Contrast gain control is a reparameterization of a population response curve.

Neurons in primary visual cortex (area V1) adapt in different degrees to the average contrast of the environment, suggesting that the representation of visual stimuli may interact with the state of cortical gain control in complex ways. To investigate this possibility, we measured and analyzed the responses of neural populations to visual stimuli as a function of contrast in different environments, each characterized by a unique distribution of contrast. Our findings reveal that, for a given stimulus, the population response can be described by a vector function r ( g e c ) , where the gain g e is a decreasing function of the mean contrast of the environment. Thus, gain control can be viewed as a reparameterization of a population response curve, which is invariant across environments. Different stimuli are mapped to distinct curves, all originating from a common origin, corresponding to a zero-contrast response. Altogether, our findings provide a straightforward, geometric interpretation of contrast gain control at the population level and show that changes in gain are well coordinated among members of a neural population.

Linear feedback control of spreading dynamics in stochastic nonlinear network models: epileptic seizures.

The development of models and approaches for controlling the spreading dynamics of epileptic seizures is an essential step towards new therapies for people with pharmacologically resistant epilepsy. Beyond resective neurosurgery, in which epileptogenic zones (EZs) are the target of surgery, closed-loop control based on intracranial electrical stimulation, applied at the very early stage of seizure evolution, has been the main alternative, e.g. the RNS system from NeuroPace (Mountain View, CA). In this approach the electrical stimulation is delivered to target brain areas after detection of seizure initiation in the EZ. Here, we examined, on model simulations, some of the closed-loop control aspects of the problem. Seizure dynamics and spread are typically modeled with highly nonlinear dynamics on complex brain networks. Despite the nonlinearity and complexity, currently available optimal feedback control approaches are mostly based on linear approximations. Alternative machine learning control approaches might require amounts of data beyond what is commonly available in the intended application. We thus examined how standard linear feedback control approaches perform when applied to nonlinear models of neural dynamics of seizure generation and spread. In particular, we considered patient-specific epileptor network models for seizure onset and spread. The models incorporate network connectivity derived from (diffusion MRI) white-matter tractography, have been shown to capture the qualitative dynamics of epileptic seizures and can be fit to patient data. For control, we considered simple linear quadratic Gaussian (LQG) regulators. The LQG control was based on a discrete-time state-space model derived from the linearization of the patient-specific epileptor network model around a stable fixed point in the regime of preictal dynamics. We show in simulations that LQG regulators acting on the EZ node during the initial seizure period tend to be unstable. The LQG solution for the control law in this case leads to global feedback to the EZ-node actuator. However, if the LQG solution is constrained to depend on only local feedback originating from the EZ node itself, the controller is stable. In this case, we demonstrate that localized LQG can easily terminate the seizure at the early stage and prevent spread. In the context of optimal feedback control based on linear approximations, our results point to the need for investigating in more detail feedback localization and additional relevant control targets beyond epileptogenic zones.

Criticality in probabilistic models of spreading dynamics in brain networks: Epileptic seizures.

The spread of seizures across brain networks is the main impairing factor, often leading to loss-of-consciousness, in people with epilepsy. Despite advances in recording and modeling brain activity, uncovering the nature of seizure spreading dynamics remains an important challenge to understanding and treating pharmacologically resistant epilepsy. To address this challenge, we introduce a new probabilistic model that captures the spreading dynamics in patient-specific complex networks. Network connectivity and interaction time delays between brain areas were estimated from white-matter tractography. The model's computational tractability allows it to play an important complementary role to more detailed models of seizure dynamics. We illustrate model fitting and predictive performance in the context of patient-specific Epileptor networks. We derive the phase diagram of spread size (order parameter) as a function of brain excitability and global connectivity strength, for different patient-specific networks. Phase diagrams allow the prediction of whether a seizure will spread depending on excitability and connectivity strength. In addition, model simulations predict the temporal order of seizure spread across network nodes. Furthermore, we show that the order parameter can exhibit both discontinuous and continuous (critical) phase transitions as neural excitability and connectivity strength are varied. Existence of a critical point, where response functions and fluctuations in spread size show power-law divergence with respect to control parameters, is supported by mean-field approximations and finite-size scaling analyses. Notably, the critical point separates two distinct regimes of spreading dynamics characterized by unimodal and bimodal spread-size distributions. Our study sheds new light on the nature of phase transitions and fluctuations in seizure spreading dynamics. We expect it to play an important role in the development of closed-loop stimulation approaches for preventing seizure spread in pharmacologically resistant epilepsy. Our findings may also be of interest to related models of spreading dynamics in epidemiology, biology, finance, and statistical physics.

