Method for constructing neurovascular bidirectional coupling model based on post-stroke epilepsy

Abstract

The invention discloses a post-stroke epilepsy-based nerve-blood vessel bidirectional coupling model construction method, which comprises the following steps of: releasing ions to a micro-blood vessel model through a micro-neurodynamic model so as to obtain a nerve-blood vessel coupling dynamic model, an established ischemic blood vessel model is subjected to energy metabolism to a microscopic neurodynamic model to obtain a blood vessel-nerve coupling dynamic model, and finally the nerve-blood vessel model and the blood vessel-nerve coupling dynamic model are combined to obtain the nerve-blood vessel bidirectional coupling model. Therefore, changes of physiological parameters after a period of time can be predicted, and theoretical guidance is provided for prediction and treatment of post-stroke epilepsy. Changes of a plurality of physiological parameters in the nerve coupling process are researched in the constructed model, and more theoretical supports are provided for prediction of the post-stroke epilepsy and prediction of the treatment effect.

Description

Technical Field

[0001] This application relates to the technical field of neurovascular coupling models, and particularly to a method for constructing a bidirectional neurovascular coupling model based on post-stroke epilepsy. Background Art

[0002] Post-stroke epilepsy (PSE) refers to epileptic seizures that occur within a certain period after a cerebral stroke, with no history of epilepsy before the stroke, excluding brain and systemic diseases, and without brain structural abnormalities or metabolic disorders due to other reasons. PSE is one of the complications of stroke and a common cause of epilepsy in elderly patients. Its seizure process presents a highly non-linear relationship of neuro-blood flow interaction regulation, and seizures mostly occur in local brain regions such as the hippocampus. Stroke-related epilepsy caused by reversible or irreversible brain damage caused by stroke will prolong the hospital stay of cerebrovascular disease patients, increase the mortality rate, and long-term affect the prognosis and quality of life of patients.

[0003] Neurovascular coupling (NVC) refers to the relationship between neural activity and local cerebrovascular responses in the brain. When neurons are activated, the metabolic demand of neurons increases, which will cause an increase in local cerebral blood flow (CBF) to meet its oxygen and nutrient supply needs and maintain the normal function of neurons. This process is usually called functional hyperemia, which describes the local vasodilation and constriction caused by neural activity in the human / mammalian brain. The change in blood vessel diameter controls local cerebral blood flow, and thus controls 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 regulation mechanisms. By predicting the changes in physiological data during the interaction process between abnormal neuronal activity and vascular regulation mechanisms, it helps to identify early warning signals of post-stroke epilepsy seizures, thus providing a precise prediction tool for clinical intervention. In addition, the construction of the model can also provide an experimental basis for the development of new treatment strategies, such as preventing or reducing epileptic seizures by modulating key links in the neurovascular coupling process, such as the activity of potassium ion channels or the vasodilation mechanism. In short, this model not only enriches the research content in the field of neurovascular coupling, but also provides a new perspective and method 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 ion concentration change mechanism under different pathological conditions and its regulation of blood vessels. The Wu Ying team proposed a coupling model composed of neurons, astrocytes, and blood vessels to simulate the ischemic state of blood vessels in order to study the role of hypoxia and abnormal neurotransmitter release in the occurrence and transmission of epileptic seizures. In recent years, the team led by Professor Wu Ying in China proposed an NVC model to study the role of energy metabolism in the occurrence and transmission of PSE. The above research shows that the unidirectional coupling model with ion release as the research object between nerves and blood vessels has been widely studied, but the coupling model with energy metabolism as the research object between blood vessels and nerves has been less studied. Therefore, the dynamic change process of physiological data during neurovascular coupling cannot be predicted, and it is only a prediction. Thus, the prediction and treatment of post-stroke epilepsy cannot be carried out, which is not suitable for the actual clinical application scenario. Summary of the Invention

[0006] Based on this, in view of the above technical problems, a construction method of a neurovascular bidirectional coupling model based on post-stroke epilepsy is provided to solve the problem that the existing neurovascular coupling model for post-stroke epilepsy is not suitable for the actual clinical application scenario.

[0007] In a first aspect, a construction method of a neurovascular bidirectional coupling model based on post-stroke epilepsy, the method includes:

[0008] Construct a first neuron compartment model: establish the Hodgkin-Huxley model of neurons, 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 at the same time add the generation process of NO to simulate the release and diffusion process of NO caused by neuronal activity;

[0009] Construct a first astrocyte compartment model to simulate the detection of glutamate concentration and potassium ion concentration in neuron synapses, and simulate the process of releasing vasodilators according to the detected glutamate concentration and potassium ion concentration in the neuron synapses, and simulate calculating the BK potassium channel flux according to the vasodilator concentration, and at the same time simulate the process of releasing potassium ions into the perivascular space according to the BK channel flux;

