied tandem affinity purification (TAP) approach coupled with mass spectrometry (MS) on Arabidopsis cell suspension cultures to isolate and analyze protein complexes involved in the cell cycle. This approach allowed us to successfully map a first draft of the basic cell cycle complex machinery of Arabidopsis, providing many new insights into plant cell division.
To map the interactome, we relied on a streamlined platform comprising generic Gateway-based vectors with high cloning flexibility, the fast generation of transgenic suspension cultures, TAP adapted for plant cells, and matrix-assisted laser desorption ionization (MALDI) tandem-MS for the identification of purified proteins (Van Leene et al, 2007, 2008Van Leene et al, 2007, 2008). Complexes for 102 cell cycle proteins were analyzed using this approach, leading to a non-redundant data set of 857 interactions among 393 proteins (Figure 1A). Two subspaces were identified in this data set, domain I1, containing interactions confirmed in at least two independent experimental repeats or in the reciprocal purification experiment, and domain I2 consisting of uniquely observed interactions.
Several observations underlined the quality of both domains. All tested reverse purifications found the original interaction, and 150 known or predicted interactions were confirmed, meaning that also a huge stack of new interactions was revealed. An in-depth computational analysis revealed enrichment for many cell cycle-related features among the proteins of the network (Figure 1B), and many protein pairs were coregulated at the transcriptional level (Figure 1C). Through integration of known cell cycle-related features, more than 100 new candidate cell cycle proteins were predicted (Figure 1D). Besides common qualities of both interactome domains, their real significance appeared through mutual differences exposing two subspaces in the cell cycle interactome: a central regulatory network of stable complexes that are repeatedly isolated and represent core regulatory units, and a peripheral network comprising transient interactions identified less frequently, which are involved in other aspects of the process, such as crosstalk between core complexes or connections with other pathways. To evaluate the biological relevance of the cell cycle interactome in plants, we validated interactions from both domains by a transient split-luciferase assay in Arabidopsis plants (Marion et al, 2008), further sustaining the hypothesis-generating power of the data set to understand plant growth.
With respect to insights into the cell cycle physiology, the interactome was subdivided according to the functional classes of the baits and core protein complexes were extracted, covering cyclin-dependent kinase (CDK)/cyclin core complexes together with their positive and negative regulation networks, DNA replication complexes, the anaphase-promoting complex, and spindle checkpoint complexes. The data imply that mitotic A- and B-type cyclins exclusively form heterodimeric complexes with the plant-specific B-type CDKs and not with CDKA;1, whereas D-type cyclins seem to associate with CDKA;1. Besides the extraction of complexes previously shown in other organisms, our data also suggested many new functional links; for example, the link coupling cell division with the regulation of transcript splicing. The association of negative regulators of CDK/cyclin complexes with transcription factors suggests that their role in reallocation is not solely targeted to CDK/cyclin complexes. New members of the Siamese-related inhibitory proteins were identified, and for the first time potential inhibitors of plant-specific mitotic B-type CDKs have been found in plants. New evidence that the E2F–DP–RBR network is not only active at G1-to-S, but also at the G2-to-M transition is provided and many complexes involved in DNA replication or repair were isolated. For the first time, a plant APC has been isolated biochemically, identifying three potential new plant-specific APC interactors, and finally, complexes involved in the spindle checkpoint were isolated mapping many new but specific interactions.
Finally, to get a general view on the complex machinery, modules of interacting cyclins and core cell cycle regulators were ranked along the cell cycle phases according to the transcript expression peak of the cyclins, showing an assorted set of CDK–cyclin complexes with high regulatory differentiation (Figure 4). Even within the same subfamily (e.g. cyclin A3, B1, B2, D3, and D4), cyclins differ not only in their functional time frame but also in the type and number of CDKs, inhibitors, and scaffolding proteins they bind, further indicating their functional diversification. According to our interaction data, at least 92 different variants of CDK–cyclin complexes are found in Arabidopsis.
