Neuromodulation device for brain treatment based on synchronous stimulation of dual targets

Abstract

The present application discloses a neuromodulation device for realizing brain treatment based on synchronous stimulation of dual targets. The neuromodulation device includes: collecting magnetic resonance structural images, resting-state functional images, diffusion tensor images, and electroencephalogram signals of the brain to apply synchronous stimulation to individualized functional targets and individualized structural targets. Through the solution of the present application, it is possible to more precisely regulate nerve signal conduction and simultaneously regulate multiple nerve pathways or brain regions.

Description

Technical Field

[0001] The present application relates to the field of neuromodulation, and particularly to a neuromodulation device for brain treatment based on dual-target synchronous stimulation. Background Art

[0002] The neuromodulation background of target stimulation for brain treatment is an important research direction in the fields of neuroscience and clinical medicine. This field aims to regulate the function of the nervous system by stimulating specific neurons or brain regions, so as to treat neurological diseases or improve the function of the nervous system. The ways of target stimulation include electrical stimulation, optogenetics, chemical stimulation, etc. The usefulness of neuromodulation of target stimulation for brain treatment lies in that it can precisely regulate specific neural pathways or brain regions to restore or improve the function of the nervous system, thereby treating various neurological diseases such as Parkinson's disease, depression, etc., or improving cognitive function. The importance of this method lies in that it provides an innovative treatment means for patients, especially for those patients for whom traditional treatment methods are ineffective or have serious side effects, which has important clinical significance. Currently, the main technologies of neuromodulation of target stimulation for brain treatment include deep brain stimulation (DBS), transcranial magnetic stimulation (TMS), optogenetics, etc. The working principle of these technologies is to affect nerve signal conduction and the function of the nervous system by stimulating or regulating specific neurons or brain regions, so as to achieve the purpose of treatment or regulation. However, there are still some problems in the existing technologies, such as the instability of the stimulation effect, the risk of side effects, and the uncertainty of the long-term impact on the nervous system, etc.

[0003] Currently, for the problem of neuromodulation of target stimulation for brain treatment, a variety of methods have been proposed to solve it. These methods include improving the setting of stimulation parameters, optimizing the selection of stimulation positions, developing new stimulation technologies, etc. For example, for the DBS technology, researchers optimize the electrode position and stimulation parameters to minimize side effects and improve the treatment effect. For the TMS technology, researchers try to develop new magnetic stimulation devices to improve the accuracy and stability of stimulation. However, these methods still have some defects, such as large individual differences in the stimulation effect and the accuracy of the selection of stimulation positions needs to be improved, etc.

[0004] Therefore, there is an urgent need for a technical solution that can more precisely regulate nerve signal conduction and at the same time regulate multiple neural pathways or brain regions. Summary of the Invention

[0005] To solve the deficiencies of the existing technology, the embodiments of the present application provide a neuromodulation device for brain treatment based on dual-target synchronous stimulation. The present application solves the technical problems of the existing technology that the nerve signal conduction is not precise enough and it is impossible to regulate multiple neural pathways or brain regions simultaneously.

[0006] An embodiment of the present application provides a neuromodulation device for realizing brain treatment based on synchronous stimulation of dual targets, including: a data acquisition unit, a target determination unit, a parameter control unit, a peripheral end control unit, a central end control unit, and a synchronous regulation unit; wherein, the data acquisition unit is used to acquire magnetic resonance structural images, resting state functional images, diffusion tensor images, and electroencephalogram signals of the brain; the target determination unit is used to determine individualized functional targets, individualized structural targets, and the energy ratio threshold of the target rhythm for triggering neuromodulation based on the data acquired by the data acquisition unit; the parameter control unit is used to determine a corresponding parameter set according to the energy ratio threshold and the corresponding target nerve signal; the peripheral end control unit is used to apply one or more electrical stimulations to the peripheral nerve through the peripheral end stimulating electrode according to the parameter set determined by the parameter control unit; the central end control unit is used to apply electrical stimulation to the central nerve through the central end stimulating electrode based on the parameter set determined by the parameter control unit and the evoked potential signal generated by the peripheral nerve stimulation; the synchronous regulation unit is used to apply synchronous stimulation to the individualized functional target and the individualized structural target based on the evoked potential signal generated by the central nerve stimulation.

[0007] In a possible implementation manner, determining individualized functional targets, individualized structural targets, and the energy ratio threshold of the target rhythm for triggering neuromodulation based on the data acquired by the data acquisition unit includes: determining individualized functional targets based on magnetic resonance structural images and resting state functional images, selecting the centromedian nucleus or the nucleus lateralis centralis as the region of interest, and calculating the functional connectivity strength index to determine individualized functional targets; determining individualized structural targets based on diffusion tensor images, selecting the centromedian nucleus or the nucleus lateralis centralis as the region of interest, and performing probabilistic fiber tracking to determine individualized structural targets; and determining the energy ratio threshold of the target rhythm for triggering neuromodulation, and determining the energy ratio threshold of the target rhythm through preprocessing and analysis of electroencephalogram signals.

[0008] In a possible implementation manner, determining a corresponding parameter set according to the energy ratio threshold and the corresponding target nerve signal includes: preprocessing and analyzing the target nerve signal to determine the conduction time and stimulation parameters; wherein the stimulation parameters include peripheral nerve electrical stimulation parameters; and determining the parameter set of peripheral-central coupled stimulation according to the target nerve signal; wherein the parameter set includes the conduction time.

