A simulation method based on the cross-border of psychology and physiology and related devices and media
Patent Information
- Application Number
- CN202611091388.X
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2026-07-22
- Publication Date
- 2026-10-13
AI Technical Summary
[0005]基于上述现有技术的不足，本申请提供了一种基于心理与生理跨界的仿真方法及相关装置、介质，以解决现有技术无法保证仿真结果的准确性的问题
[0056]本申请提供的一种基于心理和生理跨界的仿真方法，构建初始化的心理应激张量和生理特征向量。其中，心理应激张量表征虚拟患者心理认知与情绪状态。生理特征向量表征虚拟患者心脏电生理和流体力学状态。然后将心理应激张量输入自主神经突触间隙化学动力学模型中，仿真出突触间隙的神经递质的浓度，并基于神经递质的浓度确定突触后膜受体激活率。接着将突触后膜受体激活率映射入目标孪生体中，依次进行心肌细胞动作电位仿真、心肌主动收缩力学仿真以及心室多相流体动力学仿真，得到当前仿真数据。其中，目标孪生体为多尺度心肌物理场孪生镜像体，且以当前的所述生理特征向量作为边界场进行仿真。最后基于当前仿真数据中的主动脉压，通过仿真外周压力感受器生理反射机制与传入神经传导的时滞效应生成中枢反馈信号，并输入心理应激张量的状态转移方程中更新出心理应激张量，返回执行将心理应激张量输入自主神经突触间隙化学动力学模型中的步骤。所以引入自主神经突触间隙化学动力学模型，将宏观心理张量通过定量化学递质浓度桥接至介观电生理与力学模型，再通过高精度流固耦合延伸至宏观血流动力学，保证了多尺度仿真的物理和化学机理准确性。最终形成心理至脑部再至心理的动力学闭环，实现了生理状态对心理应激张量的实时动态反馈修正，即形成心理和躯体的双向博弈闭环，进而可以消除长期仿真中参数漂移导致的失真，使数字孪生体具备自适应校准和突变流形遍历能力，保证仿真的准确性。
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Abstract
Description
Technical Field
[0001] This application relates to the field of disease data simulation technology, and in particular to a simulation method and related devices and media based on the cross-disciplinary approach of psychology and physiology. Background Technology
[0002] Cardiac digital twin, as a cutting-edge direction in precision medicine and smart healthcare, aims to achieve real-time monitoring, prediction of evolution, and intervention assessment of the physiological and pathological state of the human heart by constructing a virtual digital mirror of the heart.
[0003] Most existing digital twin models of the heart are limited to simulations in a "purely physical" or "purely physiological" dimension. Furthermore, they treat the heart as an isolated organ, assuming that its boundary conditions, such as resting heart rate and basic myocardial contractility, are constant throughout the simulation period. Therefore, they directly simplify neurohumoral regulation into static boundary conditions or unidirectional control inputs for simulation.
[0004] Therefore, existing technologies neglect the deep bidirectional feedback coupling mechanism between "psychological stress" and "physiological state," resulting in inaccurate simulation results. Furthermore, due to the unidirectional open-loop transmission, errors accumulate over time, further affecting the accuracy of the simulation results. Summary of the Invention
[0005] In view of the shortcomings of the prior art, this application provides a simulation method and related devices and media based on the cross-disciplinary integration of psychology and physiology, so as to solve the problem that the prior art cannot guarantee the accuracy of simulation results.
[0006] To achieve the above objectives, this application provides the following technical solution:
[0007] The first aspect of this application provides a simulation method based on the cross-disciplinary approach of psychology and physiology, including:
[0008] An initial psychological stress tensor and physiological feature vector are constructed; wherein the psychological stress tensor represents the psychological cognition and emotional state of the virtual patient; and the physiological feature vector represents the cardiac electrophysiological and hydrodynamic state of the virtual patient.
[0009] The psychological stress tensor is input into the autonomic nerve synaptic cleft chemical kinetic model to simulate the concentration of neurotransmitters in the synaptic cleft, and the postsynaptic membrane receptor activation rate is determined based on the concentration of neurotransmitters.
[0010] The activation rate of the postsynaptic membrane receptors is mapped into the target twin, and simulations of cardiomyocyte action potentials, myocardial active contraction mechanics, and ventricular multiphase fluid dynamics are performed sequentially to obtain the current simulation data; wherein, the target twin is a multi-scale myocardial physical field twin mirror image, and the current physiological feature vector is used as the boundary field for simulation;
[0011] Based on the aortic pressure in the current simulation data, a central feedback signal is generated by simulating the physiological reflex mechanism of peripheral baroreceptors and the time delay effect of afferent nerve conduction. This signal is then input into the state transition equation of the psychological stress tensor to update the psychological stress tensor. Finally, the process returns to the step of inputting the psychological stress tensor into the autonomic nerve synaptic cleft chemical kinetic model.
[0012] Optionally, in the above-mentioned simulation method based on the cross-disciplinary integration of psychology and physiology, the step of inputting the psychological stress tensor into the autonomic neural synaptic cleft chemical kinetic model to simulate the concentration of neurotransmitters in the synaptic cleft, and determining the postsynaptic membrane receptor activation rate based on the concentration of the neurotransmitters, includes:
[0013] The psychological stress tensor is converted into the action potential discharge frequency of the presynaptic membrane of the sympathetic and parasympathetic nervous systems.
[0014] Based on the discharge frequencies of the two action potentials, the concentrations of norepinephrine and acetylcholine in the synaptic cleft were simulated using the autonomic nerve synaptic cleft chemical kinetic model.
[0015] Based on the concentrations of norepinephrine and acetylcholine, the activation rates of postsynaptic receptors of the sympathetic and parasympathetic nerves on the surface of the heart are calculated.
[0016] Optionally, in the above-described simulation method based on the cross-disciplinary integration of psychology and physiology, the step of converting the psychological stress tensor into the action potential discharge frequency of the presynaptic membrane of the sympathetic and parasympathetic nervous systems includes:
[0017] The cognitive load component and the emotional arousal component in the psychological stress tensor are linearly weighted and superimposed to obtain the action potential discharge frequency of the presynaptic membrane of the sympathetic nerve.
[0018] The physiological stress component in the psychological stress tensor is mapped to the action potential discharge frequency of the presynaptic membrane of the parasympathetic nervous system through a nonlinear negative exponential decay function.
[0019] Optionally, in the above-mentioned simulation method based on the cross-disciplinary integration of psychology and physiology, the step of mapping the postsynaptic membrane receptor activation rate into the target twin, and sequentially performing cardiomyocyte action potential simulation, myocardial active contraction mechanics simulation, and ventricular multiphase fluid dynamics simulation to obtain the current simulation data includes:
[0020] The postsynaptic membrane receptor activation rate was used as a correction coefficient and input into the cardiomyocyte membrane potential regulation model in the target twin to simulate the conductivity of ion channels in cardiomyocytes; wherein, the conductivity of the ion channels includes the conductivity of L-type calcium channels and the conductivity of vagal-sensitive potassium channels;
[0021] The transient concentration of free calcium ions in cardiomyocytes was calculated based on the conductivity of the ion channel.
