Personalized rehabilitation path making method and system based on amygdala function evaluation

Abstract

The application provides a personalized rehabilitation path making method and system based on amygdala function evaluation. First, the application obtains multi-modal neural function data of an individual. Second, the amygdala function lateralization mode is determined based on amygdala sub-region functional connection data. Third, the amygdala function lateralization mode is cooperatively analyzed with preset target individual behavior representation data. Finally, the personalized rehabilitation path is generated according to the cooperative analysis result obtained by the analysis. The technical scheme provided by the application not only realizes sub-region level accurate evaluation of amygdala function by integrating multi-modal neural function data and individual behavior representation data, but also generates a highly individualized rehabilitation path with precise coupling of neural activity and rehabilitation training, thereby effectively improving the pertinence and effect of intervention.

Description

Technical Field

[0001] This application relates to the field of neurorehabilitation technology, and in particular to a method and system for developing personalized rehabilitation pathways based on amygdala function assessment. Background Technology

[0002] In the rehabilitation of neuropsychiatric disorders such as mood disorders and post-traumatic stress disorder, clinical practice urgently needs to accurately assess the neurological function of patients and formulate highly personalized rehabilitation plans accordingly. The core pathophysiological changes of these diseases often involve the functional abnormalities of key nodes in the limbic system of the brain, namely the amygdala. Therefore, the effectiveness of rehabilitation intervention largely depends on the in-depth analysis and targeted regulation of the individual's amygdala functional characteristics.

[0003] Current technical solutions attempt to use functional magnetic resonance imaging (fMRI) to assess the functional connectivity map of the whole brain in patients at rest, and based on the deviation of this map from the population norm, provide general guidance for rehabilitation training. This existing approach can reveal the overall dysfunction of brain function at the macroscopic brain network level.

[0004] However, this approach still has significant shortcomings. First, its assessment dimensions are too broad, failing to focus on the amygdala, a specific nucleus directly related to emotional processing, and failing to delve into the subregions within the amygdala with different functional characteristics, resulting in insufficient targeting of the assessment results. Second, this approach typically analyzes brain imaging data in isolation, lacking a systematic correlation and synergistic analysis with the patient's specific behavioral manifestations (such as responses to specific emotional stimuli), resulting in relatively general rehabilitation recommendations that cannot form a dynamically adjustable personalized rehabilitation path that is precisely coupled with individual neurobehavioral characteristics. Summary of the Invention

[0005] This application provides a method and system for developing personalized rehabilitation pathways based on amygdala function assessment, in order to solve the problems of insufficient targeting and low personalization of rehabilitation pathways in the prior art due to the broad assessment dimensions, failure to focus on the functional characteristics of amygdala subregions, and the disconnect between brain function data and individual behavioral representations.

[0006] Firstly, this application provides a method for developing a personalized rehabilitation pathway based on amygdala function assessment, including:

[0007] Acquire multimodal neural function data of an individual, including functional connectivity data of the amygdala subregion acquired by functional magnetic resonance imaging;

[0008] Amygdala functional lateralization patterns are determined based on the functional connectivity data of the amygdala subregions.

[0009] The amygdala functional lateralization pattern is co-analyzed with the preset target individual behavioral representation data to generate a co-analysis result. The target individual behavioral representation data includes records of specific behavioral responses to emotional stimuli.

[0010] Personalized rehabilitation pathways are generated based on the results of collaborative analysis.

[0011] Optionally, multimodal neural function data of an individual is acquired, the multimodal neural function data including functional connectivity data of the amygdala subregion acquired by functional magnetic resonance imaging, including:

[0012] Collect resting-state functional magnetic resonance imaging (fMRI) data of the brain of the target individual in a non-task-specific state;

[0013] Simultaneously, task-oriented functional magnetic resonance imaging data were collected while the target individual received emotionally stimulating materials;

[0014] The resting-state functional magnetic resonance imaging (fMRI) data and the task-state fMRI data were respectively processed to divide the amygdala into subregions. Based on the differences in the cell structure inside the amygdala, the amygdala was divided into multiple functional subregions.

[0015] Based on the divided amygdala subregions, the functional connectivity between each amygdala subregion and other brain regions was determined from resting-state functional magnetic resonance imaging (fMRI) data, and the activation response pattern of each amygdala subregion under emotional stimulation was extracted from task-state fMRI data.

[0016] The functional connectivity relationships and activation response patterns are integrated to generate multimodal neural functional data.

[0017] Optionally, determining the amygdala functional lateralization pattern based on the amygdala subregion functional connectivity data includes:

[0018] Specific brain regions that meet preset conditions are selected from the multimodal neural function data. These specific brain regions include the prefrontal cortex, anterior cingulate cortex, and insula.

[0019] Calculate the first functional connectivity strength value between the left amygdala subregion and each region of the specific brain region, and calculate the second functional connectivity strength value between the right amygdala subregion and each region of the specific brain region;

[0020] For each amygdala subregion, the first functional connectivity strength value of the left amygdala subregion is compared with the second functional connectivity strength value of the right amygdala subregion to obtain the functional connectivity strength difference between the left and right sides of each amygdala subregion.

[0021] Based on the differences in functional connectivity strength among all amygdala subregions, a functional lateralization pattern of the amygdala is formed.

[0022] Optionally, the amygdala functional lateralization pattern is co-analyzed with preset target individual behavioral representation data to generate a co-analysis result. The target individual behavioral representation data includes records of specific behavioral responses to emotional stimuli, including:

[0023] Identify behavioral response patterns to specific emotional stimuli from the target individual's behavioral representation data, wherein the behavioral response patterns include a combination of response time and response intensity information;

[0024] The functional connectivity strength differences of each amygdala subregion in the amygdala functional lateralization pattern are matched with the behavioral response pattern to establish a correspondence between neural activity patterns and behavioral performance.

[0025] Based on the aforementioned correspondence, key functional features that are stably associated with abnormal behavioral responses in the amygdala functional lateralization pattern are identified.

[0026] Based on the degree of correlation between the key functional characteristics and behavioral response patterns, collaborative analysis results are generated to guide the development of rehabilitation pathways.

