A method for detecting substances secreted by cerebral cortex

Abstract

The present invention belongs to the technical field of brain analysis and detection, and specifically discloses a method for detecting substances secreted by the cerebral cortex, comprising: creating a first folder, storing brain detection data of a tester in the first folder; configuring detection software; creating a second folder, and constructing a first association relationship between the second folder and the first folder; processing the brain detection data by the detection software to generate analysis data, and storing the analysis data in the second folder; creating a third folder, and copying the analysis data to the third folder; constructing a second association relationship between the analysis data within the third folder, and marking the analysis data in the third folder by the second association relationship and the first association relationship; outputting the third folder as a detection result; and having the following advantages: through hierarchical folder management, data backup and parallel processing, data management transparency and processing efficiency are improved, automatic analysis and data integrity are ensured, and result output is simplified.

Description

Technical Field

[0001] The present invention relates to the technical field of brain analysis and detection, and in particular to a method for detecting substances secreted by the cerebral cortex. Background Art

[0002] For mental disorders, multimodal magnetic resonance imaging (MRI) is of great value. At present, the etiology of most psychiatric diseases is unknown. Through multimodal MRI technology, changes in the patient's brain structure, function and metabolism can be observed, thereby revealing the pathogenesis and pathological characteristics of these diseases. In the study of mental disorders, multimodal MRI data fusion analysis is an important research method that can make full use of the complementarity between modal data to provide more in-depth and comprehensive analysis results, which helps to understand the pathological mechanism of mental illness more deeply and provide stronger support for the diagnosis and treatment of the disease. For example, the project team found that patients with schizophrenia have cortical abnormalities, and the frontal lobe is the most obvious; patients with refractory schizophrenia have enlarged lateral ventricles, and ventricular enlargement is related to immune regulation; in alcohol-dependent patients, the cortical thickness of the inferior parietal lobule is a potential predictor of relapse in males; in patients with tardive dyskinesia, there are disease-related cortical thickness, local consistency, abnormal functional connectivity, and caudate nucleus neuronal damage. However, there are still many challenges in multimodal MRI data processing, such as computational complexity and processing time fields: multimodal MRI data processing usually requires complex calculations, including image registration, segmentation, feature extraction and other steps. These calculation processes require a lot of time and computing resources, which limits the possibility of real-time or fast processing, causing many clinicians to be discouraged during the research process; in addition, multimodal MRI data processing involves a variety of technologies and methods, but there is currently a lack of unified standards and protocols. This can lead to inconsistent and difficult to interpret results. Some advanced data processing methods, such as deep learning models, make it difficult for doctors to understand their results, resulting in a lack of sufficient interpretability.

[0003] Secondly, the main problem of current multimodal imaging data processing is that the data processing process is too complicated, requiring clinicians to have a programming foundation, and the data processing time is long. It mainly includes data preprocessing: head motion correction, spatial standardization, Gaussian smoothing, average filtering, median filtering, etc. The final data processing includes cortical thickness calculation, subcortical volume calculation, brain white matter fiber anisotropy calculation, etc. The data processing of magnetic resonance spectroscopy is mostly based on LCModel manual processing, which is time-consuming and prone to errors.

[0004] Therefore, a method for detecting cerebral cortex secretion substances is proposed to solve the above-mentioned problems. Summary of the invention

[0005] The present invention aims to provide a method for detecting cerebral cortex secretion substances to solve or improve at least one of the above-mentioned technical problems.

[0006] In view of this, a first aspect of the present invention is to provide a method for detecting substances secreted by the cerebral cortex.

[0007] The first aspect of the present invention provides a method for detecting substances secreted by the cerebral cortex, comprising the following steps: creating a first folder based on the tester as a unit, and storing the brain detection data of the tester in the corresponding first folder; configuring multiple detection software for processing the brain detection data; when a data processing command is received, creating a second folder according to the number of the testers, and establishing a first association relationship between the second folder and the first folder; processing the brain detection data by the detection software to generate analysis data, and storing the analysis data in the second folder according to the first association relationship; marking the second folder currently executing the storage of analysis data; when a data upload command is received, creating a third folder corresponding to the second folder, and copying the analysis data in each marked second folder to the corresponding third folder; establishing a second association relationship between the analysis data within the third folder, and marking the analysis data in the third folder by the second association relationship and the first association relationship; eliminating the marks of all second folders, and outputting the selected third folder as the detection result.

[0008] In any of the above technical solutions, the brain detection data includes MRI data, DTI data, fMRI data and MRS data of the brain, and each functional module of the detection software that processes the brain detection data is marked; multiple data partitions are respectively defined in the first folder, the second folder and the third folder, and a unique partition number is assigned to each of the data partitions, and the address information of each of the data partitions is determined.

[0009] In any of the above technical solutions, the detection software includes Freesurfer software, AFNI software and LCModel software; the Freesurfer software is used to process the MRI data to generate brain structure data; the AFNI software is used to process the fMRI data to generate brain resting state analysis data, and process the DTI data to generate white matter fiber bundle data; the LCModel software is used to process the MRS data to generate quantitative data of metabolite concentrations in the brain.

[0010] In any of the above technical solutions, the analysis data includes at least one of the brain structure data, the brain resting state analysis data, the white matter fiber bundle data and the quantitative data of brain metabolite concentration.

[0011] In any of the above technical solutions, the data partitions are defined by the data types contained in the brain detection data and the analysis data, and the first association relationship is constructed through the following steps: according to the input and output calculation relationship of each of the detection software, a first mapping relationship between the data types contained in the analysis data and the data types contained in the brain detection data is obtained; through the first mapping relationship, a second mapping relationship in address information between each of the data partitions in the first folder and the second folder is obtained; and all the second mapping relationships are used as the first association relationship.

[0012] In any of the above technical solutions, the second folder and the third folder are associated through the name of the tester, and the second association relationship is constructed through the following steps: according to the control requirements of the analysis data in the analysis of cerebral cortical secretion substances, a calibration association between the data types contained in the analysis data is obtained; through the calibration association, a third mapping relationship between the address information of each data partition in the third folder is obtained; and all the third mapping relationships are used as the second association relationships.

