A method for constructing a neuro-vascular bidirectional coupling model based on post-stroke epilepsy
By constructing a neurovascular bidirectional coupling model for post-stroke epilepsy, the bidirectional interaction between neural activity and vascular response is simulated, overcoming the shortcomings of existing models in clinical application and realizing the prediction and treatment of post-stroke epilepsy.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- BEIJING UNIV OF TECH
- Filing Date
- 2025-03-27
- Publication Date
- 2026-04-21
AI Technical Summary
Existing neurovascular coupling models for post-stroke epilepsy cannot be effectively applied in clinical practice, and cannot predict or treat post-stroke epilepsy.
A neurovascular bidirectional coupling model based on post-stroke epilepsy was constructed, including the construction of spatial models of neurons, astrocytes, endothelial cells, smooth muscle cells and small arteries, to simulate the bidirectional interaction between neural activity and vascular response, and to perform coupling modeling through mechanisms such as ion release and energy metabolism.
It provides a more comprehensive understanding of the neurovascular coupling mechanism, can predict the occurrence of post-stroke epilepsy, provides theoretical data to support treatment strategies, and enables accurate prediction and treatment of post-stroke epilepsy.
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Figure CN120340876B_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of neurovascular coupling model technology, and in particular to a method for constructing a bidirectional neurovascular coupling model based on post-stroke epilepsy. Background Technology
[0002] Post-stroke epilepsy (PSE) refers to epileptic seizures that occur within a certain period after a stroke, in patients with no prior history of epilepsy, and after ruling out systemic brain diseases, structural brain abnormalities, or other metabolic disorders. PSE is a complication of stroke and a common cause of epilepsy in elderly patients. Its seizure process exhibits a highly nonlinear relationship between neuro-blood flow regulation, and seizures often occur in localized brain regions, such as the hippocampus. Stroke-related epilepsy caused by reversible or irreversible brain injury will prolong hospital stays for cerebrovascular disease patients, increase mortality, and have a long-term impact on patient prognosis and quality of life.
[0003] Neurovascular coupling (NVC) refers to the relationship between neural activity and local cerebral vascular responses in the brain. When neurons are activated, their increased metabolic demands lead to increased cerebral blood flow (CBF) to meet their oxygen and nutrient requirements and maintain normal neuronal function. This process, often referred to as functional hyperemia, describes the local vasodilation and vasoconstriction resulting from neural activity in the human / mammalian brain. Changes in vessel diameter control local cerebral blood flow, thereby controlling the supply of oxygen and glucose.
[0004] Constructing a microscopic neurovascular coupling model of post-stroke epilepsy is an important tool for studying the complex interaction between abnormal neuronal activity and vascular regulatory mechanisms. Predicting changes in physiological data related to the interaction between abnormal neuronal activity and vascular regulatory mechanisms helps identify early warning signs of post-stroke epilepsy, thus providing a precise predictive tool for clinical intervention. Furthermore, the model can provide experimental evidence for developing new treatment strategies, such as preventing or mitigating seizures by modulating key aspects of the neurovascular coupling process, such as potassium channel activity or vasodilation mechanisms. In summary, this model not only enriches the research content in the field of neurovascular coupling but also provides new perspectives and methods for the prediction and treatment of post-stroke epilepsy.
[0005] The Kainerstorfer team established a mathematical model consisting of an ion exchange model, a neurovascular coupling model, and a hemodynamic model to explore the mechanisms of ion concentration changes under different pathological conditions and their regulation of blood vessels. The Wu Ying team proposed a coupling model composed of neurons, astrocytes, and blood vessels to simulate vascular ischemia and study the role of hypoxia and abnormal neurotransmitter release in the occurrence and transmission of epileptic seizures. In recent years, Professor Wu Ying's team in China proposed an NVC model to study the role of energy metabolism in the onset and transmission of post-stroke epilepsy (PSE). These studies indicate that while unidirectional coupling models focusing on ion release in the neurovascular system are widely studied, coupling models focusing on energy metabolism in the vascular-neuronal system are less common. Therefore, they cannot predict the dynamic changes in physiological data during neurovascular coupling, providing only a one-time prediction. Consequently, they are unsuitable for predicting and treating post-stroke epilepsy and are not applicable to real-world clinical scenarios. Summary of the Invention
[0006] Based on this, and in response to the aforementioned technical problems, a method for constructing a neurovascular bidirectional coupling model based on post-stroke epilepsy is provided to address the issue that existing post-stroke epilepsy neurovascular coupling models are not suitable for practical clinical applications.