[0027] Optionally, the functional connectivity differences of each amygdala subregion in the amygdala functional lateralization pattern are matched with the behavioral response pattern to establish a correspondence between neural activity patterns and behavioral performance, including:

[0028] The target individual behavior representation data are grouped according to preset emotional stimulus types to obtain a set of behavioral response patterns under different emotional categories;

[0029] The differences in functional connectivity strength of each amygdala subregion in the amygdala functional lateralization pattern are grouped according to the same emotion category to obtain a set of neural activity patterns under different emotion categories.

[0030] For each emotion category, the trends of change in reaction time and reaction intensity in the set of behavioral response patterns are compared with the trends of change in functional connectivity strength in the corresponding amygdala subregion in the set of neural activity patterns.

[0031] Based on the comparison results, specific amygdala subregions were identified in which changes in the difference in functional connectivity strength under specific emotion categories maintained a stable co-occurrence with changes in behavioral response patterns.

[0032] Based on the stable accompaniment relationship, a correspondence is established between the neural activity patterns of the specific amygdala subregion and the behavioral performance of the target individual.

[0033] Optionally, a personalized rehabilitation path is generated based on the collaborative analysis results, including:

[0034] Based on the correlation between key functional features and behavioral response patterns identified in the collaborative analysis results, rehabilitation activity items targeting specific emotion categories and specific amygdala subregions are selected from a pre-set rehabilitation activity item library.

[0035] Based on the significance of the key functional features in the amygdala functional lateralization pattern, the implementation sequence and intensity of the selected rehabilitation activities are determined.

[0036] The rehabilitation activities targeting different emotion categories and different amygdala subregions are combined and arranged according to the implementation order and intensity to form a rehabilitation activity sequence with a time sequence structure.

[0037] Based on the specific numerical characteristics of the differences in functional connectivity strength in the amygdala functional lateralization pattern, neural modulation parameters for each rehabilitation activity are set.

[0038] The rehabilitation activity sequence is integrated with neural regulation parameters to generate a personalized rehabilitation pathway.

[0039] Optionally, based on the specific numerical characteristics of the differences in functional connectivity strength in the amygdala functional lateralization pattern, neural modulation parameters for each rehabilitation activity are set, including:

[0040] Obtain the quantitative value of the functional connectivity strength difference of each amygdala subregion in the amygdala functional lateralization mode;

[0041] The quantified values ​​of the functional connectivity strength differences of each amygdala subregion are matched with multiple preset parameter level ranges to determine a corresponding basic parameter level for each target amygdala subregion.

[0042] Based on the correlation degree of key functional features under different emotion categories in the collaborative analysis results, the basic parameter levels are adaptively adjusted to generate preliminary neural regulation parameters for specific emotion categories and specific target amygdala subregions.

[0043] Based on the amygdala subregion and emotion category targeted by different rehabilitation activity items in the rehabilitation activity sequence, the corresponding preliminary neural regulation parameters are assigned to each rehabilitation activity item.

[0044] Based on the implementation order of the rehabilitation activity sequence, the neural regulation parameters assigned to adjacent rehabilitation activity items are processed to obtain the neural regulation parameters for each rehabilitation activity item.

[0045] Secondly, this application provides a personalized rehabilitation pathway development system based on amygdala function assessment, including:

[0046] The acquisition module is used to acquire multimodal neural function data of an individual, the multimodal neural function data including functional connectivity data of the amygdala subregion acquired by functional magnetic resonance imaging;

[0047] The determination module is used to determine the amygdala functional lateralization mode based on the amygdala subregion functional connectivity data;

[0048] The parsing module is used to perform collaborative parsing of the amygdala functional lateralization pattern and the preset target individual behavioral representation data to generate collaborative parsing results. The target individual behavioral representation data includes records of specific behavioral responses to emotional stimuli.

[0049] The generation module is used to generate personalized rehabilitation pathways based on the collaborative analysis results.

[0050] Thirdly, this application provides a computing device, including a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are invoked and executed by the processing component to implement a personalized rehabilitation pathway development method based on amygdala function assessment as described in the first aspect above.

[0051] Fourthly, this application provides a computer storage medium storing a computer program, which, when executed by a computer, implements a personalized rehabilitation pathway development method based on amygdala function assessment as described in the first aspect.

[0052] This application, by acquiring multimodal neurofunctional data including functional connectivity data of amygdala subregions, can deeply analyze the functional state of an individual's amygdala at a refined subregion level, overcoming the shortcomings of existing technologies that have broad assessment dimensions and insufficient specificity. Furthermore, by identifying the amygdala functional lateralization pattern and co-analyzing it with the individual's specific behavioral representation data, a systematic correlation between neurophysiological indicators and external behavioral performance is achieved. This ensures that the final personalized rehabilitation path is based on dual evidence of neurofunctional and behavioral factors, improving the accuracy and individualization of rehabilitation intervention.

[0053] Furthermore, by mapping the quantitative numerical features in the amygdala functional lateralization model to specific parameter levels, a precise basis for neural regulation parameters was established for each rehabilitation activity item. Adaptive adjustments were also made based on the emotional category correlation in the collaborative analysis results, and parameter smooth transitions were performed according to the implementation sequence of the rehabilitation activities. This ensured that the neural regulation parameters not only accurately corresponded to the individual's specific neurological functional defects, but also achieved a natural and coherent intensity evolution during the rehabilitation process. Thus, at the micro-operational level, the precision and adaptability of the rehabilitation path execution were guaranteed, effectively avoiding abruptness or discomfort in the intensity of rehabilitation stimuli.

[0054] These or other aspects of this application will become more apparent in the following description of the embodiments. Attached Figure Description

[0055] To more clearly illustrate the technical solutions in this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0056] Figure 1 A flowchart of a personalized rehabilitation pathway development method based on amygdala function assessment provided in this application is shown;

[0057] Figure 2 A schematic diagram of the structure of a personalized rehabilitation pathway development system based on amygdala function assessment provided in this application is shown.

[0058] Figure 3 A schematic diagram of the structure of a computing device provided in this application is shown. Detailed Implementation

[0059] To enable those skilled in the art to better understand the present application, the technical solution of the present application will be clearly and completely described below with reference to the accompanying drawings.