[0010] Construct an endothelial cell compartment model to simulate the uptake of L-arginine, the conversion of L-arginine into NO catalyzed by isomerase, and the diffusion process to smooth muscle cells;

[0011] Construct a smooth muscle cell compartment model to simulate calculating the inward rectifier potassium channel KIR flux based on the detected perivascular potassium ion concentration, simulating the hyperpolarization process of smooth muscle cells based on the inward rectifier potassium channel KIR flux, simulating the process of the closure of voltage-sensitive calcium channels caused by the hyperpolarization of vascular smooth muscle cells, and simultaneously simulating the interaction between the detected NO concentration around smooth muscle cells and intracellular enzyme activation and the smooth muscle contraction process based on the calcium ion concentration around smooth muscle cells after the closure of voltage-sensitive calcium channels;

[0012] Construct an arteriole space model to simulate the relaxation of smooth muscle, thereby driving vasodilation and obtaining the blood flow process;

[0013] Connect the first neuron compartment model, the first astrocyte compartment model, the endothelial cell compartment model, the smooth muscle cell compartment model, and the arteriole space model to construct a neuro-vascular coupling dynamics model;

[0014] Construct a second astrocyte compartment model to simulate the process of glucose uptake by astrocytes according to the change in interstitial glucose concentration caused by the change in blood flow, and simulate the process of glucose being metabolized to form lactate and pyruvate according to the change in glucose concentration in star-shaped astrocytes;

[0015] Construct a second neuron model to simulate the process of lactate oxidative decomposition according to the detected lactate concentration and simultaneously regulate the action potential of neurons according to the lactate concentration;

[0016] Connect the second astrocyte compartment model and the second neuron model to construct the vascular-neural coupling dynamics model;

[0017] Construct a neurovascular bidirectional coupling model by connecting the neuro-vascular coupling dynamics model and the vascular-neural coupling dynamics model.

[0018] In the above solution, optionally, the potassium ion concentration in the neuron synapse is simulated by the following formula:

[0019]

[0020] where [K + 0 represents the potassium ion concentration in the synapse, γ represents the unit conversion factor for converting current from μA / cm 2 to mM / s, the dimensionless factor β represents the ratio of the extracellular volume to the intracellular volume; I k represents the voltage-gated potassium channel current,

[0021] J x(x = pump, diff, glia, gliapump) 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 solution, optionally, the glutamate ion concentration in the neuronal synapse is simulated by the following formula:

[0023]

[0024] where [Gli]0 represents the glutamate concentration in the synapse, t spike represents the moment when the action potential occurs, q Glu represents the amount of glutamate released per action potential, τ Glu represents the glutamate clearance time constant.

[0025] In the above solution, optionally, the BK channel flux is simulated by the following formula:

[0026]

[0027] where J BK is the BK channel flux, g BK represents the conductance, E BK represents the Nernst potential, F represents the Faraday constant, V astr represents the membrane potential. The opening probability W of the BK channel k is modeled as a process mediated by the glial cell membrane voltage V astr and depends on the internal calcium ion and EET (epoxyeicosatrienoic acid) concentrations. is the opening time constant, V Ca is the calcium ion-mediated potential; C astr represents the glial cell capacitance, J BK represents the flux of the BK channel, J leak represents the leakage flux, J gliapump represents the glial cell sodium-potassium pump flux.

[0028] In the above solution, optionally, the release amount of NO caused by the neuronal activity and the diffusion amount to the smooth muscle are simulated by the following formula:

[0029] Neuronal action generates NO production:

[0030]

[0031] where P max represents the maximum nitric oxide production rate, which is related to the activity of neuronal nitric oxide synthase; [x] n (x = O2, L-Arg) represents the amount of oxygen and the amount of L-arginine; Kx (x = O2, L-Arg) represents the relevant Michaelis constant;

[0032] The amount of NO reaching the smooth muscle after production is:

[0033]

[0034] where, [NO] n represents the total amount diffusing from neurons to the smooth muscle, P NO,n is the NO production rate of neurons, C NO,n is the total consumption of other substances in neurons, d NO,n is the diffusion flux between compartments.

[0035] In the above solution, optionally, the perivascular potassium ion concentration is simulated by the following formula:

[0036]

[0037] K p represents the potassium ion concentration in the perivascular space, J BK 、J KIR represent the KIR and BK channel currents, VP pa 、VP ps respectively represent the volume ratios of the perivascular space to astrocytes and the perivascular space to smooth muscle cells, R decay represents the lumped decay variable, K p 、K p,min respectively represent the perivascular potassium ion concentration and the potassium ion equilibrium concentration.

