Cognitive level parameter evaluation method, device, computer equipment and storage medium

Abstract

The present application relates to a cognitive level parameter assessment method, apparatus, computer equipment, and storage medium. The method comprises: determining a test subject's cognitive score; determining the test subject's brain activity sequence based on test functional magnetic resonance imaging data; constructing the test subject's emotional memory network based on the test subject's brain activity sequence; determining the test brain state corresponding to the network activation pattern of the emotional memory network; determining pattern similarity between network activation patterns based on the test brain state; determining a predictive representation map based on the pattern similarity; determining predictive indices of the test receptive field and the predictive representation map based on the predictive representation map; determining the test subject's cognitive imaging markers based on the predictive indices; constructing a cognitive level prediction model based on the cognitive score and cognitive imaging markers using a machine learning algorithm; and determining the target subject's cognitive level parameters using the cognitive level prediction model. This method can improve the accuracy of the target subject's cognitive assessment results.

Description

Technical Field

[0001] The present application relates to the field of cognitive imaging, and in particular to a method, apparatus, computer equipment, and storage medium for evaluating cognitive level parameters. Background Art

[0002] Cognitive impairment is characterized by high morbidity, high disability rate and high recurrence rate, which places a huge burden on society and individuals. Cognitive impairment may manifest as persistent low mood, decreased vitality, decreased appetite, poor sleep quality, and slower perception and movement speed. In addition, the learning, memory and attention of people with cognitive impairment may also be impaired. However, the current assessment of cognitive impairment in individuals mainly relies on the individual's cognitive performance and scale scores. The assessment cycle is long and lacks objective quantitative indicators. The accuracy of the assessment of individual cognitive level parameters is low. Therefore, how to improve the efficiency of the assessment of individual cognitive level parameters and improve the accuracy of the assessment of cognitive level parameters is a problem that needs to be solved. Summary of the Invention

[0003] Based on this, it is necessary to provide a cognitive level parameter evaluation method, device, computer equipment and storage medium that can improve the evaluation efficiency of individual cognitive level parameters and improve the accuracy of cognitive level parameter evaluation in response to the above technical problems.

[0004] In a first aspect, the present application provides a method for evaluating cognitive level parameters, the method comprising:

[0005] determining a cognitive score of a test subject and test functional magnetic resonance imaging data of the test subject while the test subject watches the candidate movie, and determining a brain activity sequence of the test subject based on the test functional magnetic resonance imaging data;

[0006] Constructing an emotional memory network of the test person according to the brain activity sequence of the test person, determining a network activation pattern of the emotional memory network, and determining a test brain state corresponding to the network activation pattern;

[0007] determining a test activation sequence of the emotional memory network based on the test brain state, determining pattern similarity between network activation patterns based on the test activation sequence, generating a high-dimensional representation relationship structure based on the pattern similarity, and generating a predictive representation map based on the high-dimensional representation relationship structure;

[0008] Determining a test receptive field according to the predictive representation map, determining a predictive index of the predictive representation map according to the test receptive field, and determining a cognitive imaging marker of the test person according to the predictive index;

[0009] Building a cognitive level prediction model based on the cognitive score and the cognitive imaging markers of the test subject through a machine learning algorithm;

[0010] The cognitive level parameters of the target person are determined by the cognitive level prediction model according to the target functional magnetic resonance imaging data when the target person watches the target movie clip.

[0011] In one embodiment, the cognitive level parameter assessment method further includes:

[0012] Determining vocabulary feature vectors of the candidate movies, and constructing a correlation network structure between the vocabulary feature vectors based on the Pearson correlation coefficients between the vocabulary feature vectors;

[0013] A correlation analysis is performed on the correlation network structure and the predictive representation map, and a regulatory paradigm of the film genre of the candidate film on the cognitive level is constructed based on the analysis results.

[0014] In one embodiment, determining a test receptive field based on the predictive representation map, and determining a predictive index of the predictive representation map based on the test receptive field, includes:

[0015] Determining a test receptive field according to the predictive representation map, determining a curve window of an activation curve corresponding to the test receptive field by a sliding window method, determining a kurtosis within the curve window corresponding to the curve window, and determining a candidate window from the curve window according to the kurtosis within the window;

[0016] De-overlapping is performed on the candidate windows to determine a target window, and a predictive index of the predictive representation map is determined according to the skewness value of the target window.

[0017] In one embodiment, the machine learning algorithm includes a support vector machine and a ridge regression model, and the cognitive level prediction model is constructed based on the cognitive score and the cognitive imaging markers of the test person by the machine learning algorithm, including:

[0018] Building a classification model based on the cognitive score and the cognitive imaging markers of the test person by using a support vector machine;

[0019] constructing a model to be tested according to the cognitive score and the cognitive imaging markers of the test person through a ridge regression model;

[0020] A cross-validation method is used to verify the reliability of the model to be tested. If the verification is passed, the model to be tested is used as the target model, and a cognitive level prediction model is constructed based on the classification model and the target model.

[0021] In one embodiment, a classification model is constructed based on the cognitive score and the cognitive imaging markers of the test person using a support vector machine, including:

[0022] Determining a training data set and a test data set based on the cognitive score and the cognitive imaging markers of the test person;

[0023] The training data set is used to train a support vector machine to determine a candidate model, and the accuracy of the candidate model is verified using the test data set. If the accuracy passes the verification, the candidate model is used as a classification model.

[0024] In one embodiment, determining the cognitive level parameters of the target person based on the target functional magnetic resonance imaging data of the target person when watching a target movie clip using the cognitive level prediction model includes:

[0025] determining a brain activity sequence of the target person based on target functional magnetic resonance imaging data of the target person while the target person watches a target movie clip;

[0026] constructing a target memory network of the target person according to the brain activity sequence of the target person, determining a target activation pattern according to the target memory network, and determining a target brain state corresponding to the target activation pattern;

[0027] determining a target activation sequence of the target memory network according to the target brain state, and generating a target prediction representation map according to the target activation sequence;

[0028] Determining a target receptive field according to the target prediction representation map, determining a target prediction index according to the target receptive field, and determining a cognitive imaging marker of the target person according to the target prediction index;

[0029] The cognitive level parameters of the target person are determined according to the cognitive imaging markers of the target person through the cognitive level prediction model.

[0030] In one embodiment, generating a predictive representation graph based on the high-dimensional representation relationship structure includes:

[0031] Determining a diagonal degree matrix according to the high-dimensional representation relationship structure, and determining a transfer matrix according to the high-dimensional representation relationship structure and the diagonal degree matrix;

[0032] A predictive representation map is determined based on the transfer matrix, the identity matrix, and the discount coefficient.