Critical dynamics in the spread of focal epileptic seizures: Network connectivity, neural excitability and phase transitions.

Focal epileptic seizures can remain localized or, alternatively, spread across brain areas, often resulting in impairment of cognitive function and loss of consciousness. Understanding the factors that promote spread is important for developing better therapeutic approaches. Here, we show that: (1) seizure spread undergoes "critical" phase transitions in models (epileptor-networks) that capture the neural dynamics of spontaneous seizures while incorporating patient-specific brain network connectivity, axonal delays and identified epileptogenic zones (EZs). We define a collective variable for the spreading dynamics as the spread size, i.e. the number of areas or nodes in the network to which a seizure has spread. Global connectivity strength and excitability in the surrounding non-epileptic areas work as phase-transition control parameters for this collective variable. (2) Phase diagrams are predicted by stability analysis of the network dynamics. (3) In addition, the components of the Jacobian's leading eigenvector, which tend to reflect the connectivity strength and path lengths from the EZ to surrounding areas, predict the temporal order of network-node recruitment into seizure. (4) However, stochastic fluctuations in spread size in a near-criticality region make predictability more challenging. Overall, our findings support the view that within-patient seizure-spread variability can be characterized by phase-transition dynamics under transient variations in network connectivity strength and excitability across brain areas. Furthermore, they point to the potential use and limitations of model-based prediction of seizure spread in closed-loop interventions for seizure control.

A unifying framework for mean-field theories of asymmetric kinetic Ising systems.

Kinetic Ising models are powerful tools for studying the non-equilibrium dynamics of complex systems. As their behavior is not tractable for large networks, many mean-field methods have been proposed for their analysis, each based on unique assumptions about the system's temporal evolution. This disparity of approaches makes it challenging to systematically advance mean-field methods beyond previous contributions. Here, we propose a unifying framework for mean-field theories of asymmetric kinetic Ising systems from an information geometry perspective. The framework is built on Plefka expansions of a system around a simplified model obtained by an orthogonal projection to a sub-manifold of tractable probability distributions. This view not only unifies previous methods but also allows us to develop novel methods that, in contrast with traditional approaches, preserve the system's correlations. We show that these new methods can outperform previous ones in predicting and assessing network properties near maximally fluctuating regimes.

Semimetal to semiconductor transition in Bi/TiO2 core/shell nanowires.

We demonstrate the full thermoelectric and structural characterization of individual bismuth-based (Bi-based) core/shell nanowires. The influence of strain on the temperature dependence of the electrical conductivity, the absolute Seebeck coefficient and the thermal conductivity of bismuth/titanium dioxide (Bi/TiO2) nanowires with different diameters is investigated and compared to bismuth (Bi) and bismuth/tellurium (Bi/Te) nanowires and bismuth bulk. Scattering at surfaces, crystal defects and interfaces between the core and the shell reduces the electrical conductivity to less than 5% and the thermal conductivity to less than 25% to 50% of the bulk value at room temperature. On behalf of a compressive strain, Bi/TiO2 core/shell nanowires show a decreasing electrical conductivity with decreasing temperature opposed to that of Bi and Bi/Te nanowires. We find that the compressive strain induced by the TiO2 shell can lead to a band opening of bismuth increasing the absolute Seebeck coefficient by 10% to 30% compared to bulk at room temperature. In the semiconducting state, the activation energy is determined to |41.3 ± 0.2| meV. We show that if the strain exceeds the elastic limit the semimetallic state is recovered due to the lattice relaxation.

