Nerve regulation and control system for cognitive impairment

Abstract

The invention discloses a nerve regulation system for cognitive impairment, and the system comprises a cognitive behavior evaluation module which is used for recognizing a cognitive defect field which needs to be improved by a target object; the image data acquisition module is used for acquiring and preprocessing task state nuclear magnetic data and T1 weighted structure images of a target object; the brain region activation analysis module is used for determining a neural regulation potential target spot according to the preprocessed task state nuclear magnetic data; the brain network analysis module is used for identifying key nodes related to cognitive defects; the stimulation target screening module is used for determining an individualized stimulation target of the target object according to the nerve regulation potential target and the key node of the target object; the time domain interference electrical stimulation simulation and implementation module is used for performing electrical stimulation on the individualized stimulation target spot; and the dynamic feedback adjustment module is used for dynamically adjusting the stimulation parameters and enhancing the personalized regulation and control effect. By adopting the method and the device, the specific brain region directly related to the cognitive impairment can be determined, so that the targeting and the accuracy of treatment are enhanced.

Description

Technical Field

[0001] The present application relates to the field of neurotechnology, and in particular to a neural regulation system for cognitive dysfunction. Background Art

[0002] In the field of neuroscience and clinical treatment, cognitive dysfunction, including significant deterioration of memory, attention, executive function and language ability, has become an increasingly serious health challenge worldwide. With the aging of the population, the incidence of cognitive dysfunction continues to rise, which has a significant impact on the target individuals, families and society. Therefore, the development of effective neuromodulation technology to improve cognitive function is of great significance to improving the quality of life of the target subjects and reducing the social burden.

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

[0004] However, most existing methods use standardized stimulation parameters, which fail to fully consider the differences in the target subjects' conditions and individual neuroanatomical structures, resulting in unstable therapeutic effects. Some neuromodulation methods 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 subjects and unable to fully improve individualized cognitive dysfunction. Summary of the invention

[0005] The present application embodiment provides a neural regulation system for cognitive dysfunction. In order to have a basic understanding of some aspects of the disclosed embodiments, a simple summary is given below. This summary is not a general review, nor is it intended to identify key / important components or describe the scope of protection of these embodiments. Its only purpose is to present some concepts in a simple form as a preface to the detailed description that follows.

[0006] In a first aspect, an embodiment of the present application provides a neural regulation system for cognitive dysfunction, the system comprising:

[0007] A cognitive behavioral assessment module to identify areas of cognitive deficits that require improvement in the target subject;

[0008] An image data acquisition module is used to collect task-state nuclear magnetic resonance data of the target object for preprocessing during the process of performing a cognitive task based on defect field selection on the target object; collect and preprocess T1-weighted structural images of the target object;

[0009] The brain region activation analysis module is used to determine the brain regions that show abnormalities in the activation analysis as potential targets for neural regulation of the target object based on the pre-processed task-state MRI data;

[0010] The brain network analysis module is used to construct a network model of the target object based on the potential targets of neural regulation and identify the areas in the network model that have abnormal connections with the brain areas of interest as key nodes related to cognitive deficits;

[0011] A stimulation target screening module is used to determine the individualized stimulation targets of the target object based on the target object's potential neural regulation targets and key nodes. The individualized stimulation targets are used to characterize the areas in the target object's brain that ultimately need to be regulated;

[0012] The time-domain interferometric electrical stimulation simulation and implementation module is used to simulate and optimize electrical stimulation parameters based on preprocessed T1-weighted structural images, and to perform electrical stimulation on individual stimulation targets based on the optimized electrical stimulation parameters, so as to achieve precise neural regulation targeting deep brain regions;

[0013] The dynamic feedback adjustment module is used to regularly collect behavioral parameters and task-state MRI data of the target object for analysis in order to dynamically adjust the electrical stimulation parameters.

[0014] Optionally, identify areas of cognitive deficits that need improvement in the target subject, including:

[0015] Conduct cognitive behavioral tests on the target subjects using a preset scale to test the target subjects' cognitive state and obtain the test results of the target subjects;

[0016] Based on the test results, determine the areas of cognitive deficits that need to be improved in the target subject.

