Context-dependent temporal interference electrical stimulation system based on a brain circuit causal model

Abstract

A time-interventional electrical stimulation system based on a brain circuit causal model is disclosed, comprising: an analysis unit for acquiring functional magnetic resonance imaging (fMRI) data of a subject and analyzing the data to obtain the abnormal connectivity direction, abnormal nature, abnormal context, and / or clinical symptom association strength of specific brain regions; a decision unit for determining target points and electrical stimulation induction contexts based on the abnormal results, and obtaining an electrical stimulation parameter set through electric field simulation and stimulation intervention results; a stimulation unit for generating a stimulation current based on the electrical stimulation parameter set and outputting the stimulation current through an electrode array; and a control unit for acquiring data from the analysis unit, decision unit, and stimulation unit, and sending control commands to control the operation of the analysis unit, decision unit, and stimulation unit. The system provides precise deep brain stimulation based on the analysis of individual brain circuit abnormalities, supports precise abnormality localization, contextual adaptation, and highly focused target modulation, and improves the specificity and effectiveness of non-invasive brain stimulation.

Description

Technical Field

[0001] This application belongs to the field of time-interference electrical stimulation technology, specifically, it relates to a situation-dependent time-interference electrical stimulation system based on a brain circuit causal model. Background Technology

[0002] Depression is a type of mood disorder with a high incidence and recurrence rate. Its core characteristics are closely related to abnormalities in neural circuits involved in emotion regulation and reward processing. Existing neuroimaging studies have shown that the bidirectional functional connectivity between the basolateral amygdala (BLA) and the nucleus accumbens (NAc) in individuals with depression commonly exhibits abnormal patterns of over-enhancement or under-enhancement, leading to increased negative mood bias, decreased brain reward sensitivity, and impaired emotion regulation. Current non-invasive brain stimulation techniques, such as transcranial direct current stimulation and transcranial alternating current stimulation, primarily act on superficial cortical regions, making it difficult to effectively modulate deep brain structures such as the BLA and NAc. Furthermore, these techniques cannot precisely target stimulation levels based on the individual's causal connectivity state of brain circuits and situational conditions, making it difficult to dynamically adjust the stimulation pattern in conjunction with situational evoked tasks.

[0003] Temporal interferential electrical stimulation (tTIS) generates a difference-frequency envelope signal in deep brain tissue using two high-frequency currents, enabling non-invasive and flexible deep neural modulation. However, current tTIS stimulation targets are mostly located using standard templates, lacking individualized identification of circuit anomalies and having limited focusing ability to generate electric fields at deep targets. It also lacks the ability to intervene in real-time based on context-dependent circuit responses, particularly in complex abnormal circuits.

[0004] Therefore, there is a need for a deep brain modulation system that can combine causal models of analysis context and brain region connectivity with the activities of context-dependent circuits to support the optimization of tTIS stimulation parameters, in order to meet the individualized and in-depth needs of mood disorder treatment. Summary of the Invention

[0005] To address the problems existing in the prior art, the purpose of this application is to provide a time-interference electrical stimulation system based on a brain circuit causal model. The analysis unit uses the subject's functional magnetic resonance imaging data to quantitatively identify the abnormal connection direction, abnormal situation, and abnormal nature of the BLA-NAc circuit under positive or negative situations to determine the target point. The decision unit constructs a simulation model and generates the optimal tTIS electrical stimulation parameter set based on the target point, so that the stimulation unit outputs stimulation current at specific electrode positions. The envelope current generated in the brain region can act on the abnormal connection target point during the period of circuit activity induced by the situation, so as to achieve situation-dependent deep brain neural modulation, effectively intervene in the subject's brain structure and improve mood in a non-invasive manner.

[0006] Specifically, this application relates to the following aspects: A time-interventional electrical stimulation system based on a brain circuit causal model includes: an analysis unit that acquires functional magnetic resonance imaging (fMRI) data from the subject and analyzes the fMRI data to obtain abnormal results in specific brain regions, including abnormal connectivity direction, abnormal nature, abnormal context, and / or clinical symptom association strength; a decision unit that determines the stimulation intervention result based on the abnormal results, including the target point and the electrical stimulation evoked context, and obtains an electrical stimulation parameter set through electric field simulation and stimulation intervention results; a stimulation unit that generates a stimulation current based on the electrical stimulation parameter set and outputs the stimulation current through an electrode assembly; and a control unit that acquires data from the analysis unit, decision unit, and stimulation unit, and sends control commands to control the operation of the analysis unit, decision unit, and stimulation unit.

[0007] According to some implementation methods, the time-interference electrical stimulation system based on the brain circuit causal model also includes a display unit; the display unit communicates with the analysis unit, the decision-making unit and the control unit, and assists the control unit in determining the first time period that needs to be provided for the evoked situation during the process of the analysis unit acquiring the functional magnetic resonance data of the subject, and the second time period that needs to be provided for the electrical stimulation evoked situation during the process of the stimulation unit outputting the stimulation current, so as to receive the control instructions of the control unit and play the corresponding situation videos in the first time period and the second time period respectively.

[0008] According to some implementation methods, the analysis unit analyzes functional magnetic resonance imaging (fMRI) data to obtain abnormal results in specific brain regions of the subject, including: extracting the activation intensity of BLA-NAc connections in the subject's fMRI data under different situational tasks to determine whether the subject's BLA-NAc connections exhibit abnormal functional performance; in response to the presence of abnormal functional performance, constructing a DCM model based on the subject's fMRI data and obtaining group-level connectivity strength to determine the abnormal connection direction, abnormal nature, and abnormal context of BLA-NAc connections; and screening abnormal connection directions, abnormal nature, and abnormal contexts that are highly correlated with specific clinical symptoms through correlation analysis to obtain the clinical symptom association strength.

[0009] According to some implementation methods, the decision-making unit determines the stimulation intervention result based on the abnormal result by: selecting a target from BLA or NAc according to the abnormal connection direction, and determining the stimulation current frequency range of the target based on the abnormality, abnormal context and the correlation strength of clinical symptoms.

[0010] According to some implementation methods, determining the stimulation current frequency range of the target point includes: determining the stimulation current frequency range required for the lateral brain region with the highest correlation to emotional symptoms in the abnormal connection direction between BLA and NAc based on the nature of the abnormality, the abnormal situation and the correlation strength of clinical symptoms, and determining the electrical stimulation evoked situation based on the abnormal situation.