[0022] The transient concentration of calcium ions was substituted into the active contraction mechanics model, and the active contraction mechanics of myocardial fibers was simulated by combining the local geometric stretch rate and stretch change rate of myocardial fibers to obtain the instantaneous active tension generated by myocardial fibers.
[0023] Based on the instantaneous active tension as the active source term, ventricular multiphase fluid dynamics simulation was performed to obtain aortic pressure and intracardiac blood flow field data.
[0024] Optionally, in the above-mentioned simulation method based on the cross-disciplinary integration of psychology and physiology, the step of generating a central feedback signal based on the aortic pressure in the current simulation data by simulating the physiological reflex mechanism of peripheral baroreceptors and the time lag effect of afferent nerve conduction, and inputting it into the state transition equation of the psychological stress tensor to update the psychological stress tensor, includes:
[0025] The aortic pressure variation rate is determined based on the aortic pressure in the current simulation data.
[0026] The pressure difference between the aortic pressure and the pressure threshold is linearly combined with the aortic pressure variability rate to simulate the impulse discharge frequency of the peripheral baroreceptor afferent nerves.
[0027] The impulse discharge frequency is subjected to time-delay convolution integration to generate an uplink central feedback signal;
[0028] The central feedback signal is used as a correction term and substituted into the state transition equation of the psychological stress tensor to correct the psychological stress tensor, thus obtaining the updated psychological stress tensor.
[0029] The second aspect of this application provides a simulation device based on the cross-disciplinary approach of psychology and physiology, comprising:
[0030] A psychophysiological state initialization module is used to construct an initial psychological stress tensor and a physiological feature vector; wherein, the psychological stress tensor represents the psychological cognition and emotional state of the virtual patient; and the physiological feature vector represents the cardiac electrophysiological and hydrodynamic state of the virtual patient.
[0031] The autonomic nerve synaptic cleft chemical dynamics module is used to input the psychological stress tensor into the autonomic nerve synaptic cleft chemical dynamics model, simulate the concentration of neurotransmitters in the synaptic cleft, and determine the postsynaptic membrane receptor activation rate based on the concentration of neurotransmitters.
[0032] A multi-scale physical field cascade simulation module is used to map the activation rate of the postsynaptic membrane receptor into the target twin, and sequentially perform simulations of cardiomyocyte action potentials, myocardial active contraction mechanics, and ventricular multiphase fluid dynamics to obtain the current simulation data; wherein, the target twin is a multi-scale myocardial physical field twin mirror image, and the current physiological feature vector is used as the boundary field for simulation;
[0033] The physiological and psychological bidirectional feedback coupling module is used to generate a central feedback signal based on the aortic pressure in the current simulation data by simulating the physiological reflex mechanism of peripheral baroreceptors and the time delay effect of afferent nerve conduction. The signal is then input into the state transition equation of the psychological stress tensor to update the psychological stress tensor and returned to the autonomic nerve synaptic cleft chemical dynamics module.
[0034] Optionally, in the aforementioned simulation device based on the cross-disciplinary integration of psychology and physiology, the autonomic neural synaptic cleft chemical dynamics module includes:
[0035] The frequency determination module is used to convert the psychological stress tensor into the action potential discharge frequency of the presynaptic membrane of the sympathetic and parasympathetic nerves;
[0036] The concentration determination module is used to simulate the concentrations of norepinephrine and acetylcholine in the synaptic cleft based on the discharge frequencies of the two action potentials, using the autonomic nerve synaptic cleft chemical kinetic model.
[0037] The activation rate determination module is used to calculate the activation rates of postsynaptic receptors of the sympathetic and parasympathetic nerves on the surface of the heart based on the concentrations of norepinephrine and acetylcholine, respectively.
[0038] Optionally, in the above-described simulation device based on the cross-disciplinary fields of psychology and physiology, the frequency determination module includes:
[0039] The first frequency determination module is used to linearly weight and superimpose the cognitive load component and the emotional arousal component in the psychological stress tensor to obtain the action potential discharge frequency of the presynaptic membrane of the sympathetic nerve.
[0040] The second frequency determination module is used to map the physiological stress component in the psychological stress tensor to the action potential discharge frequency of the presynaptic membrane of the parasympathetic nervous system through a nonlinear negative exponential decay function.
[0041] Optionally, in the above-mentioned simulation device based on the cross-disciplinary integration of psychology and physiology, the multi-scale physical field serial simulation module includes:
[0042] The conductivity simulation module is used to input the activation rate of the postsynaptic membrane receptor as a correction coefficient into the cardiomyocyte membrane potential regulation model in the target twin to simulate the conductivity of the ion channels of the cardiomyocytes; wherein, the conductivity of the ion channels includes the conductivity of L-type calcium channels and the conductivity of vagal-sensitive potassium channels;
[0043] The concentration calculation module is used to calculate the transient concentration of free calcium ions in cardiomyocytes based on the conductivity of the ion channel.
[0044] The tension simulation module is used to substitute the transient concentration of calcium ions into the active contraction mechanics model, and combine the local geometric stretch rate and stretch change rate of myocardial fibers to perform active contraction mechanics simulation of myocardial fibers, so as to obtain the instantaneous active tension generated by myocardial fibers.
[0045] The fluid dynamics simulation module is used to perform ventricular multiphase fluid dynamics simulation based on the instantaneous active tension as the active source term, and obtain aortic pressure and intracardiac blood flow field data.
[0046] Optionally, in the above-mentioned simulation device based on the cross-disciplinary integration of psychology and physiology, the physiological and psychological bidirectional feedback coupling module includes:
[0047] The rate of change determination module is used to determine the rate of change of aortic pressure based on the aortic pressure in the current simulation data.
[0048] The frequency simulation module is used to linearly combine the pressure difference between the aortic pressure and the pressure threshold with the aortic pressure variability to simulate the impulse discharge frequency of the peripheral baroreceptor afferent nerves.
[0049] The time-delay processing module is used to perform time-delay convolution integration on the impulse discharge frequency to generate the uplink central feedback signal.
[0050] The tensor update module is used to take the central feedback signal as a correction term and substitute it into the state transition equation of the psychological stress tensor to correct the psychological stress tensor and obtain the updated psychological stress tensor.
[0051] A third aspect of this application provides an electronic device, comprising:
[0052] Memory and processor;
[0053] The memory is used to store programs;
[0054] The processor is used to execute the program, which, when executed, is specifically used to implement the simulation method based on the cross-disciplinary field of psychology and physiology as described in any of the above.
[0055] The fourth aspect of this application provides a computer storage medium for storing a computer program, which, when executed by a processor, is used to implement the simulation method based on the cross-disciplinary psychological and physiological domains as described in any of the preceding claims.
