Auxiliary analysis method, device and equipment for neural circuit injury position and medium

Abstract

The invention provides an auxiliary analysis method, device and equipment for a neural circuit injury position and a medium, and relates to the field of medical image processing, and the method comprises the steps: obtaining whole brain sample data, including fMRI data and DTI data of a healthy sample and a tested sample; selecting a seed point based on the fMRI data, establishing whole brain function connection based on the seed point, and extracting a time-varying connection coefficient sequence between the seed point and the region of interest; based on the DTI data, determining a connection fiber between the seed point and the region of interest through probabilistic fiber tracking, obtaining a target liposome representing the connection fiber and a surrounding region thereof, and obtaining a white matter bold signal sequence of the target liposome; and performing difference analysis on the connection coefficient sequence and the white matter bold signal sequence of the healthy sample and the tested sample to obtain the abnormal voxel of the tested sample, and determining the neural circuit injury position according to the coordinate of the abnormal voxel, thereby providing important information for the research of neural activity.

Description

Technical Field

[0001] The present invention relates to the technical field of medical image processing. Specifically, it relates to a method, device, equipment and medium for assisting in analyzing the location of nerve loop damage. Background Technique

[0002] Nerve loops play an important role in brain function and behavior. MRI (Magnetic Resonance Imaging), as the main evaluation method for neural network damage, provides effective support for the diagnosis of neurodegenerative diseases. Existing disease diagnosis and evaluation use fMRI (Functional Magnetic Resonance Imaging) to analyze the functional connection strength between different brain regions to determine whether the nerve loop between two brain regions is damaged. However, the current method cannot specifically analyze the location of nerve loop damage. Summary of the Invention

[0003] The purpose of the present invention is to provide a method, device, equipment and medium for assisting in analyzing the location of nerve loop damage to improve the above problems. To achieve the above purpose, the technical solutions adopted by the present invention are as follows: In the first aspect, the present application provides a method for assisting in analyzing the location of nerve loop damage, including: Obtaining whole-brain sample data, where the whole-brain sample data includes fMRI data and DTI data of healthy samples and subject samples; Based on the fMRI data, selecting seed points and establishing a whole-brain functional connection based on the seed points, and extracting a sequence of connection coefficients that vary with time between the seed points and regions of interest; Based on the DTI data, determining the connecting fibers between the seed points and regions of interest through probabilistic fiber tracking, obtaining target white matter voxels representing the connecting fibers and their surrounding regions, and obtaining a white matter bold signal sequence of the target white matter voxels; Performing a difference analysis on the connection coefficient sequences and white matter bold signal sequences of healthy samples and subject samples to obtain abnormal voxels of the subject samples, and determining the location of nerve loop damage according to the coordinates of the abnormal voxels.

[0004] In the second aspect, the present application provides a device for assisting in analyzing the location of nerve loop damage, including: A data acquisition module for obtaining whole-brain sample data, where the whole-brain sample data includes fMRI data and DTI data of healthy samples and subject samples; A functional connection module for selecting seed points based on the fMRI data and establishing a whole-brain functional connection based on the seed points, and extracting a sequence of connection coefficients that vary with time between the seed points and regions of interest; A target extraction module, configured to determine connecting fibers between seed points and regions of interest based on the DTI data through probabilistic fiber tracking, obtain target white matter voxels representing the connecting fibers and their surrounding regions, and acquire white matter bold signal sequences of the target white matter voxels; An analysis module, configured to perform differential analysis on the connection coefficient sequences and white matter bold signal sequences of healthy samples and subject samples to obtain abnormal voxels of the subject samples, and determine the positions of nerve loop injuries according to the coordinates of the abnormal voxels.

[0005] In a third aspect, the present application further provides an auxiliary analysis device for the position of nerve loop injury, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the above-mentioned auxiliary analysis method for the position of nerve loop injury are implemented.

[0006] In a fourth aspect, the present application further provides a readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the above-mentioned auxiliary analysis method for the position of nerve loop injury are implemented.

