A transcranial focused ultrasound stimulation system and method for patients with cognitive impairment

Abstract

The present invention belongs to the technical field of transcranial ultrasound stimulation. A transcranial focused ultrasound stimulation system and method for patients with cognitive impairment are provided. The system includes a main control module, a communication module, a display module, a signal source module, a driving module, an impedance matching module, an ultrasonic transducer module, and an ultrasonic coupling module. The present invention can determine personalized targets according to the actual conditions of target patients and determine appropriate treatment intensities, thereby facilitating the achievement of the best treatment effects.

Description

Technical Field

[0001] The present invention relates to the technical field of transcranial ultrasound stimulation, and in particular, to a transcranial focused ultrasound stimulation system and method for patients with cognitive impairment. Background Art

[0002] For patients with cognitive impairment, the current treatment methods generally fall into drug treatment and non-drug treatment. Among non-drug treatments, neuromodulation technology is an important treatment method.

[0003] Neuromodulation technology and its applications have always been a research hotspot in the fields of neuroscience and engineering. In recent years, researchers have gradually become interested in delivering electromagnetic fields through implanted electrodes or non-invasive transcranial methods for brain treatment and stimulation, and these technologies have gradually been widely used in the treatment of brain diseases and brain science research.

[0004] Currently existing and relatively mature neuromodulation technologies include implanted electrodes, transcranial electric and magnetic field stimulation, etc. Transcranial magnetic stimulation (TMS) can safely regulate cortical function, but its spatial resolution is limited and it cannot precisely stimulate specific deep brain regions. Transcranial direct current stimulation (tDCS) and deep brain stimulation (DBS) are also common neuroelectrical stimulation technologies. Among them, tDCS has shown potential in chronic headache and memory, but its spatial resolution is low and the stimulation location is relatively shallow. DBS, as an important clinical treatment technology, has been used to suppress tremors in Parkinson's disease, but this technology requires deep brain electrode implantation surgery, increasing the risk of surgical infection. In addition, optogenetics can precisely manipulate the neural activities of individual neurons transfected with photosensitive proteins through light of a specific wavelength, can achieve targeted effects, and has unparalleled precision, but also requires surgery to achieve gene manipulation.

[0005] In 2010, Tufail et al. published a study on transcranial ultrasound modulation of neural activity in mice. Transcranial focused ultrasound stimulation (tFUS) has rapidly attracted extensive attention due to its non-invasive, safe, high spatial resolution, good penetration ability, and ease of combination with other technologies, and has developed rapidly in recent years. Ultrasonic waves refer to sound waves with a frequency exceeding 20 kHz. Such high-frequency sound waves have a short wavelength and strong energy, and have good directivity and penetration ability, so they have been widely used in medical, military, industrial and other fields. Transcranial ultrasound stimulation acts on specific brain regions through low-frequency and low-intensity ultrasonic waves, thereby triggering the activation or inhibition of neural activity in that region. Different from relying on the thermal effect of ultrasound in medical treatment, transcranial ultrasound stimulation mainly uses the mechanical effect of ultrasound to regulate neural activity. The resolution of transcranial focused ultrasound stimulation reaches the millimeter level, enabling targeted stimulation of specific brain regions, and the maximum stimulation depth can reach more than 5 cm. In addition, since ultrasonic waves are mechanical waves, transcranial ultrasound nerve stimulation does not interfere with nerve electrode technology or magnetic resonance imaging technology, which is conducive to synchronous recording of neural point activity or neural imaging data under transcranial ultrasound nerve stimulation. The following table is a comparison of transcranial focused ultrasound stimulation with other common neural modulation techniques. From the data in the table, it can be seen that the transcranial focused ultrasound stimulation technique has the advantages of being non-invasive, high spatial resolution, large stimulation depth, and good compatibility with neural monitoring technology.

[0006]

[0007] Currently, for the neural modulation of patients with cognitive impairment, transcranial magnetic stimulation and transcranial electrical stimulation are mainly used. There is no mature transcranial focused ultrasound stimulation treatment method, and most of the modulation sites are the prefrontal cortex, without the characteristics of personalized intervention. Summary of the Invention

[0008] In view of this, the present invention provides a transcranial focused ultrasound stimulation system, working method, electronic device, computer storage medium and computer program product for patients with cognitive impairment to solve the above technical problems.

[0009] The present invention discloses a transcranial focused ultrasound stimulation system for patients with cognitive impairment. The system includes a main control module, a communication module, a display module, a signal source module, a driving module, an impedance matching module, an ultrasonic transducer module, and an ultrasonic coupling module; wherein, the main control module is the core control module of the whole system, and is used to provide the required timer resources for the signal source module, provide the required display data for the display module, provide the required communication data for the communication module, and receive the data transmitted by the communication module.