[0056] This application provides a simulation method based on the cross-disciplinary integration of psychology and physiology, constructing an initial psychological stress tensor and physiological feature vector. The psychological stress tensor represents the psychological cognition and emotional state of a virtual patient, while the physiological feature vector represents the cardiac electrophysiological and hydrodynamic state of the virtual patient. The psychological stress tensor is then input into a chemical dynamics model of the autonomic nervous system synaptic cleft to simulate the concentration of neurotransmitters in the synaptic cleft, and the activation rate of postsynaptic receptors is determined based on the neurotransmitter concentration. The postsynaptic receptor activation rate is then mapped into a target twin, and simulations of cardiomyocyte action potentials, myocardial active contraction mechanics, and ventricular multiphase fluid dynamics are performed sequentially to obtain the current simulation data. The target twin is a multi-scale myocardial physical field twin mirror image, and the current physiological feature vector is used as the boundary field for simulation. Finally, based on the aortic pressure in the current simulation data, a central feedback signal is generated by simulating the physiological reflex mechanism of peripheral baroreceptors and the time lag effect of afferent nerve conduction. This signal is then input into the state transition equation of the psychological stress tensor to update the psychological stress tensor. The process then returns to the step of inputting the psychological stress tensor into the autonomic nerve synaptic cleft chemical dynamics model. Therefore, the autonomic nerve synaptic cleft chemical dynamics model is introduced, bridging the macroscopic psychological tensor to the mesoscopic electrophysiological and mechanical model through quantitative chemical neurotransmitter concentration, and then extending it to macroscopic hemodynamics through high-precision fluid-structure interaction, ensuring the accuracy of the physical and chemical mechanisms in the multi-scale simulation. Ultimately, a dynamic closed loop from psychology to the brain and back to psychology is formed, realizing real-time dynamic feedback correction of the psychological stress tensor by the physiological state, i.e., forming a two-way game closed loop between psychology and the body. This can eliminate the distortion caused by parameter drift in long-term simulation, enabling the digital twin to have adaptive calibration and mutational manifold traversal capabilities, ensuring the accuracy of the simulation. Attached Figure Description
[0057] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of this application. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.
[0058] Figure 1A flowchart illustrating a simulation method based on the cross-disciplinary principles of psychology and physiology provided in this application embodiment;
[0059] Figure 2 A flowchart illustrating a simulation method for the chemical dynamics of the autonomic neural synaptic cleft, provided for embodiments of this application;
[0060] Figure 3 A schematic diagram illustrating the mechanism of neurotransmitter release and receptor binding in an autonomic neural synaptic cleft chemical kinetic model provided in this application embodiment;
[0061] Figure 4 A flowchart illustrating a method for simulation using a multi-scale myocardial physical field twin mirror image, provided in this application embodiment;
[0062] Figure 5 A flowchart illustrating a physiological and psychological bidirectional feedback coupling method provided in this application embodiment;
[0063] Figure 6 A simulation device based on the cross-disciplinary nature of psychology and physiology is provided for the embodiments of this application;
[0064] Figure 7 This is a schematic diagram of the architecture of an electronic device provided in an embodiment of this application. Detailed Implementation
[0065] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.
[0066] In this application, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.
[0067] This application provides a simulation method based on the cross-disciplinary approach of psychology and physiology, such as... Figure 1 As shown, it includes:
[0068] S101. Construct the initial psychological stress tensor and physiological feature vector.
[0069] The psychological stress tensor represents the virtual patient's psychological cognition and emotional state. The physiological feature vector represents the virtual patient's cardiac electrophysiological and hydrodynamic state.
[0070] It should be noted that, in order to simulate the impact of the user's psychological stress on the heart, an initial psychological stress tensor S(0) representing the psychological cognition and emotional state of the virtual patient was constructed based on the external stress input.
[0071] Optionally, the external stress input can specifically be multimodal physiological characteristics. Therefore, a psychological stress tensor corresponding to the user's current physiological state can be generated based on the user's current multimodal physiological characteristics. Optionally, the multimodal physiological characteristics can be non-invasive multimodal physiological characteristics of the individual user, such as multi-band HRV, electrical conduction analysis (EDA), acoustic speech microvibrations, and electroencephalogram (EEG) features. Optionally, a non-invasive multi-sensor wearable device can be connected to collect the user's multimodal physiological characteristics.
[0072] Optionally, vectors that reflect psychological characteristics can be extracted from multimodal physiological features, and the extracted feature vectors can be input into a psychological state inference model, such as a high-dimensional Markov hidden state evolution model, to output the current psychological stress tensor.
[0073] Optionally, multimodal physiological characteristics can be obtained through a cognitive psychological state inference engine, and the current psychological stress tensor can be generated.
[0074] In order to achieve bidirectional cross-boundary coupling between "mind (physiology)" and "brain (psychology)," in addition to constructing the initial psychological stress tensor to represent the initial state of the "brain (psychology)" dimension, it is also necessary to initialize the physiological feature vector as the initial multi-scale state carrier of the "mind (physiology)" dimension, so as to facilitate subsequent multi-scale simulation based on the physiological feature vector and continuously update the physiological state.
[0075] Optionally, the physiological feature vector can be composed of parameters of various physiological states involved in the simulation. For example, specifically for the current time t, the physiological feature vector at position x can be expressed as:
[0076]
[0077] in, Transmembrane potential; This refers to the intracellular free calcium concentration. This refers to the instantaneous active tension of the myocardium; The macroscopic aortic pressure; This refers to the blood flow velocity within the heart chambers.
[0078] Alternatively, in another embodiment of this application, in order to enable simulation for the user, the following can be further performed during initialization:
[0079] The system acquires static detection data and image data from users, such as three-dimensional anatomical geometric images of the heart, aortic arch, and major blood vessels, and constructs a finite element mesh based on the three-dimensional anatomical geometric image data. Specifically, it can use static detection data and image data to reconstruct the three-dimensional anatomical geometric network of the user's heart, and map the myocardial fiber orientation tensor field onto the finite element network of the three-dimensional anatomical geometric network, thereby constructing a digital organ physical foundation for a multi-scale myocardial physical field twin mirror image (target twin).
[0080] Specifically, image segmentation algorithms (such as 3D U-Net neural networks) can be invoked to construct a 3D anatomical geometry network of the user's heart using the acquired data, including the left and right atria, left and right ventricles, and the anatomical structures of the aortic root. Then, the myocardial fiber orientation tensor field is mapped onto the finite element network of the 3D anatomical geometry network to construct a digital organ physical foundation of a multi-scale myocardial physical field twin mirror image.
[0081] S102. Input the psychological stress tensor into the autonomic nerve synaptic cleft chemical kinetic model to simulate the concentration of neurotransmitters in the synaptic cleft, and determine the postsynaptic membrane receptor activation rate based on the concentration of neurotransmitters.
[0082] To simulate the impact of psychological stress on the autonomic nervous system and bridge the gap between psychological and autonomic nervous systems, an autonomic nervous system synaptic cleft chemical kinetic model is used. This model simulates the concentrations of various neurotransmitters released from sympathetic and parasympathetic (vagus) nerve endings in the myocardial synaptic cleft under the current psychological stress tensor, such as norepinephrine (NE) and acetylcholine (ACh). Therefore, the high-dimensional, non-somatic psychological and emotional load is transformed into a concrete synaptic cleft neurotransmitter chemical kinetic concentration vector, which serves as the dynamic control boundary condition for the underlying cardiac physical field simulation. Then, the postsynaptic receptor activation rate is determined based on the neurotransmitter concentration. In other words, the autonomic nervous system synaptic cleft chemical kinetic model receives the descending activation signal from the psychological stress tensor, simulates the release of presynaptic neurotransmitters, synaptic cleft diffusion, enzymatic hydrolysis, and postsynaptic receptors based on chemical kinetic equations, and outputs the postsynaptic receptor activation rate by combining the kinetic processes.