[0007] The beneficial effects of the present invention are as follows: By studying the changes in the connection coefficients between brain regions and the bold signals of white matter fibers, the present application finds the white matter fiber regions where the bold signals are significantly reduced when the connection coefficients are abnormal, thereby assisting in determining the injury positions and providing important information for the study of nerve activities.

[0008] Other features and advantages of the present invention will be described in the subsequent description, and some of them will become obvious from the description, or can be understood by implementing the embodiments of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the drawings required for the embodiments. It should be understood that the following drawings only show some embodiments of the present invention, and therefore should not be regarded as limiting the scope. For those of ordinary skill in the art, other related drawings can be obtained based on these drawings without creative efforts.

[0010] Figure 1 It is a flowchart of the auxiliary analysis method for the position of nerve loop injury according to an embodiment of the present application; Figure 2 It is a schematic structural diagram of the auxiliary analysis device for the position of nerve loop injury according to an embodiment of the present application; Figure 3 It is a schematic structural diagram of the auxiliary analysis device for the position of nerve loop injury according to an embodiment of the present application.

[0011] Markings in the figure: 100 - Data acquisition module; 200 - Functional connection module; 300 - Target extraction module; 400 - Analysis module; 410 - First processing unit; 420 - Second processing unit; 430 - Recognition unit; 800 - Auxiliary analysis device for the location of nerve circuit damage; 801 - Processor; 802 - Memory; 803 - Multimedia component; 804 - I / O interface; 805 - Communication component. Detailed implementation mode

[0012] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. The components of the embodiments of the present invention usually described and illustrated in the accompanying drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the claimed present invention, but merely represents selected embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts belong to the scope of protection of the present invention.

[0013] It should be noted that: Similar reference numerals and letters represent similar items in the following drawings. Therefore, once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings.

[0014] Resting-state functional magnetic resonance imaging rs-fMRI based on the principle of blood oxygen saturation dependence has the advantages of convenient data acquisition, no radiation, and low cost compared with PET imaging. This non-invasive examination method will not cause any harm to patients, so it is suitable for long-term follow-up observation and evaluation of MCI patients. Through the quantitative analysis of rs-fMRI, the degree of neural network damage can be accurately evaluated, providing an important basis for the early diagnosis and treatment of MCI.

[0015] Rs-fMRI images can also be combined with a computer-aided diagnosis system to outline regions of interest through automated or semi-automated techniques and provide quantitative measurement functions, improving the accuracy and efficiency of evaluation. MRI can also be multi-modally fused with other imaging techniques (such as PET positron emission tomography, DTI diffusion tensor imaging, etc.) to comprehensively evaluate the structural and functional status of the olfactory cortex neural network in MCI patients, providing more comprehensive information for the diagnosis and treatment of the disease. This application proposes a method for assisting in analyzing the location of nerve circuit damage based on fMRI and DTI imaging techniques.

[0016] Embodiment 1: See Figure 1, this application provides an auxiliary analysis method for the location of nerve loop damage, including steps S100, S200, S300, and S400; Step S100: Obtain whole-brain sample data, where the whole-brain sample data includes fMRI data and DTI data of healthy samples and subject samples; The fMRI data includes MRI images of the skull. MRI images are usually stored in DICOM format and need to be converted into a format convenient for subsequent processing, such as.nii format. After that, processing operations such as head motion correction, skull stripping, and non-uniformity correction are also required; Step S200: Based on the fMRI data, select seed points and establish whole-brain functional connections based on the seed points, and extract the sequence of connection coefficients that change over time between the seed points and regions of interest; First, it is necessary to preprocess the fMRI data, including the steps of: Eliminate unstable images in the MRI images, such as the first 4 - 8 images, and register the remaining MRI images with a standard brain template, that is, align the same anatomical structures on different images for comparison and analysis to obtain registered images; Use a Gaussian kernel to smooth the registered images, perform denoising processing with band-pass filtering, and then perform multiple regression processing with interfering covariates to obtain preprocessed fMRI data; DTI data also needs to be preprocessed similarly, such as format conversion, head motion correction, diffusion tensor estimation, image segmentation, and noise removal, and finally obtain preprocessed DTI data.