[0033] In a second aspect, the present application further provides a cognitive level parameter evaluation device, the device comprising:

[0034] a brain activity sequence determination module, configured to determine a cognitive score of a test subject and test functional magnetic resonance imaging data of the test subject while the test subject watches a candidate movie, and determine a brain activity sequence of the test subject based on the test functional magnetic resonance imaging data;

[0035] a brain state determination module, configured to construct an emotional memory network of the test subject based on the brain activity sequence of the test subject, determine a network activation pattern of the emotional memory network, and determine a test brain state corresponding to the network activation pattern;

[0036] a representation map determination module, configured to determine a test activation sequence of the emotional memory network based on the test brain state, determine pattern similarity between network activation patterns based on the test activation sequence, generate a high-dimensional representation relationship structure based on the pattern similarity, and generate a predictive representation map based on the high-dimensional representation relationship structure;

[0037] an imaging marker determination module, configured to determine a test receptive field based on the predictive representation map, determine a predictive index of the predictive representation map based on the test receptive field, and determine a cognitive imaging marker of the test person based on the predictive index;

[0038] A model training module, configured to construct a cognitive level prediction model based on the cognitive score and the cognitive imaging markers of the test subject using a machine learning algorithm;

[0039] The cognitive level parameter evaluation module is used to determine the cognitive level parameters of the target person based on the target functional magnetic resonance imaging data when the target person watches the target movie clip through the cognitive level prediction model.

[0040] In a third aspect, the present application further provides a computer device, comprising a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the following steps are implemented:

[0041] determining a cognitive score of a test subject and test functional magnetic resonance imaging data of the test subject while the test subject watches the candidate movie, and determining a brain activity sequence of the test subject based on the test functional magnetic resonance imaging data;

[0042] Constructing an emotional memory network of the test person according to the brain activity sequence of the test person, determining a network activation pattern of the emotional memory network, and determining a test brain state corresponding to the network activation pattern;

[0043] determining a test activation sequence of the emotional memory network based on the test brain state, determining pattern similarity between network activation patterns based on the test activation sequence, generating a high-dimensional representation relationship structure based on the pattern similarity, and generating a predictive representation map based on the high-dimensional representation relationship structure;

[0044] Determining a test receptive field according to the predictive representation map, determining a predictive index of the predictive representation map according to the test receptive field, and determining a cognitive imaging marker of the test person according to the predictive index;

[0045] Building a cognitive level prediction model based on the cognitive score and the cognitive imaging markers of the test subject through a machine learning algorithm;

[0046] The cognitive level parameters of the target person are determined by the cognitive level prediction model according to the target functional magnetic resonance imaging data when the target person watches the target movie clip.

[0047] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the following steps:

[0048] determining a cognitive score of a test subject and test functional magnetic resonance imaging data of the test subject while the test subject watches the candidate movie, and determining a brain activity sequence of the test subject based on the test functional magnetic resonance imaging data;

[0049] Constructing an emotional memory network of the test person according to the brain activity sequence of the test person, determining a network activation pattern of the emotional memory network, and determining a test brain state corresponding to the network activation pattern;

[0050] determining a test activation sequence of the emotional memory network based on the test brain state, determining pattern similarity between network activation patterns based on the test activation sequence, generating a high-dimensional representation relationship structure based on the pattern similarity, and generating a predictive representation map based on the high-dimensional representation relationship structure;

[0051] Determining a test receptive field according to the predictive representation map, determining a predictive index of the predictive representation map according to the test receptive field, and determining a cognitive imaging marker of the test person according to the predictive index;

[0052] Building a cognitive level prediction model based on the cognitive score and the cognitive imaging markers of the test subject through a machine learning algorithm;

[0053] The cognitive level parameters of the target person are determined by the cognitive level prediction model according to the target functional magnetic resonance imaging data when the target person watches the target movie clip.

[0054] The above-mentioned cognitive level parameter evaluation method, device, computer equipment and storage medium determine the cognitive score of the test person and the test functional magnetic resonance imaging data of the test person when watching the candidate movie, and determine the brain activity sequence of the test person based on the test functional magnetic resonance imaging data; construct the emotional memory network of the test person based on the brain activity sequence of the test person, determine the network activation pattern of the emotional memory network, and determine the test brain state corresponding to the network activation pattern; determine the test activation sequence of the emotional memory network based on the test brain state, determine the pattern similarity between the network activation patterns based on the test activation sequence, generate a high-dimensional representation relationship structure based on the pattern similarity, and generate a predictive representation map based on the high-dimensional representation relationship structure; determine the test receptive field based on the predictive representation map, determine the predictive index of the predictive representation map based on the test receptive field, and determine the cognitive imaging marker of the test person based on the predictive index; construct a cognitive level prediction model based on the cognitive score and the cognitive imaging marker of the test person through a machine learning algorithm; determine the cognitive level parameters of the target person based on the target functional magnetic resonance imaging data of the person when watching the target movie clip through the cognitive level prediction model. This approach addresses the current problem of cognitive impairment assessments relying primarily on individual cognitive performance and scale scores, resulting in long assessment cycles, a lack of objective quantitative indicators, and low accuracy in assessing individual cognitive parameters. This approach, based on fMRI data of the test subject's emotional and memory circuits, combines modeling methods such as brain network dynamics analysis and cognitive map representation to explore potential imaging markers of individual cognition and study the neural mechanisms that influence changes in cognitive parameters. Using machine learning algorithms, a cognitive level prediction model is trained based on the test subject's fMRI data and cognitive scores. This model is then used to predict the cognitive scores of target individuals, improving both the efficiency and accuracy of cognitive assessments. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 A diagram showing an application environment of a cognitive level parameter evaluation method in one embodiment;

[0056] Figure 2 1 is a flow chart of a method for evaluating cognitive level parameters in one embodiment;

[0057] Figure 3 An example diagram of determining a test receptive field based on a predictive representation map in one embodiment;

[0058] Figure 4is a flow chart of a method for evaluating cognitive level parameters in another embodiment;

[0059] Figure 5 is a flow chart of a method for evaluating cognitive level parameters in another embodiment;

[0060] Figure 6 is a flow chart of a method for evaluating cognitive level parameters in another embodiment;

[0061] Figure 7 is a flow chart of a method for evaluating cognitive level parameters in another embodiment;

[0062] Figure 8 is a structural block diagram of a cognitive level parameter evaluation device in one embodiment;

[0063] Figure 9 FIG. 1 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0064] In order to make the purpose, technical solutions and advantages of this application more clear, the following further describes this application in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0065] The cognitive level parameter evaluation method provided in the embodiment of the present application can be applied to Figure 1In the application environment shown, the terminal 102 communicates with the server 104 via a network. The data storage system can store data that the server 104 needs to process. The data storage system can be integrated on the server 104 or placed on the cloud or other network servers. The server 104 determines the cognitive score of the test person and the test functional magnetic resonance imaging data of the test person when watching the candidate movie, and determines the brain activity sequence of the test person based on the test functional magnetic resonance imaging data; constructs the emotional memory network of the test person based on the brain activity sequence, determines the network activation pattern of the emotional memory network, and determines the test brain state corresponding to the network activation pattern; determines the test activation sequence of the emotional memory network based on the test brain state, determines the pattern similarity between the network activation patterns based on the test activation sequence, generates a high-dimensional representation relationship structure based on the pattern similarity, and generates a predictive representation map based on the high-dimensional representation relationship structure; determines the test receptive field based on the predictive representation map, determines the predictive index of the predictive representation map based on the test receptive field, determines the cognitive imaging marker of the test person based on the predictive index, and constructs a cognitive level prediction model based on the cognitive score and the cognitive imaging marker of the test person through a machine learning algorithm; determines the cognitive level parameters of the target person based on the target functional magnetic resonance imaging data when the target person watches the target movie clip through the cognitive level prediction model, and sends relevant information of the cognitive level parameter evaluation to the terminal 102 through the communication network. Terminal 102 may include, but is not limited to, various personal computers, laptops, smartphones, tablets, IoT devices, and portable wearable devices. IoT devices may include smart speakers, smart TVs, smart air conditioners, and smart car devices. Portable wearable devices may include smart watches, smart bracelets, and head-mounted devices. Server 104 may be implemented as a standalone server or a server cluster consisting of multiple servers.