[0010] The signal source module is responsible for generating an online adjustable original signal, which is a pair of PWM signals with complementary polarities and dead time output by the advanced timer of the main control module.

[0011] The drive module is used to amplify the original signal to obtain a drive signal capable of driving the ultrasonic transducer module.

[0012] The impedance matching module consists of an LC matching network. Its input is the drive signal output by the drive module, and its output is the drive signal after impedance matching.

[0013] The ultrasonic transducer module: Its input is the drive signal after impedance matching, which is used to convert the electrical drive signal into an acoustic signal, that is, to emit focused ultrasonic waves that meet the requirements; among them, the target position stimulated by the focused ultrasonic waves is confirmed according to resting-state functional magnetic resonance.

[0014] The ultrasonic coupling module is responsible for effectively transmitting ultrasonic waves between the ultrasonic transducer module and the object to be measured, and helps to eliminate the acoustic impedance difference in the air.

[0015] The display module is used to display the parameter information of the original signal and other stimulation information in real time.

[0016] The communication module is used to record and store stimulation logs and communicate with the host computer.

[0017] In some embodiments, the system further includes a target position determination module, which is connected to the communication module; the target position determination module is used to perform the following steps: S1. Use resting-state functional magnetic resonance imaging technology to measure the blood oxygenation level-dependent signal changes in the brain of the target patient in the resting state, and divide the brain into several brain regions including the frontal lobe, parietal lobe, temporal lobe, and occipital lobe.

[0018] S2. For each of the brain regions, calculate the functional connection strength between it and other brain regions, and average the functional connection strengths between each of the brain regions and other brain regions to determine the brain region with the weakest average connection strength, and use it as the target brain region; among them, the functional connection strength is realized by calculating the correlation of the blood oxygenation level-dependent signals between two brain regions.

[0019] S3. Divide the target brain region into smaller sub-brain regions, repeat the above step S2 for each of the sub-brain regions, and use the sub-brain region with the weakest average functional connection as the target for neuromodulation.

[0020] S4. Transmit the target to the main control module through the communication module, so that the main control module displays the information of the target on the display module.

[0021] In some embodiments, the correlation of the blood oxygenation level-dependent signals between two brain regions is calculated using any one of the Pearson correlation coefficient, local consistency, seed point-based functional connectivity, and functional network connectivity analysis methods.

[0022] In some embodiments, the system further includes a treatment intensity determination module, and the treatment intensity determination module is connected to the communication module; the treatment intensity determination module is configured to perform the following steps: collect first treatment big data of similar patients with the same cognitive impairment level as the target patient, and second treatment big data of similar patients with a higher cognitive impairment level than the target patient; wherein, the first treatment big data and the second treatment big data include a number of transcranial focused ultrasound stimulation data, and each of the transcranial focused ultrasound stimulation data includes target point information, treatment intensity, cognitive impairment level, and treatment effect information.

[0023] Taking the target point information, the treatment intensity, and the cognitive impairment level as the training data ontology, and taking the treatment effect information as the training data label, first training datasets and second training datasets are respectively constructed based on the first treatment big data and the second treatment big data.

[0024] The pre-constructed first deep prediction model is trained using the first training dataset, the pre-constructed second deep prediction model is trained using the second training dataset, and the pre-constructed third deep prediction model is trained using the first training dataset and the second training dataset.

[0025] The information of the target point of the target patient and the target cognitive impairment level are respectively input into the trained first deep prediction model, the second deep prediction model, and the third deep prediction model to respectively obtain the predicted first treatment intensity, second treatment intensity, and third treatment intensity, calculate the difference between the second treatment intensity and the third treatment intensity, and determine the matching first weakening coefficient according to the difference.

[0026] The fourth treatment intensity is calculated according to the first weakening coefficient and the first treatment intensity, and the fourth treatment intensity is the final treatment intensity.

[0027] In some embodiments, the determining the matching first weakening coefficient according to the difference includes: performing matching calculation according to the difference and the preset control relationship data to obtain the matching first weakening coefficient.

[0028] In some embodiments, calculating a fourth treatment intensity based on the first weakening coefficient and the first treatment intensity, where the fourth treatment intensity is the final treatment intensity, includes: obtaining historical treatment data of a target patient, calculating the number of times of treatment using transcranial focused ultrasound stimulation based on the historical treatment data, and determining a second weakening coefficient based on the number of times; calculating the fourth treatment intensity using the first weakening coefficient, the second weakening coefficient, and the first treatment intensity, where the fourth treatment intensity is the final treatment intensity.