[0017] Specifically, the fMRI data contains the coordinates of each voxel and the time series data of the bold signal corresponding to each voxel; the region of interest (ROI) can be defined as a spherical region centered on certain coordinates, or can be defined by certain brain regions corresponding on the brain atlas; custom seed points usually need to determine the coordinates of the seed points based on prior knowledge or specific research hypotheses. For example, for olfactory function damage in early Alzheimer's disease, existing research has found that it is related to nerve loop damage between the piriform cortex (PCx) and the subiculum (IL). That is, the piriform cortex is used as the seed point to extract the bold signal within the seed point.

[0018] After obtaining the bold signal data of the seed points and each other brain region, use a sliding window to intercept the bold signal data, and within each window, calculate the Pearson correlation coefficient between the seed points and each other brain region; Each bold signal data is the time series of the bold signal, presenting as a curve fluctuating along the time axis; the Pearson correlation coefficient is a statistical index measuring the degree of linear correlation between two variables, with its value range being [-1, 1]. A value close to 1 indicates a positive correlation between the two variables, a value close to -1 indicates a negative correlation between the two variables, and a value of 0 indicates that the two variables are completely independent; under the condition of normal neural circuit function, the activities of two brain regions should be correlated. If the Pearson correlation coefficient is significantly reduced, it indicates abnormal connections between brain regions.

[0019] Slide the window along the time axis and repeatedly calculate the Pearson correlation coefficient until the entire time series is covered, obtaining a set of Pearson correlation coefficient sequences between the seed point and each other brain region. Among them, each Pearson correlation coefficient sequence also presents as a curve fluctuating along the time axis; Screen the set of Pearson correlation coefficient sequences according to the region of interest to obtain the sequence of connection coefficients changing with time between the seed point and the region of interest.

[0020] S300. Based on the DTI data, determine the connecting fibers between the seed point and the region of interest through probabilistic fiber tracking, obtain the target white matter voxels representing the connecting fibers and their surrounding regions, and acquire the white matter bold signal sequence of the target white matter voxels; Probabilistic fiber tracking mainly estimates the probability distribution of the fiber bundle direction at each voxel through the ball & stick model combined with the Markov chain Monte Carlo simulation (MCMC) method. By emitting thousands of fiber bundles at each seed point voxel, it tracks the posterior distribution of the fiber bundle and calculates relevant indexes such as the connection probability and average FA of the fiber bundle. In this step, the fiber bundle connecting the seed point and the region of interest can be obtained, and the corresponding path can be obtained and its three-dimensional coordinate information can be extracted. These coordinates are usually expressed as voxel coordinates. Based on these voxel coordinates, the range can be appropriately expanded to extract the voxels within a certain range around the fiber bundle together as the target white matter voxels to be studied; Since the voxel coordinates of the DTI data and the voxel coordinates of the fMRI data may be different, coordinate transformation processing is required to make them match. The two can be standardized to the standard space to achieve the transformation: Standardize the target white matter voxels into the MNI space to obtain the target white matter voxel coordinates in the MNI coordinate system; Standardize the fMRI data into the MNI space to obtain the whole-brain voxel coordinates in the MNI coordinate system; The MNI (Montreal Neurological Institute) space is mainly used for the standardized processing of brain imaging data. When conducting brain imaging research, it is not feasible to directly compare and analyze different images due to differences in dimensions, origins, voxel sizes, etc. Therefore, it is necessary to register and standardize the images onto the same template.

[0021] Locate the target white matter voxel coordinates in the whole-brain voxel coordinates, and extract the fMRI data corresponding to the target white matter voxel coordinates to obtain the white matter bold signal sequence.