[0013] In any of the above technical solutions, the calibration association includes: the brain structure data can calibrate the fMRI data to perform spatial normalization and regional analysis of the brain; the brain structure data can also calibrate the white matter fiber bundle data to perform brain structure and white matter path analysis; the brain structure data and the fMRI data can jointly calibrate the quantitative data of metabolite concentrations in the brain to perform brain metabolism, structure and function analysis.

[0014] In any of the above technical solutions, the step of generating the quantitative data of the concentration of metabolites in the brain by processing the MRS data through the LCModel software specifically includes: dividing the MRS data into standard MRS data and MRS data of GABA; configuring the functional module marked in the LCModel software to process the standard MRS data or the MRS data of GABA to obtain a table file; traversing each value in the table file and outputting it as an excel file; and performing quantitative analysis on the values ​​contained in the excel file through the functional module marked in the LCModel software to obtain the quantitative data of the concentration of metabolites in the brain.

[0015] In any of the above technical solutions, the step of quantitatively analyzing the numerical values ​​contained in the excel file through the functional module of the LCModel software specifically includes: configuring the corresponding operation module according to the type of the standard MRS data and the MRS data of GABA; calling the marked functional module in the LCModel software through the operation module to process the excel file to generate quantitative data; configuring a character string for screening, and screening out the quantitative data of the concentration of brain metabolites from the quantitative data through the character string.

[0016] In any of the above technical solutions, the step of traversing each value in the table file and outputting it as an Excel file specifically includes: outputting the values ​​contained in the table file into an Excel file according to the metabolite name and the content of the metabolite in the subject.

[0017] Compared with the prior art, the present invention has the following beneficial effects:

[0018] The user can upload his calculation data (i.e. the brain detection data of the tester) in the first folder and submit the analysis task. A plurality of detection software for processing brain detection data are configured. When a data processing command is received, the data is automatically processed and the analysis results are generated. These results are stored in the second folder according to the first association relationship. Since the second folder being processed is marked, a preview of the intermediate results can be provided, which is convenient for the user to view the analysis progress in real time. At the same time, for calculation tasks with large sample sizes, it has the functions of automatic reminder of abnormal data and intelligent reminder supplement of missing data to ensure the accuracy and completeness of data analysis. Finally, the user can view and download the complete analysis results through the third folder.

[0019] Supports off-site disaster recovery of data. By creating a third folder corresponding to the second folder and copying the analysis data into it, a multi-level data backup mechanism is provided. During the operation of the computing task, even if a computer room-level failure occurs, the integrity of the data can be guaranteed. The creation of the third folder and data replication provide an additional layer of security for the data, allowing rapid recovery in the event of damage or loss of the original data. By building the first and second association relationships, efficient data storage and management are achieved, ensuring that the sample data provided by the researcher can be safely stored and used under any circumstances.

[0020] According to the sample data provided by the researchers, data cleaning, preprocessing and multi-dimensional analysis can be automatically performed. When processing brain detection data, the configured detection software automatically generates analysis data, which are stored in the second and third folders according to the association relationship, and supports the generation of analysis results in multiple formats, such as csv, html, jpg, etc., for researchers to download. The automatic generation and diversified presentation of these results not only improves processing efficiency, but also facilitates the interpretation and application of results, meeting different research and clinical needs.

[0021] Additional aspects and advantages of embodiments according to the present invention will become apparent in the following description or may be learned through practice of embodiments according to the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] The above and / or additional aspects and advantages of the present invention will become apparent and easily understood from the description of the embodiments in conjunction with the following drawings, in which:

[0023] Figure 1 is a flow chart of the method steps of the present invention;

[0024] Figure 2 It is a schematic diagram of the detection data of the detection software running of the present invention;

[0025] Figure 3 A schematic diagram of a brain region analysis of the present invention;

[0026] Figure 4 Another schematic diagram of brain region analysis according to the present invention;

[0027] Figure 5 Another schematic diagram of brain region analysis according to the present invention;

[0028] Figure 6 Another schematic diagram of brain region analysis according to the present invention;

[0029] Figure 7 A schematic diagram of a control of cerebral cortex secretion substances of the present invention;

[0030] Figure 8 Another comparison diagram of the cerebral cortex secretion substance of the present invention is shown;

[0031] Fig. 9 It is a system structure logic block diagram of the present invention;

[0032] Fig.10 The figure is a schematic diagram of the structure of an electronic device of the present invention. DETAILED DESCRIPTION

[0033] In order to more clearly understand the above-mentioned purpose, features and advantages of the present invention, the present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be noted that the embodiments of the present application and the features in the embodiments can be combined with each other without conflict.

[0034] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Therefore, the protection scope of the present invention is not limited to the specific embodiments disclosed below.

[0035] See also Figure 1-Figure 10 , a method for detecting cerebral cortex secretion substances according to some embodiments of the present invention is described below.

[0036] The embodiment of the first aspect of the present invention provides a method for detecting a substance secreted by the cerebral cortex. In some embodiments of the present invention, Figure 1-Figure 8 As shown, the method comprises the following steps:

[0037] S101, creating a first folder based on the tester, storing the tester's brain test data in the corresponding first folder; and configuring a plurality of test software for processing the brain test data.

[0038] Here, a separate folder is created for each subject, and the brain test data related to each subject can be systematically organized and stored. This approach facilitates the management of large amounts of data and makes it easier to access and analyze the data of each subject individually; multiple software tools can be used to perform different data processing tasks, such as image reconstruction, functional analysis, structural mapping, etc. This allows for comprehensive analysis of brain data from multiple perspectives, providing more comprehensive diagnostic information.

[0039] As can be seen from the above, a top-level directory is created in a central storage location (such as a server or local hard disk), and the data of each tester is stored in a subdirectory. The subdirectory name is usually based on the tester's unique identifier, such as ID number, name abbreviation, etc.; a script (such as Python or Shell script) is used to automate the process of creating folders. The script reads the list of testers, generates a folder for each person, and moves or copies the corresponding data files to the correct location. Multiple testing software are integrated through scripts or data processing platforms, so that data can be automatically transferred from one application to another without manual intervention; set up data processing processes to ensure that data passes through each software in a predetermined order. For example, the original scan data is first processed using reconstruction software, then functional analysis is performed using analysis software, and finally a report is generated using visualization tools; the operating parameters of each software can be pre-configured, for example, setting the scan resolution, analyzing specific brain areas, etc., and these parameters can be adjusted according to specific clinical needs or research purposes.