[0060] In some of the processes described in the specification, claims, and accompanying drawings of this application, multiple operations appearing in a specific order are included. However, it should be clearly understood that these operations may not be executed in the order they appear herein, or may be executed in parallel. The operation numbers, such as 101, 102, etc., are merely used to distinguish different operations and do not themselves represent any execution order. Furthermore, these processes may include more or fewer operations, and these operations may be executed sequentially or in parallel. It should be noted that the descriptions such as "first," "second," etc., in this document are used to distinguish different messages, devices, modules, etc., and do not represent a chronological order, nor do they limit "first" and "second" to different types.

[0061] The technical solutions of this application will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0062] Figure 1This application provides a flowchart of a method for developing a personalized rehabilitation pathway based on amygdala function assessment, such as... Figure 1 As shown, the method includes:

[0063] Step 101: Obtain multimodal neural function data of an individual, wherein the multimodal neural function data includes functional connectivity data of the amygdala subregion acquired by functional magnetic resonance imaging.

[0064] Optionally, step 101 may specifically include the following steps:

[0065] Step 1011: Collect resting-state functional magnetic resonance imaging data of the brain of the target individual in a non-specific task state;

[0066] Step 1012: Simultaneously, task-state functional magnetic resonance imaging data are acquired while the target individual receives emotional stimulation material.

[0067] Step 1013: The resting-state functional magnetic resonance imaging data and the task-state functional magnetic resonance imaging data are respectively processed to divide the amygdala into subregions. Based on the differences in the internal cell structure of the amygdala, the amygdala is divided into multiple functional subregions.

[0068] Step 1014. Based on the divided amygdala subregions, determine the functional connectivity between each amygdala subregion and other brain regions from resting-state functional magnetic resonance imaging data, and extract the activation response pattern of each amygdala subregion under emotional stimulation from task-state functional magnetic resonance imaging data.

[0069] Step 1015: Integrate the functional connectivity relationship with the activation response pattern to generate multimodal neural functional data.

[0070] In the above scheme, multimodal neural function data refers to a comprehensive set of information formed by integrating snapshots of brain activity in different states, which is used to comprehensively assess brain function;

[0071] Functional magnetic resonance imaging (fMRI) is a special magnetic resonance scanning technique that can indirectly display the intensity of neural activity in the brain.

[0072] Amygdala subregion functional connectivity data reflects the degree of coordination between the various sub-regions of the amygdala and other brain regions.

[0073] The task-free state refers to a relaxed state in which an individual remains awake but does not engage in any specific mental activity while inside a scanner.

[0074] Resting-state functional magnetic resonance imaging (fMRI) data of the brain is collected in this relaxed state and is used to analyze the basic patterns of brain activity.

[0075] Task-based functional magnetic resonance imaging (fMRI) data is collected when an individual views emotionally stimulating materials such as pictures or videos that evoke emotional responses, and is used to analyze brain activity patterns in response to specific stimuli.

[0076] The amygdala subregion division is based on the differences in the internal cellular structure of the amygdala, which finely divides this brain region into multiple smaller units with potentially different functional focuses, i.e., multiple functional subregions.

[0077] Functional connectivity is an indicator that describes the degree of synchronicity of activity rhythms in different brain regions at rest.

[0078] Activation response pattern is an indicator that records the changes in activity intensity of various brain regions when responding to emotional stimuli.

[0079] In this scheme, firstly, raw image data in two states are acquired using a functional magnetic resonance imaging (fMRI) scanner. First, the individual remains awake, relaxed, and refrains from specific thought processes within the scanner. The instrument obtains whole-brain resting-state fMRI data by detecting changes in blood oxygen level-dependent (BOLD) signals. Second, standardized emotional images or short videos (i.e., emotional stimuli) are presented to the individual during the scan, and the changes in whole-brain BOLD signals are simultaneously recorded as the individual processes this emotional information, thus obtaining task-oriented fMRI data. Next, the two raw data sets are processed for amygdala subregion segmentation. First, a pre-defined probability map of amygdala subregions based on histological structure is used as a template. An image registration algorithm spatially aligns the individual's fMRI data with the standard template, thereby dividing amygdala voxels into different functional subregions and assigning a label to each voxel. Then, feature extraction is performed based on the subregion labels. For the resting-state fMRI data, the time-series average value of all voxels within each amygdala subregion is extracted as the data. The time series of the amygdala subregion was extracted, and the time series of other brain regions of interest were also extracted. Then, by calculating the statistical correlation (such as the Pearson correlation coefficient) between the time series of the amygdala subregion and the time series of each other brain region, a series of correlation coefficient values ​​were obtained. These values ​​constitute the functional connectivity of the amygdala subregion. Simultaneously, for the task-state functional magnetic resonance imaging data, activation analysis based on a generalized linear model was used. Different emotional stimuli (such as fearful faces vs. neutral faces) were used as regression factors. After model fitting, the estimated activation intensity (beta value) of each amygdala subregion relative to baseline activity under different emotional conditions was obtained. These values ​​constitute the activation response pattern of the amygdala subregion. Finally, the functional connectivity (a set of correlation intensity values) calculated from the resting-state data and the activation response pattern (a set of activation intensity values) calculated from the task-state data from the same amygdala subregion were used together as the feature vector of the amygdala subregion. Then, the feature vectors of all amygdala subregions were combined to generate a structured, multi-modal neural function data file (such as a matrix or a data table in a specific format) containing multi-dimensional information.

[0080] For example, consider individual A who needs emotional rehabilitation. First, individual A lies inside a functional magnetic resonance imaging (fMRI) scanner. In the first 5-minute phase, he simply needs to remain awake, relaxed, and focus on the crosshair in the center of the screen; this captures his resting-state fMRI data. Next, in the second 10-minute phase, the scanner continuously plays a series of standardized facial expression images (emotional stimuli) categorized into different types, such as fear, neutrality, and happiness, while simultaneously collecting his brain activity data; this is the task-oriented fMRI data. Subsequently, the computing system uses a standard brain mapping model... The system automatically divides individual A's amygdala imaging data into several different functional subregions, such as B, C, and D. Next, it analyzes resting-state data to calculate the temporal synchronicity of activity in subregion B with areas such as the prefrontal cortex, thus obtaining the functional connectivity of subregion B. Simultaneously, it analyzes task-state data and finds that the signal intensity in subregion C significantly increases when a fearful face is presented, thereby extracting the specific activation response pattern of subregion C to fearful stimuli. Finally, the system creates a comprehensive profile for each amygdala subregion (B, C, D, etc.) of individual A; this profile constitutes multimodal neural functional data for subsequent in-depth analysis.

