The role of extracellular glutamate homeostasis dysregulated by astrocyte in epileptic discharges: a model evidence.

Glutamate (Glu) is a predominant excitatory neurotransmitter that acts on glutamate receptors to transfer signals in the central nervous system. Abnormally elevated extracellular glutamate levels is closely related to the generation and transition of epileptic seizures. However, there lacks of investigation regarding the role of extracellular glutamate homeostasis dysregulated by astrocyte in neuronal epileptic discharges. According to this, we propose a novel neuron-astrocyte computational model (NAG) by incorporating extracellular Glu concentration dynamics from three aspects of regulatory mechanisms: (1) the Glu uptake through astrocyte EAAT2; (2) the binding and release Glu via activating astrocyte mGluRs; and (3) the Glu free diffusion in the extracellular space. Then the proposed model NAG is analyzed theoretically and numerically to verify the effect of extracellular Glu homeostasis dysregulated by such three regulatory mechanisms on neuronal epileptic discharges. Our results demonstrate that the neuronal epileptic discharges can be aggravated by the downregulation expression of EAAT2, the aberrant activation of mGluRs, and the elevated Glu levels in extracellular micro-environment; as well as various discharge states (including bursting, mixed-mode spiking, and tonic firing) can be transited by their combination. Furthermore, we find that such factors can also alter the bifurcation threshold for the generation and transition of epileptic discharges. The results in this paper can be helpful for researchers to understand the astrocyte role in modulating extracellular Glu homeostasis, and provide theoretical basis for future related experimental studies.

A neural mass model for disturbance of alpha rhythm in the minimal hepatic encephalopathy.

One of the early markers of minimal hepatic encephalopathy (MHE) is the disruption of alpha rhythm observed in electroencephalogram (EEG) signals. However, the underlying mechanisms responsible for this occurrence remain poorly understood. To address this gap, we develop a novel biophysical model MHE-AWD-NCM, encompassing the communication dynamics between a cortical neuron population (CNP) and an astrocyte population (AP), aimed at investigating the relationship between alpha wave disturbance (AWD) and mechanistical principles, specifically concerning astrocyte-neuronal communication in the context of MHE. In addition, we introduce the concepts of peak power density and peak frequency within the alpha band as quantitative measures of AWD. Our model faithfully reproduces the characteristic EEG phenomenology during MHE and shows how impairments of communication between CNP and AP could promote AWD. The results suggest that the disruptions in feedback neurotransmission from AP to CNP, along with the inhibition of GABA uptake by AP from the extracellular space, contribute to the observed AWD. Moreover, we found that the variation of external excitatory stimuli on CNP may play a key role in AWD in MHE. Finally, the sensitivity analysis is also performed to assess the relative significance of above factors in influencing AWD. Our findings align with the physiological observations and provide a more comprehensive understanding of the complex interplay of astrocyte-neuronal communication that underlies the AWD observed in MHE, which potentially may help to explore the targeted therapeutic interventions for the early stage of hepatic encephalopathy.

Layer-by-layer coated probiotics with chitosan and liposomes demonstrate improved stability and antioxidant properties in vitro.

Probiotics are of increasing interest for their potential health benefits, but their survival and adhesion in the harsh gastrointestinal environment remain a concern. This study explored a single-cell encapsulation technique to enhance probiotic survival and adhesion in the gastrointestinal tract. We encapsulated probiotics in curcumin-loaded liposomes, further coated them with polymers using layer-by-layer techniques. The coated probiotics were evaluated for survival in simulated gastrointestinal conditions, adhesion to colonic mucus, and scavenging of reactive oxygen species (ROS). The results showed that multi-layer encapsulation increased probiotic size at the nanoscale, enhancing their survival in simulated gastrointestinal conditions. Upon reaching the colon, the shedding of the coating coincided with probiotic proliferation. Additionally, the coated probiotics exhibited increased adhesion to colonic mucus. Moreover, the coating acted as a protective barrier for effectively scavenging reactive oxygen radicals, ensuring probiotic survival in inflammatory environments. This study combines the synergistic effects of probiotics and curcumin, underscoring the promise of single-cell encapsulation techniques in improving the efficacy of probiotics for addressing colitis-related diseases.

A mechanistic model of calcium homeostasis leading to occurrence and propagation of secondary brain injury.