[0038] In the above solution, optionally, the NO production and the amount diffusing to the smooth muscle generated by the endothelial cell compartment model are simulated by the following formula:

[0039] Endothelial cell NO production:

[0040]

[0041] where, V max represents the maximum nitric oxide production rate of endothelial cells, related to the activity of endothelial nitric oxide isomerase; [x] e (x = O2, L-Arg) represents the amount of oxygen and L-arginine in endothelial cells; K x (x = O2, L-Arg) represents the relevant Michaelis constant;

[0042] The amount of NO reaching the smooth muscle after production is:

[0043]

[0044] Among them, [NO] e represents the total amount diffused from the endothelium to the smooth muscle; P NO,e represents the NO production generated by the endothelium, C NO,e represents the total consumption of other substances in endothelial cells, d NO,e represents the diffusion flux between compartments.

[0045] In the above solution, optionally, the KIR potassium channel flux is simulated by the following formula:

[0046]

[0047] where γ i = 1970 mvμM -1 is the conversion unit parameter, z x (x = 1, 2, 3) is a constant, V smc is the smooth muscle cell membrane potential, E KIR represents the Nernst potential, K p is the extracellular potassium ion.

[0048] In a second aspect, a computer device includes a memory and a processor, the memory stores a computer program, and is characterized in that when the processor executes the computer program, the steps of the method described in the first aspect above are implemented.

[0049] In a third aspect, a computer-readable storage medium stores a computer program thereon, and is characterized in that when the computer program is executed by a processor, the steps of the method described in the first aspect above are implemented.

[0050] This application has at least the following beneficial effects:

[0051] In this application, a neuro-vascular coupling dynamics model is obtained by releasing ions from a microscopic neural dynamics model into a microscopic blood vessel model, and the output of blood flow change driven by nerves is obtained. Secondly, a vascular-neural coupling dynamics model is obtained by establishing an ischemic blood vessel model and then performing energy metabolism to the microscopic neural dynamics model, and the output of neural change driven by blood flow is obtained. Finally, the neuro-vascular and vascular-neural coupling dynamics models are combined to obtain a bidirectional coupling model for the interactive regulation of nerve and blood flow. The bidirectional coupling modeling method provided in this application comprehensively understands the neurovascular coupling mechanism from the perspective of glioneurovascular dynamics rather than simply from the perspective of neural discharge. By simulating the occurrence process of epilepsy after stroke through a computational model, the changes of physiological parameters after a period of time can be predicted, thus providing theoretical parameters for the prediction and treatment of epilepsy after stroke. Moreover, in the constructed model, the changes of multiple physiological data (ions, energy) during the neural coupling process are studied, providing more theoretical data for the prediction of the prediction and treatment effect of epilepsy after stroke, and enabling accurate prediction of the prediction and treatment effect of epilepsy after stroke. Description of the Drawings

[0052] Figure 1 FIG. is a schematic flowchart of a method for constructing a neurovascular bidirectional coupling model based on epilepsy after stroke provided by an embodiment of the present application;

[0053] Figure 2 FIG. is a detailed schematic flowchart of a method for constructing a neurovascular bidirectional coupling model based on epilepsy after stroke provided by an embodiment of the present application;

[0054] Figure 3 FIG. is a schematic flowchart of the technical route of a method for constructing a neurovascular bidirectional coupling model based on epilepsy after stroke provided by an embodiment of the present application;

[0055] Figure 4 FIG. is a schematic diagram of the variable relationship of the arteriolar constriction subsystem modeling provided by an embodiment of the present application;

[0056] Figure 5 FIG. is a schematic diagram of the microscopic details of the microscopic neuro-blood flow bidirectional coupling dynamics modeling provided by an embodiment of the present application;

[0057] Figure 6 FIG. is a schematic diagram of partial modeling results provided by an embodiment of the present application. Detailed Embodiments

[0058] In order to make the objectives, technical solutions and advantages of the present application clearer, the present application will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0059] In one embodiment, as Figure 1 , Figure 2 and Figure 3 shown, a method for constructing a neurovascular bidirectional coupling model based on post-stroke epilepsy is provided, characterized in that the method comprises:

[0060] Step S1: Construct a first neuron compartment model: Establish a Hodgkin-Huxley model of neurons, add a synaptic transmission module to the Hodgkin-Huxley model, simulate the process of potassium ions and glutamate being released into the synaptic space due to neuronal activity, and simultaneously add the generation process of NO to simulate the release and diffusion process of NO caused by neuronal activity.

[0061] In step S1, the simulation activity causes potassium ions and glutamate to be released into the synaptic space on the one hand, and the generation of NO on the other hand. The first neuron compartment model outputs physiological indexes related to neuronal activity.

