A neuromodulation system for cognitive dysfunction

Abstract

The present application discloses a neuromodulation system for cognitive impairment. The system includes: a cognitive behavioral assessment module for identifying the cognitive deficit areas that need to be improved in the target object; an imaging data acquisition module for collecting and preprocessing the task-state nuclear magnetic resonance data and T1-weighted structural images of the target object; a brain region activation analysis module for determining potential neuromodulation targets based on the preprocessed task-state nuclear magnetic resonance data; a brain network analysis module for identifying key nodes related to cognitive deficits; a stimulation target screening module for determining individualized stimulation targets for the target object based on the potential neuromodulation targets and key nodes of the target object; a time-domain interference electrical stimulation simulation and implementation module for performing electrical stimulation on the individualized stimulation targets; and a dynamic feedback adjustment module for dynamically adjusting the stimulation parameters to enhance the personalized regulation effect. By using the present application, specific brain regions directly related to cognitive impairment can be determined, thereby enhancing the targeting and accuracy of treatment.

Description

Technical Field

[0001] This application relates to the field of neural technology, and particularly to a neural regulation system for cognitive dysfunction. Background Art

[0002] In the fields of neuroscience and clinical treatment, cognitive dysfunction, including significant degradation of memory, attention, executive function, and language ability, has become an increasingly severe health challenge globally. With the intensification of population aging, the incidence of cognitive dysfunction is rising continuously, causing a significant impact on the individual, family, and society of the target object. Therefore, developing effective neural regulation technologies to improve cognitive function is of great significance for improving the quality of life of the target object and reducing the social burden.

[0003] Currently, neural regulation technologies such as transcranial magnetic stimulation (TMS) and transcranial direct current stimulation (tDCS) have been applied to improve cognitive function. These technologies regulate the neural activities of specific brain regions by applying current or magnetic fields outside the scalp to achieve the purpose of enhancing cognitive function. Technologies such as TMS and tDCS have shown short-term effects in improving the memory and attention of Alzheimer's disease target objects, and have also been applied in improving the executive function of mild cognitive impairment target objects.

[0004] However, existing means mostly adopt standardized stimulation parameters and fail to fully consider the differences in the conditions of the target object and the differences in individual neuroanatomical structures, resulting in unstable curative effects. Some neural regulations usually select brain regions based on brain anatomy and prior knowledge, making it difficult to accurately act on the specific damaged brain regions of the target object and unable to fully improve individualized cognitive dysfunction. Summary of the Invention

[0005] The embodiments of this application provide a neural regulation system for cognitive dysfunction. To have a basic understanding of some aspects of the disclosed embodiments, a simple summary is given below. This summary part is not a general review, nor is it to identify key / important constituent elements or depict the protection scope of these embodiments. Its sole purpose is to present some concepts in a simple form as a preface to the subsequent detailed description.

[0006] In the first aspect, the embodiments of this application provide a neural regulation system for cognitive dysfunction, the system includes:

[0007] A cognitive behavioral assessment module, used to identify the areas of cognitive defects that need to be improved in the target object;

[0008] An imaging data acquisition module, used to collect the task-state nuclear magnetic data of the target object for preprocessing during the execution of a cognitive task selected based on the defect area for the target object; collect and preprocess the T1-weighted structural image of the target object;

[0009] A brain region activation analysis module, which is used to determine, according to the preprocessed task-state nuclear magnetic data, the brain regions showing abnormalities in the activation analysis as potential neural regulation targets of the target object;

[0010] A brain network analysis module, which is used to construct a network model of the target object according to the potential neural regulation targets, and identify the regions with abnormal connections with the brain regions of interest in the network model as key nodes related to cognitive deficits;

[0011] A stimulation target screening module, which is used to determine the individualized stimulation targets of the target object according to the potential neural regulation targets and key nodes of the target object, and the individualized stimulation targets are used to characterize the regions in the brain of the target object that ultimately need to be regulated;

[0012] A time-domain interference electrical stimulation simulation and implementation module, which is used to simulate and optimize the electrical stimulation parameters according to the preprocessed T1-weighted structural image, and perform electrical stimulation on the individualized stimulation targets based on the optimized electrical stimulation parameters to achieve precise neural regulation of the targeted deep brain regions;

[0013] A dynamic feedback adjustment module, which is used to regularly collect the behavioral parameters and task-state nuclear magnetic data of the target object for analysis to dynamically adjust the electrical stimulation parameters.

[0014] Optionally, identifying the areas of cognitive deficits that the target object needs to improve, including:

[0015] Performing a cognitive behavioral test on the target object using a preset scale to test the cognitive state of the target object and obtaining the test results of the target object;

[0016] Based on the test results, determining the areas of cognitive deficits that the target object needs to improve.

[0017] Optionally, determining, according to the preprocessed task-state nuclear magnetic data, the brain regions showing abnormalities in the activation analysis as potential neural regulation targets of the target object, including:

[0018] Using a general linear model and the preprocessed task-state nuclear magnetic data to construct a matrix;

[0019] Through the matrix, capturing the brain activation patterns under preset task conditions;

[0020] Comparing the captured brain activation patterns with the activation patterns of the healthy control group to identify the regions significantly deviating from the activation patterns of the healthy control group;

[0021] According to the deviated regions, determining the brain regions related to cognitive dysfunction as potential neural regulation targets of the target object.

[0022] Optionally, construct a network model of the target subject according to the potential targets of neuromodulation, including:

[0023] Select the brain regions of interest from the potential targets of neuromodulation of the target subject;

[0024] Extract the time series signals of the brain regions of interest from the preprocessed task-based MRI data, and the time series signals are used to characterize the signal changes of the physiological variables of the brain regions at different time points;

[0025] Construct a generalized psychophysiological interaction model;

[0026] Construct a generalized psychophysiological interaction model according to the time series signals, and quantify the conditional functional connectivity strength between the brain regions to which the potential targets of neuromodulation belong and other brain regions through this model;

[0027] Construct a network model of the target subject according to the conditional functional connectivity strength.