[0007] Firstly, a method for constructing a neurovascular bidirectional coupling model based on post-stroke epilepsy, the method comprising:
[0008] Constructing the first neuronal compartment model: Establishing a Hodgkin-Huxley model of the neuron, adding a synaptic transmission module to the Hodgkin-Huxley model to simulate the release of potassium ions and glutamate into the synaptic space caused by neuronal activity, and adding the NO generation process to simulate the release and diffusion of NO caused by neuronal activity;
[0009] A first astrocyte compartment model was constructed to simulate the detection of glutamate and potassium ion concentrations in neuronal synapses, and to simulate the process of releasing vasodilators based on the detected glutamate and potassium ion concentrations in the neuronal synapses, as well as to simulate the calculation of BK potassium channel flux based on the vasodilator concentration, and to simulate the process of releasing potassium ions into the perivascular space based on the BK channel flux.
[0010] Construct an endothelial cell compartment model to simulate uptake Arginine, isomerase catalyzes the above The process by which arginine is converted into NO and diffuses into smooth muscle cells;
[0011] A smooth muscle cell compartment model was constructed to simulate the calculation of the inward rectifier potassium ion channel KIR flux based on the detected perivascular potassium ion concentration, the process of smooth muscle cell hyperpolarization caused by the inward rectifier potassium ion channel KIR flux, the process of voltage-sensitive calcium channel closure caused by vascular smooth muscle cell hyperpolarization, and the smooth muscle contraction process based on the interaction between the detected perivascular NO concentration and intracellular enzyme activation, and the perivascular calcium ion concentration after the closure of voltage-sensitive calcium channels.
[0012] A spatial model of a small artery was constructed to simulate the process of smooth muscle relaxation, which in turn led to vasodilation and blood flow.
[0013] The first neuronal compartment model, the first astrocyte compartment model, the endothelial cell compartment model, the smooth muscle cell compartment model, and the small artery spatial model are interconnected to construct a neurovascular coupling dynamic model.
[0014] A second astrocyte compartment model was constructed to simulate the process of glucose uptake by astrocytes based on changes in interstitial glucose concentration caused by changes in blood flow, and to simulate the process of glucose being metabolized into lactic acid and pyruvate based on changes in glucose concentration in astrocytes.
[0015] A second neuron model was constructed to simulate the lactate oxidation and decomposition process based on the detected lactate concentration, and the process of regulating the neuron's action potential based on the lactate concentration.
[0016] The second astrocyte compartment model and the second neuron model are connected to construct the blood vessel-nerve coupling dynamics model;
[0017] A neurovascular bidirectional coupling model is constructed by connecting the neuro-vascular coupling dynamics model and the vascular-neural coupling dynamics model.
[0018] In the above scheme, optionally, the potassium ion concentration in the neuronal synapse is obtained by simulation using the following formula:
[0019]
[0020] in This indicates the concentration of potassium ions in the synapse. This indicates that the current will be transferred from Conversion Unit conversion factor, dimensionless factor It represents the ratio of extracellular volume to intracellular volume; This represents the voltage-gated potassium ion channel current.
[0021] These represent the neuronal sodium-potassium pump flux, interstitial diffusion flux, glial cell uptake flux, and glial cell sodium-potassium pump flux, respectively.
[0022] In the above scheme, optionally, the glutamate ion concentration in the neuronal synapse is obtained by simulation using the following formula:
[0023]
[0024] in, This indicates the concentration of glutamate in the synapse. Indicates the time when the action potential occurs. This indicates the amount of glutamate released with each action potential. This represents the glutamate clearance time constant.
[0025] In the above scheme, optionally, the throughput of the BK channel is obtained by simulation using the following formula:
[0026]
[0027] in, For the BK channel flux, Indicates electrical conductance. This represents the Nernst potential, and F represents the Faraday constant. Represents the membrane potential, which is the probability of the BK channel opening. Modeled as glial cell membrane voltage The mediated process depends on the concentrations of internal calcium ions and epoxyeicosatetrienoic acid (EET). The open time constant, This is a calcium ion-mediated potential; Indicates glial cell capacitance. This represents the flux of channel BK. Indicates the leakage flux. This indicates the flux of the sodium-potassium pump in glial cells.
[0028] In the above scheme, optionally, the amount of NO released and diffused into smooth muscle caused by the neuronal activity is obtained by simulation using the following formula:
[0029] Neuronal action generation NO output:
[0030]
[0031] in, This indicates the maximum rate of nitric oxide production, which is related to the activity of neuronal nitric oxide isomerase. Indicates oxygen content and Arginine content; Indicates the relevant Michaelis constant;
[0032] The amount of NO that reaches smooth muscle after production is:
[0033]
[0034] in, This represents the total amount that diffuses from neurons to smooth muscle. To generate NO production for neurons, This represents the total consumption of other substances within the neuron. This represents the diffusion flux between compartments.