[0017] Optionally, based on the pre-processed task-state MRI data, the brain regions that show abnormalities in the activation analysis are identified as potential targets for neuromodulation of the target subject, including:

[0018] The matrix was constructed using the general linear model and preprocessed task-state MRI data;

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

[0020] Compare the captured brain activation patterns with those of healthy controls to identify areas that deviate significantly from the activation patterns of healthy controls;

[0021] Based on the areas of deviation, brain regions associated with cognitive dysfunction were identified as potential targets for neuromodulation in the target subjects.

[0022] Optionally, a network model of the target object is constructed based on potential targets of neural regulation, including:

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

[0024] Extract the time series signals of the brain region of interest from the pre-processed task-state MRI data. The time series signals are used to characterize the signal changes of physiological variables in the brain region at different time points.

[0025] Construct a generalized psychophysiological interaction model;

[0026] According to the time series signal, a generalized psychophysiological interaction model is constructed, and the conditional functional connection strength between the brain region to which the potential target of neural regulation belongs and other brain regions is quantified by the model;

[0027] According to the conditional functional connectivity strength, a network model of the target object is constructed.

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

[0029] Obtain a healthy control group;

[0030] Comparing the network model of the target subject with a pre-set healthy control group to identify abnormal areas of overconnection or dysfunction in the network model;

[0031] Abnormal regions were identified as key nodes associated with cognitive deficits.

[0032] Optionally, based on the target object's potential neural regulation targets and key nodes, determine the target object's individualized stimulation targets, including:

[0033] Compare the potential targets of neural regulation of the target object with the key nodes one by one to determine the common areas that appear simultaneously in the potential targets of neural regulation of the target object and the key nodes;

[0034] Prioritize common areas that occur simultaneously;

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

[0036] High priority targets were selected as individualized stimulation targets for the target subjects.

[0037] Optionally, based on the pre-processed T1-weighted structural images, the electrical stimulation parameters are optimized by simulation, including:

[0038] The preprocessed T1-weighted structural images are segmented to obtain the individualized head anatomical model of the target object;

[0039] The individualized head anatomical model is meshed using finite element analysis software and converted into discrete units;

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

[0041] During the simulation process, the simulation optimizes the electrical stimulation parameters so that the stimulation can be accurately focused on the target brain area.

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

[0043] During the simulation process, the stimulation parameters of the time domain interferometric electrical stimulation are optimized to obtain the simulation optimization results;

[0044] Based on the simulation optimization results, determine whether the current of time-domain interferometric electrical stimulation can act on the target brain area to which the individual stimulation target belongs;

[0045] If yes, the electrical stimulation parameters at the current moment are used as optimized electrical stimulation parameters.

[0046] Optionally, behavioral parameters and task-state MRI data of the target subject are collected and analyzed regularly to dynamically adjust the electrical stimulation parameters, including:

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

[0048] Analyze behavioral parameters and task-state MRI data to evaluate the current stimulation effect and determine whether the stimulation parameters and stimulation targets need to be adjusted;

[0049] If the stimulation effect does not reach the preset conditions, the stimulation parameters and stimulation targets are adjusted and optimized; if the stimulation effect reaches the preset conditions, the step of regularly collecting the behavioral data and task-state fMRI data of the target object as behavioral parameters and task-state fMRI data is continued.

[0050] Optional cognitive behavioral testing may include:

[0051] Initial screening with the Montreal Cognitive Assessment and the Mini-Mental State Examination, and cognitive behavioral testing using at least one of the following:

[0052] The target subjects’ working memory was assessed using the Digit Span Test;

[0053] The target subjects’ episodic memory was assessed using the California Verbal Learning Test and the Logical Memory Test;

[0054] The target subjects' semantic abilities were assessed using the Boston Naming Test and the Verbal Fluency Test;

[0055] Executive functioning of the target subjects was assessed using the Stroop test and the Wisconsin Card Sorting Test;

[0056] The clock drawing test was used to measure the visual-spatial construction ability of target objects.

[0057] In the embodiments of the present application, on the one hand, through the comprehensive application of cognitive behavioral assessment module, image data acquisition module, brain region activation analysis module and 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 based on the potential targets and key nodes of neuromodulation. These targets represent the areas in the brain that ultimately need to be regulated. The time domain interference electrical stimulation simulation and implementation module simulates and optimizes the electrical stimulation parameters based on the preprocessed T1-weighted structural images, 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, which enhances the targeting and accuracy of the treatment.