[0028] Optionally, identify the regions with abnormal connections with the brain regions of interest in the network model as key nodes related to cognitive deficits, including:

[0029] Obtain a healthy control group;

[0030] Compare the network model of the target subject with the preset healthy control group to identify the abnormal regions with over-connection or dysfunction in the network model;

[0031] Regard the abnormal regions as key nodes related to cognitive deficits.

[0032] Optionally, determine the individualized stimulation targets of the target subject according to the potential targets of neuromodulation and key nodes of the target subject, including:

[0033] Compare the potential targets of neuromodulation of the target subject with the key nodes one by one to determine the common regions that appear simultaneously in the potential targets of neuromodulation and key nodes of the target subject;

[0034] Rank the common regions that appear simultaneously in terms of priority;

[0035] Select the preset number of common regions with the highest priority from the ranking results as high-priority targets;

[0036] Regard the high-priority targets as the individualized stimulation targets of the target subject.

[0037] Optionally, simulate and optimize the electrical stimulation parameters according to the preprocessed T1-weighted structural images, including:

[0038] Segment the preprocessed T1-weighted structural images to obtain an individualized head anatomical model of the target subject;

[0039] Mesh the individualized head anatomical model through finite element analysis software and convert it into discrete elements;

[0040] Based on the discrete elements, combine with the finite element method to conduct electric field simulation to simulate the propagation and diffusion effects of current in different tissue layers;

[0041] During the simulation process, optimize the electrical stimulation parameters so that the stimulation can accurately focus on the target brain region.

[0042] Optionally, during the simulation process, optimize the electrical stimulation parameters, including:

[0043] During the simulation process, optimize the stimulation parameters of time-domain interference electrical stimulation to obtain the simulation optimization result;

[0044] Based on the simulation optimization result, determine whether the current of the time-domain interference electrical stimulation can act on the target brain region to which the individualized stimulation target belongs;

[0045] If so, take the electrical stimulation parameters at the current moment as the optimized electrical stimulation parameters.

[0046] Optionally, regularly collect the behavioral parameters and task-state MRI data of the target object for analysis to dynamically adjust the electrical stimulation parameters, including:

[0047] Regularly collect the behavioral data and task-state fMRI data of the target object as the behavioral parameters and task-state MRI data;

[0048] Analyze the behavioral parameters and task-state MRI data to evaluate the current stimulation effect and determine whether it is necessary to adjust the stimulation parameters and the stimulation target;

[0049] If the stimulation effect does not reach the preset conditions, adjust and optimize the stimulation parameters and the stimulation target; if the stimulation effect reaches the preset conditions, continue to execute the step of regularly collecting the behavioral data and task-state fMRI data of the target object as the behavioral parameters and task-state MRI data.

[0050] Optionally, the cognitive behavioral tests include:

[0051] Conduct a preliminary screening through the Montreal Cognitive Assessment Scale and the Mini-Mental State Examination Scale, and conduct cognitive behavioral tests in at least one of the following ways:

[0052] Use the digit span test to evaluate the working memory of the target object;

[0053] Use the California Verbal Learning Test and the Logical Memory Test to evaluate the episodic memory of the target object;

[0054] The semantic ability of the target object is evaluated using the Boston Naming Test and the Verbal Fluency Test;

[0055] The executive function of the target object is evaluated using the Stroop Test and the Wisconsin Card Sorting Test;

[0056] The visuospatial constructional ability of the target object is measured using the Clock Drawing Test.

[0057] In the embodiments of the present application, on the one hand, through the comprehensive application of the cognitive ethology evaluation module, the imaging data acquisition module, the brain region activation analysis module, and the brain network analysis module, it is possible to accurately determine specific brain regions directly related to cognitive dysfunction, providing a clear target area for subsequent neuromodulation therapy. On the other hand, the stimulation target screening module determines the individualized stimulation targets of the target object according to the potential neuromodulation targets and key nodes. These targets characterize the regions in the brain that ultimately need to be modulated. The time-domain interference electrical stimulation simulation and implementation module simulates and optimizes the electrical stimulation parameters based on the preprocessed T1-weighted structural image, and performs electrical stimulation on the individualized stimulation targets based on the optimized electrical stimulation parameters. By simulating and optimizing the electrical stimulation parameters, accurate electrical stimulation of the individualized stimulation targets can be achieved, enhancing the targeting and accuracy of the treatment.

[0058] It should be understood that the above general description and the following detailed description are only exemplary and explanatory, and do not limit the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] The accompanying drawings herein are incorporated into and constitute a part of this specification, showing embodiments consistent with the present application, and together with the specification are used to explain the principles of the present application.

[0060] Figure 1 is a schematic structural diagram of a neuromodulation system for cognitive dysfunction provided by an embodiment of the present application;

[0061] Figure 2 is a schematic process diagram of an activation analysis provided by an embodiment of the present application;

[0062] Figure 3 is a schematic process diagram of a gPPI analysis provided by an embodiment of the present application;

[0063] Figure 4 is a schematic flow diagram of a neuromodulation method for cognitive dysfunction provided by an embodiment of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0064] The following description and the accompanying drawings fully illustrate the specific embodiments of the present application, enabling those skilled in the art to practice them.

[0065] It should be clear that the described embodiments are only a part of the embodiments of the present application, rather than all embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments in the present application without creative efforts belong to the scope of protection of the present application.

[0066] When the following description refers to the accompanying drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present application. On the contrary, they are only examples of systems consistent with some aspects of the present application as detailed in the appended claims.