[0062] Step S2: Construct a first astrocyte compartment model, simulate detecting the glutamate concentration and potassium ion concentration in the neuronal synapse, and simulate the process of releasing vasodilators according to the detected glutamate concentration and potassium ion concentration in the neuronal synapse, and simulate calculating the BK potassium channel flux according to the vasodilator concentration, and simultaneously simulate the process of releasing potassium ions into the perivascular space according to the BK channel flux;

[0063] In step S2, the simulation astrocytes detect potassium ions and glutamate in the neuronal synapse, and then release vasodilators, open the BK potassium channel, and finally release potassium ions into the perivascular space. The first astrocyte compartment model outputs indexes related to neurotransmitter regulation mediated by astrocytes and vascular function.

[0064] Step S3: Construct an endothelial cell compartment model, simulate the process of taking up L-arginine, isomerase catalyzing the conversion of L-arginine into NO, and diffusing it to smooth muscle cells.

[0065] In step S3, the endothelial cell compartment model outputs indexes related to NO generation and diffusion mediated by endothelial cells.

[0066] Step S4: Construct a smooth muscle cell compartment model, simulate calculating the inward rectifier potassium channel KIR flux according to the detected perivascular potassium ion concentration, simulate the process of hyperpolarizing smooth muscle cells according to the inward rectifier potassium channel KIR flux, simulate the process of the voltage-sensitive calcium channel closing caused by the hyperpolarization of vascular smooth muscle cells, and simultaneously simulate the smooth muscle contraction process according to the interaction between the detected NO concentration around smooth muscle cells and intracellular enzyme activation and the calcium ion concentration around smooth muscle cells after the voltage-sensitive calcium channel closes.

[0067] In step S4, the inward rectifier potassium channel KIR on SMCs (vascular smooth muscle cells) is opened by simulating the potassium ion concentration around blood vessels, the SMC membrane is hyperpolarized, and the voltage-sensitive Ca channel is closed; on the other hand, when NO reaches the SMC, it interacts with intracellular enzyme activation, and both indirectly affect the SMC contraction system and regulate SMC relaxation. The smooth muscle cell compartment model outputs indexes related to the electrophysiology and contraction state of smooth muscle cells.

[0068] Specifically, arterioles are covered by endothelial cells and smooth muscle cells. Therefore, in order to accurately simulate vascular motility, these two cell types must be considered, and NO is first produced by endothelial cells and neurons and then diffuses to smooth muscle cells to take effect. The mass balance formula is used to mathematically describe the NO kinetics in the compartments involved.

[0069] Step S5: Construct an arteriole spatial model to simulate smooth muscle relaxation, thereby driving blood vessel dilation and obtaining the process of blood flow.

[0070] Specifically, Figure 4 Schematic diagram showing the variable relationship of the arteriole contraction subsystem modeling; the Ca in the cytoplasm of the SMC compartment 2+ concentration is used as the input signal. There are four possible states for the formation of myosin: free non-phosphorylated cross-bridge (M), free phosphorylated cross-bridge (Mp), attached phosphorylated cross-bridge (AMp), and attached dephosphorylated cross-bridge (AM). The differential equations are as follows:

[0071]

[0072] [AM]+[AMp]+[Mp]+[M]=1

[0073] where K n (n = 1,…,7) are rate constants that regulate phosphorylation and cross-bridge formation, K2 = K5, K3 = 0.4, K4 = K7 = 0.1, and K1 and K6 vary with the intracellular Ca 2+ concentration:

[0074]

[0075] F r =[AMp]+[AM]

[0076]

[0077] where γ is the sensitivity constant characterizing calcium-activated myosin phosphorylation; F r is the attached cross-bridge fraction.

[0078] The arteriole spatial model outputs indexes related to blood vessel dilation and blood flow.

[0079] Step S6: Connect the first neuron compartment model, the first astrocyte compartment model, the endothelial cell compartment model, the smooth muscle cell compartment model, and the arteriole space model to construct a neuro-vascular coupling dynamics model.

[0080] Step S7: Construct a second astrocyte compartment model to simulate the change in interstitial glucose concentration caused by blood flow changes, the process of glucose uptake by astrocytes, and the process of glucose being metabolized to form lactate and pyruvate according to the change in glucose concentration in astrocytes.

[0081] In step S7, simulate an increase in blood flow, such that astrocytes take up a large amount of glucose from the interstitial space and are metabolized to form lactate and pyruvate. Lactate is released into the interstitial space, and pyruvate is metabolized to maintain astrocyte function. The second astrocyte compartment model outputs indicators related to astrocyte glucose metabolism and lactate production.

[0082] Step S8: Construct a second neuron model to simulate the process of lactate oxidative decomposition according to the detected lactate concentration and the process of regulating the action potential of neurons according to the lactate concentration.

[0083] In step S8, neurons take up lactate from the interstitial space to meet their metabolic needs, oxidize it to generate ATP, regulate the activity of the Na-K pump, provide fuel for neuronal firing activities, and thus affect the action potential of neurons. Generate enough energy to maintain neuronal firing, and then continue the process of step 1. After a period of time, it is observed that the ATP concentration in neurons drops sharply. As the metabolic feedback weakens, neuronal firing continues, and the consumption of ATP greatly exceeds the production rate of ATP. Once the ATP concentration is too low to maintain the activity of the pump and the transmembrane ion gradient is depleted, the neurons tend to depolarize. The second neuron module simulates the above process and outputs indicators related to the coupling of lactate metabolism and neuronal electrophysiology.