The roles played by IL-10, IL-23 and IL-17A in term delivery.

BACKGROUND: The immune system significantly participates in the development of the successful delivery process. The roles played by cytokine molecules in the induction of term delivery are yet to be clarified. The aim of this project was to explore the serum levels of interleukin-10 (IL-10), IL-17A, and IL-23 in the mothers with term and prolonged pregnancy and their infants. MATERIALS AND METHODS: In this study, 60 samples were collected from either mothers with term and prolonged pregnancy or their infants, collectively 240 samples. Serum levels of IL-10, IL-17A and IL-23 were explored using enzyme linked immunosorbent assay (ELISA) technique. RESULTS: IL-10 serum levels significantly decreased in the neonates with prolonged pregnancy when compared to their mothers. Serum levels of IL-23 were increased either in term or prolonged pregnancy neonates when compared to their corresponded mothers. Serum levels of IL-10 and IL-23 significantly decreased and increased, respectively, in the female in comparison to male in the prolonged pregnancy neonates. IL-10 also significantly decreased in the term mothers who had higher gravidity. CONCLUSION: Although, IL-17A does not play a key role in the delivery mechanism, IL-10 and IL-23 may be considered as potential factors in the modulation of term delivery.

Coexistence of scale-invariant and rhythmic behavior in self-organized criticality.

Scale-free behavior as well as oscillations are frequently observed in the activity of many natural systems. One important example is the cortical tissues of mammalian brain where both phenomena are simultaneously observed. Rhythmic oscillations as well as critical (scale-free) dynamics are thought to be important, but theoretically incompatible, features of a healthy brain. Motivated by the above, we study the possibility of the coexistence of scale-free avalanches along with rhythmic behavior within the framework of self-organized criticality. In particular, we add an oscillatory perturbation to local threshold condition of the continuous Zhang model and characterize the subsequent activity of the system. We observe regular oscillations embedded in well-defined avalanches which exhibit scale-free size and duration in line with observed neuronal avalanches. The average amplitude of such oscillations are shown to decrease with increasing frequency consistent with real brain oscillations. Furthermore, it is shown that optimal amplification of oscillations occur at the critical point, further providing evidence for functional advantages of criticality.

Effects of resistance training protocols on nonlinear analysis of heart rate variability in metabolic syndrome.

Despite the various standard non-linear measurements used in autonomic modulation (AM) assessments usually being applied to long time-series, such analyses can sometimes be applied to shorter term series. To overcome this disadvantage, chaotic global methods were formulated by putting together heart rate variability (HRV) linear methods. Chaos provides information about vegetative function control related to cardiovascular risks. Applying this method can be useful to investigate the complexity of the health condition after resistance training protocols, as a therapeutic intervention in AM in metabolic syndrome individuals (MetS). This study aimed to compare the effects of two resistance training programs (conventional vs functional) in MetS using nonlinear analysis of AM. MetS subjects (n=50) of both sexes aged 40 to 60 years were randomly divided into two programs; a group of 12 people served as a control group. Both groups performed 30 sessions of training. AM was assessed in the chaos domain by chaotic global techniques. The main results showed that both resistance training, functional and conventional, increased chaos when compared to the control group, respectively, observed by chaotic forward parameter (CFP)1 (13.9±17.9 vs 12.8±14.4 vs -2.23±7.96; P≤0.05) and CFP3 (15.4±19.8 vs 21.9±13.2 vs -4.82±11.4; P≤0.05). In addition, 30 sessions of both resistance programs increased chaos, and non-linear analysis enabled discrimination of AM after interventions when compared to the control group.