[0011] According to some implementation methods, before acquiring the functional magnetic resonance imaging (fMRI) data of the subjects, the analysis unit further includes: acquiring fMRI data of healthy individuals; modeling fMRI data under positive and negative situations to obtain the activation intensity of the BLA and NAc brain regions in healthy individuals under positive and negative situations; judging the interaction between situation type and brain region type and the influence of situation modulation on functional connectivity strength based on the activation intensity, so as to determine the specific causal modulation effect of situation modulation in BLA-NAc connectivity and the direction of situation modulation; and determining the type of electrical stimulation-induced situation required under specific abnormal connectivity directions and abnormal properties based on the specific causal modulation effect and the direction of situation modulation.

[0012] According to some implementation methods, the decision-making unit obtains the set of electrical stimulation parameters through electric field simulation and stimulation intervention results, including: determining the stimulation current frequency from the stimulation current frequency range based on the stimulation intervention results; and determining the electrode position, stimulation current amplitude, and / or stimulation current phase in the electrode group that produces the maximum temporal interference electric field strength and the maximum electric field spatial focusing at the target point by performing electric field simulation on the simulation model.

[0013] According to some implementation methods, the stimulation unit generates a stimulation current based on an electrical stimulation parameter set and outputs the stimulation current through an electrode group, including: generating a stimulation current based on the stimulation current frequency, stimulation current amplitude, and / or stimulation current phase; and inputting the stimulation current to the electrodes in the electrode group located at the electrode positions based on the electrode positions, so that the electrodes generate a time interference envelope waveform at the target point.

[0014] According to some implementation methods, the decision unit is also used to acquire the subject's structural magnetic resonance images to generate a finite element head model of the subject including skin, skull, cerebrospinal fluid, gray matter and white matter, and to map BLA and NAc into the finite element head model to obtain a simulation model.

[0015] According to some implementations, the control unit transmits data and control commands to the analysis unit, decision-making unit, and stimulation unit via wired or wireless communication.

[0016] The advantages of the time-interference electrical stimulation system based on the brain circuit causal model provided in this application are as follows.

[0017] The system analyzes the BLA–NAc bidirectional connectivity using a dynamic causal model, enabling it to identify abnormal connection directions rather than simply determining approximate regulatory intervals based on correlation. This fundamentally improves the accuracy of stimulus targets. The system supports context-dependent, individualized, real-time stimulus strategies, generating individualized stimulus plans based on abnormal connection directions under positive or negative emotional situations. It also ensures that stimuli are triggered synchronously with emotion-inducing tasks, allowing the envelope signal to act within the most sensitive contextual window of the loop, thereby enhancing the targeting and effectiveness of deep modulation.

[0018] The system achieves envelope regulation and control through rectification and current stabilization, and can output corresponding frequency stimulation for abnormal connections in different directions. By supporting the selective action of two deep target points, the system achieves flexible control of complex circuits and is suitable for efficient and non-invasive stimulation of deep emotion regulation structures: it generates effective difference frequency envelope signals for deep structures such as BLA and NAc without stimulating the cortex, with high safety and few side effects.

[0019] The system can therefore significantly improve the symptoms of patients with depression and has good prospects for practical application. Attached Figure Description

[0020] Figure 1 The figure shows a schematic diagram of a time-interference electrical stimulation system based on a brain circuit causal model according to an embodiment of this application.

[0021] Figure 2 The illustration shows a schematic diagram of an electric field simulation implemented by a time-interference electrical stimulation system based on a brain circuit causal model according to an embodiment of this application.

[0022] Figure 3 The illustration shows a first schematic diagram of the electrical stimulation effect of a time-interference electrical stimulation system based on a brain circuit causal model according to an embodiment of this application.

[0023] Figure 4 The illustration shows a second schematic diagram of the electrical stimulation effect of a time-interference electrical stimulation system based on a brain circuit causal model according to an embodiment of this application.

[0024] Figure 5 The illustration shows a third schematic diagram of the electrical stimulation effect of a time-interference electrical stimulation system based on a brain circuit causal model according to an embodiment of this application. Detailed Implementation

[0025] The present application is further illustrated below with reference to embodiments. It should be understood that the embodiments are only used to further illustrate and explain the present application and are not intended to limit the present application.

[0026] Unless otherwise defined, technical and scientific terms used in this specification have the same meaning as commonly understood by one of ordinary skill in the art. While similar or identical methods and materials may be applied in experimental or practical applications, materials and methods are described herein. In case of conflict, the definitions included herein shall prevail. Furthermore, materials, methods, and examples are for illustrative purposes only and are not intended to be limiting. The present application is further described below with reference to specific embodiments, but is not intended to limit the scope of the application.

[0027] refer to Figure 1The time-interference electrical stimulation system based on a brain circuit causal model according to embodiments of this application includes an analysis unit, a decision-making unit, a display unit, and a stimulation unit; it may also include an independent control unit. Each unit can communicate with each other via wired or wireless communication, for example, via a bus for wired data communication or via the Internet for wireless data communication.

[0028] The analysis unit is used to acquire functional magnetic resonance imaging (fMRI) data from subjects and analyze the data to obtain abnormal results in specific brain regions. Quantitative analysis of abnormalities includes the direction of abnormal connections, the nature of the abnormality, the context of the abnormality, and / or the strength of association with clinical symptoms. The analysis unit is further divided into sub-units based on function: data acquisition and preprocessing, health benchmark construction, and abnormality quantification and association analysis.

[0029] The data acquisition and preprocessing subunit acquires functional magnetic resonance imaging (fMRI) data of the subjects from the imaging equipment, and further, acquires fMRI data of a healthy population to facilitate subsequent analysis of the connectivity patterns of the BLA-NAc circuit and its association with positive and negative situations. This aims to identify BLA-NAc circuit activation strength, functional connectivity, and / or other abnormal features that differ between subjects with mood disorders and healthy individuals. After data acquisition, preprocessing is performed, including format conversion, inter-slice time correction, head motion correction, normalization, spatial smoothing, and noise and drift filtering to reduce noise artifacts and standardize the data. The data acquisition and preprocessing subunit can also acquire and preprocess structural MRI of the subjects using the imaging equipment to facilitate the construction of subsequent simulation models.