[0029] The present invention also discloses a working method of a transcranial focused ultrasound stimulation system for patients with cognitive impairment. The method includes the following steps: a target position determination module determines information about a target corresponding to a target patient and transmits it to a main control module through a communication module; a treatment intensity determination module determines a treatment intensity and transmits it to the main control module through the communication module; the main control module generates a treatment start instruction based on the information about the target and the treatment intensity, and a signal source module, a driving module, an impedance matching module, an ultrasonic transducer module, and an ultrasonic coupling module are enabled in sequence according to the treatment start instruction to implement transcranial focused ultrasound stimulation of the above target of the target patient.

[0030] The present invention also discloses an electronic device, including: at least one processor, a memory, and a computer program stored in the memory and executable on the at least one processor, where the processor executes the computer program to implement the method as described in any one of the previous items.

[0031] The present invention also discloses a computer storage medium. The computer-readable storage medium stores a computer program, and the computer program is executed by a processor to implement the method as described in any one of the previous items.

[0032] The present invention also discloses a computer program product. When the computer program product runs on a terminal, it causes the terminal to execute to implement the method as described in any one of the previous items.

[0033] The beneficial effects of the present invention are as follows: The present invention can determine personalized targets according to the actual condition of a target patient and determine a suitable treatment intensity, thereby facilitating obtaining the best treatment effect. Description of the Drawings

[0034] To more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the drawings required in the embodiments. It should be understood that the following drawings only show some embodiments of the present invention, and therefore should not be regarded as limiting the scope. For those of ordinary skill in the art, without creative efforts, other related drawings can also be obtained based on these drawings.

[0035] Figure 1 It is a schematic structural diagram of a transcranial focused ultrasound stimulation system for patients with cognitive impairment disclosed in an embodiment of the present invention.

[0036] Figure 2 It is the shape of one of the original signals disclosed in an embodiment of the present invention.

[0037] Figure 3 It is a schematic flowchart of a transcranial focused ultrasound stimulation method for patients with cognitive impairment disclosed in an embodiment of the present invention. Specific Embodiments

[0038] The following specific embodiments illustrate the implementation manners of the present application. Those skilled in the art can easily understand other advantages and effects of the present application from the content disclosed in this specification. Obviously, the described embodiments are part of the embodiments of the present application, rather than all of them. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present application.

[0039] In addition, the technical features involved in different implementation manners of the present application described below can be combined with each other as long as they do not conflict with each other.

[0040] As Figure 1 shown, an embodiment of the present invention discloses a transcranial focused ultrasound stimulation system for patients with cognitive impairment. The system includes a main control module, a communication module, a display module, a signal source module, a driving module, an impedance matching module, an ultrasonic transducer module, and an ultrasonic coupling module. Among them, the main control module is the core control module of the entire system, and is used to provide the required timer resources for the signal source module, provide the required display data for the display module, provide the required communication data for the communication module, and receive the data transmitted by the communication module.

[0041] Among them, the MCU of the main control module can adopt the STM32F1 series.

[0042] The signal source module is responsible for generating an online adjustable original signal, and the original signal is a pair of PWM signals with complementary polarities and dead time output by the advanced timer of the main control module.

[0043] The above original signal is a pair of PWM signals with complementary polarities and dead time output by the MCU advanced timer. The signal is actually a series of pulse groups. A single pulse group consists of several pulses, and a single pulse consists of several fundamental waves. The specific shape of one of the pair of complementary signals is as Figure 2 shown, and the other signal has the opposite polarity to it.

[0044] The signal has the following parameters: fundamental frequency: that is, the basic square wave frequency that makes up the signal; pulse duration (TBD): the duration of a single pulse; pulse repetition frequency (PRF): the frequency at which the pulses repeat; burst duration (SD): the duration of a single pulse burst; inter-stimulus interval (ISI): the interval between pulse bursts. All of the above parameters of the signal can be adjusted online by controlling the advanced timer of the MCU through buttons.

[0045] The driving module is used to amplify the original signal to obtain a driving signal capable of driving the ultrasonic transducer module.

[0046] The driving module is, for example, a full-bridge driving circuit with an IR2110 chip as the core, followed by a transformer, used to amplify the original signal to meet the requirements for driving the ultrasonic transducer. The input of the driving module is the original signal output by the signal source module, and the output is the amplified driving signal.

[0047] The impedance matching module consists of an LC matching network. The input is the driving signal output by the driving module, and the output is the impedance-matched driving signal.