[0022] Step S400: Perform differential analysis on the connectivity coefficient sequences and white matter bold signal sequences of healthy samples and subject samples to obtain the abnormal voxels of the subject samples, and determine the location of nerve circuit damage based on the coordinates of the abnormal voxels; First, by analyzing the change trends of the peak / valley values and slopes of the sequences, align the white matter bold signal sequences and connectivity coefficient sequences of healthy samples. Specifically, in step S100, the whole-brain sample data of multiple healthy individuals under the same stimulation task can be obtained, and the data of different healthy individuals are processed separately. Taking 120 samples as an example, 120 connectivity coefficient sequences and 120 groups of white matter bold signal sequences will be obtained. Moreover, the target white matter voxels of each individual are transformed into the standard space, so the target white matter voxels of different healthy individuals can correspond to the same position in the brain structure; compare and analyze the curve of the bold signal sequence of each target white matter voxel with the curve of the connectivity coefficient sequence, and extract the peak / valley value information of the two curves and the slope information of the connectivity coefficient sequence curve. For example, in 88% or more of the samples, at similar time points, the bold signal intensity of a certain white matter voxel increases, showing a peak, and the connectivity coefficient also increases accordingly, or the increase speed is faster (the slope increases). Then it can be considered that the activity of this white matter voxel promotes the functional connectivity of the brain region, and the time period when the bold signal intensity reaches the peak and the time period when the connectivity coefficient increases can be associated and aligned. Compare the connectivity coefficient sequences of healthy samples and subject samples to obtain the difference interval where the difference between the two connectivity coefficients is greater than the first threshold. The difference interval is a time interval, and the first threshold is set according to existing experimental data. When the difference is within the first threshold, it is considered a normal difference between individuals; that is, at a certain stage of neural activity, the activity correlation between two brain regions is significantly reduced, and the difference interval is the time period when the activity correlation of the subject sample is reduced, and this time period can correspond to a certain relevant time period of the bold signal sequence. Find the aligned white matter BOLD signal sequences of each target white matter voxel, and determine whether the difference in BOLD signal intensity between the subject sample and the healthy sample within the differential interval exceeds the second threshold. If so, identify the corresponding target white matter voxel as an abnormal voxel. The second threshold is set based on existing experimental data. The maximum difference in BOLD signal intensity between healthy samples can be used as the normal inter-individual difference, and 120 - 140% of this maximum difference is used as the second threshold. In this step, the BOLD signals of the subject sample are compared with those of 120 groups of healthy samples. When the BOLD signal of a certain target white matter voxel in the subject sample shows a significant decrease compared to most healthy samples, it indicates that this target white matter voxel is abnormal.

[0023] The signal transmission between brain regions is achieved through white matter fibers. That is, when there is damage to the functional connection between two brain regions, it is very likely due to abnormalities in the white matter fibers. In previous studies, due to the relatively weak activity intensity of white matter, the analysis and detection of diseases mainly relied on the activity of brain regions. However, in recent years, more and more studies have shown that the BOLD signal in white matter can be reliably detected and also shows a response to stimuli. Therefore, in this application, by studying the changes in the connection coefficient between brain regions and the BOLD signal of white matter fibers, when the connection coefficient is abnormal, the white matter fiber region with a significantly reduced BOLD signal can be found, thereby assisting in determining the damage location and providing important information for the study of neural activities. Based on the method of the present invention, long-term follow-up observations can be carried out on the subject to determine whether the number of abnormal voxels increases, providing a reference for the stage analysis of neurodegenerative diseases.

[0024] To exclude the influence of some interfering factors in the process of obtaining experimental data, it is necessary to further increase the richness of the samples: Obtain whole-brain task-state sample data under different task conditions, where the different task conditions include different stimulation intensities, different durations, and different alternating intervals; for example, when performing an olfactory stimulation task, use lavender oil at different concentrations, acetic acid at different concentrations, and copper sulfate at different concentrations.

[0025] Perform differential analysis on the connection coefficient sequences and white matter BOLD signal sequences of the whole-brain task-state sample data under each task condition to obtain the abnormal voxels under each task condition; Take the intersection of the abnormal voxels under each task condition to obtain the interference-removed abnormal voxels, and determine the location of the neural circuit damage based on the coordinates of the interference-removed abnormal voxels. When a certain voxel shows the same abnormal phenomenon under different task conditions, it indicates that this voxel affects the olfactory information transmission process and excludes the interference of factors such as vision and emotion on the analysis of neural activities under the olfactory task.