[0084] Step S9: Connect the second astrocyte compartment model and the second neuron model to construct the vascular-neural coupling dynamics model.

[0085] Step S10: Connect the neuro-vascular coupling dynamics model and the vascular-neural coupling dynamics model to construct a bidirectional coupling model for neuro-blood flow interaction regulation.

[0086] The microscopic details of the microscopic neuro-blood flow bidirectional coupling dynamics modeling are as Figure 5 shown: Figure 5In the text, neuron represents neurons, astrocyte represents astrocytes, aortic represents arterioles, Glucose represents glucose, Lactate represents lactate, Pyruvate represents pyruvate, Glu (Glutamate) represents glutamate, Ca2+ store represents Ca storage, i.e., the endoplasmic reticulum, AD represents vasodilation, and AC represents vasoconstriction. The whole model is divided into seven parts: neurons, synaptic clefts, astrocytes, perivascular spaces, smooth muscle (SMCs) and endothelium, and arteriole lumens. These compartments are mainly coupled by ion fluxes in ion channels, energy substances, and membrane potential differences. Neurons, astrocytes, smooth muscle cells (SMCs), endothelial cells, and arterioles are regarded as independent compartment space modules, and the synaptic cleft and perivascular space are regarded as two independent extracellular environments, assuming that there is no material exchange and communication between them.

[0087] Figure 5 It includes two pathways: the positive feedback of neuro-vascular transmission through ion concentration and the metabolic feedback of vaso-neural transmission through energy substances. Specifically, in the metabolic feedback, after a series of processes of neurons-astrocytes-smooth muscle cells lead to vasodilation, the rates of glucose and lactate fluxes transferred from the endothelium to the interstitial space increase, and astrocytes and neurons take up glucose and lactate from the interstitial space. Neurons oxidize lactate to produce ATP, which provides fuel for neuronal firing.

[0088] The neurovascular coupling model for post-stroke epilepsy constructed in this application is an important tool for studying the complex interaction between abnormal neuronal activities and vascular regulation mechanisms. The model includes two aspects: the regulation of blood vessels by nerves and the feedback of vascular status on nerves, reflecting the dynamic mechanism of the clinical manifestations of post-stroke epilepsy. It can be used in basic research fields for exploring disease mechanisms. Secondly, by inputting the clinically relevant physiological parameters of patients, such as neural activity parameters and vascular structure parameters, the neurovascular coupling state can be analyzed. By observing the changes in physiological parameters in each module of the output, the degree of epilepsy induced in patients can be judged, providing assistance for clinical diagnosis. The model can identify the key therapeutic targets in neurovascular coupling. By combining the physiological data related to neurovascular coupling, the effects of drugs used to block or restore channel functions can be evaluated, and directions for developing new drugs for post-stroke epilepsy can be provided. The physiological data with dynamic changes output by the neurovascular coupling model in each sub-model include, for example, the release rates of potassium ions, glutamate, and NO caused by the changes in neuron potentials in the first neuron compartment model, etc., which are used for the judgment of post-stroke epilepsy.

[0089] Post-stroke epilepsy is one of the common complications in stroke patients, but there is currently a lack of effective early prediction methods. Therefore, the neurovascular bidirectional coupling model constructed by this patented technology, combined with the initial cerebral hemodynamic data and neurophysiological data of the patient, can simulate the risk of post-stroke epilepsy. For example, by using the patient's blood flow monitoring data or metabolic data such as oxygen consumption and glucose utilization rate, input them into the model, and output the predicted cerebral hemodynamic change parameters and neurophysiological change parameters to predict the likelihood of the patient developing epilepsy after stroke.

[0090] Currently, the treatment of epilepsy mainly relies on anti-epileptic drugs, but the treatment effects vary greatly among different patients. Therefore, through the neurovascular coupling model, combined with the individual characteristics of the patient (such as cerebral blood flow status, neuron activity pattern), it can simulate the effects of different treatment regimens (such as drug dosage, drug type) on the neurovascular coupling of the patient. For example, the model can predict the regulatory effect of a certain drug on the patient's cerebral blood flow and neuron activity, thus providing personalized treatment recommendations for doctors.