[0067] In the description of the present application, it should be understood that terms such as "first", "second", etc. are only used for descriptive purposes and cannot be construed as indicating or implying relative importance. For those of ordinary skill in the art, the specific meanings of the above terms in the present application can be understood according to specific circumstances. In addition, in the description of the present application, unless otherwise specified, "a plurality of" means two or more. "And / or" describes the association relationship of associated objects and indicates that three relationships can exist. For example, A and / or B can represent: A exists alone, A and B exist simultaneously, and B exists alone. The character " / " generally represents an "or" relationship between the associated objects before and after.

[0068] Please refer to Figure 1 , Figure 1 is a schematic structural diagram of a neuroregulation system for cognitive impairment provided by an embodiment of the present application. The system includes: a cognitive behavioral assessment module, an imaging data acquisition module, a brain region activation analysis module, a brain network analysis module, a stimulation target screening module, a time-domain interference electrical stimulation simulation and implementation module, and a dynamic feedback adjustment module; among them, the cognitive behavioral assessment module, the imaging data acquisition module, the brain region activation analysis module, the brain network analysis module, the stimulation target screening module, the time-domain interference electrical stimulation simulation and implementation module, and the dynamic feedback adjustment module are communicatively connected.

[0069] In the embodiments of the present application, a cognitive behavioral assessment module is used to identify the cognitive deficit areas that need to be improved for the target object; an imaging data acquisition module is used to collect and preprocess the task-state nuclear magnetic data of the target object during the execution of the cognitive task selected based on the deficit area for the target object; collect and preprocess the T1-weighted structural image of the target object; a brain region activation analysis module is used to determine the brain regions that show abnormalities in the activation analysis as potential neural regulation targets for the target object based on the preprocessed task-state nuclear magnetic data; a brain network analysis module is used to construct a network model of the target object based on the potential neural regulation targets, and identify the regions with abnormal connections to the brain regions of interest in the network model as key nodes related to cognitive deficits; a stimulation target screening module is used to determine the individualized stimulation targets for the target object based on the potential neural regulation targets and key nodes of the target object, and the individualized stimulation targets are used to represent the regions in the brain of the target object that ultimately need to be regulated; a time-domain interference electrical stimulation simulation and implementation module is used to simulate and optimize the electrical stimulation parameters based on the preprocessed T1-weighted structural image, and perform electrical stimulation on the individualized stimulation targets based on the optimized electrical stimulation parameters to achieve precise neural regulation of the targeted deep brain regions; a dynamic feedback adjustment module is used to regularly collect and analyze the behavioral parameters and task-state nuclear magnetic data of the target object to dynamically adjust the electrical stimulation parameters.

[0070] Among them, the cognitive deficit area refers to the specific aspects where an individual shows impairments or deficiencies in cognitive functions, such as memory, attention, executive function, etc. Task-state fMRI technology is a magnetic resonance imaging technology used to measure changes in brain activity when an individual performs a specific task. Task-state nuclear magnetic data is the data reflecting the brain activity pattern collected by fMRI technology when performing a specific task. T1-weighted structural image is a magnetic resonance imaging technology that provides high-resolution brain structure images by emphasizing T1 relaxation characteristics. Abnormal brain regions are the brain regions identified in the task-state nuclear magnetic data that deviate from normal functions or structures and are related to cognitive dysfunction. Potential neural regulation targets refer to those brain regions that are considered to be able to improve cognitive functions through neural regulation technologies (such as electrical stimulation or magnetic stimulation). The network model is a model constructed based on the functional connections between brain regions and is used to describe the interactions and network connections between different regions of the brain. Individualized stimulation targets are the electrical stimulation target regions determined according to the specific conditions of an individual and are used for personalized neural regulation treatment. Electrical stimulation parameters are the key parameters in electrical stimulation treatment, including current intensity, frequency, coherence time, electrode position, and shape, etc.

[0071] In some embodiments of the present application, the specific process of identifying the cognitive deficit areas that need to be improved for the target object includes: using a preset scale to conduct a cognitive behavioral test on the target object to test the cognitive state of the target object and obtain the test results of the target object; based on the test results, determine the cognitive deficit areas that need to be improved for the target object.

[0072] Specifically, the cognitive behavioral tests include: preliminary screening through the Montreal Cognitive Assessment Scale and the Mini-Mental State Examination Scale, and the cognitive behavioral tests are conducted in at least one of the following ways: using the Digit Span Test to evaluate the working memory of the target object; using the California Verbal Learning Test and the Logical Memory Test to evaluate the episodic memory of the target object; using the Boston Naming Test and the Verbal Fluency Test to evaluate the semantic ability of the target object; using the Stroop Test and the Wisconsin Card Sorting Test to evaluate the executive function of the target object; using the Clock Drawing Test to measure the visuospatial construction ability of the target object.

[0073] For example, this application needs to conduct a comprehensive cognitive behavioral test on the target object to accurately evaluate its cognitive status in aspects such as working memory, episodic memory, semantic ability, executive function, and visuospatial construction ability. First, preliminary screening is carried out through the Montreal Cognitive Assessment (MoCA) Scale and the Mini-Mental State Examination (MMSE) Scale to quickly evaluate the overall cognitive status of the target object. According to the screening results, targeted evaluations are further carried out, including using the Digit Span Test to evaluate working memory, the California Verbal Learning Test (CVLT) and the Logical Memory Test (optional) to evaluate episodic memory, the Boston Naming Test and the Verbal Fluency Test to evaluate semantic ability, the Stroop Color and Word Test (SCWT) and the Wisconsin Card Sorting Test (WCST, optional) to evaluate executive function, and the Clock Drawing Test to further refine the measurement of visuospatial construction ability. Through these standardized behavioral tests, the cognitive deficit areas of the target object and the key obstacles affecting daily life can be accurately identified, the specific improvement needs of the target object in different cognitive functions can be determined, and the subsequent fMRI tasks and electrical stimulation parameters can be personalized to ensure the accuracy and efficiency of the treatment plan. The step-by-step and combined method can not only comprehensively cover all cognitive function areas but also reduce the fatigue burden on the target object during the evaluation process.