The Effect of the MEMS Measurement Platform Design on the Seebeck Coefficient Measurement of a Single Nanowire.

In order to study the thermoelectric properties of individual nanowires, a thermoelectric nanowire characterization platform (TNCP) has been previously developed and used in our chair. Here, we report on a redesigned platform aiming to optimize performance, mechanical stability and usability. We compare both platforms for electrical conductivity and the Seebeck coefficient for an individual Ag nanowire of the previously-used batch and for comparable measurement conditions. By this, the measurement performance of both designs can be investigated. As a result, whereas the electrical conductivity is comparable, the Seebeck coefficient shows a 50% deviation with respect to the previous studies. We discuss the possible effects of the platform design on the thermoelectric measurements. One reason for the deviation of the Seebeck coefficient is the design of the platform leading to temperature gradients along the bond pads. We further analyze the effect of bonding materials Au and Pt, as well as the effect of temperature distributions along the bond pads used for the thermovoltage acquisition. Another major reason for the variation of the measurement results is the non-homogeneous temperature distribution along the thermometer. We conclude that for the measurement of small Seebeck coefficients, an isothermal positioning of voltage-probing bond pads, as well as a constant temperature profile at the measurement zone are essential.

Effects of resistance training protocols on nonlinear analysis of heart rate variability in metabolic syndrome

Despite the various standard non-linear measurements used in autonomic modulation (AM) assessments usually being applied to long time-series, such analyses can sometimes be applied to shorter term series. To overcome this disadvantage, chaotic global methods were formulated by putting together heart rate variability (HRV) linear methods. Chaos provides information about vegetative function control related to cardiovascular risks. Applying this method can be useful to investigate the complexity of the health condition after resistance training protocols, as a therapeutic intervention in AM in metabolic syndrome individuals (MetS). This study aimed to compare the effects of two resistance training programs (conventional vs functional) in MetS using nonlinear analysis of AM. MetS subjects (n=50) of both sexes aged 40 to 60 years were randomly divided into two programs; a group of 12 people served as a control group. Both groups performed 30 sessions of training. AM was assessed in the chaos domain by chaotic global techniques. The main results showed that both resistance training, functional and conventional, increased chaos when compared to the control group, respectively, observed by chaotic forward parameter (CFP)1 (13.9±17.9 vs 12.8±14.4 vs -2.23±7.96; P≤0.05) and CFP3 (15.4±19.8 vs 21.9±13.2 vs -4.82±11.4; P≤0.05). In addition, 30 sessions of both resistance programs increased chaos, and non-linear analysis enabled discrimination of AM after interventions when compared to the control group.

Refractory period in network models of excitable nodes: self-sustaining stable dynamics, extended scaling region and oscillatory behavior.

Networks of excitable nodes have recently attracted much attention particularly in regards to neuronal dynamics, where criticality has been argued to be a fundamental property. Refractory behavior, which limits the excitability of neurons is thought to be an important dynamical property. We therefore consider a simple model of excitable nodes which is known to exhibit a transition to instability at a critical point (λ = 1), and introduce refractory period into its dynamics. We use mean-field analytical calculations as well as numerical simulations to calculate the activity dependent branching ratio that is useful to characterize the behavior of critical systems. We also define avalanches and calculate probability distribution of their size and duration. We find that in the presence of refractory period the dynamics stabilizes while various parameter regimes become accessible. A sub-critical regime with λ < 1.0, a standard critical behavior with exponents close to critical branching process for λ = 1, a regime with 1 < λ < 2 that exhibits an interesting scaling behavior, and an oscillating regime with λ > 2.0. We have therefore shown that refractory behavior leads to a wide range of scaling as well as periodic behavior which are relevant to real neuronal dynamics.

Quantifying similarity of pore-geometry in nanoporous materials.