[0030] In one example, the data acquisition and preprocessing subunit can communicate with and control a magnetic resonance imaging (MRI) device, such as a 3.0T MR device commonly used in hospitals. The control device acquires fMRI data from healthy individuals and subjects under standardized induced tasks and automatically completes the preprocessing of the obtained fMRI data. Subjects are preferably those suffering from mood disorders such as depression, or may be those with mood disorders caused by other illnesses.

[0031] The process of acquiring fMRI data from subjects and healthy individuals is combined with a situational evoked task, which is presented in the form of video playback. The display unit can store and display fixational video footage as fixational presentation content to correct for human eye gaze. Emotional video footage with positive, negative, and neutral emotional valences serves as situational trigger elements for positive, negative, and neutral situational evoked tasks, respectively. Distraction video footage containing addition and subtraction equation judgments serves as a distraction task video to test individuals' responses after receiving the task. All video footage maintains consistency in playback duration and usage scenarios. In one example, the task is divided into multiple blocks and implemented sequentially. Within each block, the display unit plays content including fixational presentation content, emotional video footage, and distraction task video footage to subjects and healthy individuals. Emotional video footage is presented in order of emotional valence from neutral to negative and negative to positive. Multiple videos can be included within the same emotional valence, and the videos can be randomly arranged.

[0032] The data acquisition and preprocessing subunit ultimately acquires fMRI images of healthy individuals and subjects, including whole-brain 3D high-resolution structural MRI images, as well as fMRI brain images acquired in the resting state and during the situational evoked task provided by the display unit. These images are used for individual brain modeling and BLA-NAc connectivity analysis.

[0033] The health baseline construction subunit is used to execute the analysis workflow of fMRI data from healthy individuals to clarify the role of BLA-NAc circuit functional connectivity in emotion regulation and the specific causal regulatory effect of situational modulation on the BLA-NAc circuit, i.e., the normal situational dependence of the BLA-NAc circuit. First, the health baseline construction subunit analyzes the activation patterns of the BLA-NAc circuit under situational evoked tasks. In one example, the health baseline construction subunit models positive and negative situational evoked tasks separately using generalized linear models. With positive and negative situational tasks as regression variables and the oxygenation level-dependent (BOLD) signals of the BLA and NAc brain regions in the fMRI data as dependent variables, a task-related activation model is constructed to obtain the BLA and NAc activation intensities for each individual in the healthy population under the two types of situational tasks. Subsequently, group-level analysis was performed on the activation intensity of brain regions for each healthy individual. In one example, a two-way factorial ANOVA was used to determine whether there were significant interactions between the factors in the positive and negative situation induced tasks and the BLA and NAc brain regions. When the interactions were significant, a post-hoc test was performed on the differences in activation intensity of different brain regions in different situation induced tasks, so as to determine the activation patterns of BLA and NAc in positive and negative situations respectively.

[0034] Then, the health baseline construction subunit analyzes functional connectivity under context-evoked tasks. In one example, the health baseline construction subunit constructs psychophysical interaction (PPI) models using BLA and NAc as seed points. The two context-evoked tasks are used as psychological variables, and the time series data of the seed points are used as physiological variables. The interaction terms between the two are calculated to interpret changes in BOLD signals in brain regions, characterizing the strength of context-modulated functional connectivity. For example, the significance of the interaction terms is statistically tested. If the interaction terms are significant, it indicates that the context-evoked task specifically modulates the functional connectivity of brain regions, determining that functional connectivity has a specific context-specific modulatory effect; that is, the context-evoked task providing a specific context has a specific effect on functional connectivity. Thus, by comparing the relative strengths of the interaction effects obtained using BLA and NAc as seed points, the main directions of positive and negative context modulation in the BLA-NAc circuit can be obtained.

[0035] Furthermore, the health baseline construction subunit analyzes the regulatory patterns under scenario-induced tasks. In one example, the health baseline construction subunit constructs multiple competing models of the connection between the BLA and NAc based on a dynamic causal model (DCM), including models from BLA to NAc, models from NAc to BLA, and models of bidirectional connections between BLA and NAc. The dominant information flow direction in the BLA-NAc loop is determined using the Bayesian Model Selection (BMS) method to obtain the model that best interprets the fMRI data as the optimal causal model. Based on the optimal causal model, the functional connectivity between BLA and NAc at the baseline state is estimated using the Bayesian Parametric Averaging (BPA) method, i.e., the functional connectivity obtained from fMRI data before the scenario-induced task, represented by matrix A. Based on matrix A, the regulatory parameter values ​​of the functional connectivity at the baseline state under positive and negative scenario-induced tasks are obtained, represented by matrix B. Then, by comparing the B matrix parameters under positive and negative situational evoked tasks, we can identify the specific modulation of causal connection strength imposed by the two situational evoked tasks in the BLA-NAc loop, that is, determine the specific effects of positive and negative situational evoked tasks on the functional connectivity of the BLA-NAc loop.

[0036] Furthermore, the health baseline construction subunit can analyze whether there is a lateralization trend in BLA-NAc circuit connections across different hemispheres of the brain. In one example, the health baseline construction subunit models the BLA-NAc circuits in the left and right hemispheres separately, and performs a two-way factorial ANOVA on the activation intensity, PPI model connectivity, and DCM B-matrix parameters of the two brain regions, using hemisphere factors as the main effect to determine whether there is functional lateralization of the circuit in different brain regions. The significance of hemispheric differences shown in the statistical results determines whether the circuit is lateralized. If so, a strategy that favors applying electrical stimulation to brain regions in the lateralized hemisphere can be considered.

[0037] In this way, the health baseline construction subunit can obtain and output functional and causal connectivity baseline parameters of the BLA-NAc circuit in healthy individuals by analyzing fMRI data. These parameters include the activation patterns of BLA and NAc under positive and negative situational evoked tasks, the main directions of action and specific effects of positive and negative situational evoked tasks on the BLA-NAc circuit, and optionally, the lateralization trend of the BLA-NAc circuit in situational evoked tasks. These parameters can provide a benchmark reference for the decision-making unit to determine the electrical stimulation protocol and the situational evoked tasks provided during electrical stimulation. Furthermore, the functional connectivity parameters of the BLA-NAc circuit in healthy individuals can provide a standard reference for the subsequent anomaly quantification and correlation analysis subunit to identify anomalies in subjects, thereby quickly determining the abnormal connectivity patterns, the nature of the anomalies, and the evoked situations required to improve the nature of the anomalies, in order to develop individualized electrical stimulation protocols.