[0035] Optionally, in the above scheme, the perivascular potassium ion concentration is obtained by simulation using the following formula:
[0036]
[0037] This indicates the concentration of potassium ions in the space surrounding blood vessels. Indicates the currents of the KIR and BK channels. These represent the volume ratios of the perivascular space to astrocytes and the perivascular space to smooth muscle cells, respectively. Represents the ensemble decay variable. These represent the perivascular potassium ion concentration and the potassium ion balance concentration, respectively.
[0038] Optionally, in the above scheme, the NO production generated by the endothelial cell compartment model and the amount diffused into smooth muscle are simulated using the following formula:
[0039] NO production by endothelial cells:
[0040]
[0041] in, The maximum rate of nitric oxide production in endothelial cells is related to the activity of endothelial nitric oxide isomerase. Indicates the amount of oxygen in endothelial cells and Arginine content; Indicates the relevant Michaelis constant;
[0042] The amount of NO that reaches smooth muscle after production is:
[0043]
[0044] in, It represents the total amount of fluid that diffuses from the endothelium to the smooth muscle. This indicates the NO production generated by the endothelium. This indicates the total consumption of other substances within endothelial cells. This represents the diffusion flux between compartments.
[0045] In the above scheme, optionally, the KIR potassium channel flux is calculated using the following formula:
[0046]
[0047] in Convert unit parameters, It is a constant. Smooth muscle cell membrane potential Represents the Nernst potential. It is extravascular potassium ions.
[0048] In a second aspect, a computer device includes a memory and a processor, the memory storing a computer program, characterized in that the processor executes the computer program to implement the steps of the method described in the first aspect.
[0049] Thirdly, a computer-readable storage medium having a computer program stored thereon, characterized in that, when the computer program is executed by a processor, it implements the steps of the method described in the first aspect above.
[0050] This application has at least the following beneficial effects:
[0051] This application employs a microscopic neurodynamic model followed by an ion release model to a microscopic vascular model to obtain a neurovascular coupling dynamic model, yielding the output of blood flow changes driven by nerves. Secondly, it establishes an ischemic vascular model and, through energy metabolism, transfers it to a microscopic neurodynamic model to obtain a vascular-nerve coupling dynamic model, again yielding the output of nerve changes driven by blood flow. Finally, it combines the neurovascular and vascular-nerve coupling dynamic models to obtain a bidirectional coupling model of neuro-blood flow interaction regulation. The bidirectional coupling modeling method provided in this application offers a more comprehensive understanding of the neurovascular coupling mechanism from the perspective of glial vascular dynamics rather than simply from the perspective of nerve discharge. By simulating the occurrence of post-stroke epilepsy through computational models, it can predict changes in physiological parameters over a period of time, thus providing theoretical parameters for the prediction and treatment of post-stroke epilepsy. Furthermore, the constructed model studies the changes in multiple physiological data (ions, energy) during the neural coupling process, providing more theoretical data for predicting the prediction and treatment effects of post-stroke epilepsy, enabling accurate prediction of post-stroke epilepsy and its treatment outcomes. Attached Figure Description
[0052] Figure 1 A flowchart illustrating a method for constructing a neurovascular bidirectional coupling model based on post-stroke epilepsy, as provided in one embodiment of this application;
[0053] Figure 2A detailed flowchart illustrating a method for constructing a neurovascular bidirectional coupling model based on post-stroke epilepsy, provided in one embodiment of this application;
[0054] Figure 3 A flowchart illustrating the technical route of a method for constructing a neurovascular bidirectional coupling model based on post-stroke epilepsy, provided in one embodiment of this application;
[0055] Figure 4 A schematic diagram illustrating the variable relationships in modeling the arteriole systolic subsystem provided in one embodiment of this application;
[0056] Figure 5 A schematic diagram of the microscopic details of the bidirectional coupling dynamics modeling of microscopic neural blood flow provided in one embodiment of this application;
[0057] Figure 6 This is a schematic diagram of partial modeling results provided for one embodiment of this application. Detailed Implementation
[0058] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.
[0059] In one embodiment, such as Figure 1 , Figure 2 and Figure 3 As shown, a method for constructing a neurovascular bidirectional coupling model based on post-stroke epilepsy is provided, characterized in that the method includes:
[0060] Step S1: Construct the first neuronal compartment model: Establish a Hodgkin-Huxley model of the neuron, add a synaptic transmission module to the Hodgkin-Huxley model to simulate the release of potassium ions and glutamate into the synaptic space caused by neuronal activity, and add the NO generation process to simulate the release and diffusion of NO caused by neuronal activity.