[0083] Optionally, since neurotransmitters are continuously degraded while being released, when analyzing the real-time concentration of neurotransmitters under the current psychological stress tensor, it is necessary to consider not only the released neurotransmitters but also the degraded neurotransmitters in order to obtain an accurate concentration of neurotransmitters.
[0084] Since the release and degradation of neurotransmitters are not instantaneous but rather a real-time, continuous process, the macroscopic, high-dimensional, non-somatic psychological stress state tensor can be quantitatively transformed into a mid-dimensional, concrete spatial concentration fluctuation of neurotransmitters in the synaptic cleft using the stress-to-synaptic cleft neurotransmitter release kinetic equation. Then, by integrating the concentration fluctuation over the current time period, the real-time concentration of the neurotransmitter can be obtained.
[0085] Optionally, since changes in the psychological stress tensor alter the firing frequency of postganglionic autonomic fibers projecting from the brainstem to the heart, in another embodiment of this application, the current psychological stress tensor is first converted into the action potential firing frequency of the presynaptic membrane. Then, using an autonomic synaptic cleft chemical kinetic model, based on the action potential firing frequency of the presynaptic membrane, the concentration of neurotransmitters released in the synaptic cleft is calculated, and subsequently, the receptor activation rate corresponding to the postsynaptic membrane is determined.
[0086] Specifically, in the embodiments of this application, the dynamic changes in neurotransmitter release, clearance, and receptor binding in the autonomic neural synaptic cleft chemical kinetic model satisfy the following ordinary differential chemical kinetic logic:
[0087] The concentration of free neurotransmitters in the synaptic cleft changes over time and is nonlinearly positively correlated with the action potential discharge frequency of the presynaptic membrane obtained by mapping the psychological stress tensor. It is negatively regulated by the rate of natural clearance and enzymatic degradation of neurotransmitters in the synaptic cleft, and is also affected by the consumption of free neurotransmitters by the specific binding reaction of unbound receptors on the postsynaptic membrane, and by the replenishment effect of the rate of neurotransmitter release from the natural dissociation of bound receptors.
[0088] The rate of change of the real-time activation rate of postsynaptic membrane receptors over time is positively correlated with the product of the concentration of free neurotransmitters in the synaptic cleft and the proportion of unactivated receptors on the postsynaptic membrane, and negatively correlated with the natural dissociation rate of activated receptors.
[0089] Therefore, in the autonomic neural synaptic cleft chemokinetics module, the dynamic process of neurotransmitter release and receptor binding satisfies the following set of differential kinetic equations:
[0090]
[0091] ( )
[0092] Wherein, [NT] represents the concentration of free neurotransmitters (such as norepinephrine NE or acetylcholine ACh) in the synaptic cleft. The action discharge frequency of the presynaptic membrane is obtained by mapping the psychological stress tensor S(t) at the current time t. is the neurotransmitter release coefficient, used to characterize the release rate of neurotransmitters. This is a neurotransmitter degradation rate constant used to characterize the clearance / enzymatic degradation rate of neurotransmitters. and These are the binding rate constant and dissociation rate constant of the neurotransmitter and postsynaptic membrane receptor, respectively. This represents the saturation or activation rate of postsynaptic membrane receptors at the current time t, i.e., the postsynaptic membrane receptor activation rate.
[0093] Optionally, in another embodiment of this application, one specific implementation of step S102 is as follows: Figure 2 As shown, it includes:
[0094] S201. Convert the psychological stress tensor into the action potential discharge frequency of the presynaptic membrane of the sympathetic and parasympathetic nervous systems.
[0095] It should be noted that, in the embodiments of this application, simulations are performed on the dynamic pathways of the sympathetic nervous system and the parasympathetic nervous system, respectively. Therefore, the corresponding components of the psychological stress tensor are input into the corresponding transformation function to solve for the action potential discharge frequencies of the presynaptic membranes of the peripheral sympathetic and parasympathetic nervous systems.
[0096] Optionally, in another embodiment of this application, the psychological stress tensor includes at least a cognitive load component. Emotional arousal component and physiological stress components In other words, the psychological stress tensor is specifically represented as:
[0097]
[0098] Accordingly, one specific implementation of step S201 in this application embodiment includes:
[0099] The cognitive load component and the emotional arousal component in the psychological stress tensor are linearly weighted and superimposed to obtain the action potential discharge frequency of the presynaptic membrane of the sympathetic nerve. The physiological stress component in the psychological stress tensor is mapped to the action potential discharge frequency of the presynaptic membrane of the parasympathetic nerve through a nonlinear negative exponential decay function. This is used to characterize the nonlinear inhibitory effect of increased physiological stress level on parasympathetic vagal tone.
[0100] Therefore, the calculation methods for the action potential discharge frequencies of the presynaptic membrane of the sympathetic nervous system and the presynaptic membrane of the parasympathetic nervous system are as follows:
[0101]
[0102]
[0103] in, , , These are the weights corresponding to the three components in the psychological stress tensor. is the nonlinear decay rate constant.
[0104] S202. Based on the discharge frequencies of two action potentials, the concentrations of norepinephrine and acetylcholine in the synaptic cleft are simulated using a chemical kinetic model of the autonomic nerve synaptic cleft.
[0105] Specifically, the concentration of norepinephrine in the synaptic cleft can be simulated based on the action potential discharge frequency of the presynaptic membrane of the sympathetic nervous system, while the concentration of acetylcholine in the synaptic cleft can be simulated based on the action potential discharge frequency of the presynaptic membrane of the parasympathetic nervous system.
[0106] S203. Based on the concentrations of norepinephrine and acetylcholine, respectively, calculate the activation rates of postsynaptic receptors of the sympathetic and parasympathetic nerves on the surface of the heart.
[0107] Specifically, the receptor activation rate of the postsynaptic membrane of the sympathetic nerves on the surface of the heart is calculated based on the concentration of norepinephrine, and the receptor activation rate of the postsynaptic membrane of the parasympathetic nerves on the surface of the heart is calculated based on the concentration of acetylcholine.
[0108] Similarly, when determining the concentrations of norepinephrine and acetylcholine, the entire process of norepinephrine (NE) and acetylcholine (ACh) release, enzymatic degradation, reabsorption, and receptor binding needs to be considered. Specifically, this can be calculated using the ordinary differential equations in the autonomic nerve synaptic cleft chemical kinetic model. That is, the calculation uses a set of differential kinetic equations related to the potential discharge frequency, satisfying the dynamic process of neurotransmitter release and receptor binding. Taking the calculation of receptor activation rate of the postsynaptic membrane of the sympathetic nervous system based on the sympathetic neurotransmitter NE (norepinephrine) as an example, the specific ordinary differential equations are as follows:
[0109]
[0110] ( )
[0111] in, This represents the maximum release rate of the sympathetic neurotransmitter NE. It represents the semi-activated discharge constant. The coefficient of degradation and recovery of NE by monoamine oxidase (MAO) and catecholamine ortho-methyltransferase (COMT) in the synaptic cleft is given. and These are the binding rate parameters and dissociation rate constants of neurotransmitters and postsynaptic membrane receptors, respectively. This refers to the activation rate of receptors on the postsynaptic membrane of the sympathetic nervous system in the myocardium. The activation rates of receptors for the parasympathetic neurotransmitters ACh and M2 are also relevant. The differential equations are similar to those of the first differential equations, except that the parameters are different, namely, the enzymatic hydrolysis coefficients need to be replaced with the degradation rate of acetylcholinesterase (AChE).