[0066] In one embodiment, Figure 2 As shown, a method for evaluating cognitive level parameters is provided. This embodiment uses the method applied to a terminal as an example. It is understandable that the method can also be applied to a server, or to a system including a terminal and a server, and implemented through interaction between the terminal and the server. In this embodiment, the method includes the following steps:

[0067] S210: Determine the cognitive score of the test person and the test functional magnetic resonance imaging data when the test person watches the candidate movie, and determine the brain activity sequence of the test person based on the test functional magnetic resonance imaging data.

[0068] The test subjects must be right-handed and have normal vision, and must be between 22 and 35 years old. The number of test subjects can be adjusted based on actual needs, for example, 50. The test functional magnetic resonance imaging (fMRI) data refers to the test subjects' fMRI data. This is an emerging neuroimaging method that uses magnetic resonance imaging to measure changes in blood dynamics caused by neuronal activity. It is primarily used to study the human and animal brain or spinal cord. The candidate films can include clips from different genres. For example, the corresponding genres may include romance, suspense, documentary, historical drama, and situation comedy. Candidate films in the romance and situation comedy genres are positively emotional, those in the suspense genre are negatively emotional, and those in the documentary and historical drama genres are neutrally emotional.

[0069] It should be noted that when selecting testers, it is necessary to exclude people with mental disorders, people with a history of organic brain diseases, people who abuse or are dependent on substances, people who are undergoing short-term medication for mental disorder-related diseases, people whose vision, hearing and comprehension abilities cannot meet the data collection requirements of the project, people who have contraindications for magnetic resonance imaging, and people who have obvious impulsive behavior and cannot cooperate with the examination.

[0070] Specifically, the required number of testers are selected according to the tester selection criteria, and the Hamilton Depression Rating Scale, Hamilton Anxiety Rating Scale and Cognitive Mini-Mental State Examination scale of the testers are collected. The fluid intelligence-related behavioral performance of the testers is determined according to the testers' episodic memory ability, cognitive flexibility, executive function, inhibitory control ability, information processing speed, and working memory ability. The crystallized intelligence-related behavioral performance of the testers is determined according to the language ability, reading decoding ability, language comprehension ability and vocabulary comprehension ability. The negative emotion score, mental health score, stress and self-efficacy score and social relationship score of the testers are also determined. The cognitive score of the tester is determined according to the Hamilton Depression Rating Scale, Hamilton Anxiety Rating Scale and Cognitive Mini-Mental State Examination scale, fluid intelligence-related behavioral performance, crystallized intelligence-related behavioral performance, negative emotion score, mental health score, stress and self-efficacy score and social relationship score of the testers. fMRI data of the test subjects while watching the candidate movies were collected as test functional magnetic resonance imaging data. Based on the test functional magnetic resonance imaging data, brain activity sequences at different locations in the brain regions of the test subjects' emotional memory circuits were extracted as the test subjects' brain activity sequences. The brain regions of the emotional memory circuits included key areas such as the hippocampus, amygdala, parahippocampal gyrus, medial and dorsolateral prefrontal cortex, anterior cingulate gyrus, posterior cingulate gyrus and precuneus.

[0071] S220. Construct an emotional memory network of the tester according to the brain activity sequence of the tester, determine a network activation pattern of the emotional memory network, and determine a test brain state corresponding to the network activation pattern.

[0072] Specifically, the study assessed the synergy between activations at different locations within the test subject's emotional memory circuitry based on brain activity sequences within the brain. Using network decomposition, the study constructed a personalized brain functional network for the emotional and memory circuitry, known as the emotional memory network. Using a hidden Markov model approach, the co-activation patterns across the emotional memory network were detected to identify brain states with distinct activation patterns within the dynamic activity of the emotional memory network, representing the test brain states corresponding to these network activation patterns.

[0073] S230. Determine a test activation sequence of the emotional memory network based on the test brain state, determine pattern similarity between network activation patterns based on the test activation sequence, generate a high-dimensional representation relationship structure based on the pattern similarity, and generate a predictive representation map based on the high-dimensional representation relationship structure.

[0074] Among them, the test activation sequence of the emotional memory network is the activation sequence of the emotional memory network extracted from each test brain state of different activation patterns.

[0075] Specifically, the test activation sequence of the emotional memory network is extracted from each test brain state with different activation patterns. The pattern similarity between the test brain states corresponding to different time periods, that is, the pattern similarity between the network activation patterns, is calculated through the searchlight multi-voxel analysis method. According to the pattern similarity between the network activation patterns, a matrix of high-dimensional representation relationship structure is generated. Through the reinforcement learning method of the successor representation model, the reinforcement learning model of the successor representation is applied to the matrix corresponding to the high-dimensional representation relationship structure to generate a predictive representation map, thereby obtaining the predictive conversion relationship between the representations of each test brain state in different time periods.

[0076] S240. Determine a test receptive field based on the predictive representation map, determine a predictive index of the predictive representation map based on the test receptive field, and determine a cognitive imaging marker of the test person based on the predictive index.

[0077] Among them, the test receptive field is extracted from the column elements in the matrix corresponding to the predictive representation map, and the position field is a typical cognitive map representation pattern.

[0078] Specifically, such as Figure 3As shown, the column elements in the matrix corresponding to the predictive representation map are mapped into a time period representation distribution map in a two-dimensional brain space. The test receptive field is determined based on the time period representation distribution map in the two-dimensional brain space. The predictive index of the predictive representation map is calculated based on the test receptive field. The difference between the predictive representation maps is determined based on the test receptive field and the predictive index. The cognitive imaging markers of the test subjects are determined based on the difference between the predictive representation maps.

[0079] S250. Through machine learning algorithms, a cognitive level prediction model is constructed based on cognitive scores and cognitive imaging markers of test subjects.

[0080] S260. Determine the cognitive level parameters of the target person based on the target functional magnetic resonance imaging data when the person watches the target movie clip using a cognitive level prediction model.

[0081] The target movie clip may correspond to a movie genre such as romance, suspense, documentary, historical drama, or situation comedy.

[0082] Specifically, the cognitive imaging markers of the target person are determined based on the target functional magnetic resonance imaging data when the person watches the target movie clip, and the cognitive imaging markers of the target person are input into the cognitive level prediction model to determine the cognitive level parameters of the target person.