[0081] This step, by jointly collecting brain function data under both resting and task states, and based on this, performing fine subregion division and feature extraction of the amygdala, successfully obtained multi-dimensional and multi-angle fusion neurofunctional data that includes both information on the brain's internal functional connectivity architecture and its dynamic response characteristics to specific emotional stimuli. This lays a comprehensive and detailed data foundation for subsequent accurate and individualized functional assessments, overcoming the perspective limitations that may exist with single-modality data.

[0082] Step 102: Determine the amygdala functional lateralization mode based on the functional connectivity data of the amygdala subregion.

[0083] Optionally, step 102 may specifically include the following steps:

[0084] Step 1021: Select specific brain regions that meet preset conditions from the multimodal neural function data. The specific brain regions include the prefrontal cortex, the anterior cingulate cortex, and the insula.

[0085] Step 1022: Calculate the first functional connectivity strength value between the left amygdala subregion of each amygdala and each region of the specific brain region, and calculate the second functional connectivity strength value between the right amygdala subregion and each region of the specific brain region.

[0086] Step 1023: For each amygdala subregion, the first functional connectivity strength value of the left amygdala subregion is compared with the second functional connectivity strength value of the right amygdala subregion to obtain the functional connectivity strength difference between the left and right sides of each amygdala subregion.

[0087] Step 1024: Based on the differences in functional connectivity strength among all amygdala subregions, amygdala functional lateralization pattern is formed.

[0088] In the above scheme, the amygdala functional lateralization model refers to a set of comprehensive indicators used to quantitatively describe the asymmetry in functional activity between the amygdala in the left and right cerebral hemispheres.

[0089] The specific brain regions in the pre-defined conditions refer to a group of brain regions that are pre-selected based on neuroscience knowledge and are known to be closely related to emotion processing functions. In this protocol, they specifically refer to the prefrontal cortex (responsible for emotion regulation and higher cognition), the anterior cingulate cortex (involved in emotional conflict processing and response monitoring), and the insula (related to subjective feelings and emotional experiences).

[0090] The left and right amygdala subregions refer to corresponding, symmetrically located subregions within the amygdala in the left and right hemispheres of the brain, respectively.

[0091] The first functional connectivity strength value refers to the quantitative value of the strength of functional connectivity between the left amygdala subregion and a specific brain region.

[0092] The second functional connectivity strength value refers to the quantitative value corresponding to the strength of functional connectivity between the right amygdala subregion and the same specific brain region.

[0093] The difference in functional connectivity strength is calculated by comparing the functional connectivity strength values ​​of symmetrical units on the left and right sides of the same amygdala subregion with the same target brain region. It is used to directly measure the degree of lateralization in the function of that subregion.

[0094] In this approach, firstly, from a dataset containing whole-brain functional connectivity information (i.e., multimodal neural functional data), based on a predefined list of known key brain regions for emotion processing (i.e., preset conditions), a subset of functional connectivity data for three target brain regions—the prefrontal cortex, anterior cingulate cortex, and insula—is selected through data indexing, thereby completing the selection of specific brain regions. Secondly, for each selected specific brain region (e.g., the prefrontal cortex), each amygdala subregion (e.g., the basolateral subregion) is processed. For each amygdala subregion, the functional connectivity strength quantification value between the subregion located in the left hemisphere (left amygdala subregion) and this specific brain region is extracted from its functional connectivity data; this is the first functional connectivity strength value. Simultaneously, the functional connectivity strength quantification value between the corresponding subregion located in the right hemisphere (right amygdala subregion) and the same specific brain region is extracted; this is the second functional connectivity strength value. This process is performed once for each subregion and each specific brain region, generating two sets of... The corresponding set of strength values ​​is obtained. Next, for the two sets of data obtained for the same amygdala subregion and the same specific brain region, namely the first functional connectivity strength value (left subregion connectivity strength) and the second functional connectivity strength value (right subregion connectivity strength), a direct numerical comparison is performed. The left value is subtracted from the right value, and the difference is the difference in functional connectivity strength of the amygdala subregion relative to the specific brain region. A positive difference value indicates stronger left-side connectivity, and a negative value indicates stronger right-side connectivity. This calculation is performed for each pair of subregions and each specific brain region. Finally, all the calculated functional connectivity strength differences (i.e., the difference values ​​of each amygdala subregion relative to the prefrontal cortex, anterior cingulate cortex, and insula) are summarized and organized according to a certain data structure (such as a vector or a difference map). The integrated dataset thus describes the left and right hemisphere asymmetry characteristics of functional connectivity of each subregion of the individual amygdala. This comprehensive feature description is the amygdala functional lateralization pattern.

[0095] Following the specific implementation of the previous scheme, individual A's multimodal neural function data is ready. The system first selects three specific brain regions from the whole-brain connectivity data based on a pre-defined list of brain regions for emotion processing: the prefrontal cortex (region P), the anterior cingulate cortex (region A), and the insula (region I). Next, for the basolateral amygdala (subregion B), the system reads the functional connectivity strength between the left basolateral subregion (LB) and region P, recording one value (first functional connectivity strength value), and simultaneously reads the functional connectivity strength between the right basolateral subregion (RB) and region P, recording another value (second functional connectivity strength value). Then the system... The system compares the connection strength values ​​of LB and region P with those of RB and region P to obtain a value representing the difference between the two, namely the difference in functional connectivity strength of subregion B with respect to region P. The system repeats this process to calculate the difference in connectivity strength of subregion B with respect to regions A and I, and to calculate the differences in connectivity strength between other amygdala subregions (such as central subregion C) and these three specific regions. Finally, the system summarizes all the calculated difference values ​​to form a comprehensive dataset. This dataset clearly shows the amygdala functional lateralization pattern of individual A, revealing the asymmetric characteristics of functional connectivity between the left and right hemispheres of its various amygdala subregions.

[0096] This step selects key emotion-related brain regions as references, quantifies and compares the differences in functional connectivity between each subregion of the left and right amygdala and these reference brain regions, and then integrates these differences to finally construct a lateralization model that can accurately and quantitatively characterize the left-right asymmetry of individual amygdala function. This provides core, quantifiable neural function indicators for subsequently linking neural activity characteristics with behavioral performance.