[0058] It should be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0060] Figure 1 is a schematic diagram of the system structure of a neural regulation system for cognitive dysfunction provided in an embodiment of the present application;

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

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

[0063] Figure 4 It is a flow chart of a neural regulation method for cognitive dysfunction provided in an embodiment of the present application. DETAILED DESCRIPTION

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

[0065] It should be clear that the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in the field without creative work are within the scope of protection of the present application.

[0066] When the following description refers to the 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. Instead, they are only examples of systems consistent with some aspects of the present application as detailed in the attached claims.

[0067] In the description of the present application, it should be understood that the terms "first", "second", etc. are used for descriptive purposes only and should not be understood 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 the specific circumstances. In addition, in the description of the present application, unless otherwise specified, "multiple" refers to two or more. "And / or" describes the association relationship of associated objects, indicating that three relationships may exist. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. The character " / " generally indicates that the objects associated before and after are in an "or" relationship.

[0068] See also Figure 1 , Figure 1 This is a system structure diagram of a neural regulation system for cognitive dysfunction provided in an embodiment of the present application, the system comprising: a cognitive behavioral assessment module, an image data acquisition module, a brain region activation analysis module, a brain network analysis module, a stimulation target screening module, a time-domain interferometric electrical stimulation simulation and implementation module, and a dynamic feedback adjustment module; wherein the cognitive behavioral assessment module, the image data acquisition module, the brain region activation analysis module, the brain network analysis module, the stimulation target screening module, the time-domain interferometric electrical stimulation simulation and implementation module, and the dynamic feedback adjustment module are communicatively connected.

[0069] In the embodiment of the present application, the cognitive behavioral assessment module is used to identify the cognitive deficiency areas that need to be improved in the target object; the image data acquisition module is used to collect the task-state nuclear magnetic resonance data of the target object for preprocessing during the cognitive task selected based on the deficiency area for the target object; the T1-weighted structural image of the target object is collected and preprocessed; the brain area activation analysis module is used to determine the brain area that shows abnormality in the activation analysis as the potential target of neural regulation of the target object based on the preprocessed task-state nuclear magnetic resonance data; the brain network analysis module is used to construct a network model of the target object based on the potential target of neural regulation, and identify the area in the network model that has abnormal connection with the brain area of ​​interest domain as a key node related to cognitive defects; a stimulation target screening module, which is used to determine the individualized stimulation targets of the target object according to the target object's potential neural regulation targets and key nodes. The individualized stimulation targets are used to characterize the areas in the target object's brain that ultimately need to be regulated; a time-domain interferometric electrical stimulation simulation and implementation module, which is used to simulate and optimize electrical stimulation parameters based on the preprocessed T1-weighted structural images, and to electrically stimulate the individualized stimulation targets based on the optimized electrical stimulation parameters, so as to achieve precise neural regulation targeting deep brain areas; a dynamic feedback adjustment module, which is used to regularly collect the target object's behavioral parameters and task-state MRI data for analysis, so as to dynamically adjust the electrical stimulation parameters.

[0070] Among them, the field of cognitive deficits refers to the specific aspects in which an individual shows impairment or deficiency in cognitive function, 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 resonance data is data reflecting brain activity patterns collected by fMRI technology when performing a specific task. T1-weighted structural imaging is a magnetic resonance imaging technology that provides high-resolution brain structural images by emphasizing T1 relaxation characteristics. Abnormal brain areas are brain areas that deviate from normal function or structure identified in task-state nuclear magnetic resonance data and are associated with cognitive dysfunction. Potential targets for neuromodulation refer to those brain areas that are believed to be able to improve cognitive function through neuromodulation techniques (such as electrical stimulation or magnetic stimulation). Network models are models built based on functional connections between brain regions, which are used to describe the interactions and network connections between different brain regions. Individualized stimulation targets are electrical stimulation target areas determined according to the specific circumstances of the individual, and are used for personalized neuromodulation treatment. Electrical stimulation parameters are key parameters in electrical stimulation therapy, 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 areas of cognitive deficiencies that need to be improved in the target subject includes: conducting cognitive behavioral tests on the target subject using a preset scale to test the cognitive state of the target subject and obtain the test results of the target subject; based on the test results, determining the areas of cognitive deficiencies that need to be improved in the target subject.