[0074] In some embodiments of the present application, the specific process of the cognitive task selected based on the defect area includes: for working memory defects, the N-back task is selected to activate the working memory network; for executive function defects, the Stroop task or the Go / No-Go task is adopted to detect the performance of the target object in inhibitory control, conflict resolution, and attention switching, and activate the prefrontal cortex; for episodic memory defects, the word pairing task is adopted to evaluate the information encoding, memory retention, and retrieval abilities of the target object, and activate the brain regions related to memory; for semantic ability defects, the semantic classification task is adopted to evaluate the semantic processing and language network functions of the target object; for visuospatial structure ability defects, the object rotation task is adopted to evaluate the spatial operation and visual spatial memory abilities of the target object; for attention defects, the sustained attention task is adopted to evaluate the attention maintenance and selective attention abilities of the target object. The arrangement of the above tasks can not only targetedly activate specific brain regions, but also accurately identify abnormal functional regions through analysis, providing a scientific basis for subsequent intervention.

[0075] In some embodiments of the present application, when performing the cognitive task selected based on the defect area on the target object, different levels of task difficulty can be set to observe the impact of task load on brain region activity. For example, in the N-back task selected for working memory defects, the task difficulty levels include 0-back (only need to identify the target stimulus), 1-back (identify whether the current stimulus is the same as the previous one), and 2-back (identify whether the current stimulus is the same as the previous two), gradually increasing the requirements for working memory. Subsequently, by comparing and analyzing the fMRI data under different task difficulty conditions, it can be observed how the intensity and range of brain region activation change with the increase in task load. For example, the prefrontal cortex and parietal lobe in the cognitive task may show stronger activation signals with the increase in task load, while the target object with decreased cognitive function may show a series of characteristics such as insufficient activation of the prefrontal cortex and parietal lobe, reduced task performance, premature bottleneck, and even compensatory activation. These changes not only reflect the decline in the operation efficiency of the working memory system, but also reveal the functional deficiencies of specific brain regions in coping with high-load tasks, helping to accurately locate potential targets for neuromodulation.

[0076] In some embodiments of the present application, the task-state nuclear magnetic resonance data of the target object is collected and preprocessed; when collecting and preprocessing the T1-weighted structural image of the target object, the preprocessing steps of the task-state nuclear magnetic resonance data include temporal slice correction, head motion correction, spatial registration and normalization, and smoothing processing, etc., to remove the systematic errors and noises brought by the scanning process, and align the task-state nuclear magnetic resonance data to the T1-weighted structural image to ensure the precise overlap of functional and structural data, providing support for subsequent anatomical localization and functional analysis. The T1 structural image is then processed by skull stripping, spatial registration to a standard template, and tissue segmentation to obtain gray matter, white matter, and cerebrospinal fluid, providing accurate anatomical information for subsequent functional localization and simulation modeling. These preprocessing steps can ensure the quality and consistency of the image data, thus providing reliable basic data for functional analysis, individualized target selection, and neuromodulation simulation.

[0077] In some embodiments of the present application, the specific process of determining the potentially neuromodulated target brain regions that show abnormalities in activation analysis based on the preprocessed task-state nuclear magnetic resonance data includes: using a general linear model and the preprocessed task-state nuclear magnetic resonance data to construct a matrix; through the matrix, capturing the brain activation patterns under preset task conditions; comparing the captured brain activation patterns with those of the healthy control group to identify regions that significantly deviate from the activation patterns of the healthy control group; based on the deviated regions, determining the brain regions related to cognitive dysfunction as the potentially neuromodulated targets of the target object.

[0078] Among them, task-state fMRI data is functional magnetic resonance imaging data collected and preprocessed when the subject performs specific cognitive or sensorimotor tasks. The general linear model (GLM) is a statistical model used to analyze data in experimental designs, especially in fMRI analysis, to estimate the effects of different conditions or tasks on brain activity. In GLM, constructing a design matrix is a key step, which contains the expected effects of all conditions in the experiment, as well as other factors that may affect brain activity (such as head motion, baseline drift, etc.). The preset task conditions refer to the tasks or stimulus conditions predefined in the fMRI experiment, used to activate specific regions of the brain to study their functions. The baseline, in fMRI analysis, usually refers to the brain activity level when no specific task is performed or no specific stimulus is received, serving as a reference point for comparing the task state. The activation regions deviating from the healthy baseline are those brain regions that show significant differences from the healthy baseline when comparing the brain activation patterns of the target object group and the healthy control group, which may indicate the presence of cognitive dysfunction. Potentially neuromodulated targets refer to the brain regions related to cognitive dysfunction determined through fMRI analysis, which can be used as the target regions for neuromodulation therapies (such as transcranial magnetic stimulation or transcranial electrical stimulation).

[0079] For example, after the data preprocessing is completed, GLM is first used to analyze the task-state fMRI data, and a design matrix is constructed to accurately capture the brain activation patterns under task conditions. The core steps of the activation analysis include design matrix construction, contrast analysis, and comparison of the activation patterns with the brain activation patterns, aiming to identify brain regions that significantly deviate from the normal activation patterns and localize them as potential targets for subsequent neuromodulation.