[0040] In any of the above embodiments, the detection software includes Freesurfer software, AFNI software and LCModel software.

[0041] Freesurfer software was used to process MRI data to generate brain structural data.

[0042] AFNI software is used to process fMRI data to generate brain resting state analysis data, and to process DTI data to generate white matter fiber bundle data.

[0043] LCModel software was used to process MRS data to generate quantitative data on brain metabolite concentrations.

[0044] In this embodiment, three professional software tools are used to process and analyze multimodal imaging data of the brain: Freesurfer, used to process MRI (magnetic resonance imaging) data to generate high-precision brain structure data; AFNI, used to process fMRI (functional magnetic resonance imaging) data for resting-state analysis, and to process DTI (diffusion tensor imaging) data to generate white matter fiber bundle data; LCModel, used to process MRS (magnetic resonance spectroscopy) data to generate quantitative data of metabolite concentrations in the brain.

[0045] Freesurfer software processes high-resolution structural MRI images to generate brain structure data including cortical reconstruction, brain region segmentation, cortical thickness measurement, and brain surface model construction; uses image processing algorithms to remove non-brain tissue in MRI images, such as scalp and skull; corrects magnetic field inhomogeneity and motion artifacts to improve image quality; uses intensity thresholds and topological constraints to accurately locate the boundaries of gray matter and white matter; obtains cortical thickness data by calculating the distance from the gray matter and white matter interface to the cerebrospinal fluid interface; automatically labels and segments the cerebral cortex and subcortical structures based on anatomical templates and probabilistic models; constructs a three-dimensional model of the brain surface that can be used for visualization and further analysis; outputs include indicators such as cortical thickness, surface area, volume, and visualization images, which are used to display brain structure and labeling results.

[0046] AFNI software processes fMRI data for resting-state functional connectivity analysis, including time correction: correcting signal deviations caused by differences in acquisition time, head motion correction: adjusting image offsets caused by subject head motion, spatial normalization: mapping individual brains to standard space for group comparison, spatial smoothing: improving the signal-to-noise ratio of signals; physiological noise removal: removing the effects of physiological signals such as breathing and heartbeat, covariate regression: removing the effects of covariates such as head motion parameters. Correlation analysis: calculating the time series correlation between voxels in the whole brain, functional network construction: constructing a brain functional connection network based on correlation results, indicator calculation: extracting network features, such as degree centrality, clustering coefficient, etc.; brain map drawing: showing the spatial distribution of functional connections, statistical comparison: comparing differences between different conditions or groups at the group level.

[0047] AFNI software processes DTI data and generates white matter fiber bundles, including: image correction: correcting distortions caused by gradient nonlinearity and eddy current effects, head motion correction: similar to fMRI data, correcting head motion; diffusion tensor estimation: using the diffusion-weighted signal of each voxel to calculate the diffusion tensor, index calculation: generating images such as fractional anisotropy (FA) and mean diffusivity (MD); deterministic tracking: tracking fiber bundles along the direction of maximum diffusion according to the main direction of the tensor, probabilistic tracking: considering uncertainty, generating probability distribution of fiber bundles; white matter fiber bundle model: three-dimensional display of brain white matter connections, statistical analysis: comparing differences in white matter structure between different groups or conditions.

[0048] LCModel software is used to analyze magnetic resonance spectroscopy data and quantitatively measure the concentrations of various metabolites in the brain, such as NAA, creatine, choline, GABA, etc. Spectral correction: correct frequency drift and phase error. Baseline correction: remove background noise and baseline drift. Spectral fitting: use the standard spectrum of known metabolites as the basis to fit the actual measured spectrum through linear combination. Iterative optimization: adjust the contribution coefficient of each metabolite so that the fitting result best matches the actual spectrum. Concentration calculation: calculate the relative or absolute concentration of each metabolite based on the fitting coefficient. Uncertainty assessment: provide the standard error or confidence interval of each metabolite concentration. Report generation: includes information such as metabolite concentration, goodness of fit, residual, etc. Spectral graph: displays the original spectrum, fitted spectrum and residual curve.

[0049] S102, when a data processing command is received, a second folder is created according to the number of test subjects, and a first association relationship is established between the second folder and the first folder; analysis data is generated by processing the brain test data through the test software, and the analysis data is stored in the second folder according to the first association relationship; and the second folder currently executing the storage of analysis data is marked.

[0050] Here, wait for or receive instructions from the user or scheduler, start processing the stored brain detection data, create a corresponding second-level folder for each tester according to the number of testers, and store the processed analysis data; establish a correspondence between the first-level folder (raw data) and the second-level folder (analysis data), use professional software to process the raw brain detection data, generate analysis results, store the generated analysis data in the corresponding second-level folder according to the previously established association, and mark the status of the second-level folder being processed or completed for easy management and monitoring. As can be seen from the above, the data processing command can be manually entered by the user or a preset timed task; after receiving the command, the system will perform a preliminary check to ensure that all necessary data and resources are ready. Read the tester information (such as the tester ID or name) in the first folder, use a script or program to create a new folder for each tester in the specified directory, and the naming method is usually tester ID_processed or a similar format. Create a mapping file (such as .csv, .json or database table) to record the correspondence between the raw data folder and the analysis data folder of each tester. Ensure that during data processing and storage, the system can accurately match the analysis results with the corresponding testers.

[0051] Mapping file:

[0052] Detector ID Raw data path Analysis data path

[0053] A / data / raw / detectorA / data / processed / detectorA_processed

[0054] B / data / raw / detector B / data / processed / detector B_processed

[0055] Then call the software to process MRI data, generate brain structure data, process fMRI data, and perform resting state analysis; process DTI data, generate white matter fiber bundle data, process MRS data, and quantitatively analyze the concentration of metabolites in the brain; automate the processing, write scripts to automatically call the above software, process the data of each tester, and set the software processing parameters such as resolution and brain area selection according to research needs. Data processing procedures, such as head motion correction, spatial standardization, denoising, etc., such as cortical thickness calculation, functional connectivity analysis, metabolite quantification, etc., output analysis result files, such as images, tables, reports, etc.