[0097] Step 103: The amygdala functional lateralization pattern is co-analyzed with the preset target individual behavioral representation data to generate a co-analysis result. The target individual behavioral representation data includes records of specific behavioral responses to emotional stimuli.

[0098] Optionally, step 103 may specifically include the following steps:

[0099] Step 1031: Identify behavioral response patterns to specific emotional stimuli from the target individual's behavioral representation data, wherein the behavioral response patterns include a combination of response time and response intensity.

[0100] Step 1032: Match the differences in functional connectivity strength of each amygdala subregion in the amygdala functional lateralization pattern with the behavioral response pattern to establish a correspondence between neural activity patterns and behavioral performance.

[0101] Step 1032 may specifically include the following steps:

[0102] The target individual's behavioral representation data are grouped according to preset emotional stimulus types to obtain a set of behavioral response patterns under different emotional categories. The differences in functional connectivity strength of each amygdala subregion in the amygdala functional lateralization pattern are grouped according to the same emotional category to obtain a set of neural activity patterns under different emotional categories. For each emotional category, the trends in reaction time and reaction intensity in the behavioral response pattern set are compared with the trends in functional connectivity strength differences in the corresponding amygdala subregion in the neural activity pattern set. Based on the comparison results, specific amygdala subregions are identified where the changes in functional connectivity strength differences and the changes in behavioral response patterns maintain a stable accompaniment relationship under a specific emotional category. Based on this stable accompaniment relationship, a correspondence is established between the neural activity patterns of the specific amygdala subregion and the target individual's behavioral performance.

[0103] Step 1033: Based on the correspondence, determine the key functional features that are stably associated with abnormal behavioral responses in the amygdala functional lateralization pattern;

[0104] Step 1034: Based on the degree of correlation between the key functional characteristics and behavioral response patterns, generate collaborative analysis results to guide the development of rehabilitation pathways.

[0105] In the above scheme, the pre-set target individual behavioral representation data refers to the systematic record of an individual's external behavioral performance collected in advance through behavioral experiments or clinical assessments;

[0106] Collaborative analysis results refer to comprehensive conclusions obtained by correlating neural activity data with behavioral data, which can reveal the intrinsic relationship between the two.

[0107] Specific behavioral response records are unique individual response data specifically recorded for different emotional stimuli from the pre-defined target individual behavioral representation data.

[0108] Behavioral response patterns are patterns extracted from these records that characterize the individual's response characteristics. They consist of a combination of information such as reaction time (the speed at which an individual responds to a stimulus) and reaction intensity (the severity of an individual's response).

[0109] The neural activity pattern here specifically refers to the neural activity characteristics represented by differences in the strength of functional connectivity obtained from brain functional data;

[0110] Behavioral performance refers to the external capabilities or state reflected in an individual's behavioral response patterns;

[0111] A behavioral response pattern set is a summary of all behavioral responses under the same emotional category after grouping behavioral data by emotion category.

[0112] The set of neural activity patterns is a summary of all neural activity features under a corresponding emotion category;

[0113] The comparison results are conclusions about the strength of the correlation between the two sets of behavior and nerves.

[0114] Specific amygdala subregions refer to the amygdala subregions that are identified in comparison and whose activity is particularly closely related to behavioral responses;

[0115] Stable covariance refers to a continuous and consistent covariance between changes in neural activity and changes in behavioral responses in a specific amygdala subregion;

[0116] Key functional features are neural characteristics that play an important role in explaining abnormal behavior, identified from the amygdala functional lateralization pattern based on this stable relationship.

[0117] In this scheme, firstly, the system reads the behavioral representation data of the target individual, which contains detailed records of the individual's responses to different specific emotional stimuli such as fear and pleasure. By analyzing these records, the system calculates the individual's reaction time and intensity for each emotional stimulus, and combines these two pieces of information to form a behavioral response pattern describing the typical characteristics of the individual's response to this type of emotional stimulus. Secondly, a correspondence is established. The target individual's behavioral representation data is first grouped according to stimulus type (e.g., fear, neutral, pleasure) to form a set of behavioral response patterns for each emotional category. At the same time, the differences in functional connectivity strength of each amygdala subregion in the amygdala functional lateralization pattern are also grouped according to the emotional category corresponding to the time of data collection, forming a set of neural activity patterns for each emotional category. Then, for each emotional category, the system compares the changing trends of reaction time and intensity in the behavioral response pattern set with the overall changing trend of functional connectivity strength differences in each amygdala subregion in the neural activity pattern set to analyze whether they change synchronously (e.g., behavioral response intensifies). During intense emotional episodes, the functional connectivity differences in a specific amygdala are compared to determine if they also increase synchronously. Based on this comparison, amygdala subregions are identified where changes in functional connectivity strength and behavioral response patterns consistently maintain a stable correlation under specific emotional categories. These subregions are then labeled as specific amygdala subregions. Finally, based on this identified covariation, a formal correspondence is established between the neural activity patterns of this specific amygdala subregion and the individual's behavioral performance under that emotional category. Then, based on this established correspondence, the neural activity features with the strongest stable correlation to clinically recognized abnormal behavioral responses (such as excessive withdrawal from fear stimuli and indifference to pleasure stimuli) are examined. These identified neural features that significantly indicate abnormal behavior are designated as key functional features. Finally, the correlation between each key functional feature and the behavioral response pattern is systematically evaluated (e.g., the stronger the correlation, the more important the feature). Based on the correlation of these features and their corresponding abnormal behavioral types, a conclusive document containing priorities and suggested directions is generated—a collaborative analysis result used to guide the development of rehabilitation pathways.

[0118] Following the specific implementation of the previous scheme, the amygdala lateralization pattern of individual A and their target individual behavioral representation data (such as reaction records when viewing images of fearful, neutral, and happy faces) are already available. The system first analyzes the behavioral data and finds that individual A's behavioral response pattern to fearful faces is: very short reaction time (rapid avoidance) and high reaction intensity (showing intense panic). Next, the system groups the behavioral data according to emotion category to obtain a set of fear emotion behavioral response patterns; at the same time, it groups the neural data according to the same category to obtain a set of fear emotion neural activity patterns. The comparison reveals that when fearful stimuli are presented, the central subregion of individual A's amygdala... The increasing trend of functional connectivity strength difference (left stronger than right) in subregion C was highly synchronized with the trend of shorter reaction time and greater reaction intensity. Therefore, subregion C was identified as a specific amygdala subregion with a stable association with fear response, and the association between the two was established. Subsequently, given that individual A's fear response was judged as hypersensitivity (abnormal behavior), the system identified this left-strong, right-weak functional connectivity difference pattern in subregion C as a key functional feature. Finally, based on the strong correlation between this key functional feature and hyper-fear response, the system clearly pointed out in the collaborative analysis results that rehabilitation training should focus on and regulate the overactive connectivity of the left central amygdala subregion relative to the right side.