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

[0073] For example, this application requires a comprehensive cognitive behavioral test of the target object to accurately assess its cognitive state in terms of working memory, episodic memory, semantic ability, executive function and visual-spatial structural ability. First, a preliminary screening is performed by the Montreal Cognitive Assessment (MoCA) and the Mini-Mental State Examination (MMSE) to quickly assess the overall cognitive state of the target object. According to the screening results, further targeted assessments are carried out, including the use of the Digit Span Test to assess working memory, the California Verbal Learning Test (CVLT) and the Logical Memory Test (Logical Memory Test, optional) to assess episodic memory, the Boston Naming Test (Boston Naming Test) and the Verbal Fluency Test (Verbal Fluency Test) to assess semantic ability, the Stroop Test (Stroop Color and Word Test, SCWT) and the Wisconsin Card Sorting Test (Wisconsin Card Sorting Test, WCST, optional) to assess executive function, and the Clock Drawing Test (Clock Drawing Test) to further refine the measurement of visual-spatial structural ability. Through these standardized behavioral tests, the target subject's cognitive deficit areas and key obstacles that affect daily life can be accurately identified, and the target subject's specific improvement needs in different cognitive functions can be determined, so that subsequent fMRI tasks and electrical stimulation parameters can be set in a personalized manner to ensure the accuracy and efficiency of the treatment plan. The step-by-step and combined approach can not only comprehensively cover various cognitive function areas, but also reduce the fatigue burden during the target subject's evaluation process.

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

[0075] In some embodiments of the present application, when the target object performs a cognitive task selected based on the defect field, different levels of task difficulty can be set to observe the impact of task load on brain activity. For example, in the N-back task selected for working memory deficiency, the task difficulty classification includes 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), and the requirements for working memory are gradually increased. By comparing and analyzing the fMRI data of different task difficulty conditions, it is possible to observe how the intensity and range of brain activation change with the increase of task load. For example, the prefrontal cortex and parietal lobe in cognitive tasks may show stronger activation signals as the task load increases, 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 bottlenecks, and even compensatory activation. These changes not only reflect the decline in the operating efficiency of the working memory system, but also reveal the functional deficiencies of specific brain regions in dealing with high-load tasks, which helps to accurately locate potential targets for neural regulation.

[0076] In some embodiments of the present application, task-state MRI data of the target object are collected for preprocessing; when collecting and preprocessing the T1-weighted structural image of the target object, the preprocessing steps of the task-state MRI data include time layer correction, head motion correction, spatial registration and standardization, and smoothing, etc., to remove the system errors and noise caused by the scanning process, and align the task-state MRI data to the T1-weighted structural image to ensure the accurate overlap of functional and structural data, and provide support for subsequent anatomical positioning and functional analysis. The T1 structural image is processed by skull removal, 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 positioning and simulation modeling. These preprocessing steps can ensure the quality and consistency of the image data, thereby providing reliable basic data for functional analysis, individualized target selection, and neural regulation simulation.

[0077] In some embodiments of the present application, the specific process of determining the brain region that performs abnormally in the activation analysis as the potential target of neural regulation of the target object based on the preprocessed task-state MRI data includes: constructing a matrix using a general linear model and the preprocessed task-state MRI data; capturing the brain activation pattern under preset task conditions through the matrix; comparing the captured brain activation pattern with the activation pattern of the healthy control group to identify the area that significantly deviates from the activation pattern of the healthy control group; and determining the brain region associated with cognitive dysfunction as the potential target of neural regulation of the target object based on the deviated area.

[0078] Among them, task-state fMRI data are functional magnetic resonance imaging data collected and preprocessed when subjects perform specific cognitive or sensorimotor tasks. The General Linear Model (GLM) is a statistical model used to analyze data in experimental design, 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 movement, baseline drift, etc.). Preset task conditions refer to pre-defined task or stimulation conditions in fMRI experiments, which are used to activate specific areas of the brain to study their functions. In fMRI analysis, the baseline usually refers to the level of brain activity when no specific task is performed or when a specific stimulus is received, which is used as a reference point for comparing task states. Activation areas that deviate from the healthy baseline are those brain areas that are significantly different from the healthy baseline when comparing the brain activation patterns of the target subject group and the healthy control group, which may indicate the presence of cognitive dysfunction. Potential targets for neuromodulation refer to brain areas associated with cognitive dysfunction identified through fMRI analysis, which can serve as target areas for neuromodulatory treatments (such as transcranial magnetic stimulation or transcranial electrical stimulation).