[0056] Read the association mapping, the system refers to the mapping file to determine the storage location of the analysis data of each tester; data transfer, move or copy the analysis result file generated by the detection software to the corresponding second folder.

[0057] Status mark file: When processing starts, a mark file, such as processing.lock, is created in the corresponding second-level folder. After processing is completed, the processing.lock file is deleted and a completed.flag file is created. This prevents multiple processes from processing the same data at the same time. Managers can understand the processing progress by checking the mark file.

[0058] In any of the above embodiments, the analysis data includes at least one of brain structure data, brain resting state analysis data, white matter fiber bundle data, and quantitative data of brain metabolite concentration.

[0059] In this embodiment, brain structure data provides detailed images of brain anatomy, supporting the identification of structural changes in neurodegenerative diseases such as Alzheimer's disease; resting state analysis data analyzes the brain's network connections at rest, such as the default mode network, to help diagnose diseases associated with abnormal neural network function; white matter fiber tract data describes the white matter fiber paths in the brain, assessing the recovery process after neurodegenerative diseases and brain injuries. Brain metabolite concentration quantitative data provides concentration data of specific metabolites in the brain, which is used for early diagnosis of neurological diseases and monitoring of treatment effects.

[0060] Use MRI technology to obtain high-resolution images, and use software such as Freesurfer to perform image segmentation to automatically identify and quantify the volume and cortical thickness of each brain region; use fMRI technology to capture brain activity at rest, use software AFNI to calculate the correlation between time series data, and construct a functional connection map between brain regions; DTI technology measures the diffusion behavior of water molecules in the brain, and uses tools such as AFNI for data analysis, including diffusion tensor calculation and fiber tracking technology, to visualize and quantify white matter fiber bundles; MRS technology non-invasively measures the concentration of brain chemicals, and software such as LCModel accurately fits the spectrum analysis to obtain the estimated value of metabolite concentration.

[0061] Specifically, the first association relationship is established through the following steps:

[0062] According to the input and output calculation relationship of each detection software, a first mapping relationship between the data types included in the analysis data and the data types included in the brain detection data is obtained.

[0063] Through the first mapping relationship, a second mapping relationship in address information of each data partition between the first folder and the second folder is obtained.

[0064] All second mapping relationships are taken as first association relationships.

[0065] In response to the above specific description, a direct mapping relationship between the input and output of the detection software is established. This step ensures that each type of analysis data can accurately correspond to the original brain detection data, which enables users to track the data processing flow and understand how each analysis result is calculated from the specific original data. This is key to data transparency and credibility in scientific research verification and clinical diagnosis. Based on the first mapping relationship, the file-level data relationship is further mapped to a specific storage address to ensure that the physical organization of the data is consistent with the logical organization. This helps to improve the efficiency of data retrieval and reduce the time of data access, especially when processing large-scale data sets.

[0066] As can be seen from the above, the input and output data types of each detection software are analyzed. For example, MRI scan data may be converted into structured brain image data; fMRI data can be mapped to functional connection diagrams through processing, automatically identifying the types of data processed by each detection software, and recording the source and structure of these data to form a mapping table; the data logical relationship defined in the first mapping relationship is converted into a specific storage path mapping. This involves the physical storage location of the data, such as the folder path on a specific server, developing an automated script or using a database management system to bind the logical mapping relationship to the physical storage address, such as storing the original MRI data and the corresponding structured image data in connected but independent folders; the second mapping relationships corresponding to all individual data are aggregated to form a complete first association relationship table. This table is the core of data management and is used to quickly find and update data relationships. By regularly updating and maintaining this association relationship table, the accuracy and latestness of the data relationship are ensured, especially when the data set is frequently updated or expanded.

[0067] Specifically, the steps for LCModel software to process MRS data to generate quantitative data of brain metabolite concentrations include:

[0068] The MRS data were divided into standard MRS data and GABA MRS data;

[0069] Configure the marked function modules in the LCModel software to process standard MRS data or GABA MRS data to obtain table files;

[0070] Traverse each value in the table file and output it as an excel file;

[0071] The numerical values ​​contained in the excel file were quantitatively analyzed through the marked functional modules in the LCModel software to obtain the quantitative data of metabolite concentrations in the brain.

[0072] In response to the above specific description, the functions of the LCModel software are mainly focused on processing magnetic resonance spectroscopy (MRS) data to generate detailed quantitative data on brain metabolite concentrations. First, the software is responsible for subdividing the MRS data into standard MRS data and specific MRS data such as GABA, which allows for specialized analysis of different metabolites to ensure the accuracy and pertinence of the analysis. Once the data is classified, LCModel processes the data by configuring its built-in functional modules to generate table files (table files), which record the preliminary concentration information of different metabolites. In addition, the software also has the function of converting table files to a more general Excel file format, making the data more easily accessible and further analyzed by other non-professional software. Finally, LCModel uses its advanced quantitative analysis function module to deeply process the data in the Excel file and accurately calculate the concentration of each metabolite in the brain.

[0073] As can be seen above, the operating principle of LCModel software is based on highly specialized algorithms to process and analyze MRS data. The software first divides the received MRS data into standard MRS data or specific MRS data such as GABA according to its spectral characteristics through a built-in classification system. This step is achieved through pre-defined spectral feature pattern matching to ensure that each data type can receive the most suitable analysis processing. Subsequently, the software loads the appropriate configuration file and adjusts the analysis parameters such as spectral fitting range and baseline correction method according to the data type to optimize the processing process. The processed data is converted into a table file. This process involves converting complex spectral data into a more concise tabular form for preliminary analysis. Next, an automated conversion tool is used to convert the table file format to Excel format, so that the data can be used in a variety of environments, including further statistical analysis and graphical representation of the data. Finally, LCModel performs a detailed quantitative analysis of the data in the Excel file and uses statistical and mathematical models to accurately estimate the concentration of each metabolite. These calculations include signal denoising, intensity correction, and a variety of advanced mathematical processing such as least squares method to ensure the accuracy and reliability of the final results.

