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

JOURNAL OF computational NEUROSCIENCE 2019年第2-3期47卷 109-124页

作者： Song, Jiang-Ling Paixao, Luis Li, Qiang Li, Si-Hui Zhang, Rui Westover, M. Brandon Northwest Univ Med Big Data Res Ctr Xian 710127 Shaanxi Peoples R China Massachusetts Gen Hosp Dept Neurol Boston MA 02114 USA

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.

关键词： Acute hepatic encephalopathy (AHE) neural computational model Liley model Generalized periodic discharges Electroencephalogram (EEG) Particle filtering

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Mechanisms underlying EEG power changes during wakefulness in insomnia patients: a model-driven study

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COGNITIVE NEURODYNAMICS 2025年第1期19卷 1-13页

作者： Li, Qiang Wang, Hanxuan Zhang, Rui Northwest Univ Med Big Data Res Ctr Xian 710127 Peoples R China Xi An Jiao Tong Univ Hlth Sci Ctr Xian 710049 Peoples R China

Insomnia, as a common sleep disorder, is the most common complaints in medical practice affecting a large proportion of the population on a situational, recurrent or chronic basis. It has been demonstrated that, during wakefulness, patients with insomnia exhibit increased EEG power in theta, beta, and gamma band. However, the relevant mechanisms underlying such power changes are still lack of understanding. In this paper, by combining the neural computational model with the real EEG data, we focus on exploring what's behind the EEG power changes for insomniac. We first develop a modified Liley model, named FSR-Liley, by respectively considering the fast and slow synaptic responses in inhibitory neurons along with the one-way projection between them. Then we introduce a parameter selection and evaluation method based on Markov chain Monte Carlo algorithm and Wasserstein distance, by which the sensitive parameters are selected automatically, and meanwhile, the optimal values of selected parameters are evaluated. Finally, through combining with EEG data, we determine the sensitive parameters in FSR-Liley and accordingly provide the mechanistic hypotheses: (1) decrease in P-eif, corresponding to the input from the thalamus to cortical inhibitory population with fast synaptic response, leads to the increased theta and beta power;(2) decrease in N-eif, corresponding to the projection from cortical excitatory population to inhibitory population with fast synaptic response, causes the increased gamma power. The results in this paper provide insights into the mechanisms of EEG power changes in insomnia and establish a theoretical foundation to support further experimental research.

关键词： Insomnia neural computational model Electroencephalogram (EEG) Power spectrum

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modeling the grid cell activity based on cognitive space transformation

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COGNITIVE NEURODYNAMICS 2024年第3期18卷 1227-1243页

作者： Zhang, Zhihui Tang, Fengzhen Li, Yiping Feng, Xisheng Univ Sci & Technol China Hefei 230026 Peoples R China Chinese Acad Sci Shenyang Inst Automat State Key Lab Robot Shenyang 110016 Peoples R China Chinese Acad Sci Inst Robot & Intelligent Mfg Shenyang 110016 Peoples R China

The grid cells in the medial entorhinal cortex are widely recognized as a critical component of spatial cognition within the entorhinal-hippocampal neuronal circuits. To account for the hexagonal patterns, several computational models have been proposed. However, there is still considerable debate regarding the interaction between grid cells and place cells. In response, we have developed a novel grid-cell computational model based on cognitive space transformation, which established a theoretical framework of the interaction between place cells and grid cells for encoding and transforming positions between the local frame and global frame. Our model not only can generate the firing patterns of the grid cells but also reproduces the biological experiment results about the grid-cell global representation of connected environments and supports the conjecture about the underlying reason. Moreover, our model provides new insights into how grid cells and place cells integrate external and self-motion cues.

关键词： Grid cell Place cell Spatial transformation neural computational model Cognitive space

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

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COGNITIVE NEURODYNAMICS 2024年第2期18卷 485-502页

作者： Li, Duo Li, Sihui Pan, Min Li, Qiang Song, Jiangling Zhang, Rui Northwest Univ Med Big Data Res Ctr Xian 710127 Peoples R China Northwest Univ Sch Math Xian 710127 Peoples R China

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.

