ERP analysis, performed after TSD, showed a less negative mean amplitude for the N2-posterior-contralateral (N2pc) difference wave compared to the baseline, and a more positive mean amplitude for the P3 wave compared to baseline. Our data indicates a considerable reduction in attentional direction/orientation processing caused by TSD, resulting in a compensatory effect within the parietal brain to partially alleviate the consequential impairments. These findings advance our knowledge of the consequences of sleep loss, providing new insights into the topic.Neural oscillation amplitude demonstrably changes with shifts in consciousness, but how the temporal progression of these oscillations at differing time scales correlates to consciousness remains largely unknown. Using spontaneous resting-state EEG recordings, we analyzed the amplitude fluctuations of oscillations in patients with disorders of consciousness (DOC), comparing them to healthy controls to investigate this question. In order to describe the temporal patterns of EEG oscillations, both detrended fluctuation analysis (DFA) and measurements of lifetime and waiting time were applied to the long-term (1-20s) and short-term (<1s) data collected from groups of patients in different states of consciousness, comprising minimally conscious state (MCS) patients, unresponsive wakefulness syndrome (UWS) patients, and healthy subjects. The results pointed to elevated DFA exponents, a sign of stronger long-range temporal correlations, most evident in the central brain region across alpha and beta bands. Over brief periods, the oscillations showed a decrease in the frequency of bursts. The UWS and MCS groups displayed less individual variation in metrics, potentially attributable to the decreased spatial variability of oscillatory dynamics. Correspondingly, the temporal characteristics of EEG oscillations demonstrated significant relationships with how the patients behaved. Through our study, we observed that the loss of consciousness is associated with fluctuations in the temporal structure of neural oscillations across multiple time scales, potentially contributing to the understanding of the neural correlates of consciousness.Supplemental content related to the online version is located at the cited URL: 101007/s11571-022-09852-9.The online version's supplementary material is linked from 101007/s11571-022-09852-9.Bi-neuron networks with similar neurons have been extensively studied in relation to the memristive electromagnetic induction effect, contrasting with the relatively limited investigation in networks with neurons exhibiting differing properties. Employing coupled heterogeneous Rulkov neurons with memristors, this paper creates a bi-neuron network, analyzing the memristive electromagnetic induction effect. Theoretical investigation indicates that the bi-neuron network maintains an equilibrium state along a straight line, the stability of which is controlled by the strength of the memristor coupling and the initial state. The stability of the line's equilibrium state undergoes a change from an unstable saddle-focus to a stable node-focus, a process mediated by a Hopf bifurcation. Dynamical behaviors associated with memristor coupling strength and initial conditions are disclosed by multiple numerical tools—Julia and MATLAB—leveraging parameters within the stable node-focus region. Numerical results confirm that the proposed heterogeneous bi-neuron Rulkov network produces point attractors, periodic orbits, chaotic states, chaos crises, and period-doubling bifurcations. It is the initial conditions of memristor and gated ion concentration that lead to extreme multistability. Demonstrated through phase portraits and time-domain waveforms, memristor initial conditions dictate the co-existence of infinitely many periodic firing patterns, each with unique periodicities, and chaotic firing patterns. The exploration of phase synchronization, intrinsically linked to the memristor coupling strength and its initial state, is conducted. The outcome highlights that substantial memristor coupling and initial conditions enable phase synchronization of the two heterogeneous neurons. Energy balance between neurons is adaptively regulated by the plasticity of the memristor synapse. Consequently, to reinforce the numerical simulations, hardware experiments based on the MCU architecture are undertaken.The cerebral cortex's synchronized activity and its modulation by synaptic plasticity have been widely studied over the last two decades. While a substantial body of work focuses on excitatory pyramidal neurons, the precise manner in which inhibitory interneurons communicate with each other continues to be a subject of research. p53 signals receptor This study investigates a complex network composed of excitatory (E) pyramidal neurons and inhibitory (I) interneurons, where interaction occurs through chemical synapses under the influence of spike-timing-dependent plasticity (STDP). We examined the impact of eSTDP and iSTDP on synchronization and oscillatory behavior in a balanced