[0119] This step, by systematically comparing quantified lateralization patterns of brain neural activity with specific external behavioral response patterns according to emotional categories, successfully identified the core amygdala subregion and its key functional characteristics that have a stable covariation relationship with specific abnormal behaviors. This precisely links abstract neural indicators with specific behavioral manifestations, providing core and actionable decision support for the next step of developing personalized rehabilitation plans with clear neuroscientific and behavioral evidence.

[0120] Step 104: Generate a personalized rehabilitation path based on the collaborative analysis results.

[0121] Optionally, step 104 may specifically include the following steps:

[0122] Step 1041: Based on the degree of correlation between the key functional features and behavioral response patterns identified in the collaborative analysis results, select rehabilitation activity items targeting specific emotion categories and specific amygdala subregions from the preset rehabilitation activity item library.

[0123] Step 1042: Determine the implementation sequence and intensity of the selected rehabilitation activities based on the significance of the key functional features in the amygdala functional lateralization pattern.

[0124] Step 1043: The rehabilitation activities targeting different emotion categories and different amygdala subregions are combined and arranged according to the implementation order and intensity to form a rehabilitation activity sequence with a time sequence structure.

[0125] Step 1044: Based on the specific numerical characteristics of the differences in functional connectivity strength in the amygdala functional lateralization pattern, set the neuromodulation parameters for each rehabilitation activity item.

[0126] Step 1044 may specifically include the following steps:

[0127] The quantitative values ​​of functional connectivity strength differences in each amygdala subregion within the amygdala functional lateralization pattern are obtained. These quantitative values ​​are then matched with multiple preset parameter level ranges to determine a corresponding basic parameter level for each target amygdala subregion. Based on the correlation degree of key functional features under different emotion categories in the collaborative analysis results, the basic parameter levels are adaptively adjusted to generate preliminary neural modulation parameters for specific emotion categories and target amygdala subregions. According to the amygdala subregions and emotion categories targeted by different rehabilitation activity items in the rehabilitation activity sequence, the corresponding preliminary neural modulation parameters are assigned to each rehabilitation activity item. Based on the implementation order of the rehabilitation activity sequence, the neural modulation parameters assigned to adjacent rehabilitation activity items are smoothly transitioned to obtain the neural modulation parameters for each rehabilitation activity item.

[0128] Step 1045: Integrate the rehabilitation activity sequence with neural regulation parameters to generate a personalized rehabilitation path.

[0129] In the above scheme, a personalized rehabilitation pathway refers to a complete rehabilitation plan tailored for a specific individual, which includes a series of sequential rehabilitation activities and corresponding adjustment parameters.

[0130] The pre-set rehabilitation activity program library is a database that stores a variety of standardized rehabilitation training programs, each designed to address specific emotional problems or regulate brain function.

[0131] A rehabilitation activity program is a specific training unit in the library, such as a specific emotion recognition task or relaxation training.

[0132] The implementation sequence refers to the chronological order in which these selected rehabilitation activities are arranged.

[0133] The intensity of implementation refers to the level of effort or duration required for each rehabilitation activity during training.

[0134] A rehabilitation activity sequence is an ordered list of all selected and arranged rehabilitation activities, which forms the backbone of the rehabilitation pathway.

[0135] Specific numerical characteristics refer to the specific numerical magnitudes of the differences in functional connectivity strength obtained from the amygdala functional lateralization pattern;

[0136] Neural modulation parameters are control variables set for each rehabilitation activity to regulate neural activity, such as the intensity or duration of stimulation.

[0137] Quantitative values ​​refer to specific numerical values ​​representing differences in functional connectivity strength;

[0138] The preset parameter level ranges are several predefined numerical intervals used to classify continuous quantified values ​​into different intensity levels.

[0139] The baseline parameter level is the level of neural regulation intensity initially determined for each amygdala subregion by matching the quantified values ​​with the parameter level range.

[0140] Preliminary neural modulation parameters are parameter settings for specific emotions and specific brain regions obtained by fine-tuning the baseline parameter levels in combination with the degree of behavioral correlation.

[0141] In this approach, firstly, the system reads the co-analysis results and identifies which key functional features (e.g., left-hand dominant connectivity of the central subregion C to fearful stimuli) are most strongly associated with an individual's behavioral response patterns (e.g., excessive fear). Based on the strength of these associations, the system selects rehabilitation activities designed to regulate fear and specifically targeting the central subregion of the amygdala from a pre-established library of rehabilitation activities. Secondly, the system further analyzes the significance of the key functional features in the co-analysis results (i.e., the degree to which the feature deviates from the normal range). Based on this significance, the system... Prioritize all selected rehabilitation activities. The more prominent the characteristic, the higher the priority of the corresponding rehabilitation activity in the overall plan, and therefore it will be scheduled earlier in the implementation order and may require a higher implementation intensity (e.g., longer single training sessions). Next, the system will arrange all selected rehabilitation activities for different emotion categories (e.g., fear, happiness) and different amygdala subregions (e.g., central subregion, basolateral subregion) according to the predetermined implementation order (priority order) and intensity, forming a structured sequence of rehabilitation activities that unfolds sequentially in time. Then… To define specific parameters for each activity, the system first obtains quantitative values ​​of the differences in functional connectivity strength in each subregion of the amygdala's functional lateralization pattern. These quantitative values ​​are then compared with a preset range of multiple parameter levels to determine a baseline parameter level for each target amygdala subregion requiring adjustment. The system then references additional information from the co-analysis results regarding the association between this key functional characteristic and behavior under different emotion categories, appropriately adjusting this baseline parameter level to generate more refined preliminary neural modulation parameters. Next, the system examines the compiled rehabilitation activity sequence and assigns the corresponding preliminary neural modulation parameters to each activity based on the emotion category it aims to train and the targeted amygdala subregion. Finally, considering the continuity of the rehabilitation experience, the system checks the parameter allocation of adjacent activities in the rehabilitation activity sequence. If parameter values ​​jump too much, transition values ​​are inserted to smooth the change, i.e., smooth transition processing. Ultimately, specific neural modulation parameters are generated for each activity in the rehabilitation activity sequence. Finally, the system binds the ordered and intensity-set rehabilitation activity sequence with the finely defined neural modulation parameters for each activity, encapsulating them into a complete and executable rehabilitation plan file—the final personalized rehabilitation path.