[0048] The function of the impedance matching module is to reduce reflection and improve efficiency, ensuring that the output impedance of the signal source matches the input impedance of the load. According to the maximum power transfer theorem, maximum power transfer can be obtained when the source impedance and the load impedance are equal.

[0049] The ultrasonic transducer module: its input is the impedance-matched driving signal, used to convert the electrical driving signal into an acoustic signal, that is, to emit focused ultrasonic waves that meet the requirements; among them, the target position of the focused ultrasonic wave stimulation is confirmed by resting-state functional magnetic resonance; the ultrasonic coupling module is responsible for effectively transmitting ultrasonic waves between the ultrasonic transducer module and the object to be measured, helping to eliminate the acoustic impedance difference in the air.

[0050] The main function of the ultrasonic coupling module is to achieve low-loss transmission of ultrasonic wave signals, usually composed of water or special ultrasonic coupling glue. By selecting a suitable coupling medium, the ultrasonic coupling module can significantly improve the signal transmission efficiency and reduce the signal loss during transmission.

[0051] The display module is used to display the parameter information of the original signal and other stimulation information in real time.

[0052] The display module can use a TFT-LCD screen, connected to the main control module through an 80 serial port, and can display the parameter information of the original signal and other stimulation information in real time. The TFT-LCD screen has the characteristics of high resolution, bright colors, fast response, energy saving, etc., and is widely used in the medical display field.

[0053] The communication module is used to record and store stimulation logs and communicate with the host computer.

[0054] The communication storage flash selects W25Q64, communicates with the main control chip through the SPI protocol, and records information such as the time of each stimulation and stimulation parameters. Through the USB-to-serial port, the host computer can communicate with the stimulation system and read the stimulation log information.

[0055] In some embodiments, the system further includes a target position determination module, and the target position determination module is connected to the communication module; the target position determination module is used to perform the following steps: S1. Use resting-state functional magnetic resonance imaging technology to measure the blood oxygenation level-dependent (BOLD) signal changes in the brain of the target patient in the resting state, and divide the brain into several brain regions including the frontal lobe, parietal lobe, temporal lobe, and occipital lobe.

[0056] These brain regions mentioned above have different functional divisions in the brain. For example, the frontal lobe is responsible for higher cognitive functions, the parietal lobe is responsible for processing sensory information, the temporal lobe is related to hearing and language, and the occipital lobe is related to visual processing.

[0057] S2. For each of the brain regions, calculate the functional connection strength between it and other brain regions, and average the functional connection strengths between each of the brain regions and other brain regions to determine the brain region with the weakest average connection strength, and use it as the target brain region; wherein, the functional connection strength is achieved by calculating the correlation of the blood oxygenation level-dependent signals between two brain regions.

[0058] The higher the correlation between two brain regions, the stronger the functional connection between the two brain regions. The target brain region may be the region most affected or functionally abnormal in the disease state.

[0059] S3. Divide the target brain region into smaller sub-brain regions, repeat the above step S2 for each of the sub-brain regions, and use the sub-brain region with the weakest average functional connection as the target for neuromodulation.

[0060] The above smaller sub-brain regions are, for example, gyri, sulci, etc. The above target may be the most critical abnormal region in the disease state, and neuromodulation of it may help improve the patient's symptoms.

[0061] S4. Transmit the target to the main control module through the communication module, so that the main control module displays the information of the target on the display module.

[0062] In some embodiments, any one of the Pearson correlation coefficient, regional homogeneity, seed-based functional connectivity, and functional network connectivity analysis methods is used to calculate the correlation of the blood oxygenation level-dependent signals between two brain regions.

[0063] In resting-state functional magnetic resonance imaging (rs-fMRI), functional connectivity strength (or connection value) generally refers to the correlation measure of the time series data of blood oxygenation level-dependent (BOLD) signals between different brain regions. This correlation can reflect the interrelationships between different brain regions or tissues and is a key indicator for analyzing brain functional connectivity.

[0064] Specifically, functional connectivity strength can be calculated in the following ways: Pearson correlation coefficient: This is one of the most commonly used methods for measuring the linear correlation degree between two time series. The value range of the Pearson correlation coefficient is from -1 to 1, where 0 indicates no correlation, a positive value indicates positive correlation, and a negative value indicates negative correlation. In rs-fMRI, by calculating the Pearson correlation coefficient of the time series of BOLD signals between two regions of interest (ROIs), the functional connectivity strength between these two regions can be obtained.

[0065] Regional homogeneity (ReHo): This is an index for measuring local connectivity within a specific region and is calculated by analyzing whether the activities of a specific voxel and its neighboring voxels are correlated (usually using the Kendall's coefficient of concordance test).