excitatory-inhibitory network by considering three diverse scenarios: a small-world network of exclusive excitatory neurons with eSTDP, a small-world network of exclusive inhibitory neurons with iSTDP, and a small-world network with a balanced mixture of excitatory and inhibitory neurons. The number of inhibitory interneurons, the density of connections in a small-world network, and the magnitude of coupling influence the synchronization and oscillatory dynamics of these networks. While eSTDP demonstrated a strong capacity to promote synchronization, iSTDP had no notable influence on this process. eSTDP and iSTDP, working in tandem, control the equilibrium of excitatory-inhibitory interactions within the balanced neuronal network, maintaining network stability and synchronization. These tools also serve to direct treatment and further research efforts into neurodegenerative diseases.This paper examines robust stochastic stabilization and H∞ control for uncertain time-delay Markovian jump quaternion-valued neural networks (MJQVNNs) with partial transition probability information. A direct quaternion method for analyzing MJQVNNs is presented, distinct from conventional methods which often rely on system decomposition. Subsequently, to acquire a more precise estimation of the upper bound on the derivative of the constructed Lyapunov-Krasovskii functional (LKF), a generalization of the real-valued convex inequality to the quaternions is performed. Designed mode-dependent state feedback controllers provide robust stochastic stabilization conditions for MJQVNNs, incorporating admissible uncertainties, thus minimizing the effect of input disturbances on the controlled output to achieve a specified performance. Lastly, two numerical examples are provided to demonstrate the strength of the suggested approach.Chronic dementia's pathophysiology, as well as the pathophysiology of chronic schizophrenia (SCZ), have both been found to involve the proteins BMP6 and noggin, and SCZ significantly increases the likelihood of developing dementia in later life. The current research project analyzed the connection between blood biomarkers of BMP6/noggin and cognitive performance in the elderly with chronic schizophrenia.A sample of 159 elderly individuals with chronic schizophrenia and 171 healthy community members were recruited for this study. All subjects underwent evaluation of blood cytokines, encompassing BMP6 and its opposing protein noggin, as well as cognitive function; 157 of these individuals were also tested for apolipoprotein E.One year after the genotype test, cognitive evaluations were performed on 208 subjects.The elderly with chronic schizophrenia showed a decrease in blood BMP6 and noggin levels, particularly significant within the dementia cohort. Noggin and blood BMP6 levels can provide a means to delineate chronic schizophrenia (SCZ) in elderly individuals from healthy controls (NC).Four carriers demonstrated reduced levels of BMP6.Non-4 carriers are found in populations with chronic schizophrenia.Chronic schizophrenia was associated with a substantial relationship between blood BMP6/noggin levels and cognitive performance.There was a pronounced link between blood BMP6/noggin levels and cognitive abilities observed in chronic schizophrenia patients.Voluntary movements' preparatory and execution stages are signaled by negative movement-related cortical potentials. Up to this point, investigations have centered on the readiness potential (RP) associated with simple movements of either the upper or lower limbs. We sought to ascertain the potential for decoding the intention behind the sit-to-stand action using the RP, which relies on data from both the upper and lower body segments. Therefore, healthy volunteers participated in a scalp electroencephalography study. To gauge the movement of the upper body segment, a gyro sensor was placed on the posterior aspect. Meanwhile, electromyogram electrodes were situated on the hamstrings and quadriceps of the lower body segment, for the purpose of detecting its movements. Our study's findings pinpoint the appearance of a negative Readiness Potential about 2 to 3 seconds before the commencement of upper body movement in the sit-to-stand action in reaction to the initial signal. Just before the upper body motion commenced, the RP registered a negative peak and a sharp decline in value between -0.08 and -0.0001 seconds. The signals indicative of readiness or performance of a sit-to-stand motion are provided by negative-going RPs. Accordingly, the method of morphological component analysis was applied to isolate the morphology of RPs from a single trial's data. The promising morphology of RPs offers potential for progress in limb neurotrophies and neurorehabilitation technology.While neuromodulation holds great promise in managing epilepsy, existing techniques lack the capacity for real-time adjustment of stimulation based on patient reactions. Consequently, a regulated method that is both systematic and scientific, and that considers patients' real-time responses, needs to be implemented.