A neural mass model of phase-amplitude coupling

Excessive hydrogen sulfide-induced activation of NMDA receptors in the colon participates in anxiety- and compulsive-like behaviors in a rodent model of hemorrhagic shock and resuscitation

OBJECTIVE

Hemorrhagic shock and resuscitation (HSR) cause inflammatory responses in the gastrointestinal tract and is associated with substantial morbidity and mortality rates. Hydrogen sulfide (H2S), a gasotransmitter with pleiotropic activity, exhibits anti-inflammatory benefits at physiological levels. However, deleterious effects are observed when its concentration increases. In this investigation, we employed a mouse model of HSR to examine the effects of an H2S scavenger on the gastrointestinal tract and brain, with emphasis on N-Methyl-d-Aspartate (NMDA) receptor function.

METHODS

Mice were immediately administered dl-propargylglycine (PAG) intragastrically as an H2S scavenger after HSR exposure. The O-maze and buried beads tests were used to assess compulsive- and anxiety-like behaviors. Pathological changes in the intestine were evaluated at 24 and 30 days after HSR. Subsequently, at 30 days after HSR, we examined electrophysiological and pathological changes in the amygdala.

RESULTS

Within 24 h of HSR exposure, animals treated with PAG showed significantly lower colonic injury. Additionally, compared to the HSR-treated mice 30 days after HSR, the PAG-treated mice displayed reduced buried beads, increased open-arm time, lower blood levels of Diamine Oxidase (DAO) and considerably improved ZO-1 intensity, a stronger association between the delta rhythm phase and beta activity amplitude, and lower neuroinflammatory response in the amygdala. MK-801, an NMDA receptor inhibitor, significantly reversed H2S-induced intestinal and cerebral injury.

CONCLUSION

This experimental data suggests that H2S-induced excessive activation of NMDA receptors contributes to anxiety- and compulsive-like behaviors caused by HSR.

EEG generation mechanism of lower limb active movement intention and its virtual reality induction enhancement: a preliminary study

Introduction

Active rehabilitation requires active neurological participation when users use rehabilitation equipment. A brain-computer interface (BCI) is a direct communication channel for detecting changes in the nervous system. Individuals with dyskinesia have unclear intentions to initiate movement due to physical or psychological factors, which is not conducive to detection. Virtual reality (VR) technology can be a potential tool to enhance the movement intention from pre-movement neural signals in clinical exercise therapy. However, its effect on electroencephalogram (EEG) signals is not yet known. Therefore, the objective of this paper is to construct a model of the EEG signal generation mechanism of lower limb active movement intention and then investigate whether VR induction could improve movement intention detection based on EEG.

Methods

Firstly, a neural dynamic model of lower limb active movement intention generation was established from the perspective of signal transmission and information processing. Secondly, the movement-related EEG signal was calculated based on the model, and the effect of VR induction was simulated. Movement-related cortical potential (MRCP) and event-related desynchronization (ERD) features were extracted to analyze the enhancement of movement intention. Finally, we recorded EEG signals of 12 subjects in normal and VR environments to verify the effectiveness and feasibility of the above model and VR induction enhancement of lower limb active movement intention for individuals with dyskinesia.

Results

Simulation and experimental results show that VR induction can effectively enhance the EEG features of subjects and improve the detectability of movement intention.

Discussion

The proposed model can simulate the EEG signal of lower limb active movement intention, and VR induction can enhance the early and accurate detectability of lower limb active movement intention. It lays the foundation for further robot control based on the actual needs of users.

A multi-scale model explains oscillatory slowing and neuronal hyperactivity in Alzheimer's disease

Alzheimer's disease is the most common cause of dementia and is linked to the spreading of pathological amyloid-β and tau proteins throughout the brain. Recent studies have highlighted stark differences in how amyloid-β and tau affect neurons at the cellular scale. On a larger scale, Alzheimer's patients are observed to undergo a period of early-stage neuronal hyperactivation followed by neurodegeneration and frequency slowing of neuronal oscillations. Herein, we model the spreading of both amyloid-β and tau across a human connectome and investigate how the neuronal dynamics are affected by disease progression. By including the effects of both amyloid-β and tau pathology, we find that our model explains AD-related frequency slowing, early-stage hyperactivation and late-stage hypoactivation. By testing different hypotheses, we show that hyperactivation and frequency slowing are not due to the topological interactions between different regions but are mostly the result of local neurotoxicity induced by amyloid-β and tau protein.

