WO2023280086A1 - 靶点确定方法、装置、电子设备、存储介质及神经调控设备 - Google Patents
靶点确定方法、装置、电子设备、存储介质及神经调控设备 Download PDFInfo
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Definitions
- the present disclosure relates to the field of computer technology, and in particular to a target determination method, device, electronic equipment, storage medium and neuromodulation equipment.
- a variety of neurological and psychiatric diseases often have no clear pathogenic focus, and only manifest as abnormal nervous system function. It is an important means to improve the symptoms of patients to directly or indirectly adjust the abnormal function network by means of neural regulation such as electricity, magnetism, light and ultrasound. How to select neuromodulatory targets in the human brain is a difficult problem. Studies have shown that for most neurological and mental diseases, due to the inability to directly analyze the etiology and locate the lesion from structural images, ideal regulation and treatment effects are usually not achieved. Therefore, an objective, accurate and quantifiable auxiliary method is needed in clinic to help doctors screen individual neuromodulation targets. Existing methods for identifying neuromodulatory targets do not meet this need.
- the present disclosure proposes a target determination method, device, electronic equipment, storage medium and neuromodulation equipment for screening individualized neuromodulation targets.
- the present disclosure provides a method for determining a target, the method comprising: acquiring scan data of a subject, wherein the scan data includes data obtained by performing magnetic resonance imaging on the brain of the subject Determining at least two regions of interest (region of interest, ROI) of the subject based on the scan data; determining at least one abnormal interest in the at least two regions of interest according to preset abnormality detection rules a region; determining a target based on the at least one abnormal region of interest.
- the determining at least two regions of interest of the subject based on the scan data includes:
- the at least two regions of interest are formed based on a brain region template of a standard brain and the brain connection matrix.
- the determining at least two regions of interest of the subject according to the scan data includes:
- the at least two regions of interest are formed by merging brain regions whose voxel connectivity between brain regions is higher than a preset brain region voxel connectivity threshold.
- At least two regions of interest of the subject are determined from the scan data based on a volumetric standard brain structure template.
- the determining at least two regions of interest of the subject according to the scan data includes:
- the determining at least one abnormal region of interest in the at least two regions of interest according to preset abnormality detection rules includes:
- the at least one abnormal region of interest is determined according to the population brain connectivity matrix and the brain connectivity matrix.
- the determining the target based on the at least one abnormal region of interest includes:
- the center of the at least one abnormal region of interest is determined as the target point, or the center of the at least one abnormal region of interest is used as the center of the sphere . Determining the area of the preset target radius as the first target ROI, and determining the target according to the position of the first target ROI;
- the at least one abnormal region of interest is not located in an adjustable brain region, determine the connectivity between the at least one abnormal region of interest and other regions of interest in the at least two regions of interest, and connect the other regions of interest to A region of interest whose connectivity with the at least one abnormal region of interest exceeds a preset connectivity threshold and is located in an adjustable region is determined as a second target candidate region;
- the target point is determined according to the position of the region of interest of the second target point.
- the center of the target candidate area is determined as the target, or the center of the target candidate area is determined as the center of the sphere and the area with the preset target radius is determined as the target area of interest, according to the The position of the region of interest of the target point determines the target point.
- the present disclosure provides an apparatus for determining a target point, the apparatus comprising: a data acquisition unit configured to acquire scan data of a subject, wherein the scan data includes an image of the brain of the subject The data obtained by performing magnetic resonance imaging; the processing unit is configured to determine at least two regions of interest of the subject based on the scan data; the abnormality detection unit is configured to perform abnormality detection according to preset abnormality detection rules in the At least one abnormal region of interest is determined from the at least two regions of interest; the target determining unit is configured to determine the target based on the at least one abnormal region of interest.
- processing unit is further configured to:
- the at least two regions of interest are formed based on a brain region template of a standard brain and the brain connection matrix.
- the at least two regions of interest are formed by merging brain regions whose voxel connectivity between brain regions is higher than a preset brain region voxel connectivity threshold.
- processing unit is further configured to:
- At least two regions of interest of the subject are determined from the scan data based on a volumetric standard brain structure template.
- processing unit is further configured to:
- the abnormality detection unit is further configured to:
- the at least one abnormal region of interest is determined according to the population brain connectivity matrix and the subject brain connectivity matrix.
- the determining the target based on the at least one abnormal region of interest includes:
- the center of the at least one abnormal region of interest is determined as the target point, or the center of the at least one abnormal region of interest is used as the center of the sphere . Determining the area of the preset target radius as the first target ROI, and determining the target according to the position of the first target ROI;
- the at least one abnormal region of interest is not located in an adjustable brain region, determine the connectivity between the at least one abnormal region of interest and other regions of interest in the at least two regions of interest, and connect the other regions of interest to A region of interest whose connectivity with the at least one abnormal region of interest exceeds a preset connectivity threshold and is located in an adjustable region is determined as a second target candidate region;
- the target point is determined according to the position of the region of interest of the second target point.
- the determining the target based on the at least one abnormal region of interest includes:
- the center of the target candidate area is determined as the target, or the center of the target candidate area is determined as the center of the sphere and the area with the preset target radius is determined as the target area of interest, according to the The position of the region of interest of the target point determines the target point.
- the magnetic resonance imaging includes: structural magnetic resonance imaging, and/or task state functional magnetic resonance imaging, and/or resting state functional magnetic resonance imaging.
- the present disclosure provides an electronic device, including: at least one processor; a storage device, at least one program is stored on the storage device, and when the at least one program is executed by the at least one processor, the The at least one processor implements the method described in any implementation manner of the first aspect.