[0083] In the above-mentioned cognitive level parameter evaluation method, the cognitive score of the test person and the test functional magnetic resonance imaging data when the test person watches the candidate movie are determined, and the brain activity sequence of the test person is determined based on the test functional magnetic resonance imaging data; the emotional memory network of the test person is constructed based on the brain activity sequence of the test person, and the network activation pattern of the emotional memory network is determined, and the test brain state corresponding to the network activation pattern is determined; the test activation sequence of the emotional memory network is determined based on the test brain state, the pattern similarity between the network activation patterns is determined based on the test activation sequence, and a high-dimensional representation relationship structure is generated based on the pattern similarity, and a predictive representation map is generated based on the high-dimensional representation relationship structure; the test receptive field is determined based on the predictive representation map, and the predictive index of the predictive representation map is determined based on the test receptive field, and the cognitive imaging marker of the test person is determined based on the predictive index; a cognitive level prediction model is constructed based on the cognitive score and the cognitive imaging marker of the test person through a machine learning algorithm; and the cognitive level parameters of the target person are determined based on the cognitive level prediction model according to the target functional magnetic resonance imaging data when the person watches the target movie clip. This approach addresses the current problem of cognitive impairment assessments relying primarily on individual cognitive performance and scale scores, resulting in long assessment cycles, a lack of objective quantitative indicators, and low accuracy in assessing individual cognitive parameters. This approach, based on fMRI data of the test subject's emotional and memory circuits, combines modeling methods such as brain network dynamics analysis and cognitive map representation to explore potential imaging markers of individual cognition and study the neural mechanisms that influence changes in cognitive parameters. Using machine learning algorithms, a cognitive level prediction model is trained based on the test subject's fMRI data and cognitive scores. This model is then used to predict the cognitive scores of target individuals, improving both the efficiency and accuracy of cognitive assessments.

[0084] In one embodiment, Figure 4 As shown, the above-mentioned cognitive level parameter evaluation method also includes:

[0085] S310: Determine the vocabulary feature vectors of the candidate movies, and construct a correlation network structure between the vocabulary feature vectors based on the Pearson correlation coefficients between the vocabulary feature vectors.

[0086] Specifically, the lexical feature vectors of the candidate movies are extracted through the WordNet lexicon, and the Pearson correlation coefficient between the lexical feature vectors is calculated. The correlation network structure between the lexical feature vectors is constructed based on the Pearson correlation coefficient.

[0087] S320. Conduct correlation analysis on the correlation network structure and the predictive representation map, and construct a regulatory paradigm of the cognitive level of the candidate film’s genre based on the analysis results.

[0088] Among them, the regulatory paradigm of the film genre of the candidate film on the cognitive level refers to the regulatory paradigm of the film genre's regulatory effect on the parameters of the individual's cognitive level.

[0089] Specifically, a correlation analysis is performed on the correlation network structure and the predictive representation map to determine the correlation between the correlation network structure corresponding to the candidate movie and the predictive representation map determined by the test subjects when watching the candidate movie, and based on the correlation between the correlation network structure corresponding to the candidate movie and the predictive representation map determined by the test subjects when watching the candidate movie, a regulatory paradigm of the film type of the candidate movie on the cognitive level is established.

[0090] The above method, combined with the natural stimulation paradigm, studies the relationship between lexical features and cognitive imaging markers corresponding to different types of movies, which can reveal the potential mechanism of changes in cognitive level parameters and provide a theoretical basis for subsequent research on the regulation and intervention strategies of cognitive level parameters.

[0091] In one embodiment, Figure 5 As shown, the test receptive field is determined according to the predictive representation map, and the predictive index of the predictive representation map is determined according to the test receptive field, including:

[0092] S410, determining a test receptive field according to the predictive representation map, determining a curve window of an activation curve corresponding to the test receptive field by a sliding window method, determining the kurtosis within the window corresponding to the curve window, and determining a candidate window from the curve window according to the kurtosis within the window.

[0093] Specifically, the test receptive field is determined according to the predictive representation map, the activation curve corresponding to the test receptive field is processed by the sliding window method, the curve window of the activation curve corresponding to the test receptive field is determined, the kurtosis within the window corresponding to each curve window is determined, and the curve windows with the top 20% kurtosis values can be selected as candidate windows.

[0094] S420 , performing de-overlapping processing on the candidate windows, determining a target window, and determining a predictive index of the predictive representation map according to the skewness value of the target window.

[0095] Specifically, the candidate windows are de-overlapped, the target windows are determined, the skewness values of the target windows are calculated, the negative numbers are determined from the skewness values, and the absolute values of the negative numbers are taken as the predictive indicators of the predictive representation map.

[0096] The above solution provides a method for determining the predictive index of the predictive characterization map, thereby improving the accuracy of the predictive index of the predictive characterization map.

[0097] In one embodiment, Figure 6As shown, the machine learning algorithm includes a support vector machine and a ridge regression model. Through the machine learning algorithm, a cognitive level prediction model is constructed based on the cognitive score and the cognitive imaging markers of the test person, including:

[0098] S510. Construct a classification model based on the cognitive scores and the cognitive imaging markers of the test persons by using a support vector machine.

[0099] Among them, the classification model is the classifier corresponding to the testers with different emotions and cognitive scores.

[0100] Specifically, a classification model is constructed based on the cognitive scores and the cognitive imaging markers of the testers through a support vector machine. That is, a classifier is trained using a support vector machine to distinguish testers with different cognitive scores, and a screening model for testers with different cognitive scores is established.

[0101] S520. Construct a model to be tested based on the cognitive scores and the cognitive imaging markers of the test persons through a ridge regression model.

[0102] Specifically, the cognitive imaging markers of the test subjects, which predict their cognitive scores, are organized into a target dataset. A portion of the target dataset is randomly extracted as model training data, and the remaining data from the target dataset is used as model testing data. The ridge regression model is trained using the model training data. The number of model training cycles can be set as needed, for example, 100 iterations. The model to be tested is determined based on the model training results.

[0103] S530. Use a cross-validation method to verify the reliability of the model to be tested. If the verification passes, use the model to be tested as the target model, and build a cognitive level prediction model based on the classification model and the target model.

[0104] Specifically, the leave-p-out cross-validation method is used to verify the reliability of the model to be tested based on the model test data. If the verification passes, the model to be tested is used as the target model, and a cognitive level prediction model is constructed based on the classification model and the target model.

[0105] The above scheme trains the support vector machine according to the cognitive score and the cognitive imaging markers of the test person to determine the classifier, trains the ridge regression model according to the cognitive score and the cognitive imaging markers of the test person, and verifies the reliability of the trained ridge regression model through the cross-validation method, and determines the cognitive level prediction model according to the classifier and the ridge regression model after training, which can improve the model prediction accuracy of the cognitive level prediction model.