[0026] Example 2: See Figure 2, the present application also provides an auxiliary analysis device for the location of nerve circuit damage, including: A data acquisition module 100, configured to acquire whole-brain sample data, where the whole-brain sample data includes fMRI data and DTI data of healthy samples and subject samples; A functional connection module 200, configured to select seed points based on the fMRI data, establish whole-brain functional connections based on the seed points, and extract a sequence of connection coefficients that vary with time between the seed points and regions of interest; A target extraction module 300, configured to determine connection fibers between seed points and regions of interest based on the DTI data through probabilistic fiber tracking, obtain target white matter voxels representing the connection fibers and their surrounding regions, and acquire a white matter bold signal sequence of the target white matter voxels; An analysis module 400, configured to perform differential analysis on the connection coefficient sequences and white matter bold signal sequences of healthy samples and subject samples to obtain abnormal voxels of the subject samples, and determine the location of nerve circuit damage according to the coordinates of the abnormal voxels.

[0027] As an optional implementation manner, the analysis module 400 includes: A first processing unit 410, configured to align the white matter bold signal sequence and the connection coefficient sequence of the healthy sample by analyzing the change trends of the peak / valley values and slopes of the sequences; A second processing unit 420, configured to compare the connection coefficient sequences of the healthy sample and the subject sample to obtain a difference interval where the difference between the connection coefficients of the two is greater than a first threshold, and the difference interval is a time interval; An identification unit 430, configured to search for the aligned white matter bold signal sequence of each target white matter voxel, and determine whether the difference in bold signal intensity between the subject sample and the healthy sample within the difference interval exceeds a second threshold. If so, identify the corresponding target white matter voxel as an abnormal voxel.

[0028] Example 3: Corresponding to the above method embodiment, an auxiliary analysis device for the location of nerve circuit damage is also provided in this embodiment. The auxiliary analysis device for the location of nerve circuit damage described below can be mutually corresponded and referred to the auxiliary analysis method for the location of nerve circuit damage described above.

[0029] Figure 3 It is a block diagram of an auxiliary analysis device 800 for the location of nerve circuit damage shown according to an exemplary embodiment. As Figure 3As shown, the auxiliary analysis device 800 for the nerve circuit damage location includes a processor 801 and a memory 802. The auxiliary analysis device 800 for the nerve circuit damage location may also include one or more of a multimedia component 803, an input / output (I / O) interface 804, and a communication component 805. Among them, the processor 801 is used to control the overall operation of the auxiliary analysis device 800 for the nerve circuit damage location to complete all or part of the steps in the above-mentioned auxiliary analysis method for the nerve circuit damage location. The memory 802 is used to store various types of data to support the operation of the auxiliary analysis device 800 for the nerve circuit damage location. These data may include, for example, commands for any application or method operating on the auxiliary analysis device 800 for the nerve circuit damage location, as well as application-related data, such as received and sent messages, pictures, audio, video, and so on. The memory 802 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, a magnetic disk, or an optical disc.

[0030] The multimedia component 803 may include a screen and an audio component. Among them, the screen may be a touch screen, for example, and the audio component is used to output and / or input audio signals. For example, the audio component may include a microphone, and the microphone is used to receive external audio signals.

[0031] The received audio signal can be further stored in the memory 802 or sent via the communication component 805. The audio component also includes at least one speaker for outputting the audio signal. The I / O interface 804 provides an interface between the processor 801 and other interface modules, and the other interface modules may be a keyboard, a mouse, buttons, etc. These buttons can be virtual buttons or physical buttons. The communication component 805 is used for the auxiliary analysis device 800 of the nerve loop damage location to communicate with other devices in a wired or wireless manner. Wireless communication, such as Wi-Fi, Bluetooth, Near Field Communication (NFC), 2G, 3G, or 4G, or a combination of one or more of them. Accordingly, the communication component 805 may include: a Wi-Fi module, a Bluetooth module, and an NFC module.