[0009] In one possible implementation, applying electrical stimulation to the central nerve through the central - end stimulating electrode based on the parameter set determined by the parameter control unit and the evoked potential signal generated by peripheral nerve stimulation includes: collecting the evoked potential signal generated by peripheral nerve stimulation to obtain the central nerve electrical stimulation parameters; and according to the parameter set of the peripheral - central coupling stimulation and the central nerve electrical stimulation parameters, applying stimulation to the subject's central nerve through the central - end stimulating electrode when the conduction time arrives.

[0010] In one possible implementation, applying synchronous stimulation to the individualized functional target and the individualized structural target based on the evoked potential signal generated by central nerve stimulation includes: using a preset filtering algorithm to allow the evoked potential signal in the target frequency range to pass through to remove high - frequency and low - frequency noise; cutting the continuous evoked potential signal into multiple time windows according to the stimulation moment; where each of the time windows contains a complete stimulation response cycle; performing baseline correction on each time window to ensure that the signal change is only related to the stimulation event; identifying the characteristic points including peaks and valleys in the evoked potential waveform; calculating the time interval from the start of stimulation to the peak of the characteristic point to determine the conduction time; and determining the stimulation parameters including intensity, frequency, and duration based on the change of the evoked potential signal.

[0011] In one possible implementation, using a preset filtering algorithm to allow the signal in the target frequency range to pass through to remove high - frequency and low - frequency noise includes: h id (n)=2f H ·sinc(2f H n)-2f L ·sinc(2f L n), where n represents the sampling point of the signal in the discrete time domain, h id (n) represents the response of the filter at different time points, f H represents the high - frequency cut - off frequency, sinc represents the impulse response of the ideal low - pass filter, f L represents the low - frequency cut - off frequency.

[0012] In one possible implementation, the parameter control unit is further configured to optimize the timing and intensity of the stimulation based on the normal distribution algorithm.

[0013] In one possible implementation, optimizing the timing and intensity of the stimulation based on the normal distribution algorithm includes: T optimal , where T optimal represents the optimal stimulation timing, I optimal represents the optimal stimulation intensity, μ T represents the mean of the stimulation timing, μ I represents the mean of the stimulation intensity, σ IDenotes the standard deviation of the stimulus intensity, σ T Denotes the standard deviation of the stimulus timing, T represents the stimulus timing, and I represents the intensity.

[0014] In a neuroregulation device for brain treatment based on dual-target synchronous stimulation provided as above, in the embodiments of the present application, through dual-target synchronous stimulation, the conduction of nerve signals can be adjusted more precisely, and multiple nerve pathways or brain regions can be regulated simultaneously. Description of the Drawings

[0015] To more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0016] Figure 1 Schematic diagram of the neuroregulation device for brain treatment based on dual-target synchronous stimulation provided by the embodiments of the present application;

[0017] Figure 2 Schematic flowchart of a method for preprocessing evoked potential signals provided by the embodiments of the present application. Detailed Embodiments

[0018] Now, various exemplary embodiments of the present application will be described in detail with reference to the drawings. It should be noted that: Unless otherwise specifically stated, the relative arrangements, numerical expressions, and numerical values of the components and steps described in these embodiments do not limit the scope of the present application.

[0019] Those skilled in the art can understand that terms such as "first" and "second" in the embodiments of the present application are only used to distinguish different steps, devices, or modules, etc., and neither represent any specific technical meaning nor indicate an inevitable logical order between them. It should also be understood that in the embodiments of the present application, "a plurality" can refer to two or more, and "at least one" can refer to one, two, or more. It should also be understood that for any component, data, or structure mentioned in the embodiments of the present application, without clear definition or contrary indication in the context, it can generally be understood as one or more. In addition, the term "and / or" in the present application is only a description of the association relationship between associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist simultaneously, and B exists alone. In addition, the character " / " in the present application generally represents an "or" relationship between the associated objects before and after. It should also be understood that the present application emphasizes the differences between the various embodiments, and the same or similar parts can be referred to each other. For the sake of brevity, they will not be repeated one by one.

[0020] Meanwhile, it should be understood that, for ease of description, the dimensions of the various parts shown in the drawings are not drawn in actual proportional relationships. The following description of at least one exemplary embodiment is actually merely illustrative and in no way limits the present application, its application or use. Technologies, methods, and devices known to those of ordinary skill in the relevant art may not be discussed in detail, but where appropriate, the said technologies, methods, and devices should be regarded as part of the specification. It should be noted that: like reference numerals and letters denote like items in the following drawings, and thus, once an item is defined in one drawing, it does not need to be further discussed in subsequent drawings.

[0021] To make the objectives, technical solutions, and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Apparently, the described embodiments are some but not all of the embodiments of the present application. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present application without making creative efforts fall within the scope of protection of the present application.

[0022] Figure 1 It is a schematic diagram of a neuromodulation device for brain treatment based on dual-target synchronous stimulation provided by an embodiment of the present application. It should be understood that the system shown in the figure is exemplary rather than restrictive. This means that the involved system architecture is not limited to a specific form or design, but is presented as an example. In other words, the architecture shown in the figure can be regarded as a way of expression to clearly describe relevant concepts and relationships, and does not exclude other forms of architectures. Therefore, when interpreting the architecture in the said picture, it should be understood that the model has flexibility and diversity, and its purpose is to provide an exemplary description rather than a restrictive regulation of a specific form.

[0023] Specifically, a neuromodulation device for brain treatment based on dual-target synchronous stimulation in an embodiment of the present application includes: a data acquisition unit 101, a target determination unit 102, a parameter control unit 103, a peripheral end control unit 104, a central end control unit 105, and a synchronous regulation unit 106.

[0024] The data acquisition unit 101 is used to acquire magnetic resonance structural images, resting-state functional images, diffusion tensor images, and electroencephalogram signals of the brain. It should be understood that the data acquisition unit 101 can be composed of multiple components, including magnetic resonance imaging devices, diffusion tensor imaging devices, electroencephalogram recording devices, etc.