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

[0080] Taking the N-back task as an example, the N-back task conditions (such as 0-back, 1-back, 2-back) and other potential interference factors (such as head movement and baseline drift) are integrated into the model. Each task condition is used as a regression variable to simulate the response pattern of the brain under different load levels and capture the activation changes of the working memory network. At the same time, a baseline condition is added to conduct comparative analysis under different task conditions, so as to estimate the response strength of the brain under each condition, such as Figure 2 As shown. Based on the design matrix, the activation of each condition is compared (such as 2-back vs. 0-back), and the response strength of the brain region under different task conditions is quantified by parameter estimation to estimate the task-related brain area activity. In this way, brain areas that are activated as the difficulty of the task increases (such as the prefrontal lobe, parietal lobe, etc.) can be identified. These brain areas are the core areas related to working memory load. In order to identify abnormal activation areas under cognitive dysfunction, the activation pattern of the target subject is statistically compared with the activation pattern of the healthy control group to find out the areas that deviate significantly from the normal range (such as excessive or low activation), which are often related to working memory and attention deficits.

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

[0082] In some embodiments of the present application, the specific process of constructing a network model of a target object based on potential targets of neural regulation includes: selecting a brain region of interest from the potential targets of neural regulation of the target object; extracting time series signals of the brain region of interest from preprocessed task-state MRI data, the time series signals being used to characterize signal changes in physiological variables of the brain region at different time points; constructing a generalized psychophysiological interaction model; constructing a generalized psychophysiological interaction model based on the time series signals, and quantifying the conditional functional connection strength between the brain region to which the potential target of neural regulation belongs and other brain regions through the model; and constructing a network model of the target object based on the conditional functional connection strength.

[0083] Among them, potential targets for neuromodulation refer to brain regions related to specific cognitive tasks determined through preliminary activation analysis. These regions are considered to be possible points of action for neuromodulation therapy. Region of Interest (ROI) is a specific brain region selected in neuroimaging studies for further analysis. Time series signals refer to changes in physiological variables (such as BOLD signals) in brain regions recorded at continuous time points. These signals can reflect the dynamic changes of brain activity over time. The generalized psychophysiological interaction model is a statistical model used to analyze functional connections between brain regions, which can quantify the strength of interactions between different brain regions under specific psychological states. Conditional functional connectivity strength refers to the strength of functional connections between different brain regions under specific conditions or tasks. Network model refers to a model built based on the strength of functional connections between brain regions, which is 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 areas in the network model that are abnormally connected to the brain region of interest 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 areas in the network model that are over-connected or dysfunctional; and treating the abnormal areas as key nodes related to cognitive deficits.

[0085] For example, when analyzing the brain functional connectivity patterns of target subjects, the ROI-based gPPI analysis algorithm can be used to identify the functional connectivity patterns between potential target brain regions and other brain regions, such as Figure 3 As shown. This method mainly constructs a multiple linear regression model for each task condition in the experiment, calculates the interaction term between each psychological variable and the physiological variable at the neural activity level, and then convolves with the HRF function to obtain the PPI interaction term at the BOLD level. After modeling and analysis, the functional connection strength between conditions can be directly compared. For example, when performing PPI analysis on tasks 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 area, 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 signals and brain activity variables at the neural level, respectively, and HRF is the hemodynamic response function.

[0091] Specifically, select appropriate ROIs (such as abnormal prefrontal cortex or parietal cortex activated in the N-back task), extract time series signals, and construct a gPPI model. Through this model, the conditional functional connectivity strength between potential target brain areas and other brain areas is quantified, thereby reflecting the functional connectivity pattern under specific task conditions. Next, the network model of the target subject is compared with that of the healthy control group to identify overconnected or dysfunctional areas in the target subject's network, thereby locating key nodes that are closely related to the target subject's specific cognitive deficits. 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 targets not only meet the personalized needs of the target subject, but also have a good regulatory effect.

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

[0093] For example, the abnormal activation areas 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 abnormal connection areas in the functional network analysis results (such as key nodes related to the working memory network) to find common areas that show abnormalities in both analyses. Common areas represent brain areas that show significant deviations in both task activation and functional connectivity. These areas may be the key source of cognitive dysfunction and have high regulatory value. Then, potential target areas are prioritized according to the intensity of abnormal activation, the degree of abnormal connectivity, and the specific cognitive function defects of the target object. Priority is given to those brain areas that are closely related to the cognitive impairment of the target object and show significant abnormalities in both activation and network. The high-priority targets finally obtained 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 neural regulation.