[0112] Therefore, the mechanism and principle of simulating neurotransmitter release and receptor binding in the entire autonomic neural synaptic cleft chemical kinetic model, such as... Figure 3 As shown, the psychological stress tensor is mapped, and then the receptor activation rates of the sympathetic and parasympathetic nervous systems are solved through the sympathetic and parasympathetic nervous system dynamic pathways, respectively. These two output receptor activation rates will then be mapped to corresponding myocardial physiological regulation, enabling multi-scale simulation through a multi-scale myocardial physical field twin mirror image.
[0113] S103. Map the postsynaptic membrane receptor activation rate into the target twin, and sequentially perform simulations of cardiomyocyte action potentials, myocardial active contraction mechanics, and ventricular multiphase fluid dynamics to obtain the current simulation data.
[0114] The current simulation may include, but is not limited to, physiological state field data such as aortic pressure, cardiac output, local shear stress, and left ventricular ejection fraction. The target twin is a multi-scale myocardial physical field twin mirror image, and the current physiological feature vector is used as the boundary field for simulation. Therefore, the current physiological feature vector is read as the initial boundary field during simulation. After multi-scale simulation, that is, after the electro-mechanical-current strongly coupled simultaneous solution is completed, the newly calculated components such as potential, calcium concentration, aortic tension, and aortic pressure are reassigned and written into the physiological feature vector to update it, that is, the physiological feature vector is updated using the current simulation data.
[0115] In order to realize the simulation of myocardial cell action potential, myocardial active contraction mechanics, and ventricular multiphase fluid dynamics, the target twin can be composed of multiple models, such as the myocardial cell membrane potential regulation model, the active contraction mechanics model, and the ventricular cavity fluid dynamics model. Therefore, the simulation is performed through each model in turn to obtain the current simulation data composed of the simulation data of each model.
[0116] It is important to note that by inputting the postsynaptic membrane receptor activation rate into a multi-scale myocardial physical field twin, and simulating myocardial cell action potentials through a microscopic model, a cross-scale cascade reaction between neurotransmitters and the conductivity or gating variable time constant of microscopic particles in the myocardial membrane is achieved, thus bridging the crucial cross-scale gap between "neurology" and "biophysics." Then, the microscopic output is input into an organ-level model for myocardial active contractile mechanics simulation. This causes the aforementioned cross-scale cascade reaction to induce action potential time-course alternation chaos in this model, triggering a sudden change in rotor curl. This simulates the conditions that induce ventricular fibrillation in the physical twin, accurately simulating the effects of psychological stress on the heart, i.e., the physiological effects of psychological stress. This avoids the problem of existing technologies, which, due to the lack of psychological stress input and neurotransmitter dynamics intermediates, always give the erroneous conclusion of "safety" when simulating an individual without significant organic stenosis of the coronary arteries.
[0117] Finally, the simulation results of the organ-level model are input into the ventricular cavity fluid dynamics model to simulate ventricular multiphase fluid dynamics, so as to simulate blood fluid parameters, such as aortic pressure and ventricular shear stress. This allows the simulation results to be encoded and transmitted back to make real-time corrections to the psychological stress tensor, forming a dynamic Nash game closed loop inside the body, avoiding the errors accumulated over a long period of simulation and making the simulation results more accurate.
[0118] Optionally, the postsynaptic membrane receptor activation rate can be used as a control parameter and introduced into the mesoscopic myocardial electrophysiological regulation. The tissue-level electrophysiological propagation and calcium dynamics calculation, active contractile mechanics calculation, and multiphase fluid-structure coupling numerical simulation combining myocardial hyperelasticity and intracardiac hemodynamics are executed sequentially in series according to the time progression step to obtain macroscopic physiological state field data including instantaneous aortic pressure in real time.
[0119] Optionally, in another embodiment of this application, one specific implementation of step S103 is as follows: Figure 4 As shown, it includes:
[0120] S401. Using the postsynaptic membrane receptor activation rate as a correction coefficient, input it into the cardiomyocyte membrane potential regulation model in the target twin to simulate the conductivity of the ion channels in the cardiomyocyte.
[0121] The conductivity of ion channels includes the conductivity of L-type calcium channels and the conductivity of vagal-sensitive potassium channels.
[0122] It should be noted that micro-meta-physiological coupling is first performed, specifically coupling the postsynaptic membrane receptor activation rate to the cardiomyocyte ion channel conductivity equation in the cardiomyocyte membrane potential regulation model, thereby simulating the conductivity of the cardiomyocyte ion channel.
[0123] Specifically, the receptor activation rates of the postsynaptic membranes of the sympathetic and parasympathetic nervous systems, obtained from the previous calculations, are used as adjustment coefficients for ion channel conductivity. This is used to correct the conductivity of L-type calcium channels and vagal-sensitive potassium channels in the cardiomyocyte membrane potential regulation model in real time, thereby simulating the electromagnetic wave propagation process of three-dimensional tissue-level transmembrane potentials. Simultaneously, the transient concentration of intracellular free calcium ions mediated by the calcium-induced calcium release mechanism within each cardiomyocyte is obtained.
[0124] In other words, based on the activation rate of receptors on the postsynaptic membrane of the sympathetic nervous system, the conductivity of L-type calcium channels is corrected to simulate the current conductivity of L-type calcium channels. Based on the activation rate of receptors on the postsynaptic membrane of the parasympathetic nervous system, the conductivity of vagal-sensitive potassium channels is simulated to simulate the current conductivity of vagal-sensitive potassium channels. Specifically, the conductivity equation for cardiomyocyte ion channels in the cardiomyocyte membrane potential regulation model is as follows:
[0125]
[0126]
[0127] in, and The values represent the conductivity of the L-type calcium channel before and after the correction. and These represent the conductivity of the vagal-sensitive potassium channel before and after correction, i.e., the conductivity of the acetylcholine-sensitive potassium channel. This is the maximum enhancement coefficient.
[0128] S402. The transient concentration of free calcium ions in cardiomyocytes is calculated based on the conductivity of ion channels.
[0129] Specifically, based on the conductivity of ion channels, the single-cell / tissue-level electrophysiological propagation equation in the myocardial cell membrane potential regulation model is run to solve for the transmembrane potential and transient calcium ion concentration.
[0130] Among them, the transmembrane potential V of the atrial and ventricular tissues m The following propagation equations for a single domain are satisfied:
[0131]
[0132] in, This represents the ratio of cell membrane area to volume. It is a film capacitor; For myocardial conductivity tensor; It is the sum of all ion currents (including L-type calcium currents regulated by synaptic dynamics, etc.). For channel gating variables; Since the concentration of calcium ions is the intracellular ion concentration, the transient concentration of free calcium ions in cardiomyocytes can be calculated.