[0074] Specifically, the steps of quantitatively analyzing the values ​​contained in the excel file through the functional modules of the LCModel software include:

[0075] Configure the corresponding operation module according to the type of standard MRS data and GABA MRS data;

[0076] The running module calls the marked function modules in the LCModel software to process the Excel file and generate quantitative data;

[0077] Configure the character string used for filtering, and use the character string to filter out the quantitative data of brain metabolite concentration from the quantitative data.

[0078] In response to the above specific description, the advanced functions of the LCModel software include processing and analyzing MRS data input through Excel files to generate accurate quantitative data of brain metabolite concentrations. This process begins by configuring the software to select the appropriate operating module based on the data type (standard MRS data or specific MRS data such as GABA). These modules are specifically optimized for different types of data to ensure the accuracy and adaptability of the analysis. Once the appropriate module is activated, LCModel processes the values ​​in the Excel file through its functional modules to generate preliminary quantitative data. In addition, the software provides a function to configure strings for filtering, allowing users to filter out specific brain metabolite concentration information from the generated quantitative data as needed. These filtered data are detailed chemical analysis results for specific research or clinical needs, providing a basis for further diagnosis or research. 。

[0079] As mentioned above, the LCModel software contains multiple predefined operating modules, each of which is optimized for processing a specific type of MRS data. These modules contain different algorithms and parameter settings. For example, specific spectrum analysis techniques are used to improve the detection accuracy of GABA signals for GABA data. Users select the module suitable for their data type through the software interface. This selection is based on the preprocessing information of the MRS data or the experimental design.

[0080] The selected run module will be applied to the values ​​stored in the Excel file. These values ​​represent the MRS data after preliminary processing, such as spectral intensity. LCModel uses complex mathematical models to analyze these data, including peak detection, baseline correction and integral calculation to estimate the concentration of metabolites. The software automatically reads the Excel file, performs the necessary mathematical and statistical analysis, and outputs quantitative data containing estimated metabolite concentrations.

[0081] After the quantitative analysis is complete, LCModel provides a feature to filter specific results by string matching. This step allows the user to focus on the metabolites that are most critical to their research or diagnosis. The user defines a set of strings that correspond to the metabolites of interest (such as "NAA", "GABA"). The software traverses the quantitative data and uses these strings to identify and extract relevant data entries.

[0082] In any of the above embodiments, the step of traversing each value in the table file and outputting it as an Excel file specifically includes:

[0083] The values ​​contained in the table file are exported into an Excel file according to the metabolite name and the content of the metabolite in the subject.

[0084] In this embodiment, the main function of the conversion process is to format and output the data in the table file generated by the LCModel software into an Excel file according to the metabolite name and its concentration information in the subject. Doing so makes the data easier to access, analyze and share. The Excel file format is widely used in scientific research, clinical research and data reporting, and supports complex data processing and visualization; researchers can use the powerful functions of Excel to further analyze the data, such as statistical analysis, chart production, etc. Clinicians and researchers can directly extract data from Excel files to produce scientific research or clinical reports. The versatility of the Excel file format makes data easier to share between different research groups.

[0085] First, the data must be read from the table file generated by the LCModel software. This step usually involves parsing the file content, identifying the metabolite names and corresponding concentration values; using a programming language or script (such as Python, R or VBA, etc.) to read the table file. These scripts are able to identify key information, such as metabolite names and concentration values, and extract this information.

[0086] The extracted data needs to be formatted into a format suitable for export to Excel. This usually involves organizing the data into rows and columns, with each row representing a subject and each column representing the concentration of a metabolite. The script will create data structures such as lists or dictionaries in a pre-set format and then convert these data structures into a table format.

[0087] Once the data is formatted in a tabular format, the next step is to write this data into an Excel file. This requires ensuring the accuracy of the data and adaptability of the format. Use a data processing script or library (such as Python's pandas library combined with ExcelWriter, or R's write.xlsx function) to create a new Excel file and fill it with the tabular data. You can set the cell format, define the header and column names, etc. in this step.

[0088] S103, when a data upload command is received, a third folder corresponding to the second folder is created, and the analysis data in each marked second folder is copied to the corresponding third folder respectively; a second association relationship is constructed between the analysis data within the third folder, and the analysis data in the third folder is marked by the second association relationship and the first association relationship.

[0089] Here, in response to the data upload command, the system will first create a corresponding third folder for each marked second folder. This step is mainly used for data backup or preparation for upload to ensure the security and integrity of the data during migration or sharing. Once the third folder is created, the system will automatically copy the analysis data from the second folder to the corresponding third folder, which not only ensures the consistency of the data, but also provides the necessary data redundancy for subsequent operations. Next, the system will build a second association relationship between the analysis data in the third folder. This is to maintain the organizational logic of the data and facilitate users to quickly understand and retrieve the dependencies between the data. Finally, the system uses the established first and second association relationships to mark the analysis data, enhance the traceability of the data, and support complex data management requirements, such as version control, data integrity verification, etc.

[0090] As can be seen from the above, after receiving the data upload command, the folder creation process is automatically triggered by the preset file management script or API. This process uses the existing second folder information (such as folder name or path) to generate a new third folder in the specified data storage location. The naming and structure of each third folder strictly correspond to its related second folder to maintain the logical consistency of the data. Subsequently, the system performs a data copy operation, which is usually completed through efficient file operation commands to ensure that the data migration from the second folder to the third folder is fast and safe. After the data copy is completed, the system will automatically build data association relationships, which involves recording the logical connections between various data files in a database or a specific metadata file to facilitate the further use and management of the data. Finally, the system adds metadata tags to each file or file group through a script. These tags include but are not limited to the source of the data, processing date, version number, etc., to facilitate future data auditing and retrospective analysis. The entire process is highly automated, which greatly reduces the burden of manually managing large-scale data sets and improves the efficiency of data processing and management. .

[0091] Specifically, the second folder and the third folder are associated with each other through the name of the detector, and the second association relationship is constructed through the following steps.

[0092] According to the control requirements of the analysis data in the analysis of cerebral cortex secretion substances, the calibration correlation between the data types included in the analysis data is obtained.

[0093] The third mapping relationship between the data partitions in the third folder on the address information is obtained through calibration association.

[0094] All third mapping relationships are taken as second association relationships.