[0038] The anomaly quantification and correlation analysis subunit is used to analyze, identify, and output individual abnormal results based on the subject's fMRI data. This includes the direction of abnormal connections between the BLA and NAc, such as determining whether the abnormal connection between the two brain regions is from BLA to NAc or from NAc to BLA, or bidirectional between BLA and NAc; the nature of the abnormality, such as whether the abnormal connection between BLA and NAc is abnormally enhanced or abnormally weakened; the eliciting context for improving the abnormal connection, such as whether the abnormal connection between BLA and NAc identified by the health benchmark construction subunit can be improved through positive or negative situation eliciting tasks; and optionally, the strength of the correlation between the identified abnormal connection and the subject's specific clinical symptoms.

[0039] Specifically, the anomaly quantification and correlation analysis subunit is used to perform differential analysis on image features in fMRI. In one example, the anomaly quantification and correlation analysis subunit extracts circuit feature indicators from fMRI data for subjects and healthy individuals under different contextually induced tasks, including BLA-NAc circuit functional connectivity and activation intensity of BLA and NAc. Statistical tests, such as two-sample t-tests or nonparametric tests, are used to compare the circuit feature indicators of subjects and healthy individuals to identify abnormal functional performance in the BLA-NAc circuit and output abnormal circuit feature indicators, thereby determining the abnormal brain regions, abnormal connections, and connection strengths of the subjects.

[0040] Furthermore, the anomaly quantification and correlation analysis subunit identifies the subject's contextual modulation patterns. In one example, the anomaly quantification and correlation analysis subunit constructs a DCM using the subject's fMRI data and estimates the A and B matrix parameters of the subject's fMRI data in the same manner as the aforementioned health baseline construction subunit. Further, statistical tests, such as a two-sample t-test, can be used to compare the functional connectivity at baseline and the specific modulation of causal connectivity strength in the BLA-NAc circuit by the two contextual evoked tasks in the baseline state of the subject and healthy individuals. BMS is then used to analyze whether there are structural distribution differences between the optimal causal model among multiple competing models of the DCM between the subject and healthy individuals. In this way, based on the healthy population baseline reference, it is determined what brain regions are abnormal, what brain region connectivity is abnormal, and what quantitative results of the abnormalities are present in different subjects with different fMRI data. It is also based on the healthy population baseline reference to determine which contextual evoked tasks should be used for improvement intervention, or based on the specific situation exhibited by the subject, to determine which contextual evoked tasks should be used for improvement intervention.

[0041] Furthermore, the anomaly quantification and correlation analysis subunit utilizes correlation analysis methods to calculate the correlation coefficients between characteristic indicators such as brain region activation intensity, PPI-based functional connectivity, and A and B matrix parameters of the DCM under situational evoked tasks, and the scores of specific clinical symptoms exhibited by subjects. This identifies the most relevant abnormal connection directions and properties in the BLA-NAc circuit to specific clinical symptoms. By screening abnormal connection directions and properties that show significant intergroup differences between subjects and healthy individuals and are most significantly correlated with the severity of clinical symptoms as abnormal outcomes, and determining the situational evoked tasks to be implemented as abnormal situations, and optionally identifying the lateralization results of the BLA-NAc circuit, a suitable target for electrical stimulation intervention is obtained.

[0042] In this way, by acquiring, processing and analyzing fMRI data from subjects and healthy individuals, the analysis unit obtains quantitative and qualitative results such as the direction and nature of abnormal connections in the BLA-NAc circuit and their correlation with clinical symptoms. It also identifies specific situational induction tasks that have a causal moderating effect on the subject's BLA-NAc circuit. This helps the decision-making unit determine the specific intervention plan that should be provided to the subject by combining tTIS with situational induction tasks.

[0043] The system also includes a decision-making unit, used to determine the stimulation intervention outcome based on quantitative and qualitative results of anomalies, including the brain region where the target is located, target connectivity, property regulation direction, and / or electrical stimulation evoked context, and to obtain the electrical stimulation parameter set through electric field simulation and stimulation intervention results. The decision-making unit is further divided into a target intelligent decision-making subunit and an electric field simulation and parameter optimization subunit.

[0044] The target intelligent decision-making subunit receives abnormal results from the analysis unit and determines the optimal electrical stimulation target for the subject based on these results. It first identifies the target based on the direction and nature of the abnormal connection in the subject's BLA-NAc circuit and its correlation with specific clinical symptoms. Specifically, for the first scenario, where the abnormal results indicate an abnormal connection only in a single specific direction, a single brain region is selected as the target. For example, if the analysis unit processes a subject's fMRI data and finds an abnormal connection from BLA to NAc under a negative situation induced task, and this abnormal connection is abnormally enhanced, and the abnormality in BLA is highly positively correlated with the subject's specific clinical emotional symptoms, such as depression, anxiety, irritability, and tension, then the upstream node BLA in the BLA-NAc circuit is identified as an inhibitory target, requiring time-interference difference frequency envelope waveforms in a specific frequency range generated by tTIS to provide inhibitory stimulation to BLA. Alternatively, for example, if the subject shows an abnormally weakened connection from NAc to BLA only in a specific direction, such as in a positive situational evoked task, and NAc is highly negatively correlated with the subject's anhedonia symptoms, then the downstream node NAc of BLA-NAc is identified as the amplifying target, and the time interference difference frequency envelope waveform in a specific frequency range generated by tTIS is needed to amplify the target.

[0045] In the second scenario, if the subject exhibits specific abnormalities in both the connection from BLA to NAc and the connection from NAc to BLA under the situational evoked task, the primary target is determined based on the abnormal connection direction most associated with the core symptom. The determination principle is the same as in the first scenario, and electrical stimulation is subsequently applied to the primary target. The core symptom can be determined by the medical staff responsible for treating the subject, for example, depression can be used as the core symptom to provide targeted intervention and improve specific negative emotions.

[0046] Then, the target intelligent decision-making subunit integrates the aforementioned multi-faceted information and the subject's context-induced task specificity obtained from the analysis unit to generate the final decision on the intervention plan. Optionally, it also applies more stimulation to target points in hemispheres with lateralization tendencies and less or no stimulation to target points in hemispheres without lateralization tendencies, based on the hemispheric lateralization results of the BLA-NAc circuit connection obtained from the analysis unit. For example, the target intelligent decision-making subunit determines that a subject's left BLA is abnormal, and the connection direction from the left BLA to the left NAc requires inhibitory stimulation, as well as electrical stimulation within a specific frequency range in conjunction with a negative emotion induced task. The target intelligent decision-making subunit sends the obtained target points and stimulation frequency ranges to the electric field simulation and parameter optimization subunit to generate individualized target coordinates, frequencies, and electrical stimulation patterns to achieve maximized, highly focused stimulation of connections in specific directions.