In most applications of nanoporous materials the pore structure is as important as the chemical composition as a determinant of performance. For example, one can alter performance in applications like carbon capture or methane storage by orders of magnitude by only modifying the pore structure. For these applications it is therefore important to identify the optimal pore geometry and use this information to find similar materials. However, the mathematical language and tools to identify materials with similar pore structures, but different composition, has been lacking. We develop a pore recognition approach to quantify similarity of pore structures and classify them using topological data analysis. This allows us to identify materials with similar pore geometries, and to screen for materials that are similar to given top-performing structures. Using methane storage as a case study, we also show that materials can be divided into topologically distinct classes requiring different optimization strategies.

Structural versus dynamical origins of mean-field behavior in a self-organized critical model of neuronal avalanches.

Critical dynamics of cortical neurons have been intensively studied over the past decade. Neuronal avalanches provide the main experimental as well as theoretical tools to consider criticality in such systems. Experimental studies show that critical neuronal avalanches show mean-field behavior. There are structural as well as recently proposed [Phys. Rev. E 89, 052139 (2014)] dynamical mechanisms that can lead to mean-field behavior. In this work we consider a simple model of neuronal dynamics based on threshold self-organized critical models with synaptic noise. We investigate the role of high-average connectivity, random long-range connections, as well as synaptic noise in achieving mean-field behavior. We employ finite-size scaling in order to extract critical exponents with good accuracy. We conclude that relevant structural mechanisms responsible for mean-field behavior cannot be justified in realistic models of the cortex. However, strong dynamical noise, which can have realistic justifications, always leads to mean-field behavior regardless of the underlying structure. Our work provides a different (dynamical) origin than the conventionally accepted (structural) mechanisms for mean-field behavior in neuronal avalanches.

Comparison of Quetiapine and Risperidone in Treatment of Acute Psychosis: A Double-Blind, Randomized-Controlled Study.

OBJECTIVE: The aim of this study was to evaluate the effectiveness of Quetiapine versus Risperidone in control of acute psychotic signs and symptoms in hospitalized patients during four weeks. METHODS: In this double-blind, randomized controlled study, a total of 90 patients with a confirmed diagnosis acute psychosis and were hospitalized in Zare Hospital, Sari, Iran, and they were treated with Quetiapine (mean 500 mg/day) or Risperidone (mean 5.2 mg/day), in a 4 week period. The positive and negative symptoms scale (PANSS) and Clinical Global Impression-Severity scale (CGI-s) were used to assess psychotic symptoms and severity of illness in first and the last day of the study. RESULTS: No significant difference found between two groups in decreasing positive and negative sub-scores in the PANSS. Risperidone was superior to Quetiapine in decreasing the PANSS general psychopathology sub-scores and total score (p<0.05). No significant difference found between two groups in decreasing CGI-s score.

Risperidone versus risperidone plus sodium valproate for treatment of bipolar disorders: a randomized, double-blind clinical-trial.

OBJECTIVE: This study compared the efficacy of risperidone monotherapy with risperidone plus valproate in bipolar I disorder, manic phase. Some studies showed the efficacy of risperidone monotherapy in the treatment of bipolar disorder, so we examined this effectiveness in this clinical-trial study. METHOD: This 7-week, randomized, single-blind study included 48 bipolar I inpatients manic phase without psychotic features divided in risperidone group (n = 23) and risperidone plus sodium valproate group (n = 25). According to clinical symptoms, 3 categories: complete remission, partial remission and no remission were mentioned in weekly follow-up. Remission rate compared with survival analysis. RESULTS: The results showed a significant difference in remission rate between risperidone monotherapy and risperidone plus sodium valproate at the 1st, 2nd and the 3rd week (p = 0.012, 0.023, 0.027 respectively), It means the remission rate in risperidone plus valproate group was higher in the first three weeks, but at the end of the seventh week, the difference was not statistically significant. There was no significant difference between the two groups in the development of adverse effects. CONCLUSIONS: Risperidone can be effective and well tolerated in both acute manic episodes of bipolar mood disorders.