[0095] In view of the above specific description, the purpose of constructing the second association relationship is to provide a systematic method to manage and index a large amount of analysis data. This process first involves associating the second and third folders through the name header of the tester. The purpose of this is to ensure that the analysis data of each tester can be accurately tracked and stored. Next, in order to meet the control requirements of the analysis data of cerebral cortical secretions, the system will establish calibration associations. These associations are based on logical and functional dependencies between data types. Through the calibration association, the system can determine the precise address of each data partition in the third folder. This precise mapping is to optimize the data access path and improve the efficiency of data processing. Finally, all these mapping relationships are summarized into the second association relationship. This relationship structure is used to support complex data queries and ensure the integrity and accessibility of the data.

[0096] As can be seen from the above, after receiving the command to create a folder and migrate data, a series of operations will be automatically performed to build and apply the second association relationship. Specifically, the system first associates the second folder and the third folder based on the name header of the tester, which is usually done by recording the correspondence between the two folders in a database or file system. Next, in order to meet the control requirements of the analysis data, the system will identify and record the various types of data contained in the analysis data, such as specific chemical concentrations or biomarker data. The system then constructs calibration associations through pre-defined logical rules that are based on the intrinsic relationship of the data content. For example, the concentration data of a certain chemical may need to be analyzed together with specific clinical parameter data.

[0097] Once the calibration association is established, the exact storage location of each data partition in the third folder is determined based on these associations to generate a third mapping relationship. This mapping relationship is usually stored in the database in the form of a key-value pair, where the key is the data type or data identifier and the value is the specific address of the data in the file system. Finally, all these mapping relationships are summarized into a comprehensive second association relationship, which is used by the system to manage the storage, retrieval and update of all data.

[0098] In this way, the second association not only improves the efficiency of data management, but also enhances the accuracy of data analysis by ensuring that the logical relationships between data are correctly maintained. This approach is particularly suitable for research projects that need to deal with large-scale, multi-type data sets.

[0099] Specifically, the calibration associations include:

[0100] Brain structural data can be used to calibrate fMRI data for spatial normalization and regional analysis of the brain.

[0101] Brain structural data can also be calibrated with white matter fiber bundle data for brain structure and white matter pathway analysis.

[0102] Brain structural data and fMRI data can be used together to calibrate quantitative data of metabolite concentrations in the brain for metabolic, structural and functional analysis of the brain.

[0103] In response to the above specific description, brain structural data (usually obtained from MRI scans) provide detailed brain anatomical images that are used as spatial references to help standardize functional signals in fMRI data so that they can be compared between individuals. In addition, brain structural data define different regions of the brain, which is necessary to localize functional activity in fMRI data to specific anatomical structures, allowing researchers to accurately determine the specific brain regions of functional activity, as well as analyze and compare functional differences between different individuals.

[0104] Brain structural data provides clear boundaries between white matter and gray matter, which helps analyze white matter fiber bundle data generated by DTI. Using brain structural data as a guide, white matter pathways within the brain can be traced and mapped more accurately, thereby revealing the structural connections between brain regions. This analysis helps understand the brain's network connections and how they change in disease states.

[0105] Combining brain structure and function data can provide anatomical and functional context for metabolite concentration data in the brain. This multimodal data integration can help researchers better understand how specific metabolic changes are associated with structural and functional characteristics of the brain, and help study the mechanisms of neurodegenerative diseases (such as Alzheimer's disease), abnormal brain metabolism, and other brain dysfunctions.

[0106] As can be seen from the above, all imaging data first undergo necessary preprocessing steps, including denoising, motion correction, spatial normalization, etc., to ensure data quality and comparability; using brain structure data as a template, other types of data (such as fMRI and DTI data) are aligned to a unified coordinate system through spatial registration technology to ensure that data from different modalities match at the same spatial position; through specialized software tools and algorithms, the processed data are fused at the pixel or voxel level, allowing researchers to analyze structural, functional and metabolic data at the same spatial position; finally, these integrated data are comprehensively used for analysis, and biological significance is extracted through statistical and machine learning methods to generate scientific research or clinical reports.

[0107] S104, clearing the marks of all second folders, and outputting the selected third folder as the detection result.

[0108] Here, all tags set during data processing and analysis are eliminated or cleared, which usually includes data processing status, version information, or any tags used for temporary classification and sorting. This step is usually performed before data preparation enters the final review or release stage to ensure that the output data does not carry process tags that may cause misunderstandings, keeping the data presentation clear and professional; a third folder is selected from possible multiple candidate folders as the final data output location, which is usually based on data integrity, latest or other quality standards. The selected folder will be used to store the final reviewed and ready-to-release data, which is suitable for report generation, data sharing or further analysis.

[0109] As mentioned above, tags are managed in the file system or database, and these tags may be stored in the form of metadata, database entries, or file attributes. The process of eliminating tags involves accessing these metadata and removing or clearing the relevant attributes, using automated scripts or manual management tools to scan all files and subfolders in the second folder, identify all tags, and delete them. This requires ensuring the permissions and security of the operation to avoid accidentally deleting important data; the process of selecting the output folder is usually based on a series of predefined criteria, such as data integrity checks, last modification dates, or specific data quality indicators, evaluating all third folders, and determining the most suitable output folder through automated quality check scripts or manual review. Once selected, the relevant data will be copied or moved to the folder in preparation for final output.

[0110] The present invention provides a method for detecting cerebral cortex secretion substances, which creates a separate first folder for each tester to store their brain detection data, and this scheme ensures orderly and systematic management of the data. Subsequently, a second folder is created according to the number of testers and an association relationship is established, and a third folder is created after data processing and the analysis data is copied. These steps increase the transparency of data operations, allowing researchers to clearly track the data flow path and its processing status.

[0111] Supports off-site data disaster recovery to ensure that data integrity is not affected in the event of a major hardware failure. The creation of a third folder and data replication provides an additional layer of security for the data, allowing for rapid recovery in the event of any original data corruption or loss. In addition, by processing data in parallel in a multi-disk system, the capabilities of the GPU computing cluster can be maximized, accelerating the data processing process and reducing data waiting and processing time.