[0094] In some embodiments of the present application, the specific process of simulating and optimizing electrical stimulation parameters based on 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; meshing the individualized head anatomical model through finite element analysis software and converting it into discrete units; performing electric field simulation based on discrete units and in combination with the finite element method 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 area.

[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 the time domain interference electrical stimulation to obtain simulation optimization results; based on the simulation optimization results, judging whether the current of the time domain interference electrical stimulation can act on the target brain area to which the individualized stimulation target belongs; if so, using the electrical stimulation parameters at the current moment as the optimized electrical stimulation parameters.

[0096] For example, after determining the stimulation target, the parameters of time-domain interferometric electrical stimulation are accurately simulated and optimized based on finite element analysis (FEA). TIS focuses on deep target areas through the interference of two high-frequency currents (such as 2kHz and 2.01kHz) without directly acting on the superficial brain area. This feature gives it a unique advantage in precise neuroregulation. 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. The model meticulously covers multi-layer structures including gray matter, white matter, cerebrospinal fluid, skull, eyeball, cancellous bone, compact bone, blood, and muscle to truly reflect the tissue characteristics and conductivity of the head. Then, the head model is meshed using a high-precision finite element discretization method to convert 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 be accurately focused on the target brain area, inducing synchronous activity of neurons in the area, while minimizing the undesired activation of surrounding non-target tissues, thereby improving the effect of neuromodulation. The simulation results will provide data support for the implementation of subsequent personalized neuromodulation, ensuring that the safety and effectiveness of stimulation are maximized while meeting treatment needs.

[0097] Among them, based on the simulation optimization results, individualized TI electrical stimulation is performed on the target object to achieve precise neural regulation targeting deep brain areas. The entire stimulation process is strictly carried out according to the parameters optimized by simulation, including the position of the electrode, current intensity, frequency and coherence time, etc., to ensure that the current is concentrated on the target brain area, while minimizing the undesired stimulation of the surrounding tissues. Through this precise current focusing, deep neuronal activity can be effectively regulated to improve the targeting and effectiveness of treatment.

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

[0099] In some embodiments of the present application, the specific process of regularly collecting the behavioral parameters and task-state fMRI data of the target object for analysis to dynamically adjust the electrical stimulation parameters includes: regularly collecting the behavioral data and task-state fMRI data of the target object as behavioral parameters and task-state fMRI data; analyzing the behavioral parameters and task-state fMRI data to evaluate the current stimulation effect and determine whether it is necessary to adjust the stimulation parameters and stimulation targets; if the stimulation effect does not reach the preset conditions, adjusting and optimizing the stimulation parameters and stimulation targets; if the stimulation effect reaches the preset conditions, continuing to execute the step of regularly collecting the behavioral data and task-state fMRI data of the target object as behavioral parameters and task-state fMRI data.

[0100] For example, to ensure long-term treatment effects, a dynamic adjustment mechanism based on feedback is established. By regularly collecting behavioral and task-state fMRI data of the target subjects, changes in cognitive function and brain activity are evaluated in real time, and stimulation targets and parameters are adjusted accordingly. Specifically, the behavioral performance and fMRI data of the target subjects are obtained regularly, and the changes in cognitive function and brain activity of the target subjects are analyzed to determine the impact of treatment on the target brain areas, identify the progress of cognitive function and areas that need further regulation. According to data feedback, parameters such as current intensity, frequency, phase and electrode position are adjusted individually. If the target subject improves in a specific function, the stimulation intensity can be appropriately reduced and gradually turned to other areas that need improvement. For multifunctional areas, a combination of stimulations of different frequencies and intensities can be used to better meet the needs of the target subjects at different stages.

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

[0102] In the embodiments of the present application, on the one hand, through the comprehensive application of cognitive behavioral assessment module, image data acquisition module, brain region activation analysis module and 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 based on the potential targets and key nodes of neuromodulation. These targets represent the areas in the brain that ultimately need to be regulated. The time domain interference electrical stimulation simulation and implementation module simulates and optimizes the electrical stimulation parameters based on the preprocessed T1-weighted structural images, 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, which enhances the targeting and accuracy of the treatment.