In conclusion, these results reflect how several rounds of gene duplication (Sterck et al, 2007) led to the evolution of a large set of cyclin paralogs and a myriad of regulators, resulting in a significant jump in the complexity of the cell cycle machinery that could accommodate unique plant-specific features such as an indeterminate mode of postembryonic development. Through their extensive regulation and connection with a myriad of up- and downstream pathways, the core cell cycle complexes might offer the plant a flexible toolkit to fine-tune cell proliferation in response to an ever-changing environment.
Cell proliferation is the main driving force for plant growth. Although genome sequence analysis revealed a high number of cell cycle genes in plants, little is known about the molecular complexes steering cell division. In a targeted proteomics approach, we mapped the core complex machinery at the heart of the Arabidopsis thaliana cell cycle control. Besides a central regulatory network of core complexes, we distinguished a peripheral network that links the core machinery to up- and downstream pathways. Over 100 new candidate cell cycle proteins were predicted and an in-depth biological interpretation demonstrated the hypothesis-generating power of the interaction data. The data set provided a comprehensive view on heterodimeric cyclin-dependent kinase (CDK)–cyclin complexes in plants. For the first time, inhibitory proteins of plant-specific B-type CDKs were discovered and the anaphase-promoting complex was characterized and extended. Important conclusions were that mitotic A- and B-type cyclins form complexes with the plant-specific B-type CDKs and not with CDKA;1, and that D-type cyclins and S-phase-specific A-type cyclins seem to be associated exclusively with CDKA;1. Furthermore, we could show that plants have evolved a combinatorial toolkit consisting of at least 92 different CDK–cyclin complex variants, which strongly underscores the functional diversification among the large family of cyclins and reflects the pivotal role of cell cycle regulation in the developmental plasticity of plants.
doi:10.1038/msb.2010.53
PMCID: PMC2950081  PMID: 20706207
Arabidopsis thaliana; cell cycle; interactome; protein complex; protein interactions
23.  Dissecting the fission yeast regulatory network reveals phase-specific control elements of its cell cycle 
BMC Systems Biology  2009;3:93.
Background
Fission yeast Schizosaccharomyces pombe and budding yeast Saccharomyces cerevisiae are among the original model organisms in the study of the cell-division cycle. Unlike budding yeast, no large-scale regulatory network has been constructed for fission yeast. It has only been partially characterized. As a result, important regulatory cascades in budding yeast have no known or complete counterpart in fission yeast.
Results
By integrating genome-wide data from multiple time course cell cycle microarray experiments we reconstructed a gene regulatory network. Based on the network, we discovered in addition to previously known regulatory hubs in M phase, a new putative regulatory hub in the form of the HMG box transcription factor SPBC19G7.04. Further, we inferred periodic activities of several less known transcription factors over the course of the cell cycle, identified over 500 putative regulatory targets and detected many new phase-specific and conserved cis-regulatory motifs. In particular, we show that SPBC19G7.04 has highly significant periodic activity that peaks in early M phase, which is coordinated with the late G2 activity of the forkhead transcription factor fkh2. Finally, using an enhanced Bayesian algorithm to co-cluster the expression data, we obtained 31 clusters of co-regulated genes 1) which constitute regulatory modules from different phases of the cell cycle, 2) whose phase order is coherent across the 10 time course experiments, and 3) which lead to identification of phase-specific control elements at both the transcriptional and post-transcriptional levels in S. pombe. In particular, the ribosome biogenesis clusters expressed in G2 phase reveal new, highly conserved RNA motifs.