[0047] Additionally, the target intelligent decision-making subunit sends the obtained situational evoked task for coordinated electrical stimulation to the display unit, so that the display unit can configure the corresponding video material to be shown to the subject at an appropriate time period, in conjunction with tTIS to intervene in the subject to improve abnormal connectivity.

[0048] The electric field simulation and parameter optimization subunit generates a complete and context- and pattern-matched individualized tTIS parameter set based on the stimulation intervention results and the subject's structural MRI. Specifically, the subunit first performs individualized head modeling and electric field simulation based on the target points. It constructs a finite element model of the subject's head based on the subject and the processed high-resolution structural MRI, and then maps the coordinates of target points, such as the left BLA and right NAc target points, into the spatial coordinate system of this head finite element model. For a specific target point, such as the abnormally enhanced upstream node BLA, the head finite element model performs electric field simulation. The purpose of the electric field simulation is to determine the parameters of the stimulation current at the target point that generates a focused electric field with sufficient intensity within the stimulation frequency range, providing the corresponding neuromodulation effect.

[0049] In one example, the electric field simulation and parameter optimization subunit determines the frequency of the time interference difference frequency envelope waveform required by the stimulation unit based on the suppression or enhancement of the modulation effect. Using electric field simulation tools, such as SimNIBS, the subunit uses a target electric field with preset intensity and focus as a reference guide. It automatically optimizes the electrode positions of electrode pairs or electrode arrays and the initial phase of the current released by the electrodes in the electrode group position library recommended by the 10-10 EEG system to perform condition optimization. The condition optimization includes maximizing the electric field intensity at the target point, optimizing spatial focus, and keeping the overall scalp current density within a preset safety range. The optimization process can be completed using a genetic algorithm to obtain the optimal electrode position and current phase.

[0050] After determining the position and phase of the current for each electrode in the electrode array, the electric field simulation and parameter optimization subunit further optimizes the frequency and amplitude of the two high-frequency stimulation currents in each electrode pair or array through simulation, so that the two high-frequency stimulation currents form a difference frequency envelope waveform with sufficient amplitude at the target point. For example, two high-frequency currents with similar frequencies, such as 2000 Hz and 2130 Hz, can be automatically or manually set by the system operator as the frequency of the stimulation current released by an electrode pair or array at its optimal electrode position to form a target difference frequency, such as 130 Hz, at the target point. The tolerable current intensity range for the subject is determined based on the electric field distribution and skin comfort, such as 2.0 mA to 2.5 mA, and then the feasibility of the frequency and amplitude is determined through simulation. The final set of electrical stimulation parameters output by the electric field simulation and parameter optimization subunit includes: electrode position, stimulation current amplitude, stimulation current frequency, and its difference frequency envelope frequency.

[0051] The system also includes a display unit, which assists the control unit in determining the first time period during which an evoked scenario needs to be provided in the process of the analysis unit acquiring the subject's functional magnetic resonance data, and the second time period during which an electrical stimulation evoked scenario needs to be provided in the process of the stimulation unit outputting stimulation current, so as to receive control instructions from the control unit and play the corresponding scenario videos in the first and second time periods respectively.

[0052] During functional magnetic resonance imaging (fMRI) scans, the display unit automatically plays standardized positive, negative, and neutral video materials according to a preset task script and sends event markers to the analysis unit to ensure accurate alignment of video presentation time points with fMRI acquisition. The display unit provides automatic switching between fixation points, emotion videos, and distraction task videos across multiple blocks, ensuring high synchronization between the task paradigm and the scanning process, and guaranteeing the quality of context-dependent data for subsequent loop modeling.

[0053] During the neuromodulation phase, the display unit automatically retrieves matching emotion-evoking video materials, such as negative or positive videos, based on the situational task coordinated with electrical stimulation from the decision-making unit, and displays them sequentially to the subject in multiple blocks. The display unit establishes a millisecond-level synchronization connection with the stimulation unit to ensure that electrical stimulation is applied only in the designated situational task block and switches the corresponding electrical stimulation parameters according to different blocks. In this way, through the coordinated and synchronized operation of the display unit and the stimulation unit, precise electrical stimulation intervention and regulation based on situational modulation can be achieved.

[0054] The system also includes a stimulation unit for generating stimulation currents based on an electrical stimulation parameter set and outputting the stimulation currents through an electrode pair. The stimulation unit includes a multi-channel programmable stimulation output subunit, which generates two or more independently controllable high-frequency stimulation currents based on the electrical stimulation parameter set transmitted by the decision unit. The waveform of the stimulation currents can be controlled, such as sine waves, square waves, sawtooth waves, etc. Each channel includes dual channels connected to an electrode pair. Each channel includes a signal generator and a current source, each used to generate a stimulation current. The frequency, phase, and amplitude of the stimulation currents can be independently programmed and controlled by the multi-channel programmable stimulation output subunit, for example, by programming the output of the signal generator and / or the current source.

[0055] Furthermore, the stimulation unit also includes a direction control subunit, which, based on the set of electrical stimulation parameters transmitted by the decision unit, can modulate the stimulation current output by the multi-channel programmable stimulation output subunit through a rectifier circuit and a current stabilizing circuit, for example, by adjusting the output of the signal generator and / or current source to generate a variable stimulation current. The difference frequency envelope parameter generated at the target point is variable, so that the stimulation unit can adapt to the changing abnormal connection direction and / or the changing abnormal connection properties in the abnormal connection direction and make relevant adjustments.

[0056] The system also includes a control unit for acquiring data from the analysis unit, decision-making unit, and stimulation unit, and sending control commands to control the operation of these units. Its main function is to coordinate and manage these functional units in real time, including process scheduling and command management, automatically controlling the execution order of fMRI and structural MRI data acquisition, situational task presentation, and electrical stimulation application according to the experimental procedure; synchronization and triggering control, achieving millisecond-level synchronous triggering of situational task presentation and electrical stimulation to ensure strict temporal matching between stimulation and brain activity induced by the situational task; safety monitoring and feedback adjustment, real-time monitoring of the current output of the stimulation unit, changes in the contact impedance between the subject and the electrodes, and the subject's current stimulation state, automatically interrupting electrical stimulation, playing situational video materials, and / or adjusting electrical stimulation parameters in case of abnormalities; and data recording and log management, automatically recording electrical stimulation parameters, contact impedance, and / or time point operation markers for each unit at each stage, and generating a complete operation log for medical personnel, researchers, and technicians to view for result analysis and maintenance.