[0103] See also Figure 4 , which is a flow chart of a closed-loop neural regulation method based on EEG and time-domain interferometric electrical stimulation, for an embodiment of the present application. Figure 4 As shown, the detection method of the embodiment of the present application may include the following steps:

[0104] S101, identify the cognitive deficit areas that need to be improved in the target subject;

[0105] S102, in the process of performing the cognitive task based on the defect field selection on the target object, collecting and preprocessing the task-state nuclear magnetic resonance data of the target object; collecting and preprocessing the T1-weighted structural image of the target object;

[0106] S103, based on the pre-processed task-state MRI data, determining the brain regions showing abnormalities in the activation analysis as potential targets for neural regulation of the target subject;

[0107] S104, constructing a network model of the target subject based on the potential targets of neural regulation, and identifying the regions in the network model that have abnormal connections with the brain regions of interest as key nodes related to cognitive deficits;

[0108] S105, determining individualized stimulation targets of the target object according to the potential targets and key nodes of neural regulation of the target object, where the individualized stimulation targets are used to characterize the areas in the brain of the target object that ultimately need to be regulated;

[0109] S106, simulating and optimizing electrical stimulation parameters according to the preprocessed T1-weighted structural images, and electrically stimulating individualized stimulation targets based on the optimized electrical stimulation parameters to achieve precise neural regulation targeting deep brain regions;

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

[0111] In the embodiments of the present application, on the one hand, through the comprehensive application of cognitive behavioral assessment module, image data acquisition module, brain region activation analysis module and 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 based on the potential targets and key nodes of neuromodulation. These targets represent the areas in the brain that ultimately need to be regulated. The time domain interference electrical stimulation simulation and implementation module simulates and optimizes the electrical stimulation parameters based on the preprocessed T1-weighted structural images, 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, which enhances the targeting and accuracy of the treatment.

[0112] The present application also provides a computer-readable medium having program instructions stored thereon, which, when executed by a processor, implement the closed-loop neural regulation method based on EEG and time-domain interferometric electrical stimulation provided by the above-mentioned method embodiments.

[0113] The present application also provides a computer program product comprising instructions, which, when executed on a computer, enables the computer to execute the closed-loop neural regulation method based on EEG and time-domain interferometric electrical stimulation of the above-mentioned various method embodiments.

[0114] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program, and the program of closed-loop neural regulation based on EEG and time-domain interferometric electrical stimulation can be stored in a computer-readable storage medium, and when the program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, the storage medium of the program of closed-loop neural regulation based on EEG and time-domain interferometric electrical stimulation can be a disk, an optical disk, a read-only storage memory, or a random access memory, etc.

[0115] The above disclosure is only the preferred embodiment of the present application, which certainly 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 are still within the scope covered by the present application.

Claims

1. A neural regulation system for cognitive dysfunction, characterized in that: The system comprises: A cognitive behavioral assessment module to identify areas of cognitive deficits that require improvement in the target subject; An image data acquisition module, used for collecting task-state nuclear magnetic resonance data of the target object for preprocessing during the process of performing the cognitive task based on the defect field selection on the target object; collecting and preprocessing T1-weighted structural images of the target object; A brain region activation analysis module is used to determine the brain regions showing abnormalities in the activation analysis as potential targets for neural regulation of the target object based on the pre-processed task-state MRI data; A brain network analysis module, for constructing a network model of the target object according to the potential targets of neural regulation, and identifying regions in the network model that are abnormally connected to the brain regions of interest as key nodes related to cognitive deficits; A stimulation target screening module, used to determine the individualized stimulation target of the target object according to the potential neural regulation target of the target object and the key node, wherein the individualized stimulation target is used to characterize the area in the brain of the target object that ultimately needs to be regulated; A time-domain interferometric electrical stimulation simulation and implementation module, which is used to simulate and optimize electrical stimulation parameters according to the preprocessed T1-weighted structural images, and to electrically stimulate the individualized stimulation target based on the optimized electrical stimulation parameters, so as to achieve precise neural regulation targeting deep brain regions; The dynamic feedback adjustment module is used to regularly collect behavioral parameters and task-state nuclear magnetic resonance data of the target object for analysis so as to dynamically adjust the electrical stimulation parameters.

2. The system according to claim 1, characterized in that The identification of cognitive deficit areas that need to be improved in the target subject includes: Conducting a cognitive behavioral test on the target subject using a preset scale to test the cognitive state of the target subject and obtain a test result of the target subject; Based on the test results, the cognitive deficiency areas that need to be improved in the target subject are determined.