[0091] Figure 6 As shown: The figure shows the changes in the neuron membrane potential and the extracellular potassium ion concentration over time. When the neuron is in the resting state, the membrane potential remains unchanged, and at the same time, the extracellular potassium ion concentration remains unchanged. During the action potential of the neuron, the extracellular potassium ion concentration is relatively high, and the change in the arteriole radius can be observed through the processes of ion transfer and myofilament sliding. The relationship between the four possible states of myosin and the vascular radius can be seen in the figure. (a) shows the normal discharge characteristics of the neuron compartment and the fluctuation of extracellular potassium ions below the threshold when the arteriole is normal; (b) shows the epileptiform discharge characteristics of the neuron compartment when the arteriole is ischemic, and the change trend of extracellular potassium ions with epileptiform discharge; (c) shows that the neuron compartment returns to the resting state when the arteriole is severely ischemic, and there is no obvious change in the internal potassium ions.

[0092] In the above method for constructing a neurovascular coupling model based on post-stroke epilepsy, a neuro-vascular coupling dynamics model is obtained by releasing ions from a microscopic neural dynamics model into a microscopic vascular model, and the output of blood flow change driven by nerves is obtained. Secondly, a vascular-neural coupling dynamics model is obtained by establishing an ischemic vascular model through energy metabolism to a microscopic neural dynamics model, and the output of neural change driven by blood flow is obtained. Finally, the neuro-vascular and vascular-neural coupling dynamics models are combined to obtain a bidirectional coupling model for the interactive regulation of nerve and blood flow. A bidirectional coupling modeling method is provided, which comprehensively understands the neurovascular coupling mechanism from the perspective of glial vascular dynamics rather than simply from the perspective of neural discharge. By simulating the occurrence process of post-stroke epilepsy through a computational model and studying its pathological mechanism, the changes of physiological parameters after a period of time can be predicted, thereby providing theoretical data for the prediction and treatment of post-stroke epilepsy. Moreover, in the constructed model, the changes of multiple physiological parameters (ions, energy) during the neural coupling process are studied, providing more theoretical data for the prediction of post-stroke epilepsy and the prediction of treatment effects, and enabling accurate prediction of post-stroke epilepsy and treatment effects.

[0093] In one embodiment, the potassium ion concentration in the neuron synapse is simulated by the following formula:

[0094]

[0095] where [K + 0 represents the potassium ion concentration in the synapse, γ represents the unit conversion factor for converting current from μA / cm 2 to mM / s, and the dimensionless factor β represents the ratio of the extracellular volume to the intracellular volume. I k represents the voltage-gated potassium ion channel current, reflecting the potassium ion efflux caused by neuronal activity;

[0096] J x (x = pump, diff, glia, gliapump) respectively represent the neuronal sodium-potassium pump flux, interstitial diffusion flux, glial cell uptake flux, and glial cell sodium-potassium pump flux.

[0097] In one embodiment, the glutamate ion concentration in the neuron synapse is simulated by the following formula:

[0098]

[0099] where, [Glu]0 represents the glutamate concentration in the synapse; t spike represents the occurrence time of the action potential, reflecting the excitatory activity of the neuron; q Glu represents the amount of glutamate released per action potential, reflecting the regulation of glutamate release by neuronal activity; τGlu Represents the glutamate clearance time constant.

[0100] In one embodiment, the BK channel flux is simulated by the following formula:

[0101]

[0102] Wherein, the opening probability W of the BK channel k Is modeled as a process mediated by the glial cell membrane voltage V astr ; Wk represents the opening probability of the BK channel, regulated by the membrane potential Vastr; g BK Represents the conductance, reflecting the degree of channel opening; E BK Represents the Nernst potential; F represents the Faraday constant; V astr Represents the membrane potential; C astr Represents the glial cell capacitance; J BK Represents the flux of the BK channel, J leak Represents the leakage flux; J gliapump Represents the glial cell sodium-potassium pump flux.

[0103] In one embodiment, the release amount of NO caused by the neuronal activity and the diffusion amount to the smooth muscle are simulated by the following formula:

[0104] Neuronal action generates NO production:

[0105]

[0106] Wherein, P max Represents the maximum nitric oxide production rate, related to the activity of neuronal nitric oxide synthase; [x] n (x = o2, L-Arg) represents the amount of oxygen and L-arginine; K x (x = o2, L-Arg) represents the relevant Michaelis constant; Pmax is positively correlated with the neuronal action potential. The neuronal action potential activates neuronal nitric oxide synthase. The greater the action potential frequency, the higher the enzyme activity, which makes Pmax increase, and ultimately increases the NO production.

[0107] The amount of NO reaching the smooth muscle after production is:

[0108]

[0109] Wherein, [NO] n Represents the total amount diffusing from the neuron to the smooth muscle, P NO,n Is the NO production of the neuron, C NO,n Is the total consumption of other substances in the neuron, d NO,n Is the inter-compartment diffusion flux.