[0061] In step S1, the simulated activity leads to the release of potassium ions and glutamate into the synaptic space, and also to the generation of NO. The first neuronal compartment model outputs physiological indicators related to neuronal activity.
[0062] Step S2: Construct a first astrocyte compartment model to simulate the detection of glutamate and potassium ion concentrations in neuronal synapses, and to simulate the process of releasing vasodilators based on the detected glutamate and potassium ion concentrations in the neuronal synapses, as well as to simulate the calculation of BK potassium channel flux based on the vasodilator concentration, and simultaneously simulate the process of releasing potassium ions into the perivascular space based on the BK channel flux.
[0063] In step S2, simulated astrocytes detect potassium ions and glutamate in neuronal synapses, thereby releasing vasodilators, opening BK potassium channels, and ultimately releasing potassium ions into the perivascular space. The first astrocyte compartment model outputs astrocyte-mediated neurotransmitter regulation and vascular function-related indicators.
[0064] Step S3: Construct an endothelial cell compartment model to simulate uptake Arginine, isomerase catalyzes the above The process by which arginine is converted into NO and diffuses into smooth muscle cells.
[0065] In step S3, the endothelial cell compartment model outputs indicators related to endothelial cell-mediated NO generation and diffusion.
[0066] Step S4: Construct a smooth muscle cell compartment model to simulate the calculation of the inward rectifier potassium ion channel KIR flux based on the detected perivascular potassium ion concentration, the process of smooth muscle cell hyperpolarization caused by the inward rectifier potassium ion channel KIR flux, the process of voltage-sensitive calcium channel closure caused by vascular smooth muscle cell hyperpolarization, and the smooth muscle contraction process based on the interaction between the detected perivascular NO concentration and intracellular enzyme activation, and the perivascular calcium ion concentration after the closure of voltage-sensitive calcium channels.
[0067] In step S4, the simulated perivascular potassium ion concentration is activated. In vascular smooth muscle cells (SMCs), inward rectifying potassium ion channels (KIR) cause hyperpolarization of the SMC membrane and closure of voltage-sensitive Ca channels. Conversely, when NO reaches the SMC, it interacts with intracellular enzyme activation, indirectly affecting the SMC contractile system and regulating SMC relaxation. The smooth muscle cell compartment model outputs electrophysiological and contractile state-related indicators of smooth muscle cells.
[0068] Specifically, arterioles are covered by endothelial cells and smooth muscle cells. Therefore, in order to accurately simulate vascular movement, both cell types must be considered. NO is first produced by endothelial cells and neurons and then diffuses to smooth muscle cells to take effect. The NO dynamics in the involved compartments are mathematically described using a mass balance formula.
[0069] Step S5: Construct a spatial model of a small artery to simulate the process of smooth muscle relaxation, which in turn leads to vasodilation and blood flow.
[0070] Specifically, Figure 4 The diagram shows the variable relationships in the modeling of the small artery systolic subsystem; SMC compartment cytoplasm is used. Concentration serves as the input signal. Myosin formation has four possible states: free non-phosphorylated cross-bridge (M), free phosphorylated cross-bridge (Mp), attached phosphorylated cross-bridge (AMp), and attached dephosphorylated cross-bridge (AM). The differential equation is as follows:
[0071]
[0072] in As a rate constant, it regulates phosphorylation and bridge formation. , and With the cells Changes with concentration:
[0073] in It is a sensitivity constant characterizing calcium-activated myosin phosphorylation; This is the score of the attached crossbridge. The small artery spatial model outputs indicators related to vascular dilation and blood flow.
[0074] Step S6: Connect the first neuronal compartment model, the first astrocyte compartment model, the endothelial cell compartment model, the smooth muscle cell compartment model, and the small artery spatial model to construct a neurovascular coupling dynamic model.
[0075] Step S7: Construct a second astrocyte compartment model to simulate the process of glucose uptake by astrocytes based on changes in interstitial glucose concentration caused by changes in blood flow, and to simulate the process of glucose being metabolized into lactic acid and pyruvate based on changes in glucose concentration in astrocytes.
[0076] In step S7, simulated increased blood flow causes astrocytes to take up large amounts of glucose from the interstitium, which is then metabolized into lactate and pyruvate. Lactic acid is released into the interstitium, while pyruvate is metabolized to maintain glial cell function. The second astrocyte compartment model outputs indicators related to astrocyte glucose metabolism and lactate production.