[0066] Seed-based functional connectivity (seed-based FC): In this method, first one or more seed points (ROIs) are selected, and then the correlation between the average time series of all voxels within these seed points and the time series of other voxels in the whole brain is calculated to obtain a whole-brain functional connectivity map (FC map).

[0067] Functional network connectivity analysis (FNC): This method combines model-driven and data-driven methods. First, subject-specific functional networks and their related fluctuations are obtained through group independent component analysis (ICA), and then the functional connectivity between these networks is calculated to generate a functional connectivity matrix containing the connectivity strengths between all networks.

[0068] In some embodiments, the system further includes a treatment intensity determination module, and the treatment intensity determination module is connected to the communication module; the treatment intensity determination module is configured to perform the following steps: collect first treatment big data of similar patients with the same cognitive impairment level as the target patient, and second treatment big data of similar patients with a higher cognitive impairment level than the target patient; wherein, the first treatment big data and the second treatment big data contain a number of transcranial focused ultrasound stimulation data, and each of the transcranial focused ultrasound stimulation data contains target point information, treatment intensity, cognitive impairment level, and treatment effect information.

[0069] Taking the target information, the treatment intensity, and the cognitive impairment level as the training data ontology, and taking the treatment effect information as the training data label, construct a first training dataset and a second training dataset based on the first treatment big data and the second treatment big data respectively.

[0070] Train a pre-constructed first deep prediction model using the first training dataset, train a pre-constructed second deep prediction model using the second training dataset, and train a pre-constructed third deep prediction model using the first training dataset and the second training dataset.

[0071] Input the information of the target of the target patient and the target cognitive impairment level into the trained first deep prediction model, the second deep prediction model, and the third deep prediction model respectively, obtain the predicted first treatment intensity, second treatment intensity, and third treatment intensity respectively, calculate the difference between the second treatment intensity and the third treatment intensity, and determine the matching first weakening coefficient according to the difference.

[0072] Calculate the fourth treatment intensity according to the first weakening coefficient and the first treatment intensity, and the fourth treatment intensity is the final treatment intensity.

[0073] In this embodiment, in transcranial focused ultrasound stimulation, the treatment intensity is a key parameter related to treatment safety and effectiveness, referring to the energy level of ultrasonic waves. The specific parameters include spatial peak temporal average intensity (I SPTA), spatial average temporal average intensity (ISATA), and spatial peak pulse average intensity (ISPPA). The specific introduction is as follows: Spatial peak temporal average intensity (I SPTA): This is the temporal average intensity of ultrasonic waves at the spatial peak position, used to describe the average energy level of ultrasonic waves within the action area.

[0074] Spatial average temporal average intensity (ISATA): This is the intensity averaged over time within the entire action area, used to describe the energy distribution of ultrasonic waves within the entire treatment area.

[0075] Spatial peak pulse average intensity (ISPPA): This is the average pulse intensity at the spatial maximum position of the ultrasonic pulse, related to short-term mechanical biological effects.

[0076] The present invention determines the targeted treatment intensity for the target patient by combining big data with deep prediction algorithms, as follows: First, collect the first treatment big data of similar patients with the same level of cognitive impairment as the target patient, and the second treatment big data of similar patients with a higher level of cognitive impairment than the target patient. Among them, cognitive impairment can be classified into different levels according to its severity. For example: Subjective Cognitive Decline (SCD): This is considered an earlier stage than Mild Cognitive Impairment (MCI). SCD refers to an individual's subjective complaint or memory or other cognitive function decline, but the objective cognitive test is still within the normal range. Mild Cognitive Impairment (MCI): This is a transitional state between normal cognitive function and dementia. MCI involves impairment of one or more functions in multiple cognitive domains such as memory, attention, language, execution, reasoning, calculation, and orientation, which can affect the social function and quality of life of patients to varying degrees. Dementia: This is a more severe stage of cognitive impairment, involving severe impairment of cognitive function and seriously affecting the ability to perform daily activities. In addition, cognitive impairment can also be classified according to its etiology and pathological mechanism, such as Alzheimer's disease (AD), vascular cognitive impairment (VCI), dementia with Lewy bodies (DLB), frontotemporal lobar degeneration (FTD), etc. In vascular cognitive impairment, according to the severity of cognitive impairment, it can be further divided into mild VCI and severe VCI (including post-stroke dementia PSD, subcortical ischemic vascular dementia SIVaD, multi-infarct dementia MID, and mixed dementia MixD). The level of cognitive impairment of the target patient is predetermined. On this basis, similar patients with the same level of cognitive impairment as the target patient and similar patients with a higher level of cognitive impairment than the target patient (i.e., more severe) can be selected according to any of the above level classification methods, and the treatment big data of these similar patients are collected through multiple data source channels. The collected treatment big data contains several transcranial focused ultrasound stimulation data, and each transcranial focused ultrasound stimulation data contains target information, treatment intensity, cognitive impairment level, and treatment effect information. It should be noted that "similar patients" refers to patients with the same type of cognitive impairment, either to the same degree or different degrees, such as all being vascular cognitive impairment patients, the former being mild VCI and the latter being severe VCI.