[0025] Specifically, magnetic resonance structural images are brain structural images obtained through magnetic resonance imaging (MRI) technology, which are used to display the anatomical structure of the brain and the location of organs. For example, magnetic resonance imaging can reveal tumors or other structural abnormalities in the brain, providing accurate diagnostic basis for clinical treatment.

[0026] Resting-state functional images, on the other hand, observe the functional activities of the brain in a resting state using functional magnetic resonance imaging (fMRI) technology, and are often used to study the functional connectivity and networks of the brain. Through fMRI technology, researchers can discover abnormal manifestations of specific neural networks such as the Default Mode Network (DMN) in mental diseases such as schizophrenia or depression.

[0027] Diffusion tensor images are images obtained through diffusion tensor imaging (DTI) technology, which are used to reveal the nerve fiber bundles and white matter connections inside the brain. DTI depicts the structure of nerve pathways by measuring the diffusion behavior of water molecules in brain tissue. For example, by comparing DTI data of Alzheimer's patients and healthy elderly people, researchers can observe the degeneration of nerve fibers in pathological conditions. DTI data are usually used to generate fiber tracking images, thereby visualizing and quantifying the fiber pathways in the brain, as shown in the following equation:

[0028]

[0029] where λ 1 , λ 2 , λ 3 are the eigenvalues of the main diffusion directions, MD is the mean diffusivity, and FA is the fractional anisotropy.

[0030] Electroencephalogram (EEG) signals are electrophysiological signals recorded through electroencephalography, which are used to study the electrical activities and rhythms of the brain. EEG technology can provide high temporal resolution information about brain electrical activities, making it an important tool for studying rapid dynamic brain function changes. Electroencephalogram signal processing usually includes signal amplification, filtering, and analysis. For example, the fast Fourier transform (FFT) is used to analyze the intensity of different frequency components, as shown in the following equation: where x(n) is the voltage value in the time series, X(k) is the frequency domain representation, k is the frequency index, and \(N\) is the number of sample points.

[0031] To more precisely regulate neural signal conduction, the present application designs a device for regulating multiple neural pathways or brain regions simultaneously based on dual targets. This method utilizes the above various imaging and signal acquisition techniques to achieve more effective neural regulation by precisely locating and stimulating specific neuron populations and neural pathways. For example, by combining resting-state functional imaging and diffusion tensor imaging, precise regulation can be simultaneously performed on the prefrontal cortex that affects emotion regulation and the hippocampus that regulates memory function. This dual-target regulation method shows potential benefits in the treatment of neurodegenerative diseases such as depression and Parkinson's disease.

[0032] When collecting data, the data acquisition unit 101 may need to localize and calibrate the brain to ensure that the acquired images and signals correspond to specific brain regions. Meanwhile, the data acquisition unit 101 may also include a data processing and storage module for processing the acquired raw data and storing it for subsequent analysis and application. Data processing includes preprocessing steps such as denoising, enhancing, and registering images and signals, as well as integrating and jointly analyzing different data sources. The storage module is responsible for saving the processed data on an accessible medium, such as a hard disk, server, or cloud storage, for subsequent research and application.

[0033] In addition to data acquisition, the data acquisition unit 101 may also include a user interface module for operating and controlling the acquisition process and displaying the acquired data and results. The user interface can be a graphical interface or a command-line interface, through which users can perform parameter settings, data viewing, and interactive display of analysis results.

[0034] The target determination unit 102 is used to determine individualized functional targets, individualized structural targets, and the energy ratio threshold of the target rhythm for triggering neural regulation based on the data collected by the data acquisition unit.

[0035] Specifically, in one embodiment, first, individualized functional targets are determined based on magnetic resonance structural imaging and resting-state functional imaging. The centromedian nucleus or the nucleus lateralis centralis of the thalamus is selected as the region of interest, and the functional connectivity strength index is calculated to determine the individualized functional targets. It should be noted that the centromedian nucleus and the nucleus lateralis centralis of the thalamus are shown to have strong neural connections with multiple brain regions in neuroanatomical studies, especially with the prefrontal cortex and cingulate gyrus related to emotion and cognitive functions. In animal models and human studies, stimulation of these regions is shown to be able to adjust emotional responses and behavioral patterns. Especially in the treatment studies of depression and anxiety disorders, deep brain stimulation (DBS) or transcranial magnetic stimulation (TMS) targeting these regions has shown positive effects. At the same time, functional magnetic resonance imaging (fMRI) and positron emission tomography (PET) studies have revealed the functional changes of these regions in disease states, supporting their selection as treatment targets.

[0036] Therefore, the main basis for selecting the centromedian nucleus or the centrolateral nucleus of the thalamus as the region of interest is the core role of these two nuclear regions in regulating brain functions such as emotion, sleep, and movement. Using functional magnetic resonance imaging (fMRI), by observing the resting-state brain activity, the functional connectivity strength between these regions and other brain regions can be calculated. The functional connectivity strength index is calculated through the following mathematical formula and is expressed as a correlation coefficient:

[0037]

[0038] where X and Y represent the signal intensities of two different brain regions, and x i and y i are the corresponding signal values, and are the average values of their respective signals. This calculation helps to confirm which brain regions have significant functional connections with the centromedian nucleus or the centrolateral nucleus of the thalamus, providing theoretical support for dual-target synchronous stimulation. The functional connectivity strength index can reflect the degree of functional connection between different brain regions, thereby helping to determine individualized functional targets. An individualized functional target refers to a target region determined according to the specific functional connection pattern of an individual. By analyzing the functional connectivity strength index, it can be determined which brain regions are important for the specific functions of an individual, thereby determining individualized functional targets.