[0142] Following the specific implementation of the previous scheme, the system has generated the co-analysis results for individual A, indicating that the overreaction of the left amygdala's central subregion (subregion C) to fear stimuli is a key issue. First, based on this co-analysis result, the system selects the projects "Progressive Exposure Training of Fear Faces" (targeting fear emotions and the central subregion) and "Mindfulness Breathing Relaxation" (as basic conditioning) from the rehabilitation activity project library. Next, due to the significant nature of this key functional characteristic, the system sets the priority of "Progressive Exposure Training of Fear Faces" to the highest level, scheduling it to begin early in rehabilitation, and setting a high implementation intensity (e.g., five times a week, 30 minutes each time). Then, the system generates a rehabilitation activity sequence: the first and second weeks primarily focus on "Mindfulness Breathing Relaxation"; starting in the third week, intensive "Progressive Exposure Training of Fear Faces" is conducted, interspersed with "Mindfulness Breathing Relaxation". "Mindful breathing relaxation"; then, the system reads the functional connectivity strength difference value of subregion C (assumed to be 0.22), and matches the basic parameter level to "intermediate" according to the preset range (e.g., 0.15-0.25 corresponds to intermediate intensity); since this key functional feature is highly associated with behavior, the level is upgraded to "intermediate-high", thereby generating preliminary neural regulation parameters for fear training and subregion C (e.g., the initial stimulus presentation time for exposure training is 500 milliseconds); the system assigns these preliminary neural regulation parameters to the "progressive exposure training of fearful faces" item in the rehabilitation activity sequence, and smooths the parameters at the beginning and end of the rehabilitation activity sequence (e.g., the initial "mindful breathing" parameter is set to "low level", smoothly transitioning to 500 milliseconds); finally, all information is integrated to generate a detailed, personalized rehabilitation path document customized for individual A.

[0143] This step directly transforms the neurobehavioral correlations revealed by the collaborative analysis results into specific elements of the rehabilitation plan, enabling multi-level, refined, and personalized customization of rehabilitation activities, implementation sequence, intensity, and neural regulation parameters. Ultimately, it generates a rehabilitation path with clear operational guidelines that precisely matches the individual's specific neurological deficits and behavioral manifestations, thereby ensuring the targetedness and effectiveness of the rehabilitation intervention.

[0144] Figure 2 This application provides a structural diagram of a personalized rehabilitation pathway development system based on amygdala function assessment, as shown in the diagram. Figure 2 As shown, the system includes:

[0145] The acquisition module 21 is used to acquire multimodal neural function data of an individual, the multimodal neural function data including functional connectivity data of the amygdala subregion acquired by functional magnetic resonance imaging;

[0146] Module 22 is used to determine the amygdala functional lateralization mode based on the amygdala subregion functional connectivity data;

[0147] The parsing module 23 is used to perform collaborative parsing of the amygdala functional lateralization pattern and the preset target individual behavioral representation data to generate collaborative parsing results. The target individual behavioral representation data includes records of specific behavioral responses to emotional stimuli.

[0148] The generation module 24 is used to generate personalized rehabilitation paths based on the collaborative analysis results.

[0149] Figure 2 The aforementioned personalized rehabilitation pathway development system based on amygdala function assessment can be implemented. Figure 1 The implementation principle and technical effects of the personalized rehabilitation pathway development method based on amygdala function assessment described in the above embodiment will not be repeated here. The specific operation methods of each module and unit in the personalized rehabilitation pathway development system based on amygdala function assessment in the above embodiment have been described in detail in the embodiments of the relevant method, and will not be elaborated here.

[0150] In one possible design, Figure 2 The personalized rehabilitation pathway development system based on amygdala function assessment shown in the embodiment can be implemented as a computing device, such as... Figure 3 As shown, the computing device may include a storage component 31 and a processing component 32;

[0151] The storage component 31 stores one or more computer instructions, wherein the one or more computer instructions are invoked and executed by the processing component 32.

[0152] The processing component 32 is used for the above Figure 1 The embodiment describes a method for developing a personalized rehabilitation pathway based on amygdala function assessment.

[0153] The processing component 32 may include one or more processors to execute computer instructions to complete all or part of the steps in the above-described method. Alternatively, the processing component may be implemented as one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the above-described method.

[0154] Storage component 31 is configured to store various types of data to support operations at the terminal. The storage component can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0155] Of course, computing devices may also include other components, such as input / output interfaces, display components, communication components, etc.

[0156] Input / output interfaces provide interfaces between processing components and peripheral interface modules, which can be output devices, input devices, etc.

[0157] The communication components are configured to facilitate wired or wireless communication between computing devices and other devices.

[0158] The computing device can be a physical device or an elastic computing host provided by a cloud computing platform. In this case, the computing device can refer to a cloud server, and the aforementioned processing components, storage components, etc., can be basic server resources rented or purchased from the cloud computing platform.

[0159] This application also provides a computer storage medium storing a computer program, which, when executed by a computer, can perform the above-described functions. Figure 1 The embodiment shown is a method for developing a personalized rehabilitation pathway based on amygdala function assessment.