Acute dystonia due to citalopram treatment: a case series.

INTRODUCTION: Abnormal movements such as acute dystonia, dyskinesia, parkinsonism, exacerbation of Parkinson disease, akathisia and possibly neuroleptic malignant syndrome may be associated with the use of selective serotonin reuptake inhibitors (SSRIs) rarely. Citalopram, a typical SSRI, used in serotonergic dysfunction related disorders, potentially can cause extrapyramidal symptoms such as acute dystonia. METHODS: In a retrospective survey on patients referred to psychiatric clinic between February 2010 and February 2011 who were prescribed citalopram by the psychiatrist. The data about Demographic, history of drug and alcohol abuse or dependence, diagnosis and citalopram consumption length collected. The daily dose of citalopram was also recorded. Acute dystonia was identified by a validated chart review and precise neurological examination. RESULTS: Nine patients were included. Citalopram was initiated at a 20 mg and titrated to a mean dose of 27 mg. The median length of acute dystonia after citalopram therapy was nine days. Other common adverse events included somnolence, gastric upset and nightmare in the cases. CONCLUSIONS: This case series was an effort to show the citalopram potential to trigger acute dystonia. Clinician needs to be aware of possible dystonia, as early recognition is necessary to prevent major adverse outcomes.

Mean-field behavior as a result of noisy local dynamics in self-organized criticality: neuroscience implications.

Motivated by recent experiments in neuroscience which indicate that neuronal avalanches exhibit scale invariant behavior similar to self-organized critical systems, we study the role of noisy (nonconservative) local dynamics on the critical behavior of a sandpile model which can be taken to mimic the dynamics of neuronal avalanches. We find that despite the fact that noise breaks the strict local conservation required to attain criticality, our system exhibits true criticality for a wide range of noise in various dimensions, given that conservation is respected on the average. Although the system remains critical, exhibiting finite-size scaling, the value of critical exponents change depending on the intensity of local noise. Interestingly, for a sufficiently strong noise level, the critical exponents approach and saturate at their mean-field values, consistent with empirical measurements of neuronal avalanches. This is confirmed for both two and three dimensional models. However, the addition of noise does not affect the exponents at the upper critical dimension (D = 4). In addition to an extensive finite-size scaling analysis of our systems, we also employ a useful time-series analysis method to establish true criticality of noisy systems. Finally, we discuss the implications of our work in neuroscience as well as some implications for the general phenomena of criticality in nonequilibrium systems.

Cooperative mechanisms involved in chronic antidiuretic response to bendroflumethiazide in rats with lithium-induced nephrogenic diabetes insipidus.

Previous studies of central diabetes insipidus suggested that thiazides acutely exerted a paradoxical antidiuresis by either indirectly activating volume-homeostatic reflexes to decrease distal fluid-delivery, or directly stimulating distal water-reabsorption. This study investigated whether the direct and indirect actions of bendroflumethiazide (BFTZ) simultaneously cooperated and also whether the renal nerves were involved in inducing long-term antidiuresis in nephrogenic diabetes insipidus (NDI). BFTZ or vehicle was gavaged into bilateral renal denervated and innervated rats with lithium-induced NDI for 10 days, constituting four groups. At one day before (D0) and one, five and ten days after starting administration of BFTZ or vehicle, rats were placed in metabolic cages to collect urine for 6 hours. BFTZ-treatment in both renal innervated and denervated rats caused equivalent reductions in urine-flow, creatinine clearance, lithium clearance and free-water clearance, but rises in urine-osmolality, fractional proximal reabsorption and fractional distal reabsorption at all days compared to D0, as well as to those of their relevant vehicle-received group. Therefore, the chronic antidiuretic response to BFTZ in conscious NDI rats was exerted through a concomitant cooperation of its direct distal effect of stimulating water-reabsorption and its indirect effect of reducing distal fluid-delivery by activating volume-homeostatic mechanisms, which appeared independent of the renal nerves.