[0080] Among them, taking the N-back task as an example, the N-back task conditions (such as 0-back, 1-back, 2-back) and other potential interfering factors (such as head movement, baseline drift) are integrated into the model. Each task condition is used as a regression variable to simulate the response patterns of the brain at different load levels and capture the activation changes of the working memory network. At the same time, a baseline condition is added for contrast analysis under different task conditions to estimate the response intensity of the brain under each condition, as Figure 2 shown. Based on the design matrix, the activation of each condition is compared (such as 2-back vs. 0-back), and the response intensity of brain regions under different task conditions is quantified through parameter estimation to estimate the task-related brain region activities. In this way, brain regions that are activated as the task difficulty increases (such as the prefrontal lobe, parietal lobe, etc.) can be identified, and these brain regions are the core regions related to working memory load. To identify abnormal activation regions under cognitive dysfunction, the activation pattern of the target object is statistically compared with the activation pattern of the healthy control group to find regions that significantly deviate from the normal range (such as excessive or insufficient activation), and these regions are often related to working memory and attention deficits.

[0081] The abnormal activation regions identified through the above steps will be used as potential targets for subsequent neuromodulation. The activation characteristics of these regions are closely related to the cognitive dysfunction of the target object. The finally determined high or low activation regions will be used in the personalized neuromodulation plan to optimize the effect of targeted treatment.

[0082] In some embodiments of the present application, the specific process of constructing the network model of the target object according to the potential targets of neuromodulation includes: selecting the brain regions of interest from the potential targets of neuromodulation of the target object; extracting the time series signals of the brain regions of interest from the preprocessed task-state nuclear magnetic data, and the time series signals are used to characterize the signal changes of the physiological variables of the brain regions at different time points; constructing a generalized psychophysiological interaction model; constructing a generalized psychophysiological interaction model according to the time series signals, and quantifying the conditional functional connection strength between the brain regions to which the potential targets of neuromodulation belong and other brain regions through this model; constructing the network model of the target object according to the conditional functional connection strength.

[0083] Among them, potential neuroregulation targets refer to brain regions determined through previous activation analysis and related to specific cognitive tasks, and these regions are considered possible points of action for neuroregulation therapy. Region of Interest (ROI) is a specific brain region selected in neuroimaging studies for further analysis. Time series signals refer to the changes in physiological variables (such as BOLD signals) of a brain region recorded at consecutive time points. These signals can reflect the dynamic changes of brain activity over time. Generalized Psychophysiological Interaction is a statistical model used to analyze functional connectivity between brain regions, which can quantify the interaction strength between different brain regions under specific mental states. Conditional functional connectivity strength refers to the functional connectivity strength between different brain regions under specific conditions or tasks. Network models refer to models constructed based on the functional connectivity strength between brain regions, used to describe the organizational structure and dynamic characteristics of the brain as a complex network.

[0084] In some embodiments of the present application, the specific process of identifying regions with abnormal connections to the ROI in the network model as key nodes related to cognitive deficits includes: obtaining a healthy control group; comparing the network model of the target object with a preset healthy control group to identify abnormal regions with over-connections or dysfunctions in the network model; taking the abnormal regions as key nodes related to cognitive deficits.

[0085] For example, when analyzing the brain functional connectivity pattern of the target object, the gPPI analysis algorithm based on ROI can be used to identify the functional connectivity pattern between potential target brain regions and other brain regions, as Figure 3 shown. This method mainly constructs a multiple linear regression model for each task condition in the experiment, calculates the interaction term between each mental variable and physiological variable at the neural activity level, and then convolves it with the HRF function to obtain the PPI interaction term at the BOLD level. After modeling analysis, the functional connectivity strength between conditions can be directly compared. For example, when performing PPI analysis on a task with three experimental conditions A, B, and C, the corresponding multiple linear regression model is:

[0086] Y phyi = 0 + 1*X psyA + 2*X psyB + 3*X psyC

[0087] + 4* phyj + 5*X ppiA + 6*X ppiB + 7*X ppic +

[0088] X phyi = phy *RF

[0089] X ppi = Z psy * phy )*RF

[0090] Among them, Yphyi represents the BOLD signal of the target region, Yphyj represents the BOLD signal of the ROI, Xpsy represents the experimental design variable, Xppi represents the psychophysiological interaction term, Zpsy and Zphy are the psychological signal and brain activity variable at the neural level respectively, and HRF is the hemodynamic response function.

[0091] Specifically, select appropriate ROIs (such as the prefrontal cortex or parietal cortex with abnormal activation in the N-back task), extract the time series signal, and construct the gPPI model. Through this model, quantify the conditional functional connection strength between the potential target brain region and other brain regions, so as to reflect the functional connection pattern under specific task conditions. Then, compare the network model of the target object with that of the healthy control group to identify the over-connected or dysfunctional regions in the target object's network, so as to locate the key nodes closely related to the specific cognitive defects of the target object. Confirm whether these key nodes play an important role in networks such as working memory, attention, or language, so as to ensure that the selected target points meet the personalized needs of the target object and have good regulation effects.

[0092] In some embodiments of the present application, the specific process of determining the individualized stimulation target points of the target object according to the neural regulation potential target points and key nodes of the target object includes: comparing the neural regulation potential target points and key nodes of the target object one by one to determine the common regions that appear simultaneously in the neural regulation potential target points and key nodes of the target object; sorting the common regions that appear simultaneously according to the priority; selecting a preset number of common regions with the highest priority from the sorting results as the high-priority target points; and using the high-priority target points as the individualized stimulation target points of the target object.

[0093] For example, the abnormally activated regions identified in the activation analysis (such as the prefrontal cortex or parietal cortex that responds abnormally in the N-back task) are compared one by one with the abnormally connected regions in the results of the functional network analysis (such as the key nodes related to the working memory network) to find the common regions that show abnormalities in both analyses. The common regions represent the brain regions that show significant deviations in both task activation and functional connectivity, and these regions may be the key sources of cognitive dysfunction and have high regulatory value. Then, according to the abnormal activation intensity, the degree of abnormal connectivity, and the specific cognitive function defects of the target object, the potential target regions are prioritized. Those brain regions that are closely related to the cognitive impairment of the target object and show significant abnormalities in both activation and the network are preferentially selected. The finally obtained high-priority targets not only meet the cognitive needs of the target object but also balance functional recovery and network stability, providing a reliable basis for subsequent simulation optimization and personalized neuromodulation.