Conclusion
Using a systems-level analysis of the phase-specific nature of the S. pombe cell cycle gene regulation, we have provided new testable evidence for post-transcriptional regulation in the G2 phase of the fission yeast cell cycle. Based on this comprehensive gene regulatory network, we demonstrated how one can generate and investigate plausible hypotheses on fission yeast cell cycle regulation which can potentially be explored experimentally.
doi:10.1186/1752-0509-3-93
PMCID: PMC2758837  PMID: 19758441
24.  Auditory steady state responses in a schizophrenia rat model probed by excitatory/inhibitory receptor manipulation 
Alterations in neural synchrony and oscillations may contribute to the pathophysiology of schizophrenia and reflect aberrations in cortical glutamatergic and GABAergic neurotransmission. We tested the effects of a GABA agonist and a NMDA antagonist on auditory steady state responses (ASSRs) in awake rats with neonatal ventral hippocampal lesions (NVHLs) as a neurodevelopmental model of schizophrenia. NVHL vs. SHAM lesioned rats were injected with saline then either ketamine (NMDA antagonist) or muscimol (GABAA agonist). Time-frequency analyses examined alterations in phase locking (consistency) across trials and changes in total power (magnitude). ASSRs were compared at 5 stimulation frequencies (10, 20, 30, 40, and 50 Hz). In SHAM rats, phase locking and power generally increased with stimulation frequency. Both ketamine and muscimol also increased phase locking and power in SHAM rats, but mostly in the 20 to 40 Hz range. NVHL and ketamine altered the frequency dependence of phase locking, while only ketamine changed power frequency dependence. Muscimol affected power, but not phase locking, in the NVHL rats. NVHL and ketamine models of schizophrenia produce similar independent effects on ASSR, potentially representing similar forms of cortical network/glutamatergic dysfunction, albeit the effects of ketamine were more robust. Muscimol produced NVHL-dependent reductions in ASSR measures, suggesting that cortical networks in this model are intolerant to post-synaptic GABAergic stimulation. These findings suggest the utility of combining lesion, pharmacological, and ASSR approaches in understanding neural mechanisms underlying disturbed synchrony in schizophrenia.
doi:10.1016/j.ijpsycho.2012.04.002
PMCID: PMC3443327  PMID: 22504207
ASSRs; GABA; NMDA; NVHL; schizophrenia; synchronization
25.  Transient process of cortical activity during Necker cube perception: from local clusters to global synchrony 
Nonlinear Biomedical Physics  2010;4(Suppl 1):S7.
Background
It has been discussed that neural phase-synchrony across distant cortical areas (or global phase-synchrony) was correlated with various aspects of consciousness. The generating process of the synchrony, however, remains largely unknown. As a first step, we investigate transient process of global phase-synchrony, focusing on phase-synchronized clusters. We hypothesize that the phase-synchronized clusters are dynamically organized before global synchrony and clustering patterns depend on perceptual conditions.
Methods
In an EEG study, Kitajo reported that phase-synchrony across distant cortical areas was selectively enhanced by top-down attention around 4 Hz in Necker cube perception. Here, we further analyzed the phase-synchronized clusters using hierarchical clustering which sequentially binds up the nearest electrodes based on similarity of phase locking between the cortical signals. First, we classified dominant components of the phase-synchronized clusters over time. We then investigated how the phase-synchronized clusters change with time, focusing on their size and spatial structure.
Results
Phase-locked clusters organized a stable spatial pattern common to the perceptual conditions. In addition, the phase-locked clusters were modulated transiently depending on the perceptual conditions and the time from the perceptual switch. When top-down attention succeeded in switching perception as subjects intended, independent clusters at frontal and occipital areas grew to connect with each other around the time of the perceptual switch. However, the clusters in the occipital and left parietal areas remained divided when top-down attention failed in switching perception. When no primary biases exist, the cluster in the occipital area grew to its maximum at the time of the perceptual switch within the occipital area.
Conclusions
Our study confirmed the existence of stable phase-synchronized clusters. Furthermore, these clusters were transiently connected with each other. The connecting pattern depended on subjects’ internal states. These results suggest that subjects’ attentional states are associated with distinct spatio-temporal patterns of the phase-locked clusters.
doi:10.1186/1753-4631-4-S1-S7
PMCID: PMC2880804  PMID: 20522268

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