[0039] Secondly, based on diffusion tensor imaging, individualized structural targets are determined. The centromedian nucleus or the centrolateral nucleus of the thalamus is selected as the region of interest, and probabilistic fiber tracking is performed to determine individualized structural targets. Diffusion tensor imaging is an imaging technique used to observe the structure and connection of white matter fiber bundles in the brain, which can provide detailed information about the brain structure. Probabilistic fiber tracking is a technique based on diffusion tensor imaging. By analyzing the diffusion direction and degree of water molecules in the diffusion tensor image, the orientation and connection of white matter fiber bundles in the brain can be inferred.

[0040] In the embodiments of the present application, through probabilistic fiber tracking technology, individualized structural targets can be determined and information about their positions and connection patterns can be provided. An individualized structural target refers to a target region determined according to the specific brain structure of an individual, and its position and connection pattern are important for the specific functions of an individual. By analyzing the results of probabilistic fiber tracking, it can be determined which brain structures have important impacts on the specific functions of an individual, thereby determining individualized structural targets. Further, by measuring the diffusion process of water molecules in brain tissue, the nerve fiber path is traced. In this process, the centromedian nucleus or the centrolateral nucleus of the thalamus is selected as the region of interest, and probabilistic fiber tracking is performed to reveal the structural connections between these regions and other parts. The mathematical model of probabilistic fiber tracking can be expressed as:

[0041]

[0042] Among them, P ij represents the connection probability from region i to region j, N is the total number of samples, and I ijk represents the indicator function of the presence or absence of fibers from region i to region j in the k-th sample. This analysis provides precise information on individualized structural targets and enhances the targeting of treatment.

[0043] Finally, determine the energy proportion threshold of the target rhythm that triggers neuromodulation. Determine the energy proportion threshold of the target rhythm through preprocessing and analysis of electroencephalogram (EEG) signals. EEG signals are bioelectric signals that record the electrical activities of the brain and can reflect the electrophysiological activities of the brain in different states. Through an EEG signal acquisition device, the electrical activities of the brain within a specific time period can be obtained. The preprocessing includes steps such as filtering and denoising, aiming to eliminate the noise and interference in the signals and extract the effective bioelectric signal components. Then, analyze the preprocessed data to determine the energy proportion threshold of the target rhythm.

[0044] The target rhythm refers to the specific frequency components of the brain's electrical activities under specific tasks or states. Through preprocessing and analysis of EEG signals, the corresponding target rhythm under specific tasks or states can be determined, and its energy proportion can be calculated. Determine the energy proportion threshold of the target rhythm that triggers neuromodulation. This threshold indicates that when the energy proportion of the target rhythm in the brain's electrical activities reaches or exceeds this threshold, the corresponding neuromodulation mechanism can be triggered to achieve specific physiological or cognitive effects. In one embodiment, it is achieved through preprocessing and frequency-domain analysis of EEG signals. Use the fast Fourier transform (FFT) to analyze the energy distribution of each frequency component in the signal, and its mathematical expression is:

[0045]

[0046] Among them, x n is the signal intensity at time point n, and E(f) represents the energy value at frequency f. Select an appropriate frequency according to the treatment target and set the corresponding energy proportion threshold to trigger the most suitable neuromodulation mode.

[0047] The parameter control unit 103 is used to determine the corresponding parameter set according to the energy proportion threshold and the corresponding target nerve signal. Specifically, preprocess and analyze the target nerve signal to determine the conduction time and stimulation parameters; where the stimulation parameters include peripheral nerve electrical stimulation parameters; and determine the parameter set of peripheral-central coupling stimulation according to the target nerve signal; where the parameter set includes the conduction time.

[0048] First, the preprocessing of the target nerve signal involves denoising, filtering, and feature extraction. Specifically, an electroencephalogram (EEG) signal is filtered using a band-pass filter to eliminate low-frequency and high-frequency noise and retain the effective signal within the target frequency band. Then, wavelet transform is used to perform multi-scale analysis on the signal to extract the energy features of different frequency components. The energy ratio threshold is set to T energy , for each segment of the preprocessed signal x(t), calculate its energy E within the target frequency band target , and compare it with the total energy E total . When E target / E total ≥T energy , it is considered that the target nerve signal is contained in this segment of the signal.

[0049] In practical applications, assume that the target nerve signal of a certain patient is mainly concentrated in the α band (8 - 13 Hz). According to the energy ratio threshold T energy = 0.5, select the time period containing the target signal. This process is achieved through the following formula:

[0050]

[0051]

[0052] Condition:

[0053] Next, determine the conduction time and stimulation parameters. The conduction time refers to the time delay of the peripheral nerve electrical stimulation signal conducting to the central nerve, which is calculated through the nerve conduction velocity v and the distance d, that is: Assume that the stimulation site is the arm, the distance to the brain is d = 0.8 m, and the nerve conduction velocity v = 60 m / s, then the conduction time is: According to the target nerve signal, determine the peripheral nerve electrical stimulation parameters, including the stimulation intensity I, frequency f, waveform W, etc. The specific parameter selection depends on the characteristics of the target nerve signal and the individual differences of the patient. Assume that the optimal stimulation parameters for a certain patient are: stimulation intensity I = 2 mA, frequency f = 10 Hz, and the waveform is a square wave.

[0054] To achieve peripheral - central coupling stimulation, it is necessary to further determine the parameter set of the coupling stimulation. The key to peripheral - central coupling stimulation lies in synchronization and coordination, and a synchronization control unit is used to monitor and adjust the central nerve and the peripheral nerve in real time. Specifically, according to the central nerve response induced by the peripheral nerve electrical stimulation, adjust the time and intensity of the central nerve stimulation to achieve the best regulation effect.