Secondary brain injury (SBI) refers to new or worsening brain insult after primary brain injury (PBI). Neurophysiological experiments show that calcium (Ca2+) is one of the major culprits that contribute to neuronal damage and death following PBI. However, mechanistic details about how alterations of Ca2+ levels contribute to SBI are not well characterized. In this paper, we first build a biophysical model for SBI related to calcium homeostasis (SBI-CH) to study the mechanistic details of PBI-induced disruption of CH, and how these disruptions affect the occurrence of SBI. Then, we construct a coupled SBI-CH model by formulating synaptic interactions to investigate how disruption of CH affects synaptic function and further promotes the propagation of SBI between neurons. Our model shows how the opening of voltage-gated calcium channels (VGCCs), decreasing of plasma membrane calcium pump (PMCA), and reversal of the Na+/Ca2+ exchanger (NCX) during and following PBI, could induce disruption of CH and further promote SBI. We also show that disruption of CH causes synaptic dysfunction, which further induces loss of excitatory-inhibitory balance in the system, and this might promote the propagation of SBI and cause neighboring tissue to be injured. Our findings offer a more comprehensive understanding of the complex interrelationship between CH and SBI.NEW & NOTEWORTHY We build a mechanistic model SBI-CH for calcium homeostasis (CH) to study how alterations of Ca2+ levels following PBI affect the occurrence and propagation of SBI. Specifically, we investigate how the opening of VGCCs, decreasing of PMCA, and reversal of NCX disrupt CH, and further induce the occurrence of SBI. We also present a coupled SBI-CH model to show how disrupted CH causes synaptic dysfunction, and further promotes the propagation of SBI between neurons.

Fraxetin suppresses reactive oxygen species-dependent autophagy by the PI3K/Akt pathway to inhibit isoflurane-induced neurotoxicity in hippocampal neuronal cells.

Isoflurane, a common volatile anesthetic, has been widely used to provide general anesthesia in operations. However, exposure to isoflurane may cause widespread neurotoxicity in the developing animal brain. Fraxetin, a natural coumarin derivative extracted from the bark of Fraxinus rhynchophylla, possesses versatile pharmacological properties including anti-oxidative, anti-inflammatory, and neuroprotective effects. However, the effect and action mechanism of fraxetin on neurotoxicity induced by isoflurane are unknown. Reactive oxygen species (ROS) generation, cell viability, lactate dehydrogenase (LDH) release, and apoptosis were estimated by 2',7'-dichlorofluorescin-diacetate (DCFH-DA) staining, MTT, LDH release, and terminal deoxynucleotidyl transferase-mediated deoxyuridine triphosphate nick end-labeling (TUNEL) staining assays, respectively. The protein levels of light chain 3 (LC3)-I, LC3-II, p62, protein kinase B (Akt), and phosphorylated Akt (p-Akt) were detected by western blot analysis. Isoflurane induced ROS, LDH release, apoptosis, and autophagy, but inhibited the viability in HT22 cells, which were overturned by fraxetin or ROS scavenger N-acetyl-L-cysteine. Fraxetin suppressed isoflurane-induced PI3K/Akt inactivation in HT22 cells. PI3K/Akt inactivation by LY294002 resisted the effects of fraxetin on isoflurane-induced autophagy and autophagy-modulated neurotoxicity in HT22 cells. In conclusion, fraxetin suppressed ROS-dependent autophagy by activating the PI3K/Akt pathway to inhibit isoflurane-induced neurotoxicity in hippocampal neuronal cells.

NPAS4 suppresses propofol-induced neurotoxicity by inhibiting autophagy in hippocampal neuronal cells.

Propofol, a general intravenous anesthetic, has been demonstrated to cause a profound neuroapoptosis in the developing brain followed by long-term neurocognitive impairment. Our study aimed to examine the neuroprotective effect of neuronal PAS domain protein 4 (NPAS4), an activity-dependent neuron-specific transcription factor, on propofol-induced neurotoxicity in hippocampal neuronal HT22 cells. The differentially expressed genes in HT22 cells after treatment with propofol were screened from Gene Expression Omnibus dataset GSE106799. NPAS4 expression in HT22 cells treated with different doses of propofol was investigated by qRT-PCR and Western blot analysis. Cell viability, lactate dehydrogenase (LDH) release, caspase-3 activity, and apoptosis were evaluated by MTT, a LDH-Cytotoxicity Assay Kit, a Caspase-3 Colorimetric Assay Kit, and TUNEL assay, respectively. The protein levels of LC3-I, LC3-II, Beclin 1, p62 and NPAS4 were detected using Western blot analysis. Propofol treatment concentration-dependently decreased NPAS4 expression in HT22 cells. Propofol treatment inhibited cell viability, increased LDH release and caspase-3 activity, and induced apoptosis and autophagy in HT22 cells. NPAS4 overexpression suppressed propofol-induced cell injury and autophagy in HT22 cells. Mechanistically, autophagy agonist rapamycin attenuated the neuroprotective effect of NPAS4 in propofol-treated HT22 cells. In conclusion, NAPS4 overexpression protected hippocampal neuronal HT22 cells against propofol-induced neurotoxicity by reducing autophagy.

A model of metabolic supply-demand mismatch leading to secondary brain injury.