[0094] In some embodiments of the present application, the specific process of simulating and optimizing the electrical stimulation parameters according to the preprocessed T1-weighted structural image includes: segmenting the preprocessed T1-weighted structural image to obtain an individualized head anatomical model of the target object; performing mesh division on the individualized head anatomical model through finite element analysis software to convert it into discrete units; based on the discrete units, and combining the finite element method to perform electric field simulation to simulate the propagation and diffusion effects of current in different tissue layers; during the simulation process, simulating and optimizing the electrical stimulation parameters so that the stimulation can accurately focus on the target brain region.

[0095] In some embodiments, during the simulation process, the specific process of simulating and optimizing the electrical stimulation parameters includes: during the simulation process, optimizing the stimulation parameters of time-domain interference electrical stimulation to obtain the simulation optimization result; based on the simulation optimization result, determining whether the current of the time-domain interference electrical stimulation can act on the target brain region to which the individualized stimulation target belongs; if so, taking the electrical stimulation parameters at the current moment as the optimized electrical stimulation parameters.

[0096] For example, after determining the stimulation target, based on Finite Element Analysis (FEA), the parameters of time-domain interferential electrical stimulation are precisely simulated and optimized. TIS focuses on deep target areas through the interference of two high-frequency currents (such as 2 kHz and 2.01 kHz) without directly acting on the superficial brain regions, which gives it a unique advantage in precise neuromodulation. To ensure the personalization and accuracy of the simulation results, an individualized head anatomical model is first constructed based on the T1 segmentation results of the target object. This model meticulously covers multiple layers of structures including gray matter, white matter, cerebrospinal fluid, skull, eyeballs, cancellous bone, cortical bone, blood, and muscle to truly reflect the tissue characteristics and conductivity of the head. Then, a high-precision finite element discretization method is used to mesh the head model, transforming the complex three-dimensional anatomical structure into discrete units suitable for electric field simulation to simulate the actual propagation and diffusion effects of current in different tissue layers. During the simulation process, parameters such as current intensity, frequency, coherence time, electrode position, and shape are continuously optimized to ensure that the low-frequency interference wave can precisely focus on the target brain region, induce synchronous neuronal activity in this region, and simultaneously minimize the unwanted activation of surrounding non-target tissues, thereby improving the effect of neuromodulation. The simulation results will provide data support for the subsequent implementation of personalized neuromodulation to ensure the maximum improvement of the safety and effectiveness of stimulation while meeting the treatment requirements.

[0097] Among them, based on the simulation optimization results, individualized TI electrical stimulation is performed on the target object to achieve precise neuromodulation targeting deep brain regions. The entire stimulation process is strictly carried out according to the parameters optimized by the simulation, including the position of the electrodes, current intensity, frequency, and coherence time, etc., to ensure that the current acts concentratedly on the target brain region while minimizing the unwanted stimulation of surrounding tissues. Through this precise current focusing, the neuronal activities in the deep layer can be effectively regulated, improving the targeting and effect of the treatment.

[0098] Among them, to ensure the safety and tolerance of the target subject, the physiological parameters of the target subject (such as heart rate, blood pressure, blood oxygen saturation, etc.) are monitored in real time during the entire neuromodulation process. These physiological indicators help to promptly identify potential side effects or abnormal reactions. In addition, the subjective feedback of the target subject will also be recorded in detail, including the sensations in the stimulated area, any discomfort or pain, etc. Through this monitoring and feedback system, medical staff can adjust the parameters in a timely manner, avoid imposing unnecessary burdens on the target subject, and ensure the safety and controllability of the treatment process. To maximize the treatment effect, the implementation plan of TIS adopts a multiple-step and progressive approach. The stimulation intensity and frequency are relatively low in the initial stage, and as the adaptability of the target subject improves, the stimulation intensity and frequency are gradually increased to gradually accumulate the effect of neuromodulation. This progressive approach helps to induce the plasticity of neurons in the target area, enables the deep brain regions to gradually adapt to and respond to the stimulation, promotes the reshaping of neural circuits, and thus gradually improves the cognitive function of the target subject. Through this long-term and highly adaptable TIS plan, the function recovery or enhancement of specific brain regions in the brain is gradually promoted, providing a lasting neural basis for the improvement of the cognitive function of the target subject.

[0099] In some embodiments of the present application, the specific process of regularly collecting the behavioral parameters and task-based MRI data of the target subject for analysis to dynamically adjust the electrical stimulation parameters includes: regularly collecting the behavioral data and task-based fMRI data of the target subject as the behavioral parameters and task-based MRI data; analyzing the behavioral parameters and task-based MRI data to evaluate the current stimulation effect and determine whether it is necessary to adjust the stimulation parameters and the stimulation target; if the stimulation effect does not reach the preset condition, then adjust and optimize the stimulation parameters and the stimulation target; if the stimulation effect reaches the preset condition, then continue to execute the step of regularly collecting the behavioral data and task-based fMRI data of the target subject as the behavioral parameters and task-based MRI data.

[0100] For example, to ensure the long-term treatment effect, a feedback-based dynamic adjustment mechanism is established. By regularly collecting the behavioral and task-based fMRI data of the target subject, the changes in cognitive function and brain region activity are evaluated in real time, and the stimulation target and parameters are adjusted accordingly. Specifically, the behavioral performance and fMRI data of the target subject are obtained regularly, the changes in the cognitive function and brain region activity of the target subject are analyzed, the impact of the treatment on the target brain region is judged, and the progress of the cognitive function and the regions that need further regulation are identified. According to the data feedback, parameters such as current intensity, frequency, phase, and electrode position are adjusted individually. If the target subject shows improvement in a specific function, the stimulation intensity can be appropriately reduced and gradually shifted to other regions that need improvement. For multi-functional regions, a combination of different frequencies and intensities can be used for stimulation to better adapt to the needs of the target subject at different stages.