[0106] In one embodiment, a classification model is constructed based on the cognitive scores and the cognitive imaging markers of the test subjects using a support vector machine, including:

[0107] The training data set and the test data set are determined based on the cognitive scores and the cognitive imaging markers of the testers; the support vector machine is trained using the training data set to determine the candidate model, and the accuracy of the candidate model is verified using the test data set. If the verification passes, the candidate model is used as the classification model.

[0108] Specifically, the cognitive imaging markers used to predict the cognitive scores of the test subjects are organized into a target dataset. A portion of the data from the target dataset is randomly extracted as the training dataset, and the data from the target dataset other than the training dataset is used as the test dataset. The support vector machine is iteratively trained using the training dataset to determine a candidate model. The number of iterative training iterations can be 100. The accuracy of the candidate model is verified using the test dataset. If the verification passes, the candidate model is used as the classification model.

[0109] The above solution can improve the reliability of the classification model.

[0110] Exemplarily, the cognitive level parameters of the target person are determined by using a cognitive level prediction model based on target functional magnetic resonance imaging data of the target person when watching a target movie clip, including:

[0111] Based on the target functional magnetic resonance imaging data of the target person when watching the target movie clip, the brain activity sequence of the target person is determined; based on the brain activity sequence of the target person, a target memory network of the target person is constructed, and the target activation pattern is determined based on the target memory network, and the target brain state corresponding to the target activation pattern is determined; based on the target brain state, the target activation sequence of the target memory network is determined, and a target prediction representation map is generated based on the target activation sequence; based on the target prediction representation map, the target receptive field is determined, and the target prediction index is determined based on the target receptive field, and the cognitive imaging markers of the target person are determined based on the target prediction index; through the cognitive level prediction model, the cognitive level parameters of the target person are determined based on the cognitive imaging markers of the target person.

[0112] Among them, the target memory network is the emotional memory network of the target person, the target activation mode refers to the network activation mode of the target memory network, the target prediction representation map refers to the predictive representation map corresponding to the target memory network, the target receptive field refers to the receptive field corresponding to the target prediction representation map, and the target prediction index refers to the predictive index corresponding to the target receptive field.

[0113] The above scheme, when evaluating the cognitive score of the target person, can determine the cognitive imaging markers of the target person based on the target functional magnetic resonance imaging data when the target person watches the target movie clip, and determine the cognitive score of the target person based on the cognitive imaging markers of the target person through the cognitive level prediction model, so as to evaluate the cognitive level parameters of the target person based on the cognitive score of the target person, thereby improving the efficiency of evaluating the cognitive level parameters of the target person.

[0114] In one embodiment, Figure 7 As shown in Figure 2, a predictive representation graph is generated based on the high-dimensional representation relationship structure, including:

[0115] S610: Determine a diagonal degree matrix according to the high-dimensional representation relationship structure, and determine a transfer matrix according to the high-dimensional representation relationship structure and the diagonal degree matrix.

[0116] The values in the diagonal degree matrix are the sum of the values of the corresponding rows in the high-dimensional matrix representing the relational structure.

[0117] Specifically, the diagonal matrix is determined based on the values of each row in the matrix representing the high-dimensional relational structure and the values of each row in the diagonal matrix. The calculation formula of the transfer matrix is shown in formula (1):

[0118] (1)

[0119] Among them, T is the diagonal matrix, D is the diagonal degree matrix, and W is the high-dimensional representation relationship structure.

[0120] S620: Determine a predictive representation map based on the transfer matrix, the identity matrix, and the discount coefficient.

[0121] Specifically, the calculation formula of the predictive representation map is shown in formula (2):

[0122] (2)

[0123] Where M is the predictive representation map, I is the identity matrix, is the discount factor.

[0124] The above solution provides a method for calculating a predictive characterization map, which can improve the accuracy of the predictive characterization map.

[0125] In one embodiment, the cognitive level parameter assessment method further includes:

[0126] The required number of testers were selected according to the tester selection criteria, and their Hamilton Depression Rating Scale, Hamilton Anxiety Rating Scale and Cognitive Mini-Mental State Examination scores were collected. The testers' fluid intelligence-related behavioral performance was determined based on their episodic memory ability, cognitive flexibility, executive function, inhibitory control ability, information processing speed and working memory ability. The testers' crystallized intelligence-related behavioral performance was determined based on their language ability, reading decoding ability, language comprehension ability and vocabulary comprehension ability. The testers' negative emotion score, mental health score, stress and self-efficacy score and social relationship score were also determined. The testers' cognitive score was determined based on the testers' Hamilton Depression Rating Scale, Hamilton Anxiety Rating Scale and Cognitive Mini-Mental State Examination scores, fluid intelligence-related behavioral performance, crystallized intelligence-related behavioral performance, negative emotion score, mental health score, stress and self-efficacy score and social relationship score. fMRI data of the test subjects while watching the candidate movies were collected as test functional magnetic resonance imaging data. Based on the test functional magnetic resonance imaging data, brain activity sequences at different locations in the brain regions of the test subjects' emotional memory circuits were extracted as the test subjects' brain activity sequences. The brain regions of the emotional memory circuits included key areas such as the hippocampus, amygdala, parahippocampal gyrus, medial and dorsolateral prefrontal cortex, anterior cingulate gyrus, posterior cingulate gyrus and precuneus.

[0127] Based on the brain activity sequences at different locations within the test subject's emotional memory circuit, the synergy between activations at different locations within the brain region was assessed. Using network decomposition, a personalized brain functional network of the brain's emotion and memory circuits, namely the emotional memory network, was constructed. Using the Hidden Markov Model method, by detecting the co-activation patterns at various locations within the emotional memory network, brain states with different activation patterns during dynamic activity within the emotional memory network were identified, namely, the test brain states corresponding to the network activation patterns.

[0128] The test activation sequence of the emotional memory network is extracted from each test brain state with different activation patterns. The pattern similarity between the test brain states corresponding to different time periods, that is, the pattern similarity between the network activation patterns, is calculated through the searchlight multi-voxel analysis method. According to the pattern similarity between the network activation patterns, a matrix of high-dimensional representation relationship structure is generated. Through the reinforcement learning method of the successor representation model, the reinforcement learning model of the successor representation is applied to the matrix corresponding to the high-dimensional representation relationship structure to generate a predictive representation map, thereby obtaining the predictive conversion relationship between the representations of each test brain state in different time periods.

[0129] The column elements in the matrix corresponding to the predictive representation map are mapped into a time segment representation distribution map in two-dimensional brain space. The test receptive field is determined based on the predictive representation map. The activation curve corresponding to the test receptive field is processed using a sliding window method to determine the curve window of the activation curve corresponding to the test receptive field. The kurtosis within each curve window is determined, and the curve windows with the top 20% kurtosis values are selected as candidate windows. The candidate windows are de-overlapped to determine the target window. The skewness of each target window is calculated, and the negative number is determined from the skewness value. The absolute value of the negative number is taken as the predictive index of the predictive representation map. The differences between the predictive representation maps are determined based on the test receptive field and the predictive index. Based on the differences between the predictive representation maps, the cognitive imaging markers of the test subjects are determined.