Secondary brain injury (SBI) is defined as new or worsening injury to the brain after an initial neurologic insult, such as hemorrhage, trauma, ischemic stroke, or infection. It is a common and potentially preventable complication following many types of primary brain injury (PBI). However, mechanistic details about how PBI leads to additional brain injury and evolves into SBI are poorly characterized. In this work, we propose a mechanistic model for the metabolic supply demand mismatch hypothesis (MSDMH) of SBI. Our model, based on the Hodgkin-Huxley model, supplemented with additional dynamics for extracellular potassium, oxygen concentration, and excitotoxity, provides a high-level unified explanation for why patients with acute brain injury frequently develop SBI. We investigate how decreased oxygen, increased extracellular potassium, excitotoxicity, and seizures can induce SBI and suggest three underlying paths for how events following PBI may lead to SBI. The proposed model also helps explain several important empirical observations, including the common association of acute brain injury with seizures, the association of seizures with tissue hypoxia and so on. In contrast to current practices which assume that ischemia plays the predominant role in SBI, our model suggests that metabolic crisis involved in SBI can also be nonischemic. Our findings offer a more comprehensive understanding of the complex interrelationship among potassium, oxygen, excitotoxicity, seizures, and SBI.NEW & NOTEWORTHY We present a novel mechanistic model for the metabolic supply demand mismatch hypothesis (MSDMH), which attempts to explain why patients with acute brain injury frequently develop seizure activity and secondary brain injury (SBI). Specifically, we investigate how decreased oxygen, increased extracellular potassium, excitotoxicity, seizures, all common sequalae of primary brain injury (PBI), can induce SBI and suggest three underlying paths for how events following PBI may lead to SBI.

Do Triphasic Waves and Nonconvulsive Status Epilepticus Arise From Similar Mechanisms? A Computational Model.

PURPOSE: Triphasic waves arising in patients with toxic metabolic encephalopathy (TME) are often considered different from generalized periodic discharges (GPDs) in patients with generalized nonconvulsive status epilepticus (GNCSE). The primary objective of this study was to investigate whether a common mechanism can explain key aspects of both triphasic waves in TME and GPDs in GNCSE. METHOD: A neural mass model was used for the simulation of EEG patterns in patients with acute hepatic encephalopathy, a common etiology of TME. Increased neuronal excitability and impaired synaptic transmission because of elevated ammonia levels in acute hepatic encephalopathy patients were used to explain how triphasic waves and GNCSE arise. The effect of gamma-aminobutyric acid-ergic drugs on epileptiform activity, simulated with a prolonged duration of the inhibitory postsynaptic potential, was also studied. RESULTS: The simulations show that a model that includes increased neuronal excitability and impaired synaptic transmission can account for both the emergence of GPDs and GNCSE and their suppression by gamma-aminobutyric acid-ergic drugs. CONCLUSIONS: The results of this study add to evidence from other studies calling into question the dichotomy between triphasic waves in TME and GPDs in GNCSE and support the hypothesis that all GPDs, including those arising in TME patients, occur via a common mechanism.

Seizure tracking of epileptic EEGs using a model-driven approach.

OBJECTIVE: As a chronic neurological disorder, epilepsy is characterized by recurrent and unprovoked epileptic seizures that can disrupt the normal neuro-biologic, cognitive, psychological conditions of patients. Therefore, it is worthwhile to give a detailed account of how the epileptic EEG evolves during a period of seizure so that an effective control can be guided for epileptic patients in clinics. APPROACH: Considering the successful application of the neural mass model (NMM) in exploring the insights into brain activities for epilepsy, in this paper, we aim to construct a model-driven approach to track the development of seizures using epileptic EEGs. We first propose a new time-delay Wendling model with sub-populations (TD-W-SP model) with respect to three aspects of improvements. Then we introduce a model-driven seizure tracking approach, where a model training method is designed based on extracted features from epileptic EEGs and a tracking index is defined as a function of the trained model parameters. MAIN RESULTS: Numerical results on eight patients on CHB-MIT database demonstrate that our proposed method performs well in simulating epileptic-like EEGs as well as tracking the evolution of three stages (that is, from pre-ictal to ictal and from ictal to post-ictal) during a period of epileptic seizure. SIGNIFICANCE: A useful attempt to track epileptic seizures by combining the NMM with the data analysis.

A New Neural Mass Model Driven Method and Its Application in Early Epileptic Seizure Detection.