Analysis of the neural mechanism of spectra decrease in MCI by a thalamo-cortical coupled neural mass model

Objective.In order to deeply understand the neurophysiological mechanism of the spectra decrease in mild cognitive impairment (MCI), this paper studies a new neural mass model, which can simulate various intracerebral electrophysiological activities.Approach. In this study, a thalamo-cortical coupled neural mass model (TCC-NMM) is proposed. The influences of the coupling coefficients and other key parameters on the model spectra are simulated. Then, the unscented Kalman filter (UKF) algorithm is used to reversely identify the parameters in the TCC-NMM. Furthermore, the TCC-NMM and UKF are combined to analyze the spectra reduction mechanism of electroencephalogram (EEG) signals in MCI patients. The independent sample t-test is carried out to statistical analyze the differences of the identified parameters between MCI and normal controls. The Pearson correlation analysis is used to analyze the intrinsic relationship between parameters and the scores of the comprehensive competence assessment scale.Main results.The simulation results show that the decreased cortical synaptic connectivity constantsC₁can result in spectra decrease of the TCC-NMM outputs. The real EEG analysis results show that the identified values of parameterC₁are significant lower in the MCI group than in control group in frontal and occipital areas and the parametersC₁are positively correlated with the Montreal Cognitive Assessment (MoCA) scores in the two areas. This consistency suggests that the cortical synaptic connectivity loss from pyramidal cells to excitatory interneurons (eIN) may be one of the neural mechanisms of EEG spectra decrease in MCI.Significance. (a) In this study, a new mathematical model TCCNMM based on anatomy and neurophysiology is proposed. (b) All key parameters in TCC-NMM are studied in detail through the forward and reverse analysis and the influence of these parameters on the output spectra of the model is pointed out. (c) The possible neural mechanism of the decreased spectra in MCI patients is pointed out by the joint analysis of simulation in forward with TCC-NMM and analysis of the actual EEG signals in reverse with UKF identification algorithm. (d) We find that the identified parameter C1 of MCI patients is significantly lower than that of the control group, which is consistent with the simulation analysis of TCC-NMM. So, we suggest that the decreased MCI alpha power spectrum is likely related to the cortical synaptic connection loss from pyramidal cells to eIN.

Adaptive rewiring in nonuniform coupled oscillators

Structural plasticity of the brain can be represented in a highly simplified form as adaptive rewiring, the relay of connections according to the spontaneous dynamic synchronization in network activity. Adaptive rewiring, over time, leads from initial random networks to brain-like complex networks, that is, networks with modular small-world structures and a rich-club effect. Adaptive rewiring has only been studied, however, in networks of identical oscillators with uniform or random coupling strengths. To implement information-processing functions (e.g., stimulus selection or memory storage), it is necessary to consider symmetry-breaking perturbations of oscillator amplitudes and coupling strengths. We studied whether nonuniformities in amplitude or connection strength could operate in tandem with adaptive rewiring. Throughout network evolution, either amplitude or connection strength of a subset of oscillators was kept different from the rest. In these extreme conditions, subsets might become isolated from the rest of the network or otherwise interfere with the development of network complexity. However, whereas these subsets form distinctive structural and functional communities, they generally maintain connectivity with the rest of the network and allow the development of network complexity. Pathological development was observed only in a small proportion of the models. These results suggest that adaptive rewiring can robustly operate alongside information processing in biological and artificial neural networks.

Pathological responses to single-pulse electrical stimuli in epilepsy: The role of feedforward inhibition

Delineation of epileptogenic cortex in focal epilepsy patients may profit from single‐pulse electrical stimulation during intracranial EEG recordings. Single‐pulse electrical stimulation evokes early and delayed responses. Early responses represent connectivity. Delayed responses are a biomarker for epileptogenic cortex, but up till now, the precise mechanism generating delayed responses remains elusive. We used a data‐driven modelling approach to study early and delayed responses. We hypothesized that delayed responses represent indirect responses triggered by early response activity and investigated this for 11 patients. Using two coupled neural masses, we modelled early and delayed responses by combining simulations and bifurcation analysis. An important feature of the model is the inclusion of feedforward inhibitory connections. The waveform of early responses can be explained by feedforward inhibition. Delayed responses can be viewed as second‐order responses in the early response network which appear when input to a neural mass falls below a threshold forcing it temporarily to a spiking state. The combination of the threshold with noisy background input explains the typical stochastic appearance of delayed responses. The intrinsic excitability of a neural mass and the strength of its input influence the probability at which delayed responses to occur. Our work gives a theoretical basis for the use of delayed responses as a biomarker for the epileptogenic zone, confirming earlier clinical observations. The combination of early responses revealing effective connectivity, and delayed responses showing intrinsic excitability, makes single‐pulse electrical stimulation an interesting tool to obtain data for computational models of epilepsy surgery.