[0110] In one embodiment, the perivascular potassium ion concentration is simulated by the following formula:

[0111]

[0112] K p represents the potassium ion concentration in the perivascular space, J BK 、J KIR represent the KIR and BK channel currents, VP pa 、VP ps respectively represent the volume ratios of the perivascular space to astrocytes and the perivascular space to smooth muscle cells, R decay represents the lumped decay variable, K p 、K p,min respectively represent the perivascular potassium ion concentration and the potassium ion equilibrium concentration.

[0113] In one embodiment, the NO production generated by the endothelial cell compartment model and the amount diffusing into the smooth muscle are simulated by the following formula:

[0114] NO production by endothelial cells:

[0115]

[0116] where V max represents the maximum rate of nitric oxide production by endothelial cells, related to the activity of endothelial nitric oxide isomerase; [x] e (x = O2, L-Arg) represents the amount of oxygen and L-arginine in endothelial cells; K x (x = O2, L-Arg) represents the related Michaelis constant.

[0117] The amount of NO reaching the smooth muscle after production is:

[0118]

[0119] where [NO] e represents the total amount diffusing from the endothelium to the smooth muscle. P NO,e represents the NO production by endothelial cells, C NO,e represents the total consumption of other substances in endothelial cells, d NO,e represents the diffusion flux between compartments.

[0120] In one embodiment, the KIR potassium channel flux is simulated by the following formula:

[0121]

[0122] where γ i = 1970 mv μM-1 is the conversion unit parameter, z x (x = 1, 2, 3) is obtained by linear fitting of the data by Filosa et al. and has specific parameters, V smc is the smooth muscle cell membrane potential, E KIR represents the Nernst potential, K p is the extracellular potassium ion.

[0123] In one embodiment, a computer device is provided. The computer device may be a server. The computer device includes a processor, a memory, and a network interface connected by a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, it is used to implement the above-mentioned method for constructing a neurovascular coupling model based on post-stroke epilepsy.

[0124] In one embodiment, a computer-readable storage medium is further provided, on which a computer program is stored, which relates to all or part of the processes in the above-mentioned embodiment method.

[0125] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it may include the processes of the embodiments of the above-mentioned methods. Among them, any reference to a memory, storage, database, or other medium used in the various embodiments provided in the present application may include at least one of non-volatile and volatile memories. Non-volatile memory may include read-only memory (ROM), magnetic tape, floppy disk, flash memory, or optical memory, etc. Volatile memory may 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.

[0126] The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity of description, 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, it should be considered as the scope recorded in this specification.

[0127] The above-described embodiments merely represent several implementation manners of the present application. The description is relatively specific and detailed, but it should not be construed as a limitation on the scope of the invention patent. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present application, several variations and improvements can still be made, and these all belong to the protection scope of the present application. Therefore, the protection scope of the patent of the present application shall be subject to 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 a first neuron compartment model: establishing the Hodgkin-Huxley model of neurons, adding a synaptic transmission module to the Hodgkin-Huxley model, simulating the release of potassium ions and glutamate into the synaptic space due to neuron activity, and simultaneously adding the generation process of NO, and simulating the release and diffusion process of NO caused by neuron activity; Constructing a first astrocyte compartment model, simulating the detection of glutamate concentration and potassium ion concentration in neuron synapses, and simulating the process of releasing vasodilators according to the detected glutamate concentration and potassium ion concentration in the neuron synapses, and simulating the calculation of BK potassium channel flux according to the vasodilator concentration, and simultaneously simulating the process of releasing potassium ions into the perivascular space according to the BK channel flux; Constructing an endothelial cell compartment model, simulating the uptake of L-arginine, the conversion of L-arginine into NO catalyzed by isomerase, and the diffusion to smooth muscle cells; Constructing a smooth muscle cell compartment model, simulating the calculation of the inward rectifier potassium ion channel KIR flux according to the detected perivascular potassium ion concentration, simulating the hyperpolarization process of smooth muscle cells according to the inward rectifier potassium ion channel KIR flux, simulating the process of the closure of voltage-sensitive calcium channels caused by the hyperpolarization of vascular smooth muscle cells, and simultaneously simulating the interaction between the detected NO concentration around smooth muscle cells and intracellular enzyme activation and the smooth muscle contraction process according to the calcium ion concentration around smooth muscle cells after the closure of voltage-sensitive calcium channels; Constructing an arteriole space model, simulating smooth muscle relaxation, and then driving vasodilation to obtain the blood flow; Connecting the first neuron compartment model, the first astrocyte compartment model, the endothelial cell compartment model, the smooth muscle cell compartment model, and the arteriole space model to construct a neurovascular coupling dynamics model; Constructing a second astrocyte compartment model, simulating the uptake process of glucose by astrocytes according to the change in interstitial glucose concentration caused by blood flow change, and simulating the process of glucose being metabolized into lactate and pyruvate according to the change in glucose concentration in astrocytes; Constructing a second neuron model, simulating the oxidative decomposition process of lactate according to the detected lactate concentration, and simultaneously regulating the action potential of neurons according to the lactate concentration; Connecting the second astrocyte compartment model and the second neuron model to construct the vascular-neural coupling dynamics model; Connecting the neurovascular coupling dynamics model and the vascular-neural coupling dynamics model to construct a neurovascular bidirectional coupling model.