[0077] Step S8: Construct a second neuron model to simulate the lactate oxidation and decomposition process based on the detected lactate concentration, and the process of adjusting the neuron's action potential based on the lactate concentration.
[0078] In step S8, neurons take up lactate from the interstitium to meet their metabolic needs, oxidize it to generate ATP, and regulate... The pump activity fuels neuronal firing, thus influencing the neuron's action potential. Sufficient energy is generated to sustain neuronal firing, allowing the process in step 1 to continue. After a period of time, a sharp decrease in neuronal ATP concentration is observed. Despite the weakening of metabolic feedback, neuronal firing continues, with ATP consumption far exceeding ATP production. Once the ATP concentration becomes too low to maintain pump activity, the transmembrane ion gradient is depleted, and the neuron tends to depolarize. The second neuron module simulates this process, outputting indicators related to the coupling of lactate metabolism and neuronal electrophysiology.
[0079] Step S9: Connect the second astrocyte compartment model and the second neuron model to construct the blood vessel-nerve coupling dynamics model.
[0080] Step S10: A bidirectional coupling model for the regulation of nerve-blood flow interaction is constructed by connecting the neural-vascular coupling dynamic model and the vascular-neural coupling dynamic model.
[0081] Microscopic neural blood flow bidirectional coupling dynamic modeling microscopic details such as Figure 5 As shown: Figure 5 In this context, "neuron" represents a neuron, "astrocyte" represents astrocytes, "aortic" represents arterioles, "glucose" represents glucose, "lactate" represents lactate, "pyruvate" represents pyruvate, and "glutamate" represents glutamate. The endoplasmic reticulum represents calcium storage, AD represents vasodilation, and AC represents vasoconstriction. The entire model is divided into seven parts: neurons, synaptic cleft, astrocytes, perivascular space, smooth muscle cells (SMCs), endothelium, and arteriole lumen. These compartments are mainly coupled through ion flux, energy substances, and membrane potential differences in ion channels. Neurons, astrocytes, smooth muscle cells (SMCs), endothelial cells, and arterioles are considered as independent compartmental space modules, while the synaptic cleft and perivascular space are considered as two independent extracellular environments, assuming that they do not involve material exchange or communication.
[0082] Figure 5 This involves two pathways: positive feedback via ion concentration transfer between nerves and blood vessels, and metabolic feedback via energy transfer between blood vessels and nerves. Specifically, metabolic feedback refers to a series of processes involving neurons, astrocytes, and smooth muscle cells that lead to vasodilation. This results in an increased rate of glucose and lactate flux transfer through the endothelium to the interstitium, and astrocytes and neurons take up glucose and lactate from the interstitium. Neurons oxidize lactate to produce ATP, thus providing fuel for neuronal firing.
[0083] This application constructs a microscopic neurovascular coupling model of post-stroke epilepsy, an important tool for studying the complex interaction between abnormal neuronal activity and vascular regulatory mechanisms. The model comprises two aspects: neural regulation of blood vessels and vascular feedback to nerves, reflecting the dynamic mechanism of the clinical manifestations of post-stroke epilepsy. It can be used in basic research and for exploring disease mechanisms. Secondly, by inputting relevant clinical physiological parameters of the patient, such as neural activity parameters and vascular structure parameters, the neurovascular coupling state can be analyzed. Changes in physiological parameters in each output module can determine the degree of epilepsy induced in the patient, providing assistance for clinical diagnosis. The model can identify key therapeutic targets in neurovascular coupling. By combining neurovascular coupling-related physiological data, the effects of drugs used to block or restore channel function can be evaluated, providing direction for the development of novel drugs for post-stroke epilepsy. The neurovascular coupling model outputs dynamic physiological data from each sub-model, including, for example, the release rates of potassium ions, glutamate, and NO caused by changes in neuronal potential in the first neuronal compartment model, for the assessment of post-stroke epilepsy.
[0084] Post-stroke epilepsy is a common complication of stroke, but effective early prediction methods are currently lacking. Therefore, this patented technology constructs a neurovascular bidirectional coupling model, combining the patient's initial cerebral hemodynamic and neurophysiological data, to simulate the risk of developing post-stroke epilepsy. For example, by inputting patient blood flow monitoring data or metabolic data such as oxygen consumption and glucose utilization into the model, the model outputs predicted cerebral hemodynamic and neurophysiological parameters to predict the likelihood of post-stroke epilepsy.
[0085] Currently, the treatment of epilepsy mainly relies on anti-epileptic drugs, but the treatment effect varies greatly among different patients.