[0101] During each adjustment process, the treatment parameters and the target subject's responses are recorded in detail, including current intensity, frequency, stimulation duration, stimulation effect, and any subjective feedback. By continuously recording, a systematic treatment dataset is formed, which can track the specific effects of each treatment and provide a historical basis for subsequent personalized adjustments. These data also provide valuable information resources for the further optimization and popularization of treatment plans. This feedback-based dynamic adjustment mechanism ensures the accuracy and flexibility of treatment, can continuously adapt to the individual differences and rehabilitation progress of the target subject, and ultimately achieves long-term and stable improvement of cognitive function.

[0102] In the embodiments of the present application, on the one hand, through the comprehensive application of the cognitive behavioral assessment module, the imaging data acquisition module, the brain region activation analysis module, and the brain network analysis module, it is possible to accurately determine specific brain regions directly related to cognitive dysfunction, providing a clear target area for subsequent neuromodulation treatment. On the other hand, the stimulation target screening module determines the individualized stimulation targets of the target subject according to the potential neuromodulation targets and key nodes. These targets represent the regions in the brain that ultimately need to be modulated. The time-domain interference electrical stimulation simulation and implementation module simulates and optimizes the electrical stimulation parameters according to the preprocessed T1-weighted structural image, and performs electrical stimulation on the individualized stimulation targets based on the optimized electrical stimulation parameters. By simulating and optimizing the electrical stimulation parameters, accurate electrical stimulation of the individualized stimulation targets can be achieved, enhancing the targeting and accuracy of the treatment.

[0103] Please refer to Figure 4 , which is a schematic flow chart of a closed-loop neuromodulation method based on electroencephalogram and time-domain interference electrical stimulation provided by the embodiments of the present application. As Figure 4 shown, the detection method of the embodiments of the present application may include the following steps:

[0104] S101, identifying the cognitive deficit areas that the target subject needs to improve;

[0105] S102, during the process of performing a cognitive task selected based on the deficit area on the target subject, collecting and preprocessing the task-state nuclear magnetic data of the target subject; collecting and preprocessing the T1-weighted structural image of the target subject;

[0106] S103, according to the preprocessed task-state nuclear magnetic data, determining the brain regions with abnormal performance in the activation analysis as the potential neuromodulation targets of the target subject;

[0107] S104, according to the potential neuromodulation targets, constructing a network model of the target subject, and identifying the regions with abnormal connections with the regions of interest in the network model as the key nodes related to cognitive deficits;

[0108] S105. Determine the individualized stimulation target of the target object according to the potential neuromodulation target and key node of the target object. The individualized stimulation target is used to characterize the area in the brain of the target object that ultimately needs to be modulated.

[0109] S106. Simulate and optimize the electrical stimulation parameters according to the preprocessed T1-weighted structural image, and perform electrical stimulation on the individualized stimulation target based on the optimized electrical stimulation parameters to achieve precise neuromodulation targeting deep brain regions.

[0110] S107. Regularly collect the behavioral parameters and task-state MRI data of the target object for analysis to dynamically adjust the electrical stimulation parameters.

[0111] In the embodiments of the present application, on the one hand, through the comprehensive application of the cognitive behavioral assessment module, the imaging data acquisition module, the brain region activation analysis module, and the brain network analysis module, it is possible to accurately determine the specific brain regions directly related to cognitive dysfunction, providing a clear target area for subsequent neuromodulation treatment. On the other hand, the stimulation target screening module determines the individualized stimulation targets of the target object according to the potential neuromodulation targets and key nodes. These targets characterize the areas in the brain that ultimately need to be modulated. The time-domain interference electrical stimulation simulation and implementation module simulates and optimizes the electrical stimulation parameters according to the preprocessed T1-weighted structural image, and performs electrical stimulation on the individualized stimulation targets based on the optimized electrical stimulation parameters. By simulating and optimizing the electrical stimulation parameters, precise electrical stimulation of the individualized stimulation targets can be achieved, enhancing the targeting and accuracy of the treatment.

[0112] The present application also provides a computer-readable medium, on which program instructions are stored. When the program instructions are executed by a processor, the closed-loop neuromodulation method based on electroencephalogram and time-domain interference electrical stimulation provided by the above-mentioned various method embodiments is implemented.

[0113] The present application also provides a computer program product containing instructions. When it runs on a computer, it enables the computer to execute the closed-loop neuromodulation method based on electroencephalogram and time-domain interference electrical stimulation of the above-mentioned various method embodiments.

[0114] Those of ordinary skill in the art can understand that all or part of the processes of implementing the methods in the above embodiments can be completed by instructing relevant hardware through a computer program. The program for closed-loop neuromodulation based on electroencephalogram and time-domain interference electrical stimulation can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the above-mentioned method embodiments. Among them, the storage medium of the program for closed-loop neuromodulation based on electroencephalogram and time-domain interference electrical stimulation can be a magnetic disk, an optical disk, a read-only memory, or a random access memory, etc.

[0115] The above disclosure is only for the preferred embodiments of the present application. Of course, it cannot be used to limit the scope of rights of the present application. Therefore, equivalent changes made according to the claims of the present application still fall within the scope covered by the present application.