[0055] Assume that the evoked potential of the peripheral nerve stimulation of a certain patient reaches the peak after the conduction time, and the optimal time point for the central nerve stimulation is topt = t conduction + Δt, where Δt is the phase adjustment time. Through experiments, it is determined that Δt = 5 ms, then the optimal time point for central nerve stimulation is: t opt = 13.33 ms + 5 ms = 18.33 ms.

[0056] Furthermore, the parameter control unit 103 is also used to optimize the timing and intensity of stimulation based on the normal distribution algorithm. In one embodiment, it includes:

[0057]

[0058] where T optimal represents the optimal stimulation timing, and I optimal represents the optimal stimulation intensity., μ T represents the mean of the stimulation timing, μ I represents the mean of the stimulation intensity, σ I represents the standard deviation of the stimulation intensity, σ T represents the standard deviation of the stimulation timing, T represents the stimulation timing, and I represents the intensity.

[0059] In a neuromodulation system, the algorithm not only needs to process and analyze a large amount of complex brain data (such as structural images, functional images, and electroencephalogram signals), but also needs to adjust the stimulation parameters in real time to adapt to individual differences and immediate physiological responses. The goal of optimizing the algorithm is to improve the pertinence and efficiency of treatment, reduce unnecessary stimulation, thereby reducing side effects and increasing the treatment effect. The normal distribution is a probability distribution that is ubiquitous in natural and social sciences and is often used to describe and process random phenomena.

[0060] In the algorithm optimization of a neuromodulation device, the Gaussian distribution can be used to simulate and predict the variability of neural responses, helping to determine the most effective stimulation timing and intensity. The determination of the stimulation timing depends on the precise analysis of the evoked potential signal. Regarding the response time of the evoked potential signal (the time from the start of stimulation to the response of the electroencephalogram signal) as a random variable, it is assumed that these response times conform to the Gaussian distribution. By statistically analyzing the mean and standard deviation of these response times, the stimulation timing that is most likely to produce the maximum therapeutic effect can be determined. The adjustment of the stimulation intensity can also be optimized using the Gaussian distribution. Collect data on the stimulation intensity and treatment effect in multiple stimulation experiments, assuming that these data are close to the Gaussian distribution. By analyzing the distribution characteristics of the data, the most effective stimulation intensity can be determined.

[0061] The peripheral control unit 104 is used to apply one or more electrical stimulations to the peripheral nerve through the peripheral stimulation electrode according to the parameter set determined by the parameter control unit. Specifically, the peripheral control unit 104 first receives the parameter set from the parameter control unit, and this parameter set includes detailed parameters such as stimulation intensity, current waveform, frequency, pulse width, and stimulation timing. Through these parameters, the peripheral control unit can precisely control the electrical stimulation process of the peripheral nerve.

[0062] In one embodiment, assume that the peripheral nerve target of patient A is the left sciatic nerve, and the stimulation electrode is precisely placed on this nerve path. According to the output of the parameter control unit, the peripheral control unit decides to perform multiple repeated electrical stimulations on this nerve, and the parameters of each stimulation are as follows: stimulation intensity I = 3 mA, pulse frequency f = 50 Hz, pulse width τ = 200 μs, stimulation duration T = 30 s.

[0063] The peripheral control unit uses a pulse generator to generate the corresponding electrical stimulation waveform and adjusts the waveform in real time through a digital signal processor (DSP) to ensure the accuracy and stability of the electrical stimulation. The specific electrical stimulation waveform can be expressed as: V(t) = I·W(t)·sin(2πft), where W(t) is the pulse width control function used to define the duration of each pulse. Under the above parameters, the waveform diagram of the electrical stimulation is as Figure 1 shown, demonstrating the relationship between the voltage of multiple consecutive pulses and time.

[0064] To ensure the effectiveness and safety of the electrical stimulation, the peripheral control unit is also equipped with a feedback control mechanism. By real-time monitoring the physiological response of the peripheral nerve (such as the electromyogram EMG signal), the system can dynamically adjust the electrical stimulation parameters to avoid over-stimulation or under-stimulation. Assume that during an electrical stimulation process, the monitored EMG signal shows an abnormally strong muscle response, and the feedback control mechanism will immediately adjust the stimulation intensity or frequency to ensure the comfort and treatment effect of the patient.

[0065] In another embodiment, for patient B with Parkinson's disease, the peripheral nerve stimulation target is the vagus nerve. The peripheral control unit implements the following electrical stimulation scheme according to the parameter set provided by the parameter control unit: stimulation intensity I = 1.5 mA, frequency f = 20 Hz, pulse width τ = 100 μs, stimulation duration T = 60 s. By precisely controlling these parameters, the peripheral control unit can effectively regulate the patient's nerve activity and relieve the symptoms of Parkinson's disease.

[0066] To further optimize the electrostimulation effect, the peripheral control unit can adjust parameters in combination with neural network algorithms. Using a trained deep learning model, the system can predict the impact of different stimulation parameters on neural responses and select the optimal parameter set for electrostimulation. Specifically, a model that combines a convolutional neural network (CNN) and a long short-term memory network (LSTM) is used to train and predict a large amount of historical electrostimulation data, thereby achieving intelligent optimization of parameters.

[0067] Suppose the training data includes a dataset of neural responses under different stimulation parameters {(I i , f i , τ i , T i , R i )}, where R i represents the neural response under the corresponding parameters. The neural network model learns this data to establish a mapping relationship between stimulation parameters and neural responses. The prediction model can be expressed as: R pred = CNN + LSTM(I, f, τ, T). In practical applications, the peripheral control unit obtains the patient's neural response data in real time, inputs this data into the neural network model, predicts the neural response under the current parameters, and adjusts the stimulation parameters according to the prediction results to achieve the best treatment effect.