[0086] Therefore, by using neurovascular coupling models, combined with individual patient characteristics (such as cerebral blood flow status and neuronal activity patterns), it is possible to simulate the impact of different treatment regimens (such as drug dosage and type) on the patient's neurovascular coupling. For example, the model can predict the regulatory effect of a certain drug on the patient's cerebral blood flow and neuronal activity, thereby providing doctors with personalized treatment recommendations.
[0087] Figure 6As shown: The figure illustrates the changes in neuronal membrane potential and extracellular potassium ion concentration over time. When neurons are in a resting state, the membrane potential and extracellular potassium ion concentration remain constant. During the action potential, the extracellular potassium ion concentration is relatively high, and changes in arteriolar radius can be observed through ion transport and myofibril sliding. The figure shows the relationship between four possible states of myosin and vascular radius. (a) indicates the normal discharge characteristics of neuronal compartments and the fluctuation of extracellular potassium ions below the threshold when the arteriolar vessel is normal; (b) indicates the epileptiform discharge characteristics of neuronal compartments when the arteriolar vessel is ischemic, and the trend of extracellular potassium ion changes with epileptiform discharge; (c) indicates that when the arteriolar vessel is severely ischemic, the neuronal compartments return to a resting state, and there is no significant change in internal potassium ions.
[0088] In the aforementioned method for constructing a neurovascular coupling model based on post-stroke epilepsy, a neurovascular coupling dynamic model is obtained by transferring ions from a microscopic neurodynamic model to a microscopic vascular model, thus obtaining the output of blood flow changes driven by nerves. Secondly, an ischemic vascular model is established and its energy metabolism is transferred to a microscopic neurodynamic model to obtain a vascular-nerve coupling dynamic model, thus obtaining the output of nerve changes driven by blood flow. Finally, the neuro-vascular and vascular-nerve coupling dynamic models are combined to obtain a bidirectional coupling model of neuro-blood flow interaction regulation. This provides a bidirectional coupling modeling method that comprehensively understands the neurovascular coupling mechanism from the perspective of glial vascular dynamics rather than simply from the perspective of nerve discharge. By simulating the occurrence process of post-stroke epilepsy through computational models, its pathological mechanisms can be studied, thereby predicting changes in physiological parameters over a period of time. This provides theoretical data for the prediction and treatment of post-stroke epilepsy. Furthermore, the constructed model studies the changes in multiple physiological parameters (ions, energy) during the neural coupling process, providing more theoretical data for predicting the prediction and treatment effects of post-stroke epilepsy, enabling accurate prediction of post-stroke epilepsy and its treatment outcomes.
[0089] In one embodiment, the potassium ion concentration in the neuronal synapse is simulated using the following formula:
[0090]
[0091] in This indicates the concentration of potassium ions in the synapse. This indicates that the current will be transferred from Conversion Unit conversion factor, dimensionless factor It represents the ratio of extracellular volume to intracellular volume; This represents the voltage-gated potassium ion channel current, reflecting the potassium ion outflow caused by neuronal activity.
[0092] These represent the neuronal sodium-potassium pump flux, interstitial diffusion flux, glial cell uptake flux, and glial cell sodium-potassium pump flux, respectively.
[0093] In one embodiment, the glutamate ion concentration in the neuronal synapse is simulated using the following formula:
[0094]
[0095] in, This indicates the concentration of glutamate in the synapse. It indicates the timing of the action potential and reflects the excitatory activity of the neuron; This represents the amount of glutamate released with each action potential, reflecting the regulation of glutamate release by neuronal activity; This represents the glutamate clearance time constant.
[0096] In one embodiment, the BK channel flux is simulated using the following formula:
[0097]
[0098] Among them, the open probability of the BK channel. Modeled as glial cell membrane voltage The mediated process; Wk represents the opening probability of the BK channel, which is affected by the membrane potential. Regulation; It indicates electrical conductivity, reflecting the degree of openness of the channel; Represents the Nernst potential; F represents the Faraday constant; Indicates membrane potential; Indicates glial cell capacitance; This represents the flux of channel BK. Indicates the leakage flux; This indicates the flux of the sodium-potassium pump in glial cells.
[0099] In one embodiment, the amount of NO released and diffused into smooth muscle caused by the neuronal activity is simulated using the following formula:
[0100] Neuronal action generation NO output:
[0101]
[0102] in, This indicates the maximum rate of nitric oxide production, which is related to the activity of neuronal nitric oxide isomerase. Indicates oxygen content and Arginine content; The value represents the Michaelis constant. Pmax is positively correlated with neuronal action potential. Neuronal action potential activates neuronal nitric oxide isomerase. The larger the action potential and the higher the frequency, the higher the enzyme activity, which increases Pmax and ultimately increases NO production.