[0032] In another exemplary embodiment, a computer-readable storage medium including program commands is also provided. When the program commands are executed by a processor, the steps of the above-described auxiliary analysis method for the nerve loop damage location are implemented. For example, the computer-readable storage medium may be the above-mentioned memory 802 including program commands, and the above program commands can be executed by the processor 801 of the auxiliary analysis device 800 of the nerve loop damage location to complete the above-described auxiliary analysis method for the nerve loop damage location.

[0033] Embodiment 4: Corresponding to the above-described embodiment of the auxiliary analysis method for the nerve loop damage location, a readable storage medium is also provided in this embodiment. A readable storage medium described below can be correspondingly referred to with the above-described auxiliary analysis method for the nerve loop damage location.

[0034] A readable storage medium stores a computer program. When the computer program is executed by a processor, the steps of the above-described embodiment of the auxiliary analysis method for the nerve loop damage location are implemented.

[0035] Specifically, the readable storage medium may be various readable storage media such as a USB flash drive, a mobile hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disc that can store program codes.

[0036] It should be noted that in this text, relational terms such as "first" and "second" are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprising", "including" or any other variation thereof are intended to cover non-exclusive inclusion, such that a process, method, article or device comprising a series of elements not only includes those elements but also includes other elements not expressly listed, or elements inherent to such process, method, article or device. Without further limitation, an element defined by the statement "comprising an..." does not exclude the presence of additional identical elements in the process, method, article or device comprising the element.

[0037] As described above, the above are only specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention can easily think of changes or substitutions, which should all be covered within the protection scope of the present invention. Therefore, the protection scope of the present invention should be subject to the protection scope of the claims.

Claims

1. An auxiliary analysis method for the location of nerve loop injury, characterized in that Comprising: Obtaining whole-brain sample data, where the whole-brain sample data includes fMRI data and DTI data of healthy samples and subject samples; Based on the fMRI data, selecting seed points and establishing whole-brain functional connectivity based on the seed points, and extracting the sequence of connection coefficients that change over time between the seed points and regions of interest; Based on the DTI data, determining the connecting fibers between the seed points and regions of interest through probabilistic fiber tracking, obtaining target white matter voxels representing the connecting fibers and their surrounding regions, and obtaining the white matter bold signal sequence of the target white matter voxels; Performing differential analysis on the connection coefficient sequences and white matter bold signal sequences of healthy samples and subject samples to obtain abnormal voxels of the subject samples, and determining the location of nerve circuit damage according to the coordinates of the abnormal voxels.

2. The auxiliary analysis method for the damaged position of a neural circuit according to claim 1, wherein The performing differential analysis on the connection coefficient sequences and white matter bold signal sequences of healthy samples and subject samples to obtain abnormal voxels of the subject samples includes: By analyzing the change trends of the peak / valley values and slopes of the sequences, aligning the white matter bold signal sequences and connection coefficient sequences of healthy samples; Comparing the connection coefficient sequences of healthy samples and subject samples to obtain a difference interval where the difference between the connection coefficients of the two is greater than a first threshold, and the difference interval is a time interval; Searching for the aligned white matter bold signal sequences of each target white matter voxel, and determining whether the difference in bold signal intensity between the subject sample and the healthy sample within the difference interval exceeds a second threshold. If so, identifying the corresponding target white matter voxel as an abnormal voxel.

3. An auxiliary analysis method for the damage location of a nerve circuit according to claim 1, characterized in that, The determining the connecting fibers between the seed points and regions of interest through probabilistic fiber tracking, obtaining target white matter voxels representing the connecting fibers and their surrounding regions, and obtaining the white matter bold signal sequence of the target white matter voxels includes: Normalizing the target white matter voxels into the MNI space to obtain the coordinates of the target white matter voxels in the MNI coordinate system; Normalizing the fMRI data into the MNI space to obtain the whole-brain voxel coordinates in the MNI coordinate system; Searching for the coordinates of the target white matter voxels in the whole-brain voxel coordinates, and extracting the fMRI data corresponding to the coordinates of the target white matter voxels to obtain the white matter bold signal sequence.