[0077] Then, using the target information, treatment intensity, and cognitive impairment level in each transcranial focused ultrasound stimulation data as the training data ontology, and the treatment effect information as the training data label, the first training dataset and the second training dataset can be constructed respectively. The pre-constructed first deep prediction model is trained using the first training dataset, the pre-constructed second deep prediction model is trained using the second training dataset, and the pre-constructed third deep prediction model is trained using the first training dataset and the second training dataset. The first deep prediction model, the second deep prediction model, and the third deep prediction model are all preferably constructed based on the Transformer algorithm.

[0078] Based on the information of the above-mentioned target points and the target cognitive impairment level (obtained in advance) of the target patient, the well-trained first deep prediction model, second deep prediction model, and third deep prediction model can respectively deduce the treatment intensity that can achieve the best treatment effect. On this basis, the present invention further calculates the difference (absolute value) between the third treatment intensity and the second treatment intensity, and then determines the matching first weakening coefficient according to this difference. Using this first weakening coefficient to moderately reduce the previously predicted first treatment intensity, the fourth treatment intensity with higher safety is obtained, and this fourth treatment intensity is used as the initial treatment intensity for the target patient. If the subsequent treatment effect is good, the treatment intensity can be gradually reduced.

[0079] It should be noted that the second deep prediction model and the third deep prediction model in the present invention are trained using different training data, and the difference between the treatment intensities analyzed by the two for the same input data, that is, the information of the target points of the target patient and the target cognitive impairment level, is analyzed. This difference reflects the "disagreement" of different models on the treatment intensity of the same patient. Therefore, to ensure the safety of treatment, the present invention sets that when this "disagreement", that is, the difference is larger, the corresponding first weakening coefficient is smaller (for example, 0.8), that is, a smaller first weakening coefficient is used to lower the previously predicted first treatment intensity to a greater extent; when this "disagreement", that is, the difference is smaller, the corresponding first weakening coefficient is larger (for example, 0.9), that is, a larger first weakening coefficient is used to lower the previously predicted first treatment intensity to a smaller extent.

[0080] In some embodiments, determining the matching first weakening coefficient according to the difference includes: performing matching calculations according to the difference and the preset control relationship data to obtain the matching first weakening coefficient.

[0081] In this embodiment, the control relationship data between the difference and the first weakening coefficient can be established by actual measurement or simulation model speculation. After obtaining the above difference, the corresponding first weakening coefficient can be quickly obtained through matching analysis with the preset control relationship data.

[0082] In some embodiments, calculating a fourth treatment intensity based on the first weakening coefficient and the first treatment intensity, where the fourth treatment intensity is the final treatment intensity, includes: obtaining historical treatment data of a target patient, calculating the number of times of treatment using transcranial focused ultrasound stimulation based on the historical treatment data, and determining a second weakening coefficient based on the number of times; calculating the fourth treatment intensity using the first weakening coefficient, the second weakening coefficient, and the first treatment intensity, where the fourth treatment intensity is the final treatment intensity.

[0083] In this embodiment, the target patient may have received transcranial focused ultrasound stimulation treatment in other medical institutions before, so the initial treatment intensity for this time can be appropriately weakened again accordingly. Specifically, first obtain the historical treatment data of the target patient, extract the treatment records using transcranial focused ultrasound stimulation from the historical treatment data, and count the number of records, and determine the second weakening coefficient based on the number of times. Then, simultaneously weaken the first treatment intensity using the first weakening coefficient and the second weakening coefficient to obtain the final fourth treatment intensity.

[0084] Among them, both the first weakening coefficient and the second weakening coefficient are values less than or equal to 1, and the second weakening coefficient is negatively correlated with the above-mentioned number of times, that is, the larger the above-mentioned number of times, the smaller the second weakening coefficient (for example, 0.8), and the smaller the above-mentioned number of times, the larger the second weakening coefficient (for example, 0.9). However, the present invention does not limit the corresponding relationship between the second weakening coefficient and the above-mentioned number of times, which can be either a conversion formula or, for example, the aforementioned preset control relationship data.