[0103] The amount of NO that reaches smooth muscle after production is:
[0104]
[0105] in, This represents the total amount that diffuses from neurons to smooth muscle. To generate NO production for neurons, This represents the total consumption of other substances within the neuron. This represents the diffusion flux between compartments.
[0106] In one embodiment, the perivascular potassium ion concentration is simulated using the following formula:
[0107]
[0108] This indicates the concentration of potassium ions in the space surrounding blood vessels. Indicates the currents of the KIR and BK channels. These represent the volume ratios of the perivascular space to astrocytes and the perivascular space to smooth muscle cells, respectively. Represents the ensemble decay variable. These represent the perivascular potassium ion concentration and the potassium ion balance concentration, respectively.
[0109] In one embodiment, the NO production generated by the endothelial cell compartment model and the amount diffused into smooth muscle are simulated using the following formula:
[0110] NO production by endothelial cells:
[0111]
[0112] in, The maximum rate of nitric oxide production in endothelial cells is related to the activity of endothelial nitric oxide isomerase. Indicates the amount of oxygen in endothelial cells and Arginine content; This represents the relevant Michaelis constant.
[0113] The amount of NO that reaches smooth muscle after production is:
[0114]
[0115] in, It represents the total amount that diffuses from the endothelium to the smooth muscle. This indicates the NO production generated by the endothelium. This indicates the total consumption of other substances within endothelial cells. This represents the diffusion flux between compartments.
[0116] In one embodiment, the KIR potassium channel flux is simulated using the following formula:
[0117]
[0118] in It is a unit conversion parameter. It was obtained through linear fitting of data from Filosa et al. and has specific parameters. It is the smooth muscle cell membrane potential. Represents the Nernst potential. It is extravascular potassium ions.
[0119] In one embodiment, a computer device is provided, which may be a server. The computer device includes a processor, memory, and a network interface connected via a system bus. The processor provides computing and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage medium. The network interface is used to communicate with external terminals via a network connection. When the computer program is executed by the processor, it implements the aforementioned method for constructing a neurovascular coupling model based on post-stroke epilepsy.
[0120] In one embodiment, a computer-readable storage medium is also provided, on which a computer program is stored relating to all or part of the processes in the methods of the above embodiments.
[0121] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the methods described above. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, or optical storage, etc. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc.
[0122] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.
[0123] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the invention patent. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this patent application should be determined by the appended claims.
Claims
1. A method for constructing a neurovascular bidirectional coupling model based on post-stroke epilepsy, characterized in that, The method includes: Constructing the first neuronal compartment model: Establishing a Hodgkin-Huxley model of the neuron, adding a synaptic transmission module to the Hodgkin-Huxley model to simulate the release of potassium ions and glutamate into the synaptic space caused by neuronal activity, and adding the NO generation process to simulate the release and diffusion of NO caused by neuronal activity; A first astrocyte compartment model was constructed to simulate the detection of glutamate and potassium ion concentrations in neuronal synapses, and to simulate the release of vasodilators based on the detected glutamate and potassium ion concentrations in the neuronal synapses. The model also simulated the calculation of BK potassium channel flux based on the vasodilator concentration, and the release of potassium ions into the perivascular space based on the BK channel flux. Constructing endothelial cell compartment model to simulate uptake Arginine is converted to NO by the enzyme Arginine is converted to NO by the enzyme A smooth muscle cell compartment model was constructed to simulate the calculation of the inward rectifier potassium ion channel KIR flux based on the detected perivascular potassium ion concentration, the process of smooth muscle cell hyperpolarization caused by the inward rectifier potassium ion channel KIR flux, the process of voltage-sensitive calcium channel closure caused by vascular smooth muscle cell hyperpolarization, and the smooth muscle contraction process based on the interaction between the detected perivascular NO concentration and intracellular enzyme activation, and the perivascular calcium ion concentration after the closure of voltage-sensitive calcium channels. A spatial model of a small artery was constructed to simulate the process of smooth muscle relaxation, which in turn led to vasodilation and blood flow. The first neuronal compartment model, the first astrocyte compartment model, the endothelial cell compartment model, the smooth muscle cell compartment model, and the small artery spatial model are interconnected to construct a neurovascular coupling dynamic model. A second astrocyte compartment model was constructed to simulate the process of glucose uptake by astrocytes based on changes in interstitial glucose concentration caused by changes in blood flow, and to simulate the process of glucose being metabolized into lactic acid and pyruvate based on changes in glucose concentration in astrocytes. A second neuron model was constructed to simulate the lactate oxidation and decomposition process based on the detected lactate concentration, and the process of regulating the neuron's action potential based on the lactate concentration. The second astrocyte compartment model and the second neuron model are connected to construct the blood vessel-nerve coupling dynamics model; A neurovascular bidirectional coupling model is constructed by connecting the neuro-vascular coupling dynamics model and the vascular-neural coupling dynamics model.