[0112] It can automatically perform data cleaning, preprocessing and multi-dimensional analysis, and generate result formats (such as csv, html, jpg) that are easy to understand and apply, which not only improves processing efficiency, but also optimizes the presentation and use of results. In addition, it also has the functions of automatic reminder of abnormal data and intelligent reminder of missing data. These intelligent reminders ensure the accuracy and completeness of data analysis and help researchers to discover and solve possible data problems in a timely manner.

[0113] Clearing all the second folder marks and selecting the third folder as the final test result output simplifies the final review and release process of the data and ensures the accuracy of the output data. The final data output is the selected third folder, which makes data distribution and sharing easier and more controllable.

[0114] In any of the above embodiments, the brain detection data includes MRI data, DTI data, fMRI data and MRS data of the brain, and each functional module of the detection software that processes the brain detection data is marked.

[0115] A plurality of data partitions are defined in the first folder, the second folder and the third folder respectively, and a unique partition number is assigned to each data partition, and address information of each data partition is determined.

[0116] In this embodiment, each type of brain detection data (MRI, DTI, fMRI and MRS) is stored in a first folder that is labeled separately, and the data processed by each detection software is further labeled in these folders through functional modules. Such organization makes the data easy to identify and access, ensures that the data is organized in a way that meets the needs of processing and analysis, and provides a clear tracking and backtracking path; multiple data partitions are created in the first, second and third folders, each partition is assigned a unique partition number and the specific address of each data partition is determined. This structured data storage method improves the access efficiency and security of data, supports efficient data management, and ensures that each piece of data can be accurately located and processed, especially when a large amount of data is involved.

[0117] Through automated scripts or manual configuration, unique storage paths and tags are created for each data type and processing module. These paths and tags are designed according to the source and type of data, and a database management system or file management tool is used to create and maintain the folder structure, while the detailed information of each file and folder is recorded through metadata or database; the first, second, and third folders represent different stages of data processing, and the data partitions within each folder are managed by unique labels and clear address information. This multi-level and partitioned design helps to achieve step-by-step processing and safe storage of data, configure partitions for each folder in the file system, and record the label and address of each partition in the database. This information is used for data storage, retrieval, and migration; by defining clear address information on each data partition, the system can quickly locate data and support concurrent access and processing. It helps to use high-performance computing resources such as GPU computing clusters to process large-scale data, achieve efficient storage and fast access to data, especially in computing tasks that require frequent read and write operations, and optimize performance and throughput through multi-disk parallel operations.

[0118] In any of the above embodiments, data partitions are defined based on the types of data contained in the brain detection data and analysis data.

[0119] In this embodiment, by partitioning the data according to the types of brain detection data and analysis data, data storage can be organized more specifically, making data access and processing more efficient. Each data type (such as MRI, DTI, fMRI and MRS data) and its corresponding analysis results are assigned to a dedicated data partition to facilitate specific data queries and operations. This organization method helps researchers quickly find the required data type and perform data processing and analysis, especially when facing large and diverse data sets; specific to each data type partition of brain detection data, it can support more complex data analysis needs, such as multimodal analysis or cross-validation research. Accurate partitioning of data types also helps to apply specific analysis tools and algorithms, thereby improving the accuracy and efficiency of analysis. For example, MRI data partitioning can be used specifically for structural analysis, while fMRI data partitioning focuses on functional analysis. This partitioning method can effectively support various specialized needs.

[0120] Data partitioning is based on the type and source of data. Each type of brain detection data and its derived analytical data are systematically stored in predefined partitions by type. These partitions are usually pre-set in the logical structure of data storage and implemented through a file system or database management system. When new data is imported into the system, the data management system will automatically archive it to the corresponding partition based on the metadata tag of the data (such as the data type tag). For example, all fMRI data and related analysis results will be stored in the fMRI data partition; by maintaining clear data partitions, the system can quickly respond to data queries from researchers. Each partition can be independently indexed to improve retrieval speed and data processing efficiency. When researchers or analysis tools request specific types of data, the data management system can directly locate the corresponding data partition and quickly provide the required data. This process is supported by efficient database query operations to ensure rapid response.

[0121] The second aspect of the present invention provides a system 2 for implementing the method for detecting cerebral cortex secretion substances in any of the above embodiments, such as Fig. 9 As shown, the system 2 includes:

[0122] The data management module 201 is used to create an exclusive first folder for each examinee in the system, store the brain detection data (including MRI, DTI, fMRI and MRS data) of each examinee in the corresponding first folder, configure multiple detection software (such as Freesurfer, AFNI, LCModel) for processing brain detection data, and set corresponding parameters and paths for them in the system.

[0123] The data processing module 202 is used to create a corresponding second folder for each tester according to the number of testers, establish a mapping relationship between the first folder and the second folder to facilitate data tracking and management, call the configured detection software, process brain detection data, generate analysis data, and store the analysis data in the corresponding second folder according to the first association relationship, and mark the second folder currently being executed.

[0124] The data upload and association construction module 203 is used to create a corresponding third folder for each marked second folder, copy or move the analysis data in each marked second folder to the corresponding third folder, establish an association relationship between the analysis data in the third folder (such as the association between brain structure data and functional data), and use the first and second association relationships to mark the analysis data in the third folder to facilitate data management and subsequent query.

[0125] The result output module 204 is used to remove the marking status of all second folders, release system resources, select a specific third folder as the final test result output as needed, export the selected test result in a specified format for user download or further analysis, and record the log of the entire data processing process, and notify relevant personnel that the results have been generated.

[0126] The present invention provides a system in which each module corresponds to a specific function, which facilitates system maintenance and expansion; the entire process from data uploading to result output is highly automated, reducing manual intervention; through multi-level folders and association relationships, orderly storage and rapid access to data are ensured; result output in multiple data formats is supported to meet different analysis and application requirements; and comprehensive support for the detection method of cerebral cortex secretion substances is achieved, ensuring the efficiency, accuracy and reliability of data processing.

[0127] Embodiments of the third aspect of the present invention provide electronic devices. In some embodiments of the present invention, such as Fig.10 As shown, an electronic device is provided, which includes: electronic devices such as desktop computers, notebooks, handheld computers, and cloud servers. The electronic device 3 may include but is not limited to a processor 301 and a memory 302. Those skilled in the art will understand that Fig.10 The electronic device 3 is merely an example and does not limit the electronic device 3 , and may include more or less components than those shown in the figure, or different components.