[0057] The control unit also provides a communication interface between the various units, acting as a data relay station. In one example, the control unit uses a standardized data interface to exchange data and transmit commands between images, simulations, and electrical stimulation, ensuring the overall stable operation of the system.

[0058] It should be understood that the analysis unit, decision-making unit, and control unit of the system correspond to software units implemented on computer devices or embedded devices. For example, the analysis unit provides data acquisition and analysis, the decision-making unit obtains the electrical stimulation parameter set based on electric field simulation, and the control unit's functions of acquiring data and sending control commands can all be conveniently implemented on computer devices or embedded devices by running one or more corresponding computer program instructions or embedded program instructions. The stimulation unit may include an independent processor, which is electrically connected to the computer device or embedded device running the aforementioned computer program instructions or embedded program instructions, or it may also receive the electrical stimulation parameter set via wired / wireless communication with the computer device or embedded device running the aforementioned computer program instructions or embedded program instructions. Based on the content of the electrical stimulation parameter set, the stimulation unit and its direction control subunit are controlled to perform corresponding functions, so that the output stimulation current meets the requirements specified by the electrical stimulation parameter set.

[0059] The following examples illustrate the application and effect of the time-interference electrical stimulation system based on the brain circuit causal model according to the present application in the brain region intervention task of the subjects with depression.

[0060] Example 1: Feasibility Verification of a Time-Interference Electrical Stimulation System Based on a Brain Circuit Causal Model This embodiment constructs an individualized head model and generates electric field simulation results for time-interference electrical stimulation (tTIS) to verify the feasibility of implementing tTIS using the brain circuit causal model-based time-interference electrical stimulation system of this application. First, the subject's structural magnetic resonance imaging (MRI) images are imported into the SimNIBS simulation software, which automatically generates a high-precision finite element head model including skin, skull, cerebrospinal fluid, gray matter, and white matter as the subject's simulation model. Pre-defined deep brain tTIS target points, including the left / right basolateral brain arterial nerve endings (BLA) and nucleus accumbens (NAc), are precisely mapped into the simulation model. Subsequently, electrodes are arranged on the scalp surface according to the internationally accepted 10-10 EEG system. Electric field simulation is used to optimize different electrode combinations and stimulation current intensities to obtain the optimal electrical stimulation scheme that achieves the highest envelope electric field strength at the target points while keeping the cortical current density within a safe range. Finally, the individualized stimulation parameters were obtained as follows: The stimulation protocol for the right BLA included electrode combination F8 (1.4 mA), F9 (-1.4 mA) and TP8 (2.6 mA), O10 (-2.6 mA); the protocol for the left BLA was FT7 (2 mA), FT9 (-2 mA) and FP1 (2 mA), PO10 (-2 mA); the protocol for the right NAc was F9 (2 mA), FC5 (-2 mA) and FT8 (2 mA), P8 (-2 mA); the protocol for the left NAc was AF7 (2.14 mA), FPZ (-2.14 mA) and T7 (1.86 mA), O9 (-1.86 mA). Figure 2 As shown, the results of electric field simulations demonstrate that the combination of the electrodes and stimulation current can generate a focused difference-frequency envelope electric field in the deep brain structure where the target point is located, while the cortical exposure is minimized. This proves that the system described in this application can achieve precise stimulation of the deep brain based on the individualized electric field optimization strategy provided by the given target point, providing a reliable basis for subsequent clinical applications.

[0061] Example 2: Preliminary Verification of the Intervention Effect of a Time-Interference Electrical Stimulation System Based on a Brain Circuit Causal Model This embodiment was used to verify the modulatory effect of the tTIS provided by the system on the right amygdala of a subject with depression, in order to evaluate the clinical feasibility and preliminary therapeutic effectiveness of the system. Specifically, after review by the Ethics Committee of Beijing Anding Hospital, seven patients who met the diagnostic criteria for depression were enrolled as subjects at the outpatient clinic of Beijing Anding Hospital, and all subjects signed informed consent forms. All subjects received precise targeted tTIS stimulation with the right amygdala as the target point, determined based on their individual structural MRI and functional MRI images. The stimulation used a 130 Hz envelope frequency, formed by the difference frequency of two sets of high-frequency currents; the electrical stimulation intervention protocol was 3 times a day, 30 minutes each time, for 5 consecutive days. The electrode placement scheme of all subjects was optimized by electric field simulation based on the simulation model generated from their MRI images to ensure the formation of an effective 130 Hz envelope electric field focused on the deep right amygdala. Resting-state functional magnetic resonance imaging (rs-fMRI) of subjects was collected before and after treatment to assess changes in their circuit functional connectivity, and the Montgomery Depression Rating Scale (MADRS) was used to assess clinical symptoms; the treatment efficacy rate was defined as a reduction in MADRS score of >50%, and the improvement rate was defined as a reduction in MADRS score of >20%.

[0062] Figure 3 This is a schematic diagram illustrating the changes in clinical symptoms of the subjects after right amygdala tTIS stimulation intervention. Figure 3 Part A on the left is a line graph showing the MADRS score reduction rate of 7 subjects before and after receiving 5 consecutive days of tTIS stimulation intervention. The horizontal axis represents the subject number (1-7), and the vertical axis represents the MADRS score reduction rate. It can be seen that the MADRS scores of all subjects decreased, and the reduction rate was greater than 20%, indicating that all subjects showed improvement in clinical symptoms after treatment. Figure 3 Part B on the right is a line graph connecting the baseline before tTIS stimulation intervention and the absolute MADRS score after tTIS intervention for each subject. The horizontal axis represents time points, and the vertical axis represents MADRS scores. The MADRS scores of all subjects decreased significantly after treatment, and the differences between groups were statistically significant (p<0.001). These results indicate that tTIS stimulation of the right amygdala by the described system effectively improved the depressive symptoms of the subjects.

