Cognitive Neurodynamics

U1 snRNA over-expression affects neural oscillations and short-term memory deficits in mice Abstract Small nuclear RNAs (snRNAs) and other RNA spliceosomal components are involved in neurological and psychiatric disorders. U1 snRNA has recently been demonstrated to be altered in pathology in some neurodegenerative diseases, but whether it has a causative role is not clear. Here we have studied this by overexpressing U1 snRNA in mice and measured their hippocampal oscillatory patterns and brain functions. Novel object recognition test showed that the recognition index was significantly decreased in the U1 snRNA over-expression mice compared to that in the C57BL mice. U1 snRNA over-expression regulated not only the pattern of neural oscillations but also the expression of neuron excitatory and inhibitory proteins. Here we show that U1 snRNA over-expression contains the shrinkage distribution of theta-power, theta-phase lock synchronization, and theta and low-gamma cross-frequency coupling in the hippocampus. The alternations of neuron receptors by the U1 snRNA overexpression also modulated the decreasing of recognition index, the energy distribution of theta power spectrum with the reductions of theta phase synchronization and phase-amplitude coupling between theta and low-gamma. Linking these all together, our results suggest that U1 snRNA overexpression particularly causes a deficit in short-term memory. These findings make a bedrock of our research that U1 snRNA bridges the gap about the mechanism behind short-term memory based on the molecular and mesoscopic level.

Plasma total antioxidant status and cognitive impairments in first-episode drug-naïve patients with schizophrenia Abstract Accumulating evidence suggest that excessive reactive oxygen species-induced oxidative damage may underlie neurodegeneration and cognitive impairment in several disorders including schizophrenia. In this study we examined the association of oxidative stress with cognitive deficits in first-episode drug-naïve (FEDN) patients with schizophrenia. We recruited 54 FEDN patients and 50 age- and sex-matched healthy controls and examined the Measurement and Treatment Research to Improve Cognition in Schizophrenia Consensus cognitive Battery (MCCB) and plasma total antioxidant status (TAS). Psychopathological symptoms were assessed using the Positive and Negative Syndrome Scale. The results showed that plasma TAS levels were significantly lower in the patients than those in the healthy subjects (94.7 ± 25.0 U/ml vs 156.6 ± 46.7 U/ml, p < 0.0001). The patients scored lower than healthy controls on the MCCB total score, speed of processing, attention/vigilance and managing emotion test index and STROOP test. For the patients, TAS was associated with some domains of cognitive deficits in schizophrenia, such as speed of processing, attention/vigilance and emotion managing. Our results suggested that oxidative stress may be involved in the pathophysiology of schizophrenia at the early of stage and its cognitive impairment.

Bifurcation analysis and diverse firing activities of a modified excitable neuron model Abstract Electrical activities of excitable cells produce diverse spiking-bursting patterns. The dynamics of the neuronal responses can be changed due to the variations of ionic concentrations between outside and inside the cell membrane. We investigate such type of spiking-bursting patterns under the effect of an electromagnetic induction on an excitable neuron model. The effect of electromagnetic induction across the membrane potential can be considered to analyze the collective behavior for signal processing. The paper addresses the issue of the electromagnetic flow on a modified Hindmarsh–Rose model (H–R) which preserves biophysical neurocomputational properties of a class of neuron models. The different types of firing activities such as square wave bursting, chattering, fast spiking, periodic spiking, mixed-mode oscillations etc. can be observed using different injected current stimulus. The improved version of the model includes more parameter sets and the multiple electrical activities are exhibited in different parameter regimes. We perform the bifurcation analysis analytically and numerically with respect to the key parameters which reveals the properties of the fast-slow system for neuronal responses. The firing activities can be suppressed/enhanced using the different external stimulus current and by allowing a noise induced current. To study the electrical activities of neural computation, the improved neuron model is suitable for further investigation.