3. The system according to claim 1, characterized in that The step of determining the brain region showing abnormal performance in activation analysis as a potential target for neural regulation of the target object based on the pre-processed task-state MRI data includes: The matrix was constructed using the general linear model and preprocessed task-state MRI data; Through the matrix, the brain activation patterns under the preset task conditions are captured; Compare the captured brain activation patterns with those of healthy controls to identify areas that deviate significantly from the activation patterns of healthy controls; Based on the deviated area, a brain area associated with cognitive dysfunction is determined as a potential target for neural regulation of the target object.

4. The system according to claim 1, characterized in that The step of constructing a network model of the target object according to the potential neural regulation targets includes: Selecting a brain region of interest from potential neural regulation targets of the target subject; Extracting time series signals of the brain region of interest from the pre-processed task-state MRI data, wherein the time series signals are used to characterize signal changes of physiological variables of the brain region at different time points; Construct a generalized psychophysiological interaction model; According to the time series signal, a generalized psychophysiological interaction model is constructed, and the conditional functional connection strength between the brain region to which the potential target of neural regulation belongs and other brain regions is quantified by the model; A network model of the target object is constructed according to the conditional functional connection strength.

5. The system according to claim 1, characterized in that The method of identifying the region in the network model that is abnormally connected to the brain region of interest as a key node associated with cognitive deficits includes: Obtain a healthy control group; Comparing the network model of the target subject with a preset healthy control group to identify abnormal areas of overconnection or dysfunction in the network model; The abnormal regions are regarded as key nodes associated with cognitive deficits.

6. The system according to claim 1, characterized in that The step of determining the individualized stimulation target of the target object according to the potential neural regulation target of the target object and the key node comprises: Comparing the potential targets of neural regulation of the target object with the key nodes one by one to determine the common areas where the potential targets of neural regulation of the target object and the key nodes appear simultaneously; Prioritize common areas that occur simultaneously; Select a preset number of common regions with the highest priority from the sorting results as high-priority targets; The high priority target is used as the individualized stimulation target for the target object.

7. The system according to claim 1, characterized in that The method of simulating and optimizing electrical stimulation parameters based on the preprocessed T1-weighted structural image comprises: Segmenting the preprocessed T1-weighted structural image to obtain an individualized head anatomical model of the target object; The individualized head anatomical model is meshed by finite element analysis software and converted into discrete units; Based on the discrete units, electric field simulation is performed in combination with the finite element method to simulate the propagation and diffusion effects of current in different tissue layers; During the simulation process, the simulation optimizes the electrical stimulation parameters so that the stimulation can be accurately focused on the target brain area.

8. The system according to claim 7, characterized in that In the simulation process, the simulation optimizes the electrical stimulation parameters, including: During the simulation process, the stimulation parameters of the time domain interferometric electrical stimulation are optimized to obtain the simulation optimization results; Based on the simulation optimization result, determining whether the current of the time domain interferometric electrical stimulation can act on the target brain area to which the individualized stimulation target belongs; If yes, the electrical stimulation parameters at the current moment are used as optimized electrical stimulation parameters.

9. The system according to claim 1, characterized in that The periodic collection of behavioral parameters and task-state nuclear magnetic resonance data of the target object for analysis to dynamically adjust electrical stimulation parameters includes: Regularly collecting behavioral data and task-state fMRI data of the target subject as behavioral parameters and task-state fMRI data; Analyze the behavioral parameters and task-state MRI data to evaluate the current stimulation effect and determine whether the stimulation parameters and stimulation targets need to be adjusted; If the stimulation effect does not reach the preset conditions, the stimulation parameters and stimulation targets are adjusted and optimized; if the stimulation effect reaches the preset conditions, the step of regularly collecting the behavioral data and task-state fMRI data of the target object as behavioral parameters and task-state fMRI data is continued.

10. The system according to claim 2, characterized in that The cognitive behavioral tests include: Initial screening with the Montreal Cognitive Assessment and the Mini-Mental State Examination, and cognitive behavioral testing using at least one of the following: The working memory of the target subjects was assessed using the Digit Span Test; The target subjects were assessed for episodic memory using the California Verbal Learning Test and the Logical Memory Test; The Boston Naming Test and the Verbal Fluency Test were used to assess the semantic abilities of the target subjects; The executive functions of the target subjects were assessed using the Stroop test and the Wisconsin Card Sorting Test; The clock drawing test was used to measure the visual-spatial construction ability of the target subjects.

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