[0063] Example 3: Preliminary Verification of the Intervention Effect of a Time-Interference Electrical Stimulation System Based on a Brain Circuit Causal Model (Part 2) This embodiment is used to verify the accessibility and regulatory effect of the system described in this application on deep brain structures in healthy individuals. Specifically, after review by the Ethics Committee of Beijing Anding Hospital, five healthy adult subjects were recruited, and all subjects voluntarily signed informed consent forms. First, a simulation model was established based on the structural MRI images of each subject, and the NAc was precisely located as the deep brain stimulation target point in this embodiment. The left and right NAcs were stimulated separately to improve the emotion-reward circuit of the subject's BLA-NAc. The decision unit automatically calculated the optimal electrode placement scheme according to the target point location and generated two high-frequency currents, including current frequency settings of 2 kHz and 2.13 kHz, while still ensuring that the frequency of the difference frequency envelope waveform generated was 130 Hz. During the actual stimulation phase, a one-time tTIS stimulation was applied to the subject, and the envelope frequency was maintained at 130 Hz. rs-fMRI data were collected before and after stimulation to assess changes in local activity in deep brain regions and functional connectivity of cross-regional circuits. First, the amplitude of low-frequency oscillations (ALFF) was used as an indicator of local neural activity. Subsequently, based on functional connectivity analysis, the changes in the connection strength between BLA and NAc in the emotion-reward loop were calculated.

[0064] Figure 4 This diagram illustrates the changes in deep electric field distribution, ALFF, and functional connectivity strength of the emotion-reward circuit before and after a single NAc tTIS treatment in five healthy subjects. Figure 4 Part A on the left shows the electric field simulation results of targeted electrical stimulation of the NAc. The simulation model calculations show that the difference-frequency envelope electric field forms a significantly focused distribution in the NAc region. Figure 4 Part B on the upper right shows the trend of ALFF changes in NAc before and after a single tTIS stimulation. After stimulation, the ALFF values ​​of both left and right NAc increased significantly, suggesting that tTIS can enhance local neural activity in this deep brain region. Figure 4 Part C on the lower right shows the changes in ipsilateral BLA-NAc functional connectivity in the emotion-reward circuit before and after stimulation. It can be observed that the functional connectivity strength of both the left (lhBLA - lhNAc) and right (rhBLA - rhNAc) pathways increased after stimulation compared to baseline, indicating that tTIS can immediately modulate and improve the connectivity of deep emotion-reward related circuits.

[0065] Example 4: Feasibility Verification of Analyzing the Contextual Dependence of BLA-NAc Circuits in Healthy Individuals Using a Time-Interference Electrical Stimulation System Based on a Brain Circuit Causal Model This embodiment is used to verify that the system described in this application can obtain the baseline parameters from healthy individuals required for the decision unit to determine the electrical stimulation parameter set based on healthy individuals. Specifically, after review by the Ethics Committee of Beijing Anding Hospital, fMRI data of 20 healthy subjects based on a situation-evoked task were collected at the outpatient clinic of Beijing Anding Hospital. All subjects voluntarily signed informed consent forms and participated in data collection. First, for each subject, voxel-based functional connectivity matrices were calculated between the left BLA and NAc, and between the right BLA and NAc, under the happy and sad situations provided by the situation-evoked task. Then, a Happy-Sad functional connectivity difference matrix was constructed and binarized according to whether it was greater than or equal to 0, in order to evaluate the changes in BLA-NAc connectivity patterns under different emotional states. Specifically, the permutation test was used to compare whether ipsilateral BLA-NAc connectivity was significantly enhanced under the condition that Happy > Sad (i.e., left-sided connectivity lhBLA-lhNAc > contralateral connectivity lhBLA-rhNAc, or right-sided connectivity rhBLA-rhNAc > contralateral connectivity rhBLA-lhNAc); then, under the condition that Sad > Happy, the test was used to examine whether contralateral BLA-NAc connectivity was more significant (i.e., left-sided connectivity lhBLA-lhNAc < contralateral connectivity lhBLA-rhNAc, or right-sided connectivity rhBLA-rhNAc < contralateral connectivity rhBLA-lhNAc). Paired t-tests were used for within-group comparisons to further validate the stability of the results.

[0066] Figure 5 The test results are shown; under the condition that Happy > Sad, such as Figure 5 As shown in sections A and C on the left, more subjects showed a significant enhancement of ipsilateral BLA-NAc connectivity; under the Sad>Happy condition, as Figure 5 As shown in sections B and D on the right, more subjects exhibited a significant enhancement in contralateral BLA-NAc connectivity. Furthermore, the paired t-test results revealed that under the Happy>Sad condition, functional connectivity between the BLA-NAc pathways was significant in 5 subjects, with 3 showing a pattern of ipsilateral greater connectivity than contralateral connectivity; similarly, under the Sad>Happy condition, a contralateral greater connectivity pattern was observed. These results indicate that directional connectivity within the BLA-NAc circuitry is context-dependent.

[0067] The above embodiments fully demonstrate the following advantages of the system: the system has strong accessibility to brain stimulation, enabling the non-invasive focusing of low-frequency difference-frequency envelope electric fields onto deep target points in the BLA and NAc; the system can significantly enhance local activity, for example, by providing short-duration single stimulation to enhance local neural activity within the NAc; the system can achieve real-time modulation of cross-regional circuits to enhance the functional connectivity of the key connection circuit BLA-NAc in a specific direction within the emotion-reward circuit. The system has scientific basis and engineering feasibility for clinical intervention applications in mood disorders such as depression and a wider range of mental illnesses.

[0068] The basic principles of this application have been described above with reference to specific embodiments. However, it should be noted that the advantages, benefits, and effects mentioned in this application are merely examples and not limitations, and should not be considered as essential features of each embodiment of this application. Furthermore, the specific details disclosed above are for illustrative and facilitative purposes only, and are not limitations. These details do not limit the application to the necessity of employing the aforementioned specific details for implementation.

[0069] The block diagrams of devices, apparatuses, devices, and systems involved in this application are merely illustrative examples and are not intended to require or imply that they must be connected, arranged, or configured in the manner shown in the block diagrams. As those skilled in the art will recognize, these devices, apparatuses, devices, and systems can be connected, arranged, and configured in any manner. Words such as “comprising,” “including,” “having,” etc., are open-ended terms meaning “including but not limited to,” and are used interchangeably with them. The terms “or” and “and” as used herein refer to the terms “and / or,” and are used interchangeably with them unless the context clearly indicates otherwise. The term “such as” as used herein refers to the phrase “such as but not limited to,” and is used interchangeably with it.