[0133] S403. Substitute the transient concentration of calcium ions into the active contraction mechanics model, and combine the local geometric stretch rate and stretch change rate of myocardial fibers to perform active contraction mechanics simulation of myocardial fibers, so as to obtain the instantaneous active tension generated by myocardial fibers.
[0134] Specifically, in the mesoscopic electromechanical coupling process, the transient concentration of intracellular free calcium ions is substituted as a driving variable into the active contraction mechanics model. Combined with the local geometric stretch rate and stretch change rate of myocardial fibers, the instantaneous active tension generated by myocardial fibers during the contraction phase is calculated and output.
[0135] Optionally, the active contraction mechanical model is used to solve for the instantaneous active tension T. a Specifically, it can be:
[0136]
[0137] in, For maximum equal-length active tension; This refers to the transient concentration of calcium ions. It is the activation function of calcium ions; This represents the local geometric stretching ratio of myocardial fibers; The rate of change of tension. This represents a mechanical dependence function.
[0138] S404. Based on the instantaneous active tension as the active source term, perform ventricular multiphase fluid dynamics simulation to obtain aortic pressure and intracardiac blood flow field data.
[0139] Specifically, instantaneous active tension is superimposed as an active source term into the constitutive model of a passively anisotropic hyperelastic material of the myocardium to construct solid mechanics governing equations and solve for the geometric deformation and displacement field of the heart wall. Then, a fluid-structure interaction analysis method is used, with the displacement and mechanical equilibrium at the endocardial boundary as strongly coupled constraints at the fluid-structure interface. The fluid dynamics computational mesh is adaptively updated, and the governing equations of incompressible non-Newtonian fluid dynamics of blood within the cardiac chambers are solved simultaneously. Real-time integral output of instantaneous aortic pressure and intracardiac blood flow field data is generated.
[0140] The governing equation for solid mechanics, namely the solid mechanics equilibrium equation for myocardial tissue, is as follows:
[0141]
[0142] in, This refers to myocardial density. This is a fixed displacement offset. It is the force field vector per unit volume. Let be the total stress tensor. The total stress tensor is:
[0143]
[0144] in, The passive stress tensor of myocardium is the elastic resistance generated by the passive stretching of myocardial tissue, which can be described by the Holzapfel-Ogden constitutive model; Vector along the direction of myocardial fibers The generated active stress; It is the tensor product.
[0145] The blood within the heart chambers is considered an incompressible non-Newtonian fluid, satisfying the following Navier-Stokes equations:
[0146]
[0147]
[0148] in, Blood density; For the blood velocity vector; This is the instantaneous pressure within the heart chambers, which is the pressure field, so the instantaneous aortic pressure can be extracted from it; The rate of mesh deformation due to large deformation of the left ventricular wall is determined in the previous step. Seek; Blood dynamic viscosity.
[0149] The fluid domain and the solid domain at the endocardial boundary Strong compatibility constraints on displacement and stress at the fluid-structure interface (i.e., the fluid-structure interface) are as follows:
[0150]
[0151]
[0152] in, Let be the unit outward normal vector of the solid domain; is the unit outward normal vector of the fluid domain.
[0153] Therefore, by solving the above strongly coupled control equations, the macroscopic fluid state field data of the heart can be output, and then the aortic pressure and intracardiac blood flow field data can be extracted from it.
[0154] S104. Based on the aortic pressure in the current simulation data, a central feedback signal is generated by simulating the physiological reflex mechanism of peripheral baroreceptors and the time delay effect of afferent nerve conduction, and then input into the state transition equation of the psychological stress tensor to update the psychological stress tensor.
[0155] After updating the psychological stress tensor, the process returns to step S102 based on the updated psychological stress tensor.
[0156] Specifically, the aortic pressure in the current simulation data is extracted. Based on this aortic pressure, an effective central feedback signal is generated by simulating the physiological reflex mechanism of peripheral baroreceptors and the time delay effect of afferent nerve conduction. This signal is then introduced as an inhibition damping term into the state transition equation of the psychological stress tensor to dynamically correct the value of the psychological stress tensor. Finally, the corrected psychological stress tensor is returned to the simulation for re-simulation, thereby constructing a two-way closed-loop feedback pathway between psychology and physiology.
[0157] Optionally, in another embodiment of this application, one specific implementation of step S104 is as follows: Figure 5 As shown, it includes:
[0158] S501. Determine the aortic pressure variation rate based on the aortic pressure in the current simulation data.
[0159] S502. The pressure difference between the aortic pressure and the pressure threshold is linearly combined with the aortic pressure variation rate to simulate the impulse discharge frequency of the peripheral baroreceptor afferent nerve.
[0160] Specifically, the simulation model of ascending nerve discharge, that is, the model simulating the impulse discharge frequency of peripheral baroreceptor afferent nerves, is as follows:
[0161]
[0162] in, The static gain can be set to 1.2 Hz / mmHg; The dynamic gain can be set to 0.5 Hz / (mmHg / s); The pressure threshold can be set to 80 mmHG; Aortic pressure.
[0163] S503. Perform time-delay convolution integration on the impulse discharge frequency to generate the uplink central feedback signal.
[0164] It should be noted that, considering the spatial conduction distance of nerve axons and the time delay attenuation effect of multi-level relay conduction at the synapse, the impulse discharge frequency can be obtained by using the time delay exponential decay kernel function in the target twin to perform time delay convolution integral, thereby obtaining the effective electrical feedback control signal reaching the central nervous system, i.e., the central feedback signal.
[0165] Specifically, the time-delay exponential decay kernel function, i.e., the central feedback signal, can be:
[0166]
[0167] in, represents the time lag in peripheral afferent nerve conduction to the central nervous system; represents the attenuation factor. For kernel function, ; The time delay decay half-life is used for physiological conduction.
[0168] S504. Substitute the central feedback signal as a correction term into the state transition equation of the psychological stress tensor to correct the psychological stress tensor and obtain the updated psychological stress tensor.
[0169] Specifically, the state transition equation for the psychological stress tensor can be:
[0170]
[0171] in, The weighting of feedback effects and psychological components; This is an external stress input. Therefore, when the feedback signal increases, it will directly inhibit and reduce the components representing physiological and emotional tension in the psychological stress tensor.
[0172] This application provides a simulation method based on the cross-disciplinary fields of psychology and physiology, constructing an initialized psychological stress tensor and physiological feature vector. The psychological stress tensor represents the psychological cognition and emotional state of a virtual patient, while the physiological feature vector represents the cardiac electrophysiological and hydrodynamic state of the virtual patient. The psychological stress tensor is then input into a chemical dynamics model of the autonomic nervous system synaptic cleft to simulate the concentration of neurotransmitters in the synaptic cleft, and the activation rate of postsynaptic receptors is determined based on the neurotransmitter concentration. Next, the activation rate of postsynaptic receptors is mapped into a target twin, and simulations of cardiomyocyte action potentials, myocardial active contraction mechanics, and ventricular multiphase fluid dynamics are performed sequentially to obtain the current simulation data. The target twin is a multi-scale myocardial physical field twin mirror image. Finally, based on the aortic pressure in the current simulation data, a central feedback signal is generated by simulating the physiological reflex mechanism of peripheral baroreceptors and the time lag effect of afferent nerve conduction, and input into the state transition equation of the psychological stress tensor to update the psychological stress tensor. The process then returns to the step of inputting the psychological stress tensor into the chemical dynamics model of the autonomic nervous system synaptic cleft. Therefore, an autonomic neural synaptic cleft chemical kinetic model is introduced, bridging the macroscopic psychological tensor to the mesoscopic electrophysiological and mechanical model through quantitative chemical neurotransmitter concentration. This is then extended to macroscopic hemodynamics via high-precision fluid-structure interaction, ensuring the accuracy of the physical and chemical mechanisms in multi-scale simulations. Ultimately, a dynamic closed loop is formed from psychology to the brain and back to psychology, achieving real-time dynamic feedback correction of the psychological stress tensor to physiological states. This creates a two-way game-theory closed loop between psychology and the body, eliminating distortions caused by parameter drift in long-term simulations. This enables the digital twin to have adaptive calibration and mutational manifold traversal capabilities, ensuring the accuracy of the simulation.