[0160] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0161] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0162] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0163] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. A method for developing a personalized rehabilitation pathway based on amygdala function assessment, characterized in that, include: This method acquires multimodal neural function data for an individual, including functional connectivity data of the amygdala subregions acquired through functional magnetic resonance imaging (fMRI). The acquisition includes: collecting resting-state fMRI data of the target individual's brain in a task-free state; simultaneously collecting task-oriented fMRI data while the target individual receives emotional stimuli; dividing the resting-state and task-oriented fMRI data into amygdala subregions based on differences in cellular architecture; determining the functional connectivity between each amygdala subregion and other brain regions from the resting-state fMRI data, and extracting the activation response pattern of each amygdala subregion under emotional stimulation from the task-oriented fMRI data; and integrating the functional connectivity and activation response patterns to generate multimodal neural function data. Determining an amygdala functional lateralization pattern based on the functional connectivity data of the amygdala subregions includes: selecting specific brain regions that meet preset conditions from the multimodal neural function data, wherein the specific brain regions include the prefrontal cortex, anterior cingulate cortex, and insula; calculating the first functional connectivity strength value between the left amygdala subregion and each region of the specific brain region, and calculating the second functional connectivity strength value between the right amygdala subregion and each region of the specific brain region; for each amygdala subregion, comparing the first functional connectivity strength value of the left amygdala subregion with the second functional connectivity strength value of the right amygdala subregion to obtain the functional connectivity strength difference between the left and right sides of each amygdala subregion; and combining the functional connectivity strength differences of all amygdala subregions to form an amygdala functional lateralization pattern. The amygdala functional lateralization pattern is co-analyzed with the preset target individual behavioral representation data to generate a co-analysis result. The target individual behavioral representation data includes records of specific behavioral responses to emotional stimuli. Personalized rehabilitation pathways are generated based on the results of collaborative analysis.

2. The method according to claim 1, characterized in that, The amygdala functional lateralization pattern is co-analyzed with preset target individual behavioral representation data to generate co-analysis results. The target individual behavioral representation data includes records of specific behavioral responses to emotional stimuli, including: Identify behavioral response patterns to specific emotional stimuli from the target individual's behavioral representation data, wherein the behavioral response patterns include a combination of response time and response intensity information; The functional connectivity strength differences of each amygdala subregion in the amygdala functional lateralization pattern are matched with the behavioral response pattern to establish a correspondence between neural activity patterns and behavioral performance. Based on the aforementioned correspondence, key functional features that are stably associated with abnormal behavioral responses in the amygdala functional lateralization pattern are identified. Based on the degree of correlation between the key functional characteristics and behavioral response patterns, collaborative analysis results are generated to guide the development of rehabilitation pathways.

3. The method according to claim 2, characterized in that, The functional connectivity differences in each amygdala subregion within the amygdala lateralization pattern are matched with the behavioral response pattern to establish a correspondence between neural activity patterns and behavioral performance, including: The target individual behavior representation data are grouped according to preset emotional stimulus types to obtain a set of behavioral response patterns under different emotional categories; The differences in functional connectivity strength of each amygdala subregion in the amygdala functional lateralization pattern are grouped according to the same emotion category to obtain a set of neural activity patterns under different emotion categories. For each emotion category, the trends of change in reaction time and reaction intensity in the set of behavioral response patterns are compared with the trends of change in functional connectivity strength in the corresponding amygdala subregion in the set of neural activity patterns. Based on the comparison results, specific amygdala subregions were identified in which changes in the difference in functional connectivity strength under specific emotion categories maintained a stable co-occurrence with changes in behavioral response patterns. Based on the stable accompaniment relationship, a correspondence is established between the neural activity patterns of the specific amygdala subregion and the behavioral performance of the target individual.

4. The method according to claim 1, characterized in that, Personalized rehabilitation pathways are generated based on the collaborative analysis results, including: Based on the correlation between key functional features and behavioral response patterns identified in the collaborative analysis results, rehabilitation activity items targeting specific emotion categories and specific amygdala subregions are selected from a pre-set rehabilitation activity item library. Based on the significance of the key functional features in the amygdala functional lateralization pattern, the implementation sequence and intensity of the selected rehabilitation activities are determined. The rehabilitation activities targeting different emotion categories and different amygdala subregions are combined and arranged according to the implementation order and intensity to form a rehabilitation activity sequence with a time sequence structure. Based on the specific numerical characteristics of the differences in functional connectivity strength in the amygdala functional lateralization pattern, neural modulation parameters for each rehabilitation activity are set. The rehabilitation activity sequence is integrated with neural regulation parameters to generate a personalized rehabilitation pathway.

5. The method according to claim 4, characterized in that, Based on the specific numerical characteristics of the differences in functional connectivity strength in the amygdala functional lateralization pattern, neural modulation parameters for each rehabilitation activity are set, including: Obtain the quantitative value of the functional connectivity strength difference of each amygdala subregion in the amygdala functional lateralization mode; The quantified values ​​of the functional connectivity strength differences of each amygdala subregion are matched with multiple preset parameter level ranges to determine a corresponding basic parameter level for each target amygdala subregion. Based on the correlation degree of key functional features under different emotion categories in the collaborative analysis results, the basic parameter levels are adaptively adjusted to generate preliminary neural regulation parameters for specific emotion categories and specific target amygdala subregions. Based on the amygdala subregion and emotion category targeted by different rehabilitation activity items in the rehabilitation activity sequence, the corresponding preliminary neural regulation parameters are assigned to each rehabilitation activity item. Based on the implementation order of the rehabilitation activity sequence, the neural regulation parameters assigned to adjacent rehabilitation activity items are processed to obtain the neural regulation parameters for each rehabilitation activity item.

6. A personalized rehabilitation pathway development system based on amygdala function assessment, applied to the personalized rehabilitation pathway development method based on amygdala function assessment as described in any one of claims 1-5, characterized in that, include: The acquisition module is used to acquire multimodal neural function data of an individual, the multimodal neural function data including functional connectivity data of the amygdala subregion acquired by functional magnetic resonance imaging; The determination module is used to determine the amygdala functional lateralization mode based on the amygdala subregion functional connectivity data; The parsing module is used to perform collaborative parsing of the amygdala functional lateralization pattern and the preset target individual behavioral representation data to generate collaborative parsing results. The target individual behavioral representation data includes records of specific behavioral responses to emotional stimuli. The generation module is used to generate personalized rehabilitation pathways based on the collaborative analysis results.

7. A computing device, characterized in that, It includes a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are invoked and executed by the processing component to implement a personalized rehabilitation pathway development method based on amygdala function assessment as described in any one of claims 1 to 5.

8. A computer storage medium, characterized in that, The device contains a computer program that, when executed by a computer, implements a personalized rehabilitation pathway development method based on amygdala function assessment as described in any one of claims 1 to 5.

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