Frontal delta-beta cross-frequency coupling in high and low social anxiety: An index of stress regulation?

Cross-frequency coupling (CFC) between frontal delta (1–4 Hz) and beta (14–30 Hz) oscillations has been suggested as a candidate neural correlate of social anxiety disorder, a disorder characterized by fear and avoidance of social and performance situations. Prior studies have used amplitude-amplitude correlation (AAC) as a CFC measure and hypothesized it as a candidate neural mechanism of affective control. However, using this metric has yielded inconsistent results regarding the direction of CFC, and the functional significance of coupling strength is uncertain. To offer a better understanding of CFC in social anxiety, we compared frontal delta-beta AAC with phase-amplitude coupling (PAC) – a mechanism for information transfer through neural circuits. Twenty high socially anxious (HSA) and 32 low socially anxious (LSA) female undergraduates participated in a social performance task (SPT). Delta-beta PAC and AAC were estimated during the resting state, as well as the anticipation and recovery conditions. Results showed significantly more AAC in LSA than HSA participants during early anticipation, as well as significant values during all conditions in LSA participants only. PAC did not distinguish between LSA and HSA participants, and instead was found to correlate with state nervousness during early anticipation, but in LSA participants only. Together, these findings are interpreted to suggest that delta-beta AAC is a plausible neurobiological index of adaptive stress regulation and can distinguish between trait high and low social anxiety during stress, while delta-beta PAC might be sensitive enough to reflect mild state anxiety in LSA participants.

Cross-Frequency Coupling Based Neuromodulation for Treating Neurological Disorders

Synchronous, rhythmic changes in the membrane polarization of neurons form oscillations in local field potentials. It is hypothesized that high-frequency brain oscillations reflect local cortical information processing, and low-frequency brain oscillations project information flow across larger cortical networks. This provides complex forms of information transmission due to interactions between oscillations at different frequency bands, which can be rendered with cross-frequency coupling (CFC) metrics. Phase-amplitude coupling (PAC) is one of the most common representations of the CFC. PAC reflects the coupling of the phase of oscillations in a specific frequency band to the amplitude of oscillations in another frequency band. In a normal brain, PAC accompanies multi-item working memory in the hippocampus, and changes in PAC have been associated with diseases such as schizophrenia, obsessive-compulsive disorder (OCD), Alzheimer disease (AD), epilepsy, and Parkinson's disease (PD). The purpose of this article is to explore CFC across the central nervous system and demonstrate its correlation to neurological disorders. Results from previously published studies are reviewed to explore the significant role of CFC in large neuronal network communication and its abnormal behavior in neurological disease. Specifically, the association of effective treatment in PD such as dopaminergic medication and deep brain stimulation with PAC changes is described. Lastly, CFC analysis of the electrocorticographic (ECoG) signals recorded from the motor cortex of a Parkinson's disease patient and the parahippocampal gyrus of an epilepsy patient are demonstrated. This information taken together illuminates possible roles of CFC in the nervous system and its potential as a therapeutic target in disease states. This will require new neural interface technologies such as phase-dependent stimulation triggered by PAC changes, for the accurate recording, monitoring, and modulation of the CFC signal.

A neural mass model of cross frequency coupling

Electrophysiological signals of cortical activity show a range of possible frequency and amplitude modulations, both within and across regions, collectively known as cross-frequency coupling. To investigate whether these modulations could be considered as manifestations of the same underlying mechanism, we developed a neural mass model. The model provides five out of the theoretically proposed six different coupling types. Within model components, slow and fast activity engage in phase-frequency coupling in conditions of low ambient noise level and with high noise level engage in phase-amplitude coupling. Between model components, these couplings can be coordinated via slow activity, giving rise to more complex modulations. The model, thus, provides a coherent account of cross-frequency coupling, both within and between components, with which regional and cross-regional frequency and amplitude modulations could be addressed.