[0068] The central control unit 105 is used to apply electrostimulation to the central nerve through the central stimulation electrode based on the parameter set determined by the parameter control unit and the evoked potential signal generated by peripheral nerve stimulation. Specifically, the evoked potential signal generated by peripheral nerve stimulation is collected to obtain the central nerve electrostimulation parameters; according to the parameter set of the peripheral-central coupled stimulation and the central nerve electrostimulation parameters, when the conduction time arrives, electrostimulation is applied to the subject's central nerve through the central stimulation electrode.

[0069] The central control unit 105 includes a highly sensitive evoked potential detection module, which can monitor and record in real time the evoked potential signal (EP) generated by peripheral nerve stimulation. Specifically, the evoked potential detection module uses a precision amplifier and a filter to perform high-precision acquisition and analysis of the potential changes generated in the central nervous system after peripheral nerve stimulation. In an experiment, for the evoked potential signal V EP (t) generated by peripheral nerve stimulation, the detection module can accurately capture its peak value and characteristic moments, thereby providing a basis for subsequent central nerve electrostimulation.

[0070] To determine the central nerve electrostimulation parameters, it is first necessary to perform a detailed analysis and processing of the evoked potential signal. The Fourier Transform is used to perform frequency domain analysis on the signal to extract the main frequency components, and the formula is as follows: Among them, X(f) represents the signal strength at frequency f. By analyzing X(f), the main frequency components and their energy distribution of the evoked potential signal can be identified, thereby determining the frequency parameter f of the central nerve stimulation. CNS .

[0071] In practical applications, assuming that the main frequency component of a certain evoked potential signal is 10 Hz, then the frequency parameter f of the central nerve stimulation CNS should also be set to 10 Hz to ensure the synchronization and effectiveness of the stimulation. In addition, by analyzing the time domain of the signal, the conduction time t conduction is determined, that is, the time required for the peripheral nerve stimulation signal to conduct to the central nervous system. Assuming the conduction time is 20 ms, an appropriate time delay device is set in the central control unit to ensure that the electrical stimulation is applied when the conduction time arrives.

[0072] For the determination of the central nerve stimulation parameters, the stimulation intensity I CNS and the pulse width τ CNS also need to be considered. The selection of these parameters is based on the amplitude and morphology of the evoked potential signal of the peripheral nerve stimulation. By calculating the peak voltage V peak of the evoked potential signal, the intensity parameter of the central nerve stimulation can be derived: I CNS = k·V peak where k is a proportionality constant, which is adjusted according to different clinical applications and individual patient differences. In the treatment of a Parkinson's disease patient, assuming that the peak value of the detected evoked potential signal is 100 μV, by setting the proportionality constant k = 1.5, the intensity parameter I CNS of the central nerve stimulation should be set to 150 μA.

[0073] For the determination of the pulse width parameter τ CNS , it also needs to be adjusted according to the time domain characteristics of the evoked potential signal. By analyzing the rise time and fall time of the evoked potential signal, an appropriate pulse width is determined to simulate natural nerve activity to the greatest extent. Assuming that the rise time and fall time of a certain evoked potential signal are 5 ms and 10 ms respectively, the pulse width τ CNS of the central nerve stimulation can be set to 7.5 ms.

[0074] In summary, the central control unit 105 determines the specific parameter set of the central nerve stimulation through precise evoked potential detection and analysis, and applies electrical stimulation to the central nerve of the subject through the central stimulation electrode when the conduction time arrives.

[0075] The synchronous regulation unit 106 is used to apply synchronous stimulation to the individualized functional target and individualized structural target based on the evoked potential signals generated by central nerve stimulation. Specifically, the synchronous regulation unit 106 receives and processes the evoked potential signals generated by central nerve stimulation to precisely adjust the electrical stimulation parameters of the individualized functional target and structural target, realizing synchronous regulation of multiple neural pathways or brain regions, so as to achieve more precise regulation of neural signal conduction.

[0076] First, the synchronous regulation unit 106 collects the evoked potential signals (Evoked Potentials, EP) generated by central nerve stimulation through a high-precision amplifier and filter. These signals reflect the response of the central nervous system to external electrical stimulation and are the key basis for adjusting the electrical stimulation parameters of the individualized target. Suppose in an experiment, the evoked potential signal V EP (t) generated by central nerve stimulation. To further analyze these signals, the synchronous regulation unit 106 uses Fourier Transform (FT) to perform frequency-domain analysis on the signals and extract the main frequency components (such as Figure 2 shown, and the specific method will be elaborated Figure 2 at).

[0077] In practical applications, assume that the main frequency component of a certain evoked potential signal is 10 Hz, then the frequency parameter f target of the target electrical stimulation should also be set to 10 Hz to ensure the synchronization and effectiveness of the stimulation. In addition, through time-domain analysis of the signals, the conduction time t conduction is determined, that is, the time required for the central nerve stimulation signal to conduct to the functional or structural target. Suppose the conduction time is 20 ms, then an appropriate delay device is set in the synchronous regulation unit to ensure that electrical stimulation is applied when the conduction time arrives.

[0078] To ensure synchronous regulation of multiple neural pathways or brain regions, the synchronous regulation unit 106 needs to process multiple evoked potential signals simultaneously and dynamically adjust the electrical stimulation parameters according to these signals. In the specific implementation process, the evoked potential signals of different targets are simultaneously recorded through a multi-channel data acquisition system, and the multi-channel Fourier transform algorithm is used for frequency-domain analysis. The formula is as follows: Among them, represents the evoked potential signal of the i-th target, and X i (f) represents the frequency-domain signal of the i-th target. Through frequency-domain analysis of all target signals, the synchronous regulation unit can determine the optimal set of electrical stimulation parameters for each target.