OBJECTIVE: Despite numerous neural computational models proposed to explain physiological and pathological mechanisms of brain activity, a large gap remains between theory and application of the models. Building on the successful application of data-driven methods in epileptic seizure detection, we aim to build a bridge between data and models in this paper. METHODS: We first propose a novel model-driven seizure detection method based on dynamic features in epileptic EEGs, where the rationale for dynamic features in epileptic EEGs can be clarified in theory by characterizing the variation of parameters of the model. Then we apply the proposed D&F-model-driven method to the problem of early epileptic seizure detection, where the evolution of model parameters selected and optimized by the proposed method is measured and used to detect the starting point of the seizure. RESULTS: Numerical results on two open EEG databases demonstrate that our proposed method does a good job of early epileptic seizure detection. The average detection sensitivity, false positive rate and early detection period attain 100%, 0.1/h, and 7.1 s respectively. CONCLUSION: This paper provides a strategy to characterize EEG signals using a NMM-related method and the model parameters optimized by real EEG may then serve as features in their own right for early seizure detection. SIGNIFICANCE: An useful attempt to early detect epileptic seizures by combining the neural mass model with data analysis.

A novel neural computational model of generalized periodic discharges in acute hepatic encephalopathy.

Acute hepatic encephalopathy (AHE) due to acute liver failure is a common form of delirium, a state of confusion, impaired attention, and decreased arousal. The electroencephalogram (EEG) in AHE often exhibits a striking abnormal pattern of brain activity, which epileptiform discharges repeat in a regular repeating pattern. This pattern is known as generalized periodic discharges, or triphasic-waves (TPWs). While much is known about the neurophysiological mechanisms underlying AHE, how these mechanisms relate to TPWs is poorly understood. In order to develop hypotheses how TPWs arise, our work builds a computational model of AHE (AHE-CM), based on three modifications of the well-studied Liley model which emulate mechanisms believed central to brain dysfunction in AHE: increased neuronal excitability, impaired synaptic transmission, and enhanced postsynaptic inhibition. To relate our AHE-CM to clinical EEG data from patients with AHE, we design a model parameter optimization method based on particle filtering (PF-POM). Based on results from 7 AHE patients, we find that the proposed AHE-CM not only performs well in reproducing important aspects of the EEG, namely the periodicity of triphasic waves (TPWs), but is also helpful in suggesting mechanisms underlying variation in EEG patterns seen in AHE. In particular, our model helps explain what conditions lead to increased frequency of TPWs. In this way, our model represents a starting point for exploring the underlying mechanisms of brain dynamics in delirium by relating microscopic mechanisms to EEG patterns.

Effects of Cholinergic Neuromodulation on Thalamocortical Rhythms During NREM Sleep: A Model Study.

It has been suggested that cholinergic neurons shape the oscillatory activity of the thalamocortical (TC) network in behavioral and electrophysiological experiments. However, theoretical modeling demonstrating how cholinergic neuromodulation of thalamocortical rhythms during non-rapid eye movement (NREM) sleep might occur has been lacking. In this paper, we first develop a novel computational model (TC-ACH) by incorporating a cholinergic neuron population (CH) into the classical thalamo-cortical circuitry, where connections between populations are modeled in accordance with existing knowledge. The neurotransmitter acetylcholine (ACH) released by neurons in CH, which is able to change the discharge activity of thalamocortical neurons, is the primary focus of our work. Simulation results with our TC-ACH model reveal that the cholinergic projection activity is a key factor in modulating oscillation patterns in three ways: (1) transitions between different patterns of thalamocortical oscillations are dramatically modulated through diverse projection pathways; (2) the model expresses a stable spindle oscillation state with certain parameter settings for the cholinergic projection from CH to thalamus, and more spindles appear when the strength of cholinergic input from CH to thalamocortical neurons increases; (3) the duration of oscillation patterns during NREM sleep including K-complexes, spindles, and slow oscillations is longer when cholinergic input from CH to thalamocortical neurons becomes stronger. Our modeling results provide insights into the mechanisms by which the sleep state is controlled, and provide a theoretical basis for future experimental and clinical studies.

A Mean Field Model of Acute Hepatic Encephalopathy.

Acute hepatic encephalopathy (AHE) is a common form of delirium, a state of confusion, impaired attention, and decreased arousal due to acute liver failure. However, the neurophysiological mechanisms underlying AHE are poorly understood. In order to develop hypotheses for mechanisms of AHE, our work builds on an existing neural mean field model for similar EEG patterns in cerebral anoxia, the bursting Liley model. The model proposes that generalized periodic discharges, similar to the triphasic waves (TPWs) seen in severe AHE, arise through three types of processes a) increased neuronal excitability; b) defective brain energy metabolism leading to impaired synaptic transmission; c) and enhanced postsynaptic inhibition mediated by increased GABA-ergic and glycinergic transmission. We relate the model parameters to human EEG data using a particle-filter based optimization method that matches the TPW inter-event-interval distribution of the model with that observed in patients EEGs. In this way our model relates microscopic mechanisms to EEG patterns. Our model represents a starting point for exploring the underlying mechanisms of brain dynamics in delirium.