[0128] The processor 301 can be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc.

[0129] The memory 302 may be an internal storage unit of the electronic device 3, for example, a hard disk or memory of the electronic device 3. The memory 302 may also be an external storage device of the electronic device 3, for example, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), etc. equipped on the electronic device 3. The memory 302 may also include both an internal storage unit of the electronic device 3 and an external storage device. The memory 302 is used to store computer programs and other programs and data required by the electronic device.

[0130] The embodiment of the fourth aspect of the present invention proposes a computer-readable storage medium. In some embodiments of the present invention, a computer-readable storage medium is provided, and when the computer-readable storage medium is executed by the processor 301, the steps of the above method are implemented. Therefore, the computer-readable storage medium provided by the third aspect of the present invention has all the technical effects of the above steps, which will not be repeated here.

[0131] The technicians in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In practical applications, the above-mentioned function allocation can be completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated in a processing unit, or each unit can exist physically separately, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, which will not be repeated here.

[0132] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this disclosure.

[0133] In the embodiments provided in the present disclosure, it should be understood that the disclosed devices / electronic devices and methods can be implemented in other ways. For example, the device / electronic device embodiments described above are merely schematic. For example, the division of modules or units is only a logical function division. There may be other division methods in actual implementation. Multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed may be through some interfaces, indirect coupling or communication connection of devices or units, which may be electrical, mechanical or other forms.

[0134] If the integrated module / unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present disclosure implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. The computer program may include computer program code, and the computer program code may be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying computer program code, recording medium, U disk, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal and software distribution medium. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, the computer-readable medium does not include electric carrier signals and telecommunication signals.

[0135] The above embodiments are only used to illustrate the technical solutions of the present disclosure, rather than to limit them. Although the present disclosure has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present disclosure, and should all be included in the protection scope of the present disclosure.

Claims

1. A method for detecting substances secreted by the cerebral cortex, characterized in that: The steps include: A first folder is created for each tester, and the brain test data of the tester is stored in the corresponding first folder; a plurality of test software for processing the brain test data is configured, wherein the brain test data includes MRI data, DTI data, fMRI data and MRS data of the brain, and a functional module for processing the brain test data of each test software is marked; When receiving a data processing command, creating a second folder according to the number of the detectors, and establishing a first association relationship between the second folder and the first folder; Processing the brain detection data by the detection software to generate analysis data, and storing the analysis data in the second folder according to the first association relationship; Marking the second folder where the currently executed storage analysis data is stored; When a data upload command is received, a third folder corresponding to the second folder is created, and the analysis data in each marked second folder is copied to the corresponding third folder respectively, and a plurality of data partitions are defined in the first folder, the second folder and the third folder respectively, and a unique partition number is assigned to each of the data partitions, and the address information of each of the data partitions is determined; the data partitions are defined by the data types included in the brain detection data and the analysis data, and the first association relationship is constructed by the following steps: according to the input and output calculation relationship of each of the detection software, a first mapping relationship between the data types included in the analysis data and the data types included in the brain detection data is obtained; through the first mapping relationship, a second mapping relationship between the address information of each of the data partitions in the first folder and the second folder is obtained; all the second mapping relationships are used as the first association relationship; A second association relationship between the analysis data in the third folder is constructed, and the analysis data is marked in the third folder by the second association relationship and the first association relationship; the second folder and the third folder are associated by the name of the tester, and the second association relationship is constructed by the following steps: according to the analysis data control requirements in the analysis of cerebral cortex secretion substances, a calibration association between the data types included in the analysis data is obtained; a third mapping relationship between the address information of each data partition in the third folder is obtained by the calibration association; all the third mapping relationships are used as the second association relationship; All marks of the second folders are removed, and the selected third folder is output as the detection result.

2. The method for detecting cerebral cortex secretion substances according to claim 1, characterized in that: The detection software includes Freesurfer software, AFNI software and LCModel software; The Freesurfer software is used to process the MRI data to generate brain structure data; The AFNI software is used to process the fMRI data to generate brain resting state analysis data, and to process the DTI data to generate white matter fiber bundle data; The LCModel software is used to process the MRS data to generate quantitative data of metabolite concentrations in the brain.

3. The method for detecting cerebral cortex secretion substances according to claim 2, characterized in that: The analysis data includes at least one of the brain structure data, the brain resting state analysis data, the white matter fiber bundle data, and the brain metabolite concentration quantitative data.

4. The method for detecting cerebral cortex secretion substances according to claim 3, characterized in that: The calibration association comprises: The brain structure data can calibrate the fMRI data to perform spatial normalization and regional analysis of the brain; The brain structure data can also calibrate the white matter fiber bundle data to perform brain structure and white matter path analysis; The brain structure data and the fMRI data can be used together to calibrate the quantitative data of metabolite concentrations in the brain to perform metabolic, structural and functional analysis of the brain.

5. The method for detecting cerebral cortex secretion substances according to claim 4, characterized in that: The step of processing the MRS data by the LCModel software to generate the quantitative data of the brain metabolite concentration specifically includes: The MRS data are divided into standard MRS data and GABA MRS data; Configure the marked function modules in the LCModel software to process standard MRS data or GABA MRS data to obtain a table file; Traverse each value in the table file and output it as an excel file; The numerical values ​​contained in the Excel file are quantitatively analyzed by the functional modules marked in the LCModel software to obtain the quantitative data of the metabolite concentration in the brain.

6. The method for detecting cerebral cortex secretion substances according to claim 5, characterized in that: The step of quantitatively analyzing the values ​​contained in the Excel file through the functional modules marked in the LCModel software specifically includes: Configure corresponding operation modules according to the types of the standard MRS data and the MRS data of GABA; Calling the function module marked in the LCModel software through the operation module to process the Excel file and generate quantitative data; A character string for screening is configured, and the quantitative data of the concentration of metabolites in the brain is screened out from the quantitative data by using the character string.

7. The method for detecting cerebral cortex secretion substances according to claim 6, characterized in that: The step of traversing each value in the table file and outputting it as an Excel file specifically includes: The numerical values ​​contained in the table file are output into an Excel file according to the metabolite name and the content of the metabolite.

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