Claims

1. A neuromodulation system for cognitive impairment, characterized in that, The system includes: A cognitive behavioral assessment module for identifying the cognitive deficit areas that the target object needs to improve; An imaging data acquisition module for collecting and preprocessing the task-state MRI data of the target object during the execution of a cognitive task selected based on the deficit area for the target object; collecting and preprocessing the T1-weighted structural images of the target object; A brain region activation analysis module for determining, based on the preprocessed task-state MRI data, the brain regions that show abnormalities in the activation analysis as potential neuromodulation targets for the target object; wherein, The determining, based on the preprocessed task-state MRI data, the brain regions that show abnormalities in the activation analysis as potential neuromodulation targets for the target object includes: Using a general linear model and the preprocessed task-state MRI data to construct a matrix; Capturing the brain activation pattern under preset task conditions through the matrix; Comparing the captured brain activation pattern with the activation pattern of a healthy control group to identify the regions that significantly deviate from the activation pattern of the healthy control group; Determining the brain regions related to cognitive dysfunction as potential neuromodulation targets for the target object based on the deviated regions; A brain network analysis module for constructing a network model of the target object based on the potential neuromodulation targets, and identifying the regions with abnormal connections to the brain regions of interest in the network model as key nodes related to cognitive deficits; A stimulation target screening module for determining the individualized stimulation targets for the target object based on the potential neuromodulation targets and the key nodes of the target object, where the individualized stimulation targets are used to represent the regions in the brain of the target object that ultimately need to be modulated; The determining the individualized stimulation targets for the target object based on the potential neuromodulation targets and the key nodes of the target object includes: Comparing the potential neuromodulation targets of the target object with the key nodes one by one to determine the common regions that appear simultaneously in the potential neuromodulation targets and the key nodes of the target object; Sorting the simultaneously appearing common regions by priority; Selecting a preset number of the highest-priority common regions from the sorting results as high-priority targets; Taking the high-priority targets as the individualized stimulation targets for the target object; A time-domain interference electrical stimulation simulation and implementation module for simulating and optimizing the electrical stimulation parameters based on the preprocessed T1-weighted structural images, and performing electrical stimulation on the individualized stimulation targets based on the optimized electrical stimulation parameters to achieve precise neuromodulation of the targeted deep brain regions; A dynamic feedback adjustment module for regularly collecting the behavioral parameters and task-state MRI data of the target object for analysis to dynamically adjust the electrical stimulation parameters.

2. The system according to claim 1, wherein The identifying the cognitive deficit areas that the target object needs to improve includes: Performing a cognitive behavioral test on the target object using a preset scale to test the cognitive state of the target object and obtaining the test results of the target object; Determining the cognitive deficit areas that the target object needs to improve based on the test results.

3. The system according to claim 1, characterized in that The constructing the network model of the target object based on the potential neuromodulation targets includes: Select a brain region of interest from the potential neuroregulation targets of the target object; Extract the time series signals of the brain region of interest from the preprocessed task-state MRI data, where the time series signals are used to characterize the signal changes of the physiological variables of the brain region at different time points; Construct a generalized psychophysiological interaction model; Construct a generalized psychophysiological interaction model according to the time series signals, and quantify the conditional functional connectivity strength between the brain region to which the potential neuroregulation target belongs and other brain regions through this model; Construct a network model of the target object according to the conditional functional connectivity strength.

4. The system according to claim 1, wherein Identify the regions with abnormal connections with the brain region of interest in the network model as key nodes related to cognitive deficits, including: Obtain a healthy control group; Compare the network model of the target object with a preset healthy control group to identify abnormal regions with over-connection or dysfunction in the network model; Regard the abnormal regions as key nodes related to cognitive deficits.

5. The system according to claim 1, characterized in that, Simulate and optimize the electrical stimulation parameters according to the preprocessed T1-weighted structural images, including: Segment the preprocessed T1-weighted structural images to obtain an individualized head anatomical model of the target object; Perform mesh division on the individualized head anatomical model through finite element analysis software and convert it into discrete units; Based on the discrete units, combine with the finite element method to perform electric field simulation to simulate the propagation and diffusion effects of current in different tissue layers; During the simulation process, simulate and optimize the electrical stimulation parameters so that the stimulation focuses on the target brain region.

6. The system according to claim 5, characterized in that, During the simulation process, simulate and optimize the electrical stimulation parameters, including: During the simulation process, optimize the stimulation parameters of time-domain interference electrical stimulation to obtain a simulation optimization result; Based on the simulation optimization result, judge whether the current of the time-domain interference electrical stimulation can act on the target brain region to which the individualized stimulation target belongs; If so, regard the electrical stimulation parameters at the current moment as the optimized electrical stimulation parameters.

7. The system according to claim 1, characterized in that, Regularly collect the behavioral parameters and task-state MRI data of the target object for analysis to dynamically adjust the electrical stimulation parameters, including: Regularly collect the behavioral data and task-state fMRI data of the target object as behavioral parameters and task-state MRI data; Analyze the behavioral parameters and task-state MRI data to evaluate the current stimulation effect and determine whether it is necessary to adjust the stimulation parameters and the stimulation target; If the stimulation effect does not reach the preset conditions, adjust and optimize the stimulation parameters and the stimulation target; if the stimulation effect reaches the preset conditions, continue to perform the step of regularly collecting the behavioral data and task-state fMRI data of the target object as behavioral parameters and task-state MRI data.

8. The system according to claim 2, characterized in that The cognitive behavioral tests include: Conduct a preliminary screening through the Montreal Cognitive Assessment Scale and the Mini-Mental State Examination Scale, and conduct cognitive behavioral tests in at least one of the following ways: Use the digit span test to evaluate the working memory of the target object; Use the California Verbal Learning Test and the Logical Memory Test to evaluate the episodic memory of the target object; The semantic ability of the target object is evaluated using the Boston Naming Test and the Verbal Fluency Test; The executive function of the target object is evaluated using the Stroop Test and the Wisconsin Card Sorting Test; The visuospatial constructional ability of the target object is measured using the Clock Drawing Test.

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