[0079] In one embodiment, the individual functional target of the patient is the motor cortex, and the structural target is the basal ganglia. Through the synchronous regulation unit 106, the evoked potential signals of these two targets are respectively recorded, and their electrical stimulation parameters are determined. Assume that the optimal stimulation parameters for the motor cortex are: stimulation intensity I M1 = 1.2 mA, frequency f M1 = 12 Hz, pulse width τ M1 = 150 μs; the optimal stimulation parameters for the basal ganglia are: stimulation intensity I BG = 1.5 mA, frequency f BG = 10 Hz, pulse width τ BG = 200 μs. Through the synchronous regulation unit, these parameters are applied simultaneously when the conduction time arrives to achieve synchronous regulation of multiple neural pathways.

[0080] To further optimize the synchronous regulation effect, the synchronous regulation unit 106 introduces a feedback control mechanism. By real-time monitoring the neural response signals of each target (such as electroencephalogram EEG or functional magnetic resonance fMRI data), the system can dynamically adjust the electrical stimulation parameters. Assume that during the electrical stimulation process, an abnormal neural response signal of a certain target is monitored. The synchronous regulation unit can immediately adjust the electrical stimulation intensity or frequency of this target to ensure the overall regulation effect.

[0081] Assume that during a certain electrical stimulation process, an abnormal enhancement of the neural response signal of the basal ganglia is monitored. Then the system dynamically adjusts the stimulation intensity through the following feedback control formula: I BG = I BG -ΔI, where ΔI is the preset intensity adjustment step size. Through this real-time adjustment mechanism, the situations of over-stimulation or under-stimulation can be effectively avoided, ensuring the accuracy and safety of the regulation.

[0082] In summary, the synchronous regulation unit 106 realizes synchronous and precise regulation of multiple neural pathways or brain regions through high-precision signal acquisition and analysis, dynamic parameter adjustment, and real-time feedback control. In specific clinical applications, this unit demonstrates significant therapeutic effects, providing a new technical path for the treatment of complex neurological diseases. Through this technical solution, the neural signal conduction can be adjusted more precisely, and multiple neural pathways or brain regions can be regulated simultaneously based on dual targets, achieving efficient control and management of the complex nervous system.

[0083] Figure 2 This is a schematic flowchart of a method for preprocessing evoked potential signals provided by an embodiment of the present application. As Figure 2 shown, at step S201, a preset filtering algorithm is used to allow signals within the target frequency range to pass through to remove high-frequency and low-frequency noises. In the present application, the preset filtering algorithm includes:

[0084] h id h(n) = 2f H ·sinc(2fn H - 2f L ·sinc(2fn L ),

[0085] where n represents the sampling points of the signal in the discrete - time domain, and h id (n) represents the response of the filter at different time points, f H represents the high - frequency cut - off frequency, sinc represents the impulse response of an ideal low - pass filter, and f L represents the low - frequency cut - off frequency. The core of filter design lies in the frequency response of the filter. The frequency response defines the gain (or attenuation) of the filter at different frequencies.

[0086] In one embodiment, a FIR band - pass filter is designed to process a visual evoked potential (VEP) signal. The selected low - frequency cut - off is 1 Hz, the high - frequency cut - off is 100 Hz, the sampling frequency is 500 Hz, and a Hanning window is used to reduce sidelobe leakage. First, calculate the ideal band - pass impulse response: h id (n) = 2·0.2·sinc(0.2n)-2·0.002·sinc(0.002n), where Then apply the window function, and use the Hanning window w(n) to adjust h id (n). Finally, filter the VEP signal: apply the designed filter to the original VEP data to remove noise and highlight the useful signal components.

[0087] At step S202, the continuous evoked potential signal is cut into multiple time windows according to the stimulation time; each of the time windows contains a complete stimulation - response cycle. This is a time - segmentation technique used to isolate and analyze the potential changes caused by each stimulation. By this method, the dynamic changes of the nerve response after each stimulation can be accurately observed, thus ensuring the accuracy and repeatability of the data. This segmentation also helps with subsequent data processing and analysis because it allows researchers to focus on the effects of a single stimulation and avoid the mutual interference of continuous - stimulation effects.

[0088] At step S203, baseline correction is performed on each time window to ensure that the signal changes are only related to the stimulation event. Baseline correction is achieved by subtracting the average activity level during the non - stimulation period from the actual signal, which can more clearly reveal the signal changes related to the stimulation and more accurately interpret the subtle changes in neural electrical activity.

[0089] At step S204, characteristic points including peaks and valleys in the evoked potential waveform are identified. The identification of characteristic points is the key to understanding the characteristics of neural responses. Peaks usually represent the peaks of synchronous discharges of neural cell populations, while valleys may indicate a temporary reduction in activity or the interaction between different neuron populations. Accurately identifying these characteristic points is the basis for understanding the information encoding and decoding mechanisms in the process of neural signal transmission. Next, at step S205, the time interval from the start of the stimulus to the peak of the characteristic point is calculated to determine the conduction time. At step S206, stimulus parameters including intensity, frequency, and duration are determined based on the changes in the evoked potential signal.

[0090] Furthermore, an embodiment of the present application also provides a control chip for neuromodulation to achieve brain treatment based on dual-target synchronous stimulation, which is characterized by including: a processor, a memory, and a system bus; wherein, the processor and the memory are connected through the system bus; the memory is used to store one or more programs, and the one or more programs include instructions, and when the instructions are executed by the processor, the processor executes the modulation method of the neuromodulation device in the above embodiment.