[0173] Another embodiment of this application provides a simulation device based on the cross-disciplinary approach of psychology and physiology, such as... Figure 6 As shown, it includes:
[0174] The psychophysiological state initialization module 601 is used to construct the initial psychological stress tensor and physiological feature vector.
[0175] Among them, the psychological stress tensor represents the virtual patient's psychological cognition and emotional state. The physiological feature vector represents the virtual patient's cardiac electrophysiological and hydrodynamic state.
[0176] The autonomic nerve synaptic cleft chemical dynamics module 602 is used to input the psychological stress tensor into the autonomic nerve synaptic cleft chemical dynamics model, simulate the concentration of neurotransmitters in the synaptic cleft, and determine the postsynaptic membrane receptor activation rate based on the concentration of neurotransmitters.
[0177] The multi-scale physical field cascade simulation module 603 is used to map the postsynaptic membrane receptor activation rate into the target twin, and sequentially perform simulations of cardiomyocyte action potentials, myocardial active contraction mechanics, and ventricular multiphase fluid dynamics to obtain the current simulation data. The target twin is a multi-scale myocardial physical field twin mirror image, and the current physiological feature vector is used as the boundary field for simulation.
[0178] The physiological and psychological bidirectional feedback coupling module 604 is used to generate a central feedback signal based on the aortic pressure in the current simulation data by simulating the physiological reflex mechanism of peripheral baroreceptors and the time delay effect of afferent nerve conduction. The signal is then input into the state transition equation of the psychological stress tensor to update the psychological stress tensor and return to the autonomic nerve synaptic cleft chemical dynamics module 602.
[0179] Optionally, in another embodiment of the simulation device based on the cross-disciplinary integration of psychology and physiology provided in this application, the autonomic neural synaptic cleft chemical dynamics module includes:
[0180] The frequency determination module is used to convert the psychological stress tensor into the action potential discharge frequency of the presynaptic membrane of the sympathetic and parasympathetic nervous systems.
[0181] The concentration determination module is used to simulate the concentrations of norepinephrine and acetylcholine in the synaptic cleft based on the discharge frequencies of the two action potentials, using a chemical kinetic model of the autonomic nerve synaptic cleft.
[0182] The activation rate determination module is used to calculate the activation rates of postsynaptic receptors of the sympathetic and parasympathetic nerves on the surface of the heart based on the concentrations of norepinephrine and acetylcholine, respectively.
[0183] Optionally, in another embodiment of the simulation device based on the cross-disciplinary fields of psychology and physiology provided in this application, the frequency determination module includes:
[0184] The first frequency determination module is used to linearly weight and superimpose the cognitive load component and the emotional arousal component in the psychological stress tensor to obtain the action potential discharge frequency of the presynaptic membrane of the sympathetic nerve.
[0185] The second frequency determination module is used to map the physiological stress component in the psychological stress tensor to the action potential discharge frequency of the presynaptic membrane of the parasympathetic nervous system through a nonlinear negative exponential decay function.
[0186] Optionally, in another embodiment of the simulation device based on the cross-disciplinary integration of psychology and physiology provided in this application, the multi-scale physical field serial simulation module includes:
[0187] The conductivity simulation module uses the postsynaptic membrane receptor activation rate as a correction coefficient, inputting it into the cardiomyocyte membrane potential regulation model in the target twin, to simulate the conductivity of ion channels in cardiomyocytes. The conductivity of these ion channels includes the conductivity of L-type calcium channels and vagal-sensitive potassium channels.
[0188] The concentration calculation module is used to calculate the transient concentration of free calcium ions in cardiomyocytes based on the conductivity of ion channels.
[0189] The tension simulation module is used to substitute the transient concentration of calcium ions into the active contraction mechanics model, and combine the local geometric stretch rate and stretch change rate of myocardial fibers to perform active contraction mechanics simulation of myocardial fibers, so as to obtain the instantaneous active tension generated by myocardial fibers.
[0190] The fluid dynamics simulation module is used to perform multiphase fluid dynamics simulation of the ventricle based on instantaneous active tension as the active source term, and to obtain aortic pressure and intracardiac blood flow field data.
[0191] Optionally, in another embodiment of the simulation device based on the cross-disciplinary integration of psychology and physiology provided in this application, the physiological and psychological bidirectional feedback coupling module includes:
[0192] The rate of change determination module is used to determine the rate of change of aortic pressure based on the aortic pressure in the current simulation data.
[0193] The frequency simulation module is used to linearly combine the pressure difference between the aortic pressure and the pressure threshold with the aortic pressure variability to simulate the impulse discharge frequency of the peripheral baroreceptor afferent nerves.
[0194] The time-delay processing module is used to perform time-delay convolution integration on the impulse discharge frequency to generate the uplink central feedback signal.
[0195] The tensor update module is used to take the central feedback signal as a correction term and substitute it into the state transition equation of the psychological stress tensor to correct the psychological stress tensor and obtain the updated psychological stress tensor.
[0196] It should be noted that the specific working process of each module provided in the above embodiments of this application can be referred to the implementation process of the corresponding steps in the above method embodiments, and will not be repeated here.
[0197] Another embodiment of this application provides an electronic device, such as... Figure 7 As shown, it includes:
[0198] Memory 701 and processor 702.
[0199] The memory 701 is used to store the program.
[0200] The processor 702 is used to execute programs, which, when executed, are specifically used to implement simulation methods based on the cross-disciplinary fields of psychology and physiology, as described above.
[0201] Another embodiment of this application provides a computer storage medium for storing a computer program, which, when executed by a processor, is used to implement the simulation method based on the psychological and physiological cross-domain as provided in any of the above embodiments.
[0202] Computer storage media, including both permanent and non-permanent, removable and non-removable media, can store information using any method or technology. Information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.
[0203] Those skilled in the art will further recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.