Flanker paradigm contains conflict and distraction factors with distinct neural mechanisms: an ERP analysis in a 2-1 mapping task Abstract Behavioral studies using the flanker 2-1 mapping task suggest that both stimulus and response conflicts contribute to flanker conflict effect. However, both are intertwined with distraction effect. Their underlying neural mechanisms remain unclear. We applied a perceptual flanker 2-1 mapping task to 24 healthy young adults, while the event-related potentials were recorded. The task included stimulus-incongruent (SI), response-incongruent (RI), congruent (CO) and neutral (NE) stimuli. Our reaction time data demonstrated conflict effect, distraction effect and their interaction. Furthermore, the conflict factor successively enhanced the frontal P2 (160–240 ms), the posterior N2pc (200–240 ms), the fronto-central and the right frontal N2b (240–420 ms), and the posterior N2c (320–420 ms). Only the frontal P2 was larger for RI than SI. The distraction factor increased the right N2pc and reduced the left parietal P3b (460–480 ms). Overall, our findings suggested that the flanker conflict involved an early attentional processing of task-relevant and distractive information, and a later processing of conflict evaluation and response inhibition.

Complex temporal patterns processing by a neural mass model of a cortical column Abstract It is well known that neuronal networks are capable of transmitting complex spatiotemporal information in the form of precise sequences of neuronal discharges characterized by recurrent patterns. At the same time, the synchronized activity of large ensembles produces local field potentials that propagate through highly dynamic oscillatory waves, such that, at the whole brain scale, complex spatiotemporal dynamics of electroencephalographic (EEG) signals may be associated to sensorimotor decision making processes. Despite these experimental evidences, the link between highly temporally organized input patterns and EEG waves has not been studied in detail. Here, we use a neural mass model to investigate to what extent precise temporal information, carried by deterministic nonlinear attractor mappings, is filtered and transformed into fluctuations in phase, frequency and amplitude of oscillatory brain activity. The phase shift that we observe, when we drive the neural mass model with specific chaotic inputs, shows that the local field potential amplitude peak appears in less than one full cycle, thus allowing traveling waves to encode temporal information. After converting phase and amplitude changes obtained into point processes, we quantify input–output similarity following a threshold-filtering algorithm onto the amplitude wave peaks. Our analysis shows that the neural mass model has the capacity for gating the input signal and propagate selected temporal features of that signal. Finally, we discuss the effect of local excitatory/inhibitory balance on these results and how excitability in cortical columns, controlled by neuromodulatory innervation of the cerebral cortex, may contribute to set a fine tuning and gating of the information fed to the cortex.

Complex network based models of ECoG signals for detection of induced epileptic seizures in rats Abstract The automatic detection of seizures bears a considerable significance in epileptic diagnosis as it can efficiently lead to a considerable reduction of the workload of the medical staff. The present study aims at automatic detecting epileptic seizures in epileptic rats. To this end, seizures were induced in rats implementing the pentylenetetrazole model, with the electrocorticogram (ECoG) signals during, before and after the seizure periods being recorded. For this purpose, five algorithms for transforming time series into complex networks based on visibility graph (VG) algorithm were used. In this study, VG based methods were used for the first time to analyze ECoG signals in rats. Afterward, Standard measures in network science (graph properties) were made to examine the topological structure of these networks produced on the basis of ECoG signals. Then these measures were given to a classifier as input features so that the ECoG signals could be classified into seizure periods and seizure-free periods. Artificial Neural Network, considered a popular classifier, was used in this work. The experimental results showed that the method managed to detect epileptic seizure in rats with a high accuracy of 92.13%. Our proposed method was also applied to the recorded EEG signals from Bonn database to show the efficiency of the proposed method for human seizure detection.

Consensus of uncertain multi-agent systems with input delay and disturbances Abstract In this paper, the problem of robust consensus for multi-agent systems affected by external disturbances is discussed. A novel consensus control is developed by using a feedback controller based on disturbance rejection and Smith predictor scheme. Specifically, the disturbance rejection performance of the uncertain multi-agent systems is improved according to the estimation of equivalent-input-disturbance and the effect of time delay in the control system is reduced via Smith predictor scheme by shifting the delay outside the feedback loop. Furthermore, by combining Lyapunov theory, matrix inequality techniques and properties of Kronecker product, a robust feedback controller for each agent is designed such that the desired consensus of the uncertain multi-agent systems affected by external disturbances can be ensured. Finally, to illustrate the validity of the designed control scheme, two numerical examples with simulation results are provided.