[0085] In addition, some usage details of the above system of the present invention are specifically introduced as follows: Electroencephalogram (EEG) data collection before stimulation: Collect resting-state EEG, including five minutes with eyes open and five minutes with eyes closed.

[0086] Stimulation protocol: Stimulate continuously for 5 days and then rest for 2 days. One week is a cycle, and stimulate continuously for three cycles, that is, three weeks, and stimulate for 15 minutes every day.

[0087] EEG data collection after stimulation: After the complete stimulation protocol is completed, collect resting-state EEG, including five minutes with eyes open and five minutes with eyes closed.

[0088] EEG data preprocessing: For the EEG collected before and after stimulation, perform filtering, downsampling, deleting bad segments, interpolating bad leads, removing ICA components, rereferencing, and data segmentation in sequence for subsequent processing.

[0089] EEG data processing: For the preprocessed EEG data, perform processing and analysis from three perspectives: EEG complexity, EEG power spectrum, and brain network functional connectivity.

[0090] (1) EEG complexity: The EEG complexity of patients with cognitive impairment is generally lower than that of the healthy population. Calculate the multi-scale entropy of the EEG data before and after stimulation respectively, and analyze whether the EEG complexity after stimulation is higher than that before stimulation. If it is increased, the stimulation is effective.

[0091] (2) EEG power spectrum: The EEG power spectrum of patients with cognitive impairment is different from that of the healthy population. Specifically, compared with the healthy population, the power of the α and β frequency bands of patients with cognitive impairment is lower, and the power of the δ and θ frequency bands is higher. Calculate the power of each frequency band of the EEG data before and after stimulation respectively, and analyze whether there is improvement in the power spectrum.

[0092] (3) Brain network functional connectivity: The brain network functional connectivity of patients with cognitive impairment is weaker than that of the healthy population. Calculate the functional connectivity indexes of the EEG data before and after stimulation, such as COH and PLI, and analyze whether the brain network functional connectivity is enhanced after stimulation.

[0093] As Figure 3 shown, the embodiment of the present invention also discloses a working method of a transcranial focused ultrasound stimulation system for patients with cognitive impairment. The method includes the following steps: The target position determination module determines the information of the target corresponding to the target patient, and transmits it to the main control module through the communication module.

[0094] The treatment intensity determination module determines the treatment intensity, and transmits it to the main control module through the communication module.

[0095] The main control module generates a treatment start instruction according to the information of the target and the treatment intensity. The signal source module, the drive module, the impedance matching module, the ultrasonic transducer module, and the ultrasonic coupling module are enabled in sequence according to the treatment start instruction to realize the transcranial focused ultrasound stimulation of the above-mentioned target of the target patient.

[0096] The embodiment of the present invention also discloses an electronic device, including: at least one processor, a memory, and a computer program stored in the memory and executable on the at least one processor, wherein: the processor executes the computer program to implement the method as described in the foregoing embodiment.

[0097] The embodiment of the present invention also discloses a computer storage medium, the computer storage medium stores a computer program, wherein: the computer program is executed by a processor to implement the method as described in the foregoing embodiment.

[0098] The embodiment of the present invention also discloses a computer program product, when the computer program product runs on a terminal, the terminal is enabled to execute to implement the method as described in the foregoing embodiment.

[0099] The present invention is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to embodiments of the present invention. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, as well as the combination of flows and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, such that the instructions executed by the processor of the computer or other programmable data processing devices generate a means for implementing the functions specified in the Figure 1 one or more flows and / or blocks Figure 1 means for implementing the functions specified in one or more blocks.

[0100] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, such that the instructions stored in the computer-readable memory generate a manufactured article including instruction means that implement the functions specified in the Figure 1 one or more flows and / or blocks Figure 1 means for implementing the functions specified in one or more blocks.

[0101] These computer program instructions can also be loaded onto a computer or other programmable data processing device, such that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process, so that the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in the Figure 1 one or more flows and / or blocks Figure 1 means for implementing the functions specified in one or more blocks.

[0102] As mentioned above, it is only a preferred embodiment of the present invention and is not used to limit the protection scope of the present invention.