2. The method of claim 1, wherein the post-stroke epilepsy-based neurovascular bidirectional coupling model is constructed by, The potassium ion concentration in the neuronal synapse was obtained by simulation using the following formula: wherein represents the potassium ion concentration in the synapse, represents the conversion of current from conversion unit conversion factor, dimensionless factor represents the ratio of extracellular volume to intracellular volume; represents the voltage-gated potassium ion channel current, Np, Nm, Ng, Np represent the neuronal Na / K pump flux, interstitial diffusion flux, glial uptake flux, and glial Na / K pump flux, respectively.
3. The method of claim 1, wherein the post-stroke epilepsy-based neurovascular bidirectional coupling model is constructed by, The concentration of glutamate ions in the neuronal synapse was obtained by simulation using the following formula: wherein, represents the concentration of glutamate in the synapse, represents the time of occurrence of the action potential, represents the amount of glutamate released per action potential, represents the glutamate clearance time constant.
4. The method of claim 1, wherein the post-stroke epilepsy-based neurovascular bidirectional coupling model is constructed by, The flux of the BK channel is obtained by simulation using the following formula: wherein, is the BK channel flux, denotes the conductance, denotes the Nernst potential, F denotes the Faraday constant, denotes the membrane potential, the open probability of the BK channel is modeled as a glial cell membrane voltage mediated process, dependent on internal calcium ion and epoxyeicosatrienoic acid EET concentration, is the open time constant, is the calcium ion mediated potential; denotes the glial cell capacitance, denotes the BK channel flux, denotes the leak flux, denotes the glial cell sodium-potassium pump flux.
5. The method of claim 1, wherein the post-stroke epilepsy-based neurovascular bidirectional coupling model is constructed by: The amount of NO released and diffused into smooth muscle caused by the neuronal activity was simulated using the following formula: Neuronal action generation NO output: wherein represents the maximum production rate of nitric oxide, which is related to the activity of neuronal nitric oxide synthase; represents the amount of oxygen and the amount of arginine; represents the associated Michaelis constant; The amount of NO that reaches smooth muscle after production is: wherein, represents the total amount of diffusion from neurons to smooth muscle, is the production of NO by neurons, is the total consumption of other substances within neurons, is the inter-compartment diffusion flux.
6. The method of claim 1, wherein the post-stroke epilepsy-based neurovascular bidirectional coupling model is constructed by: The perivascular potassium ion concentration was obtained through simulation using the following formula: potassium ion concentration in the perivascular space, KIR and BK channel currents, volume ratios of perivascular space to astrocytes and perivascular space to smooth muscle cells, respectively, lumped decay variable, perivascular potassium ion concentration and potassium ion equilibrium concentration, respectively.
7. The method of claim 1, wherein the post-stroke epilepsy-based neurovascular bidirectional coupling model is constructed by: The NO production generated by the endothelial cell compartment model and the amount diffused into smooth muscle were simulated using the following formula: NO production by endothelial cells: wherein The maximum production rate of nitric oxide by endothelial cells is related to the activity of endothelial-type nitric oxide isomerase; represents the amount of oxygen in the endothelial cells and arginine; represents the associated Michaelis constant; The amount of NO that reaches smooth muscle after production is: wherein, represents the total amount diffusing from the endothelium to the smooth muscle; represents the endothelial NO production, represents the total consumption of other substances in the endothelial cell, represents the inter-compartment diffusion flux.
8. The method of claim 1, wherein the post-stroke epilepsy-based neurovascular bidirectional coupling model is constructed by, KIR potassium channel flux is calculated by the following equation: wherein a conversion unit parameter, is a constant, a smooth muscle cell membrane potential, denotes the Nernst potential, is the extracellular potassium ion. 9.A computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the computer device is configured to perform the method according to any one of claims 1-8 when the computer program is executed by the processor. The computer program, when executed by the processor, implements the steps of the method of any one of claims 1 to 8.
10. A computer-readable storage medium having stored thereon a computer program, characterized in that, The computer program, when executed by the processor, implements the steps of the method of any one of claims 1 to 8.