[0130] The cognitive imaging markers of the test subjects are used to predict their cognitive scores, which are organized into a target dataset. A portion of the data is randomly extracted from the target dataset as the training dataset, and the data in the target dataset other than the training dataset is used as the test dataset. The support vector machine is iteratively trained using the training dataset to determine a candidate model. The number of iterative training iterations can be 100. The accuracy of the candidate model is verified using the test dataset. If the verification passes, the candidate model is used as the classification model.

[0131] A portion of data is randomly extracted from the target dataset as model training data, and the remaining data in the target dataset is used as model testing data. The ridge regression model is trained using the model training data. The number of model training cycles can be set as needed, for example, 100 iterations. The model to be tested is determined based on the model training results. The reliability of the model to be tested is verified using the leave-out cross-validation method based on the model testing data. If the reliability is verified, the model to be tested is used as the target model, and a cognitive level prediction model is constructed based on the classification model and the target model.

[0132] Lexical feature vectors of candidate films were extracted using the WordNet lexicon, and the Pearson correlation coefficient between the lexical feature vectors was calculated. A correlation network structure between the lexical feature vectors was constructed based on the Pearson correlation coefficient. A correlation analysis was performed between the correlation network structure and the predictive representation map to determine the association between the correlation network structure corresponding to the candidate film and the predictive representation map determined by the test subjects after viewing the candidate film. Based on this association between the correlation network structure corresponding to the candidate film and the predictive representation map determined by the test subjects after viewing the candidate film, a model for the regulation of the film genre of the candidate film on cognitive level was established.

[0133] The above scheme determines the cognitive score of the test person and the test functional magnetic resonance imaging data when the test person watches the candidate movie, and determines the brain activity sequence of the test person based on the test functional magnetic resonance imaging data; constructs the test person's emotional memory network based on the brain activity sequence of the test person, determines the network activation pattern of the emotional memory network, and determines the test brain state corresponding to the network activation pattern; determines the test activation sequence of the emotional memory network based on the test brain state, determines the pattern similarity between the network activation patterns based on the test activation sequence, generates a high-dimensional representation relationship structure based on the pattern similarity, and generates a predictive representation map based on the high-dimensional representation relationship structure; determines the test receptive field based on the predictive representation map, determines the predictive index of the predictive representation map based on the test receptive field, and determines the cognitive imaging markers of the test person based on the predictive index; constructs a cognitive level prediction model based on the cognitive score and the cognitive imaging markers of the test person through a machine learning algorithm; determines the cognitive level parameters of the target person based on the target functional magnetic resonance imaging data when the person watches the target movie clip through the cognitive level prediction model. This approach addresses the current problem of cognitive impairment assessments relying primarily on individual cognitive performance and scale scores, resulting in long assessment cycles, a lack of objective quantitative indicators, and low accuracy in assessing individual cognitive parameters. This approach, based on fMRI data of the test subject's emotional and memory circuits, combines modeling methods such as brain network dynamics analysis and cognitive map representation to explore potential imaging markers of individual cognition and study the neural mechanisms that influence changes in cognitive parameters. Using machine learning algorithms, a cognitive level prediction model is trained based on the test subject's fMRI data and cognitive scores. This model is then used to predict the cognitive scores of target individuals, improving both the efficiency and accuracy of cognitive assessments.

[0134] It should be understood that, although the various steps in the flowcharts involved in the various embodiments described above are displayed in sequence according to the instructions of the arrows, these steps are not necessarily executed in sequence in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and these steps can be executed in other orders. Moreover, at least a portion of the steps in the flowcharts involved in the various embodiments described above can include multiple steps or multiple stages, and these steps or stages are not necessarily executed and completed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a portion of steps or stages in other steps.

[0135] Based on the same inventive concept, the present application also provides a cognitive level parameter assessment device for implementing the cognitive level parameter assessment method described above. The implementation solution provided by this device is similar to the implementation solution described in the above method. Therefore, the specific limitations of one or more cognitive level parameter assessment device embodiments provided below can be found in the limitations of the cognitive level parameter assessment method described above and will not be repeated here.

[0136] In one embodiment, Figure 8 As shown, a cognitive level parameter evaluation device is provided, comprising: a brain activity sequence determination module 701, a brain state determination module 702, a representation map determination module 703, an imaging marker determination module 704, a model training module 705, and a cognitive level parameter evaluation module 706, wherein:

[0137] a brain activity sequence determination module 701 for determining a cognitive score of a test subject and test functional magnetic resonance imaging data of the test subject while watching a candidate movie, and determining a brain activity sequence of the test subject based on the test functional magnetic resonance imaging data;

[0138] A brain state determination module 702 is configured to construct an emotional memory network of the test subject based on the test subject's brain activity sequence, determine a network activation pattern of the emotional memory network, and determine a test brain state corresponding to the network activation pattern;

[0139] a representation map determination module 703 for determining a test activation sequence of the emotional memory network based on the test brain state, determining pattern similarity between network activation patterns based on the test activation sequence, generating a high-dimensional representation relationship structure based on the pattern similarity, and generating a predictive representation map based on the high-dimensional representation relationship structure;

[0140] An imaging marker determination module 704 is configured to determine a test receptive field based on the predictive representation map, determine a predictive index of the predictive representation map based on the test receptive field, and determine a cognitive imaging marker of the test subject based on the predictive index;

[0141] A model training module 705 is used to construct a cognitive level prediction model based on the cognitive scores and cognitive imaging markers of the test subjects through a machine learning algorithm;

[0142] The cognitive level parameter evaluation module 706 is used to determine the cognitive level parameters of the target person based on the target functional magnetic resonance imaging data when the target person watches the target movie clip through the cognitive level prediction model.

[0143] Exemplarily, the above-mentioned cognitive level parameter evaluation device further includes:

[0144] A correlation network determination module is used to determine the vocabulary feature vectors of the candidate movies and construct a correlation network structure between the vocabulary feature vectors based on the Pearson correlation coefficient between the vocabulary feature vectors;

[0145] The correlation analysis module is used to perform correlation analysis on the correlation network structure and the predictive representation map, and to construct a regulatory paradigm of the cognitive level of the candidate film genre based on the analysis results.

[0146] Exemplarily, the imaging marker determination module 704 is specifically configured to:

[0147] Determine a test receptive field according to the predictive representation map, determine a curve window of an activation curve corresponding to the test receptive field by a sliding window method, determine the kurtosis within the window corresponding to the curve window, and determine a candidate window from the curve window according to the kurtosis within the window;

[0148] The candidate windows are de-overlapped to determine the target window, and the predictive index of the predictive representation map is determined according to the skewness value of the target window.

[0149] Exemplarily, the above-mentioned machine learning algorithm includes a support vector machine and a ridge regression model. Furthermore, the model training module 705 is specifically used to:

[0150] A classification model was constructed based on the cognitive scores and the cognitive imaging markers of the test subjects using support vector machines;

[0151] The model to be tested was constructed based on the cognitive scores and cognitive imaging markers of the test subjects through the ridge regression model;

[0152] The cross-validation method is used to verify the reliability of the model to be tested. If the verification is passed, the model to be tested is used as the target model, and a cognitive level prediction model is constructed based on the classification model and the target model.