[0091] Furthermore, an embodiment of the present application also provides a computer program product, which, when running on a terminal device, enables the terminal device to execute any one of the above modulation methods.

[0092] From the description of the above embodiments, those skilled in the art can clearly understand that all or part of the steps in the methods of the above embodiments can be implemented by means of software plus a necessary general hardware platform. Based on such an understanding, the technical solution of the present application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. The computer software product can be stored in a storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network communication device such as a media gateway, etc.) to execute the methods described in each embodiment or some parts of the embodiments of the present application.

[0093] It should be noted that the embodiments in this specification are described in a progressive manner, and the key point of each embodiment is to illustrate the differences from other embodiments. The same or similar parts among the embodiments can be referred to each other. For the devices disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple, and the relevant parts can be referred to the description of the method part.

[0094] It should also be noted that, in this text, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device comprising a series of elements not only includes those elements, but also includes other elements not expressly listed, or also includes elements inherent to such process, method, article or device. Without further limitation, an element defined by the statement "comprising an..." does not exclude the presence of additional identical elements in the process, method, article or device comprising the said element.

[0095] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be obvious to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but rather to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A neuromodulatory device for achieving brain treatment based on dual-target synchronous stimulation, characterized in that: include: Data acquisition unit, target determination unit, parameter control unit, peripheral end control unit, central end control unit and synchronization control unit; wherein, The data acquisition unit is used to acquire magnetic resonance structural images, resting state functional images, diffusion tensor images and electroencephalogram signals of the brain; The target determination unit is used to determine individualized functional targets and individualized structural targets, as well as an energy proportion threshold of a target rhythm that triggers neural regulation based on the data collected by the data collection unit; The parameter control unit is used to determine a corresponding parameter set according to the energy proportion threshold and the corresponding target nerve signal; including: preprocessing and analyzing the target nerve signal to determine the conduction time and stimulation parameters; wherein the stimulation parameters include peripheral nerve electrical stimulation parameters; and determining a parameter set for peripheral-central coupled stimulation according to the target nerve signal; wherein the parameter set includes conduction time, peripheral nerve electrical stimulation intensity, peripheral nerve electrical stimulation frequency, peripheral nerve electrical stimulation waveform and / or central nerve stimulation phase time; the parameter control unit is also used to optimize the timing and intensity of stimulation based on a normal distribution algorithm, including: ， in, Indicates the best time to stimulate. represents the optimal stimulation intensity, represents the mean of the stimulus timing, represents the mean value of stimulus intensity, represents the standard deviation of stimulus intensity, represents the standard deviation of stimulus timing, Indicates the timing of stimulation, Indicates strength; The peripheral end control unit is used to apply one or more electrical stimulations to the peripheral nerves through the peripheral end stimulation electrodes according to the parameter set determined by the parameter control unit, and train the neural network model using the neural response data set under different stimulation parameters to establish a mapping relationship between the stimulation parameters and the neural response to perform parameter adjustment; The central end control unit is used to apply electrical stimulation to the central nerve through the central end stimulation electrode based on the parameter set determined by the parameter control unit and the evoked potential signal generated by the peripheral nerve stimulation; including: collecting the evoked potential signal generated by the peripheral nerve stimulation to obtain the central nerve electrical stimulation parameter; according to the parameter set of the peripheral-central coupling stimulation and the central nerve electrical stimulation parameter, when the conduction time arrives, applying stimulation to the subject's central nerve through the central end stimulation electrode; The synchronous control unit is used to apply synchronous stimulation to individualized functional targets and individualized structural targets based on the evoked potential signal generated by central nervous system stimulation; including: using a preset filtering algorithm to allow evoked potential signals in the target frequency range to pass through to remove high-frequency and low-frequency noise; cutting the continuous evoked potential signal into multiple time windows according to the stimulation time; each of the time windows contains a complete stimulation response cycle; performing baseline correction on each time window to ensure that the signal change is only related to the stimulation event; identifying characteristic points including peaks and troughs in the evoked potential waveform; calculating the time interval from the start of stimulation to the peak of the characteristic point to determine the conduction time; and determining the stimulation parameters including intensity, frequency and duration based on the changes in the evoked potential signal.

2. The neural regulation device according to claim 1, characterized in that: Wherein, based on the data collected by the data collection unit, determining the individualized functional target and the individualized structural target, and the energy proportion threshold of the target rhythm that triggers neural regulation include: Based on the magnetic resonance imaging and resting-state functional images, the individualized functional targets were determined, the central middle nucleus or central lateral nucleus of the thalamus was selected as the region of interest, and the functional connectivity strength index was calculated to determine the individualized functional targets; Based on the diffusion tensor image, the individualized structural target is determined. The central middle nucleus or central lateral nucleus of the thalamus is selected as the region of interest, and probabilistic fiber tracking is performed to determine the individualized structural target. The mathematical model of probabilistic fiber tracking is expressed as: ， in, represents the connection probability from region i to region j, N is the total number of samples, An indicator function indicating whether fibers from region i to region j exist in the kth sample; and a threshold value of the energy proportion of the target rhythm that triggers neural regulation, which is determined by preprocessing and analyzing EEG signals.

3. The neural regulation device according to claim 1, characterized in that: The preset filtering algorithm is used to allow the signal of the target frequency range to pass through to remove high-frequency and low-frequency noise, including: ， in, represents the sampling points of the signal in the discrete time domain, represents the response of the filter at different time points, represents the high frequency cutoff frequency, represents the impulse response of an ideal low-pass filter, Indicates the low-frequency cutoff frequency.

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