[0204] The above description of the disclosed embodiments enables those skilled in the art to make or use this application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims
1. A simulation method based on the cross-disciplinary fields of psychology and physiology, characterized in that, include: An initial psychological stress tensor and physiological feature vector are constructed; wherein the psychological stress tensor represents the psychological cognition and emotional state of the virtual patient; and the physiological feature vector represents the cardiac electrophysiological and hydrodynamic state of the virtual patient. The psychological stress tensor is input into the autonomic nerve synaptic cleft chemical kinetic model to simulate the concentration of neurotransmitters in the synaptic cleft, and the postsynaptic membrane receptor activation rate is determined based on the concentration of neurotransmitters. The activation rate of the postsynaptic membrane receptors is mapped into the target twin, and simulations of cardiomyocyte action potentials, myocardial active contraction mechanics, and ventricular multiphase fluid dynamics are performed sequentially to obtain the current simulation data; wherein, the target twin is a multi-scale myocardial physical field twin mirror image, and the current physiological feature vector is used as the boundary field for simulation; Based on the aortic pressure in the current simulation data, a central feedback signal is generated by simulating the physiological reflex mechanism of peripheral baroreceptors and the time delay effect of afferent nerve conduction. This signal is then input into the state transition equation of the psychological stress tensor to update the psychological stress tensor. Finally, the process returns to the step of inputting the psychological stress tensor into the autonomic nerve synaptic cleft chemical kinetic model.
2. The method according to claim 1, characterized in that, The step of inputting the psychological stress tensor into the autonomic neural synaptic cleft chemical kinetic model to simulate the concentration of neurotransmitters in the synaptic cleft, and determining the postsynaptic membrane receptor activation rate based on the concentration of neurotransmitters, includes: The psychological stress tensor is converted into the action potential discharge frequency of the presynaptic membrane of the sympathetic and parasympathetic nervous systems. Based on the discharge frequencies of the two action potentials, the concentrations of norepinephrine and acetylcholine in the synaptic cleft were simulated using the autonomic nerve synaptic cleft chemical kinetic model. Based on the concentrations of norepinephrine and acetylcholine, the activation rates of postsynaptic receptors of the sympathetic and parasympathetic nerves on the surface of the heart are calculated.
3. The method according to claim 1, characterized in that, The process of converting the psychological stress tensor into the action potential discharge frequency of the presynaptic membrane of the sympathetic and parasympathetic nervous systems includes: The cognitive load component and the emotional arousal component in the psychological stress tensor are linearly weighted and superimposed to obtain the action potential discharge frequency of the presynaptic membrane of the sympathetic nerve. The physiological stress component in the psychological stress tensor is mapped to the action potential discharge frequency of the presynaptic membrane of the parasympathetic nervous system through a nonlinear negative exponential decay function.
4. The method according to claim 1, characterized in that, The process involves mapping the postsynaptic membrane receptor activation rate into the target twin, followed by sequential simulations of cardiomyocyte action potentials, myocardial active contraction mechanics, and ventricular multiphase fluid dynamics to obtain current simulation data, including: The postsynaptic membrane receptor activation rate was used as a correction coefficient and input into the cardiomyocyte membrane potential regulation model in the target twin to simulate the conductivity of ion channels in cardiomyocytes; wherein, the conductivity of the ion channels includes the conductivity of L-type calcium channels and the conductivity of vagal-sensitive potassium channels; The transient concentration of free calcium ions in cardiomyocytes was calculated based on the conductivity of the ion channel. The transient concentration of calcium ions was substituted into the active contraction mechanics model, and the active contraction mechanics of myocardial fibers was simulated by combining the local geometric stretch rate and stretch change rate of myocardial fibers to obtain the instantaneous active tension generated by myocardial fibers. Based on the instantaneous active tension as the active source term, ventricular multiphase fluid dynamics simulation was performed to obtain aortic pressure and intracardiac blood flow field data.
5. The method according to claim 1, characterized in that, Based on the aortic pressure in the current simulation data, a central feedback signal is generated by simulating the physiological reflex mechanism of peripheral baroreceptors and the time lag effect of afferent nerve conduction. This signal is then input into the state transition equation of the psychological stress tensor to update the psychological stress tensor, including: The aortic pressure variation rate is determined based on the aortic pressure in the current simulation data. The pressure difference between the aortic pressure and the pressure threshold is linearly combined with the aortic pressure variability rate to simulate the impulse discharge frequency of the peripheral baroreceptor afferent nerves. The impulse discharge frequency is subjected to time-delay convolution integration to generate an uplink central feedback signal; The central feedback signal is used as a correction term and substituted into the state transition equation of the psychological stress tensor to correct the psychological stress tensor, thus obtaining the updated psychological stress tensor.
6. A simulation device based on the cross-disciplinary fields of psychology and physiology, characterized in that, include: A psychophysiological state initialization module is used to construct an initial psychological stress tensor and a physiological feature vector; wherein, the psychological stress tensor represents the psychological cognition and emotional state of the virtual patient; and the physiological feature vector represents the cardiac electrophysiological and hydrodynamic state of the virtual patient. The autonomic nerve synaptic cleft chemical dynamics module is used to input the psychological stress tensor into the autonomic nerve synaptic cleft chemical dynamics model, simulate the concentration of neurotransmitters in the synaptic cleft, and determine the postsynaptic membrane receptor activation rate based on the concentration of neurotransmitters. A multi-scale physical field cascade simulation module is used to map the activation rate of the postsynaptic membrane receptor into the target twin, and sequentially perform simulations of cardiomyocyte action potentials, myocardial active contraction mechanics, and ventricular multiphase fluid dynamics to obtain the current simulation data; wherein, the target twin is a multi-scale myocardial physical field twin mirror image, and the current physiological feature vector is used as the boundary field for simulation; The physiological and psychological bidirectional feedback coupling module is used to generate a central feedback signal based on the aortic pressure in the current simulation data by simulating the physiological reflex mechanism of peripheral baroreceptors and the time delay effect of afferent nerve conduction. The signal is then input into the state transition equation of the psychological stress tensor to update the psychological stress tensor and returned to the autonomic nerve synaptic cleft chemical dynamics module.
7. The apparatus according to claim 6, characterized in that, The autonomic neural synaptic cleft chemical dynamics module includes: The frequency determination module is used to convert the psychological stress tensor into the action potential discharge frequency of the presynaptic membrane of the sympathetic and parasympathetic nerves; The concentration determination module is used to simulate the concentrations of norepinephrine and acetylcholine in the synaptic cleft based on the discharge frequencies of the two action potentials, using the autonomic nerve synaptic cleft chemical kinetic model. The activation rate determination module is used to calculate the activation rates of postsynaptic receptors of the sympathetic and parasympathetic nerves on the surface of the heart based on the concentrations of norepinephrine and acetylcholine, respectively.
8. The apparatus according to claim 7, characterized in that, The frequency determination module includes: The first frequency determination module is used to linearly weight and superimpose the cognitive load component and the emotional arousal component in the psychological stress tensor to obtain the action potential discharge frequency of the presynaptic membrane of the sympathetic nerve. The second frequency determination module is used to map the physiological stress component in the psychological stress tensor to the action potential discharge frequency of the presynaptic membrane of the parasympathetic nervous system through a nonlinear negative exponential decay function.
9. An electronic device, characterized in that, include: Memory and processor; The memory is used to store programs; The processor is used to execute the program, which, when executed, is specifically used to implement the simulation method based on the cross-disciplinary psychological and physiological approach as described in any one of claims 1 to 5.
10. A computer storage medium, characterized in that, Used to store computer programs, which, when executed by a processor, are used to implement the simulation method based on the cross-disciplinary psychological and physiological framework as described in any one of claims 1 to 5.