[0153] Furthermore, the model training module 705 is further specifically configured to:

[0154] Determine the training dataset and the test dataset based on the cognitive scores and cognitive imaging markers of the testers;

[0155] The support vector machine is trained using the training data set to determine the candidate model, and the accuracy of the candidate model is verified using the test data set. If the verification passes, the candidate model is used as the classification model.

[0156] Exemplarily, the cognitive level parameter evaluation module 706 is specifically configured to:

[0157] determining the target person's brain activity sequence based on the target functional magnetic resonance imaging data when the target person watches the target movie clip;

[0158] Constructing a target memory network of the target person based on the target person's brain activity sequence, determining a target activation pattern based on the target memory network, and determining a target brain state corresponding to the target activation pattern;

[0159] Determine the target activation sequence of the target memory network according to the target brain state, and generate a target prediction representation map based on the target activation sequence;

[0160] Determine the target receptive field according to the target prediction representation map, determine the target prediction index according to the target receptive field, and determine the cognitive imaging marker of the target person according to the target prediction index;

[0161] Through the cognitive level prediction model, the cognitive level parameters of the target person are determined according to the cognitive imaging markers of the target person.

[0162] Exemplarily, the characterization map determination module 703 is specifically configured to:

[0163] Determine a diagonal degree matrix based on the high-dimensional representation relationship structure, and determine a transfer matrix based on the high-dimensional representation relationship structure and the diagonal degree matrix;

[0164] A predictive representation map is determined based on the transfer matrix, the identity matrix, and the discount coefficient.

[0165] In one embodiment, a computer device is provided. The computer device may be a terminal, and its internal structure diagram may be as follows: Figure 9 As shown. The computer device includes a processor, memory, an input / output interface, a communication interface, a display unit, and an input device. The processor, memory, and input / output interface are connected via a system bus, and the communication interface, display unit, and input device are connected to the system bus via the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The input / output interface of the computer device is used to exchange information between the processor and external devices. The communication interface of the computer device is used to communicate with external terminals via wired or wireless means, and the wireless means can be achieved via Wi-Fi, mobile cellular networks, NFC (near-field communication), or other technologies. When executed by the processor, the computer program implements a method for assessing cognitive level parameters. The display unit of the computer device is used to produce a visually visible image and can be a display screen, a projection device, or a virtual reality imaging device. The display screen can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covering the display screen, or a button, trackball or touchpad set on the computer device casing, or an external keyboard, touchpad or mouse.

[0166] Those skilled in the art will understand that Figure 9 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0167] In one embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and when the processor executes the computer program, the following steps are implemented:

[0168] Step 1: determining the cognitive score of the test subject and the test functional magnetic resonance imaging data of the test subject when watching the candidate movie, and determining the brain activity sequence of the test subject based on the test functional magnetic resonance imaging data;

[0169] Step 2: Construct the test subject's emotional memory network based on the test subject's brain activity sequence, determine the network activation pattern of the emotional memory network, and determine the test brain state corresponding to the network activation pattern;

[0170] Step 3: determining a test activation sequence of the emotional memory network based on the test brain state, determining pattern similarity between network activation patterns based on the test activation sequence, generating a high-dimensional representation relationship structure based on the pattern similarity, and generating a predictive representation map based on the high-dimensional representation relationship structure;

[0171] Step 4: determining a test receptive field based on the predictive representation map, determining a predictive index of the predictive representation map based on the test receptive field, and determining a cognitive imaging marker of the test person based on the predictive index;

[0172] Step 5: Using machine learning algorithms, a cognitive level prediction model is constructed based on the cognitive scores and the cognitive imaging markers of the test subjects;

[0173] Step 6: Determine the cognitive level parameters of the target person based on the target functional magnetic resonance imaging data when the target person watches the target movie clip through the cognitive level prediction model.

[0174] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the following steps are implemented:

[0175] Step 1: determining the cognitive score of the test subject and the test functional magnetic resonance imaging data of the test subject when watching the candidate movie, and determining the brain activity sequence of the test subject based on the test functional magnetic resonance imaging data;

[0176] Step 2: Construct the test subject's emotional memory network based on the test subject's brain activity sequence, determine the network activation pattern of the emotional memory network, and determine the test brain state corresponding to the network activation pattern;

[0177] Step 3: determining a test activation sequence of the emotional memory network based on the test brain state, determining pattern similarity between network activation patterns based on the test activation sequence, generating a high-dimensional representation relationship structure based on the pattern similarity, and generating a predictive representation map based on the high-dimensional representation relationship structure;

[0178] Step 4: determining a test receptive field based on the predictive representation map, determining a predictive index of the predictive representation map based on the test receptive field, and determining a cognitive imaging marker of the test person based on the predictive index;

[0179] Step 5: Using machine learning algorithms, a cognitive level prediction model is constructed based on the cognitive scores and the cognitive imaging markers of the test subjects;

[0180] Step 6: Determine the cognitive level parameters of the target person based on the target functional magnetic resonance imaging data when the target person watches the target movie clip through the cognitive level prediction model.

[0181] In one embodiment, a computer program product is provided, comprising a computer program, which, when executed by a processor, implements the following steps:

[0182] Step 1: determining the cognitive score of the test subject and the test functional magnetic resonance imaging data of the test subject when watching the candidate movie, and determining the brain activity sequence of the test subject based on the test functional magnetic resonance imaging data;

[0183] Step 2: Construct the test subject's emotional memory network based on the test subject's brain activity sequence, determine the network activation pattern of the emotional memory network, and determine the test brain state corresponding to the network activation pattern;

[0184] Step 3: determining a test activation sequence of the emotional memory network based on the test brain state, determining pattern similarity between network activation patterns based on the test activation sequence, generating a high-dimensional representation relationship structure based on the pattern similarity, and generating a predictive representation map based on the high-dimensional representation relationship structure;

[0185] Step 4: determining a test receptive field based on the predictive representation map, determining a predictive index of the predictive representation map based on the test receptive field, and determining a cognitive imaging marker of the test person based on the predictive index;

[0186] Step 5: Using machine learning algorithms, a cognitive level prediction model is constructed based on the cognitive scores and the cognitive imaging markers of the test subjects;

[0187] Step 6: Determine the cognitive level parameters of the target person based on the target functional magnetic resonance imaging data when the target person watches the target movie clip through the cognitive level prediction model.

[0188] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with the relevant laws, regulations and standards of relevant countries and regions.

[0189] Those skilled in the art will appreciate that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the above-mentioned embodiments. In particular, any reference to memory, database, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The databases involved in the various embodiments provided herein may include at least one of a relational database and a non-relational database. Non-relational databases may include, but are not limited to, distributed databases based on blockchains. The processors involved in the various embodiments provided herein may be, but are not limited to, general-purpose processors, central processing units (CPUs), graphics processors (GPUs), digital signal processors (DSPs), programmable logic devices (PLDs), data processing logic devices based on quantum computing, and the like.