2. The construction method of the neurovascular bidirectional coupling model based on post-stroke epilepsy according to claim 1, characterized in that The potassium ion concentration in the neuron synapse is simulated by the following formula: where [K + 0 represents the potassium ion concentration in the synapse, γ represents the unit conversion factor for converting current from μA / cm 2 to the unit of mM / s, and the dimensionless factor β represents the ratio of the extracellular volume to the intracellular volume; I k represents the current of the voltage-gated potassium ion channel, J x (x = pump, diff, glia, gliapump) represent the neuronal sodium-potassium pump flux, interstitial diffusion flux, glial uptake flux, and glial sodium-potassium pump flux, respectively.

3. The construction method of the neurovascular bidirectional coupling model based on post-stroke epilepsy according to claim 1, wherein The glutamate ion concentration in the neuron synapse is simulated by the following formula: Among them, [Glu]0 represents the glutamate concentration in the synapse, and t spike represents the moment when the action potential occurs, and q Glu represents the amount of glutamate released by each action potential, and τ Glu represents the glutamate clearance time constant.

4. The construction method of the neurovascular bidirectional coupling model based on post-stroke epilepsy according to claim 1, wherein, The BK channel flux is simulated by the following formula: Among them, J BK is the BK channel flux, g BK represents the conductance, E BK represents the Nernst potential, F represents the Faraday constant, V astr represents the membrane potential, and the opening probability W of the BK channel k is modeled as a process mediated by the glial cell membrane voltage V astr and depends on the internal calcium ion and EET (epoxyeicosatrienoic acid) concentrations. is the opening time constant, V Ca is the calcium ion-mediated potential; C astr represents the glial cell capacitance, J BK represents the flux of the BK channel, J leak represents the leakage flux, J gliapump represents the glial cell sodium-potassium pump flux.

5. The construction method of the neurovascular bidirectional coupling model based on post-stroke epilepsy according to claim 1, wherein The release amount of NO caused by neuron activity and the diffusion amount to smooth muscle are simulated by the following formula: The amount of NO produced by neuron action: Among them, P max represents the maximum nitric oxide generation rate and is related to the activity of neuronal nitric oxide synthase; [x] n (x = O2, L-Arg) represents the amounts of oxygen and L-arginine; K x (x = O2, L-Arg) represents the relevant Michaelis constant; The amount of NO reaching smooth muscle after production is: Among them, [NO] n represents the total amount diffused from neurons to smooth muscle, P NO,n is the NO production generated by neurons, C NO,n is the total consumption of other substances in neurons, d NO,n is the diffusion flux between compartments.

6. The construction method of the neurovascular bidirectional coupling model based on post-stroke epilepsy according to claim 1, wherein, The perivascular potassium ion concentration is simulated by the following formula: K p represents the potassium ion concentration in the perivascular space, J BK 、J KIR represent the KIR and BK channel currents, VP pa 、VP ps represent the volume ratios of the perivascular space to astrocytes and the perivascular space to smooth muscle cells, respectively, R decay represents the lumped decay variable, K p 、K p,min represent the perivascular potassium ion concentration and the potassium ion equilibrium concentration, respectively.

7. The construction method of the neurovascular bidirectional coupling model based on post-stroke epilepsy according to claim 1, wherein The NO production generated by the endothelial cell compartment model and the amount diffusing into smooth muscle are simulated by the following formula: Endothelial cell-generated NO production: Among them, V max represents the maximum nitric oxide production rate of endothelial cells, which is related to the activity of endothelial nitric oxide isomerase; [x] e (x = O2, L-Arg) represents the amount of oxygen and L-arginine in endothelial cells; K x (x = O2, L-Arg) represents the relevant Michaelis constant; The amount reaching smooth muscle after NO production is: Among them, [NO] e represents the total amount diffused from the endothelium to the smooth muscle; P NO,e represents the NO production generated by the endothelium, C NO,e represents the total consumption of other substances in endothelial cells, d NO,e represents the diffusion flux between compartments.

8. The construction method of the neurovascular bidirectional coupling model based on post-stroke epilepsy according to claim 1, characterized in that, The KIR potassium channel flux is calculated by the following formula: where γ i = 1970 mvμM -1 is the conversion unit parameter, z x (x = 1, 2, 3) are constants, V smc is the smooth muscle cell membrane potential, E KIR represents the Nernst potential, K p is the extracellular potassium ion.

9. A computer device, comprising a memory and a processor, the memory storing a computer program, characterized in that, When the processor executes the computer program, the steps of the method according to any one of claims 1 to 8 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 8 are implemented.

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