[0070] It should also be noted that in the apparatus, equipment, and methods of this application, the components or steps can be disassembled and / or recombined. These disassemblies and / or recombinations should be considered as equivalent solutions of this application.

[0071] The above description of the disclosed aspects is provided to enable any person skilled in the art to make or use this application. Various modifications to these aspects will be readily apparent to those skilled in the art, and the general principles defined herein can be applied to other aspects without departing from the scope of this application. Therefore, this application is not intended to be limited to the aspects shown herein, but rather to be accorded the widest scope consistent with the principles and novel features disclosed herein.

[0072] The above description has been given for purposes of illustration and description. Furthermore, this description is not intended to limit the embodiments of this application to the forms disclosed herein. Although numerous exemplary aspects and embodiments have been discussed above, those skilled in the art will recognize certain variations, modifications, alterations, additions, and sub-combinations thereof.

Claims

1. A time-interference electrical stimulation system based on a brain circuit causal model, characterized in that, include: The analysis unit acquires the subject's functional magnetic resonance imaging (fMRI) data and analyzes the fMRI data to obtain abnormal results in specific brain regions, including abnormal connectivity direction, abnormal nature, abnormal context, and / or clinical symptom association strength. The decision-making unit determines the stimulus intervention result based on the abnormal result, including the target and the electrical stimulation evoked situation, and obtains the electrical stimulation parameter set through electric field simulation and the stimulus intervention result. The stimulation unit generates a stimulation current based on the electrical stimulation parameter set and outputs the stimulation current through an electrode group. The control unit is used to acquire data from the analysis unit, decision unit, and stimulation unit, and to send control commands to control the operation of the analysis unit, decision unit, and stimulation unit.

2. The time-interference electrical stimulation system based on a brain circuit causal model according to claim 1 further includes a display unit; The display unit communicates with the analysis unit, the decision-making unit, and the control unit. The control unit determines that a first time period of evoked scenarios needs to be provided during the process of the analysis unit acquiring the subject's functional magnetic resonance data, and determines that a second time period of electrical stimulation evoked scenarios needs to be provided during the process of the stimulation unit outputting the stimulation current. The display unit receives control commands from the control unit and plays corresponding scenario videos during the first time period and the second time period, respectively.

3. The time-interference electrical stimulation system based on a brain circuit causal model according to claim 1, wherein, The analysis unit analyzes the functional magnetic resonance imaging data to obtain abnormal results in specific brain regions of the subject, including: The activation intensity of BLA-NAc connections in the functional magnetic resonance imaging data of the subjects under different situational tasks was extracted to determine whether the subjects' BLA-NAc connections had abnormal functional performance. In response to abnormal functional manifestations, a DCM model was constructed based on the functional magnetic resonance data of the subjects and the group-level connectivity strength was obtained to determine the abnormal connectivity direction, abnormal nature, and abnormal context of BLA-NAc connectivity. Correlation analysis is used to screen the abnormal connection directions, abnormal natures, and abnormal contexts that are highly correlated with specific clinical symptoms, and the correlation strength of the clinical symptoms is obtained.

4. The time-interference electrical stimulation system based on a brain circuit causal model according to claim 1, wherein, The decision-making unit determines the stimulus intervention outcome based on the abnormal results, including: The target point is selected from BLA and NAc according to the abnormal connection direction, and the stimulation current frequency range of the target point is determined according to the abnormality, abnormal situation and clinical symptom correlation strength.

5. The time-interference electrical stimulation system based on a brain circuit causal model according to claim 4, wherein, Determining the frequency range of the stimulation current for the target point includes: Based on the nature of the abnormality, the abnormal context, and the correlation strength with clinical symptoms, the required stimulation current frequency range for the lateral brain region with the highest correlation to emotional symptoms along the abnormal connection direction between BLA and NAc is determined, and the electrical stimulation evoked context is determined based on the abnormal context.

6. The time-interference electrical stimulation system based on a brain circuit causal model according to claim 1, wherein, Before acquiring the subject's functional magnetic resonance imaging (fMRI) data, the analysis unit also includes: Functional magnetic resonance imaging (fMRI) data of healthy individuals was acquired. The fMRI data under positive and negative scenarios were modeled to obtain the activation intensity of the BLA and NAc brain regions in healthy individuals under positive and negative scenarios. Based on the activation intensity, the interaction between scenario type and brain region type and the influence of scenario modulation on functional connectivity strength were determined to identify the specific causal regulatory effect of scenario modulation in BLA-NAc connectivity and the direction of scenario modulation. Based on the specific causal modulation effect and the direction of situational modulation, the type of electrical stimulation-induced situation required under specific abnormal connection directions and abnormal properties is determined.

7. The time-interference electrical stimulation system based on a brain circuit causal model according to claim 1, wherein, The decision-making unit obtains a set of electrical stimulation parameters based on electric field simulation and the results of the stimulation intervention, including: Based on the results of the stimulation intervention, the stimulation current frequency is further determined from the stimulation current frequency range; By performing electric field simulation on a simulation model, the electrode positions, stimulation current amplitudes, and / or stimulation current phases in the electrode group that produce the maximum temporal interference electric field strength and the maximum spatial focusing of the electric field at the target point are determined.

8. The time-interference electrical stimulation system based on a brain circuit causal model according to claim 7, wherein, The stimulation unit generates a stimulation current based on the electrical stimulation parameter set and outputs the stimulation current through the electrode group, including: A stimulation current is generated based on the stimulation current frequency, stimulation current amplitude, and / or stimulation current phase. Based on the electrode position, a stimulation current is input to the electrode in the electrode group located at the electrode position, so that the electrode generates a time interference envelope waveform at the target point.

9. The time-interference electrical stimulation system based on a brain circuit causal model according to claim 7, wherein, The decision unit is also used to acquire the subject's structural magnetic resonance images to generate a finite element head model of the subject including skin, skull, cerebrospinal fluid, gray matter and white matter, and to map BLA and NAc into the finite element head model to obtain the simulation model.

10. The time-interference electrical stimulation system based on a brain circuit causal model according to any one of claims 1-9, wherein, The control unit transmits data and control commands to the analysis unit, decision-making unit, and stimulation unit via wired or wireless communication.

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