关键词： Neuron Astrocyte Glutamate Epileptic discharges neural computational model

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

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JOURNAL OF NEUROPHYSIOLOGY 2021年第2期126卷 653-667页

作者： Song, Jiang-Ling Kim, Jennifer A. Struck, Aaron F. Zhang, Rui Westover, M. Brandon Northwest Univ Med Big Data Res Ctr Xian Peoples R China Harvard Med Sch Massachusetts Gen Hosp Dept Neurol Boston MA 02115 USA Yale New Haven Hosp Dept Neurol 20 York St New Haven CT 06504 USA Univ Wisconsin Dept Neurol Madison WI 53706 USA William S Middleton Mem Vet Adm Med Ctr Madison WI USA

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 m

关键词： excitotoxicity metabolic supply-demand mismatch hypothesis neural computational model secondary brain injury seizures

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model based optimal multipolar stimulation without a priori knowledge of nerve structure: application to vagus nerve stimulation

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JOURNAL OF neural ENGINEERING 2018年第4期15卷 046018页

作者： Dali, Melissa Rossel, Olivier Andreu, David Laporte, Laure Hernandez, Alfredo Laforet, Jeremy Marijon, Eloi Hagege, Albert Clerc, Maureen Henry, Christine Guiraud, David Univ Montpellier CNRS INRIA Montpellier France Livanova Clamart France Univ Rennes INSERM LTSI UMR 1099 Rennes France Univ Technol Compiegne CNRS Lab Biomecan & Bioingn Compiegne France Univ Paris 05 Sorbonne Paris Cite INSERM UMRS 970 Paris 6 France HEGP AP HP Paris France Univ Cote dAzur Inria Sophia Antipolis Mediterranee Athena Project Team Nice France

Objective. Multipolar cuff electrode can selectively stimulate areas of peripheral nerves and therefore enable to control independent functions. However, the branching and fascicularization are known for a limited set of nerves and the specific organization remains subject-dependent. This paper presents general modeling and optimization methods in the context of multipolar stimulation using a cuff electrode without a priori knowledge of the nerve structure. Vagus nerve stimulation experiments based on the optimization results were then investigated. Approach. The model consisted of two independent components: a lead field matrix representing the transfer function from the applied current to the extracellular voltage present on the nodes of Ranvier along each axon, and a linear activation model. The optimization process consisted in finding the best current repartition (ratios) to reach activation of a targeted area depending on three criteria: selectivity, efficiency and robustness. Main results. The results showed that state-of-the-art configurations (tripolar transverse, tripolar longitudinal) were part of the optimized solutions but new ones could emerge depending on the trade-off between the three criteria and the targeted area. Besides, the choice of appropriate current ratios was more important than the choice of the stimulation amplitude for a stimulation without a priori knowledge of the nerve structure. We successfully assessed the solutions in vivo to selectively induce a decrease in cardiac rhythm through vagus nerve stimulation while limiting side effects. Compared to the standard whole ring configuration, a selective solution found by simulation provided on average 2.6 less adverse effects. Significance. The preliminary results showed the rightness of the simulation, using a generic nerve geometry. It suggested that this approach will have broader applications that would benefit from multicontact cuff electrodes to elicit selective responses. In the con

关键词： selective neural stimulation electrode-nerve modeling neural computational model neural stimulation current optimization vagus nerve stimulation

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A neural Circuit model for nCRF's Dynamic Adjustment and its Application on Image Representation

A Neural Circuit Model for nCRF's Dynamic Adjustment and its...

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International Joint Conference on neural Networks (IJCNN)

作者： Wei, Hui Wang, Xiao-Mei Fudan Univ Dept Comp Sci Lab Cognit Model & Algorithm Brain Sci Res Ctr Shanghai 200433 Peoples R China

ISBN: (纸本)9781424496365

According to Biology there is a large disinhibitory area outside the classical receptive field (CRF), which is called as non-classical receptive field (nCRF). Combining CRF with nCRF could increase the sparseness, reliability and precision of the neuronal responses. This paper is aimed at the realization of the neural circuit and the dynamic adjustment mechanism of the receptive field (RF) with respect to nCRF. On the basis of anatomical and electrophysiological evidence, we constructed a neural computational model, which can represent natural images faithfully, simply and rapidly. And the representation can significantly improve the subsequent operation efficiency such as segmentation or integration. This study is of particular significance in the development of efficient image processing algorithms based on neurobiological mechanisms. The RF mechanism of ganglion cell (GC) is the result of a long term of evolution and optimization of self-adaptability and high representation efficiency. So its performance evaluation in natural image processing is worthy of further study.

关键词： computational modeling Equations Feedback control Integrated circuit modeling Mathematical model Radio frequency Retina bioelectric phenomena biology biology ganglion cell image processing algorithms image representation image representation nCRF natural image processing neural circuit model neural computational model neural nets neurobiological mechanisms non-classical receptive field

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