Theoretical models of reaction times arising from simple-choice tasks Abstract In this work we present a group of theoretical models for reaction times arising from simple-choice task tests. In particular, we argue for the inclusion of a shifted version of the Gamma distribution as a theoretical model based on a mathematical result on first hitting times. We contrast the goodness-of-fit of those models with the Ex-Gaussian distribution, using data from recently published experiments. The evidence of the results obtained highlights the convenience of proposing theoretical models for reaction times instead of models acting exclusively as quantitative distribution measurements.

Cluster burst synchronization in a scale-free network of inhibitory bursting neurons Abstract We consider a scale-free network of inhibitory Hindmarsh–Rose (HR) bursting neurons, and make a computational study on coupling-induced cluster burst synchronization by varying the average coupling strength \(J_0\) . For sufficiently small \(J_0\) , non-cluster desynchronized states exist. However, when passing a critical point \(J^*_c~(\simeq 0.16)\) , the whole population is segregated into 3 clusters via a constructive role of synaptic inhibition to stimulate dynamical clustering between individual burstings, and thus 3-cluster desynchronized states appear. As \(J_0\) is further increased and passes a lower threshold \(J^*_l~(\simeq 0.78)\) , a transition to 3-cluster burst synchronization occurs due to another constructive role of synaptic inhibition to favor population synchronization. In this case, HR neurons in each cluster make burstings every 3rd cycle of the instantaneous burst rate \(R_w(t)\) of the whole population, and exhibit burst synchronization. However, as \(J_0\) passes an intermediate threshold \(J^*_m~(\simeq 5.2)\) , HR neurons fire burstings intermittently at a 4th cycle of \(R_w(t)\) via burst skipping rather than at its 3rd cycle, and hence they begin to make intermittent hoppings between the 3 clusters. Due to such intermittent intercluster hoppings via burst skippings, the 3 clusters become broken up (i.e., the 3 clusters are integrated into a single one). However, in spite of such break-up (i.e., disappearance) of the 3-cluster states, (non-cluster) burst synchronization persists in the whole population, which is well visualized in the raster plot of burst onset times where bursting stripes (composed of burst onset times and indicating burst synchronization) appear successively. With further increase in \(J_0\) , intercluster hoppings are intensified, and bursting stripes also become dispersed more and more due to a destructive role of synaptic inhibition to spoil the burst synchronization. Eventually, when passing a higher threshold \(J^*_h~(\simeq 17.8)\) a transition to desynchronization occurs via complete overlap between the bursting stripes. Finally, we also investigate the effects of stochastic noise on both 3-cluster burst synchronization and intercluster hoppings.

Temperature effect on memristive ion channels Abstract Neuron shows distinct dependence of electrical activities on membrane patch temperature, and the mode transition of electrical activity is induced by the patch temperature through modulating the opening and closing rates of ion channels. In this paper, inspired by the physical effect of memristor, the potassium and sodium ion channels embedded in the membrane patch are updated by using memristor-based voltage gate variables, and an external stimulus is applied to detect the variety of mode selection in electrical activities under different patch temperatures. It is found that each ion channel can be regarded as a physical memristor, and the shape of pinched hysteresis loop of memristor is dependent on both input voltage and patch temperature. The pinched hysteresis loops of two ion-channel memristors are dramatically enlarged by increasing patch temperature, and the hysteresis lobe areas are monotonously reduced with the increasing of excitation frequency if the frequency of external stimulus exceeds certain threshold. However, for the memristive potassium channel, the AREA1 corresponding to the threshold frequency is increased with the increasing of patch temperature. The amplitude of conductance for two ion-channel memristors depends on the variation of patch temperature. The results of this paper might provide insights to modulate the neural activities in appropriate temperature condition completely, and involvement of external stimulus enhance the effect of patch temperature.