Claims

1. A transcranial focused ultrasound stimulation system for patients with cognitive impairment, characterized in that: The system includes a main control module, a communication module, a display module, a signal source module, a driving module, an impedance matching module, an ultrasonic transducer module, and an ultrasonic coupling module; among them, the main control module is the core control module of the entire system, and is used to provide the required timer resources for the signal source module, provide the required display data for the display module, provide the required communication data for the communication module, and receive the data transmitted by the communication module; The signal source module is responsible for generating an online adjustable original signal, and the original signal is a pair of PWM signals with complementary polarities and dead time output by the advanced timer of the main control module; The driving module is used to amplify the original signal to obtain a driving signal capable of driving the ultrasonic transducer module; The impedance matching module consists of an LC matching network, with the input being the driving signal output by the driving module and the output being the impedance-matched driving signal; The ultrasonic transducer module has an input of the impedance-matched driving signal and is used to convert the electrical driving signal into an acoustic signal, that is, to emit focused ultrasonic waves that meet the requirements; among them, the target position stimulated by the focused ultrasonic waves is confirmed according to resting-state functional magnetic resonance; The ultrasonic coupling module is responsible for effectively transmitting ultrasonic waves between the ultrasonic transducer module and the object to be measured, and helps to eliminate the acoustic impedance difference in the air; The display module is used to display the parameter information of the original signal and other stimulation information in real time; the communication module is used to record and store stimulation logs and communicate with the host computer; The system further includes a treatment intensity determination module, and the treatment intensity determination module is connected to the communication module; the treatment intensity determination module is used to perform the following steps: Collect the first treatment big data of similar patients with the same cognitive impairment level as the target patient, and the second treatment big data of similar patients with a higher cognitive impairment level than the target patient; among them, the first treatment big data and the second treatment big data contain a number of transcranial focused ultrasound stimulation data, and each transcranial focused ultrasound stimulation data contains target information, treatment intensity, cognitive impairment level, and treatment effect information; Use the target information, the treatment intensity, and the cognitive impairment level as the training data ontology, and use the treatment effect information as the training data label, and construct a first training data set and a second training data set respectively based on the first treatment big data and the second treatment big data; Train a pre - constructed first depth prediction model using the first training dataset, train a pre - constructed second depth prediction model using the second training dataset, and train a pre - constructed third depth prediction model using the first training dataset and the second training dataset; input the information of the target point and the target cognitive impairment level of the target patient into the trained first depth prediction model, the second depth prediction model, and the third depth prediction model respectively, obtain the predicted first treatment intensity, second treatment intensity, and third treatment intensity respectively, calculate the difference between the second treatment intensity and the third treatment intensity, and determine a matching first weakening coefficient according to the difference; the first weakening coefficient is negatively correlated with the difference; Calculate a fourth treatment intensity according to the first weakening coefficient and the first treatment intensity, and the fourth treatment intensity is the final treatment intensity; The calculating a fourth treatment intensity according to the first weakening coefficient and the first treatment intensity, and the fourth treatment intensity is the final treatment intensity includes: Obtain the historical treatment data of the target patient, calculate the number of times of treatment using transcranial focused ultrasound stimulation according to the historical treatment data, and determine a second weakening coefficient according to the number of times; the second weakening coefficient is negatively correlated with the number of times; Calculate the fourth treatment intensity using the first weakening coefficient, the second weakening coefficient, and the first treatment intensity, and the fourth treatment intensity is the final treatment intensity.

2. The transcranial focused ultrasound stimulation system for patients with cognitive impairment according to claim 1, characterized in that: The system further includes a target location determination module, and the target location determination module is connected to the communication module; the target location determination module is used to perform the following steps: S1. Measure the blood oxygenation level - dependent signal change of the target patient's brain in the resting state using resting - state functional magnetic resonance imaging technology, and divide the brain into several brain regions including the frontal lobe, parietal lobe, temporal lobe, and occipital lobe; S2. For each of the brain regions, calculate the functional connection strength between it and other brain regions, and average the functional connection strengths between each of the brain regions and other brain regions to determine the brain region with the weakest average connection strength as the target brain region; wherein, the functional connection strength is achieved by calculating the correlation of the blood oxygenation level - dependent signals between two brain regions; S3. Divide the target brain region into smaller sub - brain regions, repeat the above step S2 for each of the sub - brain regions, and use the sub - brain region with the weakest average functional connection as the target for neuromodulation; S4. Transmit the target to the main control module through the communication module so that the main control module can display the information of the target on the display module.

3. The transcranial focused ultrasound stimulation system for patients with cognitive impairment according to claim 1, wherein: Calculate the correlation of the blood oxygenation level - dependent signals between two brain regions using any one of the Pearson correlation coefficient, regional homogeneity, seed - based functional connectivity, and functional network connectivity analysis methods.

4. The transcranial focused ultrasound stimulation system for patients with cognitive impairment according to claim 1, characterized in that: The determining a matching first weakening coefficient according to the difference includes: performing a matching calculation according to the difference and the preset control relation data to obtain the matching first weakening coefficient.

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