4. The auxiliary analysis method for the injury location of a nerve circuit according to claim 3, characterized in that The method further includes: Obtaining whole-brain task-state sample data under different task conditions, where the different task conditions include different stimulation intensities, different durations, and different alternating intervals; Performing differential analysis on the connection coefficient sequences and white matter bold signal sequences of the whole-brain task-state sample data under each task condition to obtain abnormal voxels under each task condition; Taking the intersection of the abnormal voxels under each task condition to obtain interference-removed abnormal voxels, and determining the location of nerve circuit damage according to the coordinates of the interference-removed abnormal voxels.

5. The auxiliary analysis method for the damaged position of a neural circuit according to claim 1, wherein The selecting seed points and establishing whole-brain functional connectivity based on the seed points, and extracting the sequence of connection coefficients that change over time between the seed points and regions of interest includes: Obtaining the bold signal data of the seed points and each other brain region, using a sliding window to intercept the bold signal data, and calculating the Pearson correlation coefficient between the seed points and each other brain region within each window; Slide the window along the time axis and repeatedly calculate the Pearson correlation coefficient until the entire time series is covered, obtaining a set of Pearson correlation coefficient sequences between the seed point and each other brain region. Filter the set of Pearson correlation coefficient sequences according to the region of interest to obtain a sequence of connection coefficients that vary over time between the seed point and the region of interest.

6. The auxiliary analysis method for the location of nerve loop injury according to claim 1, characterized in that, The fMRI data includes MRI images of the head; based on the fMRI data, selecting a seed point and establishing a whole-brain functional connection based on the seed point includes: Eliminate unstable images in the MRI images, register the remaining MRI images with a standard brain template to obtain registered images. Smooth the registered images using a Gaussian kernel, perform denoising using band-pass filtering, and then apply interference covariates for multiple regression processing to obtain preprocessed fMRI data. Select a seed point based on the preprocessed fMRI data and establish a whole-brain functional connection based on the seed point.

7. An auxiliary analysis device for the location of nerve loop injury, characterized in that, Including: A data acquisition module for acquiring whole-brain sample data, where the whole-brain sample data includes fMRI data and DTI data of healthy samples and subject samples. A functional connection module for selecting a seed point based on the fMRI data and establishing a whole-brain functional connection based on the seed point, and extracting a sequence of connection coefficients that vary over time between the seed point and the region of interest. A target extraction module for determining the connecting fibers between the seed point and the region of interest through probabilistic fiber tracking based on the DTI data, obtaining target white matter voxels representing the connecting fibers and their surrounding regions, and acquiring the white matter bold signal sequence of the target white matter voxels. An analysis module for performing differential analysis on the connection coefficient sequences and white matter bold signal sequences of healthy samples and subject samples to obtain abnormal voxels of the subject samples, and determining the location of nerve loop damage according to the coordinates of the abnormal voxels.

8. The auxiliary analysis device for the damaged position of a nerve circuit according to claim 7, characterized in that, The analysis module includes: A first processing unit for aligning the white matter bold signal sequence and the connection coefficient sequence of the healthy sample by analyzing the change trends of the peak / valley values and slopes of the sequences. A second processing unit for comparing the connection coefficient sequences of the healthy sample and the subject sample to obtain a difference interval where the difference between the connection coefficients of the two is greater than a first threshold, and the difference interval is a time interval. An identification unit for searching for the aligned white matter bold signal sequence of each target white matter voxel and determining whether the difference in bold signal intensity between the subject sample and the healthy sample within the difference interval exceeds a second threshold. If so, the corresponding target white matter voxel is identified as an abnormal voxel.

9. An auxiliary analysis device for the location of nerve circuit damage, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the method for assisting in analyzing the location of nerve loop damage according to any one of claims 1 to 6.

10. A readable storage medium, characterized in that, A computer program is stored on the readable storage medium, and when the computer program is executed by the processor, it implements the steps of the method for assisting in analyzing the location of nerve loop damage according to any one of claims 1 to 6.

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