[0190] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0191] The above-described embodiments merely represent several implementation methods of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present application. It should be noted that a person of ordinary skill in the art may make various modifications and improvements without departing from the spirit of the present application, and these modifications and improvements fall within the scope of protection of the present application. Therefore, the scope of protection of the present application shall be determined by the appended claims.

Claims

1. A method for evaluating cognitive level parameters, characterized in that: include: Determining a cognitive score of a test subject and test functional magnetic resonance imaging data of the test subject while watching the candidate movie, and determining a brain activity sequence of the test subject based on the test functional magnetic resonance imaging data; the brain activity sequence refers to a brain activity sequence at different locations within the brain region of the test subject's emotional memory circuit; An emotional memory network of the test subject is constructed based on the brain activity sequence of the test subject, and a network activation pattern of the emotional memory network is determined, and a test brain state corresponding to the network activation pattern is determined; the emotional memory network refers to an individualized brain functional network of the brain's emotion and memory circuits; the test brain state corresponding to the network activation pattern refers to a brain state with different activation patterns in the dynamic activity of the emotional memory network; determining a test activation sequence of the emotional memory network based on the test brain state, determining pattern similarity between network activation patterns based on the test activation sequence, generating a high-dimensional representation relationship structure based on the pattern similarity, and generating a predictive representation map based on the high-dimensional representation relationship structure; Determining a test receptive field according to the predictive representation map, determining a predictive index of the predictive representation map according to the test receptive field, and determining a cognitive imaging marker of the test person according to the predictive index; Building a cognitive level prediction model based on the cognitive score and the cognitive imaging markers of the test subject through a machine learning algorithm; Determining the cognitive level parameters of the target person based on the target functional magnetic resonance imaging data when the target person watches the target movie clip using the cognitive level prediction model; The machine learning algorithm includes a support vector machine and a ridge regression model. The machine learning algorithm is used to construct a cognitive level prediction model based on the cognitive score and the cognitive imaging markers of the test person, including: Building a classification model based on the cognitive score and the cognitive imaging markers of the test person by using a support vector machine; constructing a model to be tested according to the cognitive score and the cognitive imaging markers of the test person through a ridge regression model; A cross-validation method is used to verify the reliability of the model to be tested. If the verification is passed, the model to be tested is used as the target model, and a cognitive level prediction model is constructed based on the classification model and the target model.

2. The method according to claim 1, characterized in that include: Determining the vocabulary feature vectors of the candidate movies, and constructing a correlation network structure between the vocabulary feature vectors based on the Pearson correlation coefficients between the vocabulary feature vectors; A correlation analysis is performed on the correlation network structure and the predictive representation map, and a regulatory paradigm of the film genre of the candidate film on the cognitive level is constructed based on the analysis results.

3. The method according to claim 1, characterized in that Determining a test receptive field according to the predictive representation map, and determining a predictive index of the predictive representation map according to the test receptive field, including: Determining a test receptive field according to the predictive representation map, determining a curve window of an activation curve corresponding to the test receptive field by a sliding window method, determining a kurtosis within the curve window corresponding to the curve window, and determining a candidate window from the curve window according to the kurtosis within the window; De-overlapping is performed on the candidate windows to determine a target window, and a predictive index of the predictive representation map is determined according to the skewness value of the target window.

4. The method according to claim 1, wherein A classification model is constructed based on the cognitive score and the cognitive imaging markers of the test person by a support vector machine, including: Determining a training data set and a test data set based on the cognitive score and the cognitive imaging markers of the test person; The training data set is used to train a support vector machine to determine a candidate model, and the accuracy of the candidate model is verified using the test data set. If the accuracy passes the verification, the candidate model is used as a classification model.

5. The method according to claim 1, wherein Determining the cognitive level parameters of the target person based on the target functional magnetic resonance imaging data of the target person when watching the target movie clip using the cognitive level prediction model includes: determining a brain activity sequence of the target person based on target functional magnetic resonance imaging data of the target person while the target person watches a target movie clip; constructing a target memory network of the target person according to the brain activity sequence of the target person, determining a target activation pattern according to the target memory network, and determining a target brain state corresponding to the target activation pattern; determining a target activation sequence of the target memory network according to the target brain state, and generating a target prediction representation map according to the target activation sequence; Determining a target receptive field according to the target prediction representation map, determining a target prediction index according to the target receptive field, and determining a cognitive imaging marker of the target person according to the target prediction index; The cognitive level parameters of the target person are determined according to the cognitive imaging markers of the target person through the cognitive level prediction model.

6. The method according to claim 1, characterized in that Generating a predictive representation graph according to the high-dimensional representation relationship structure includes: Determining a diagonal degree matrix according to the high-dimensional representation relationship structure, and determining a transfer matrix according to the high-dimensional representation relationship structure and the diagonal degree matrix; A predictive representation map is determined based on the transfer matrix, the identity matrix, and the discount coefficient.

7. A cognitive level parameter evaluation device, characterized in that: The cognitive level parameter evaluation device comprises: a brain activity sequence determination module, configured to determine a test subject's cognitive score and test functional magnetic resonance imaging data of the test subject while watching a candidate movie, and to determine the test subject's brain activity sequence based on the test functional magnetic resonance imaging data; the brain activity sequence refers to a brain activity sequence at different locations within the brain region of the test subject's emotional memory circuit; a brain state determination module, configured to construct an emotional memory network of the test subject based on the test subject's brain activity sequence, determine a network activation pattern of the emotional memory network, and determine a test brain state corresponding to the network activation pattern; the emotional memory network refers to an individualized brain functional network of the brain's emotion and memory circuits; the test brain state corresponding to the network activation pattern refers to a brain state with different activation patterns in the dynamic activity of the emotional memory network; a representation map determination module, configured to determine a test activation sequence of the emotional memory network based on the test brain state, determine pattern similarity between network activation patterns based on the test activation sequence, generate a high-dimensional representation relationship structure based on the pattern similarity, and generate a predictive representation map based on the high-dimensional representation relationship structure; an imaging marker determination module, configured to determine a test receptive field based on the predictive representation map, determine a predictive index of the predictive representation map based on the test receptive field, and determine a cognitive imaging marker of the test person based on the predictive index; A model training module, configured to construct a cognitive level prediction model based on the cognitive score and the cognitive imaging markers of the test subject using a machine learning algorithm; a cognitive level parameter evaluation module, configured to determine the cognitive level parameters of the target person based on the target functional magnetic resonance imaging data when the target person watches the target movie clip, using the cognitive level prediction model; The machine learning algorithm includes a support vector machine and a ridge regression model, and the model training module is further used to construct a classification model based on the cognitive score and the cognitive imaging markers of the test person through the support vector machine; constructing a model to be tested according to the cognitive score and the cognitive imaging markers of the test person through a ridge regression model; A cross-validation method is used to verify the reliability of the model to be tested. If the verification is passed, the model to be tested is used as the target model, and a cognitive level prediction model is constructed based on the classification model and the target model.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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