- the present disclosure provides a computer-readable storage medium, where a computer program is stored on the computer-readable storage medium, wherein when the computer program is executed by at least one processor, any one of the first aspect can be implemented.
- the method described by Implementation is not limited to:
- the present disclosure provides a neuromodulation device configured to neuromodulate a subject's target according to a preset neuromodulation scheme; wherein, the target is according to any one of the first aspect Determined by the method described in the implementation.
- the preset control scheme includes at least one of the following: deep brain stimulation; transcranial electrical stimulation; electroconvulsive therapy; electrical stimulation based on cortical EEG electrodes; transcranial magnetic stimulation; Ultrasound focused neural regulation; MRI-guided high-energy ultrasound focused therapy regulation; light stimulation regulation.
- fMRI functional magnetic resonance imaging
- rTMS Repetitive transcranial magnetic stimulation
- DLPFC left dorsal lateral frontal cortex
- 5cm treatment-resistant depression
- PET scans are expensive, which increases the medical burden; there is certain radiation in the scan process; PET scans are applicable to neurological and mental diseases Limited; the image signal-to-noise ratio is low, the boundary of anatomical structure is not clear, which affects the accuracy of target determination, and the effectiveness of clinical treatment is low.
- the target determination method, device, electronic equipment, storage medium, and neuromodulation equipment provided in the present disclosure obtain scan data of a subject, wherein the scan data includes magnetic resonance imaging of the brain of the subject According to the obtained data, at least two regions of interest of the subject are determined based on the scan data; at least one abnormal region of interest is determined in the at least two regions of interest according to preset abnormality detection rules; based on the obtained The at least one abnormal region of interest is used to determine the target.
- the embodiments of the present disclosure use functional magnetic resonance imaging to provide the subject's brain scan data to determine the subject's brain region. On the basis of fully considering individual differences, it can effectively solve the problems caused by traditional methods that do not consider individual structural or functional differences.
- the problem of inaccurate neuromodulation targets has realized the positioning of the subjects' individual neuromodulation targets.
- the individualized anomaly detection method combines brain structural information and functional information, which can efficiently detect brain regions with abnormal brain connection patterns compared with normal people, and can effectively solve neuromodulation targets caused by individual structural or functional differences that are not considered in traditional methods Inaccurate question.
- FIG. 1 is an exemplary system architecture diagram to which an embodiment of the present disclosure can be applied;
- FIG. 2 is a schematic flow diagram of an embodiment of a target determination method according to the present disclosure
- Fig. 3 is an exploded schematic diagram of an embodiment of step 202 in the target point determination method shown in Fig. 2;
- Fig. 4 is an exploded schematic diagram of another embodiment of step 202 in the target point determination method shown in Fig. 2;
- Fig. 5 is a schematic structural diagram of an embodiment of a target point determination device according to the present disclosure.
- FIG. 6 is a schematic structural diagram of a computer system suitable for implementing a terminal device or a server of the present disclosure.
- connection should be understood in a broad sense, for example, it can be an electrical connection, it can also be the internal communication of two elements, and it can be a direct connection , can also be indirectly connected through an intermediary, and those of ordinary skill in the art can understand the specific meanings of the above terms according to specific situations.
- first ⁇ second ⁇ third involved in the embodiments of the present disclosure is only to distinguish similar objects, and does not represent a specific ordering of objects. Understandably, “first ⁇ second ⁇ “Third” can be interchanged for a specific order or sequence where allowed. It should be understood that the terms “first ⁇ second ⁇ third” can be interchanged under appropriate circumstances so that the embodiments of the disclosure described herein can be implemented in sequences other than those illustrated or described herein.
- FIG. 1 shows an exemplary system architecture 100 to which embodiments of the target determination method or target determination device of the present disclosure can be applied.
- a system architecture 100 may include terminal devices 101 , 102 , 103 , a network 104 and a server 105 .
- the network 104 is used as a medium for providing communication links between the terminal devices 101 , 102 , 103 and the server 105 .
- Network 104 may include various connection types, such as wires, wireless communication links, or fiber optic cables, among others.
- Terminal devices 101 , 102 , 103 Users can use terminal devices 101 , 102 , 103 to interact with server 105 via network 104 to receive or send messages and the like.
- Various communication client applications can be installed on the terminal devices 101, 102, and 103, such as magnetic resonance imaging control applications, functional magnetic resonance imaging control applications, web browser applications, shopping applications, search applications, instant messaging tools, mailboxes, etc. Client, social platform software, etc.
- the terminal devices 101, 102, and 103 may be hardware or software.
- the terminal devices 101, 102, 103 When the terminal devices 101, 102, 103 are hardware, they may be various electronic devices with display screens, including but not limited to smartphones, tablet computers, laptop computers, desktop computers, and the like.
- the terminal devices 101, 102, and 103 are software, they can be installed in the above-mentioned electronically determined multiple brain area devices of the subject. It can be implemented as a plurality of software or software modules (for example, to provide processing of brain maps), or as a single software or software module. No specific limitation is made here.
- the server 105 may be a server that provides various services, such as a background data processing server that processes the scan data sent by the terminal devices 101 , 102 , 103 .
- the background data processing server can determine multiple brain regions of the subject and the corresponding voxels of each brain region based on the scan data and feed them back to the terminal device.
- the server 105 may be hardware or software.
- the server 105 can be implemented as a distributed server cluster composed of multiple servers, or as a single server.
- the server 105 is software, it can be implemented as multiple software or software modules (for example, for providing distributed services), or as a single software or software module. No specific limitation is made here.
- the target point determination method provided in the present disclosure is generally executed by the server 105 , and correspondingly, the target point determination device is generally set in the server 105 .
- the target determination method provided by the present disclosure can be executed by the server 105, or by the terminal devices 101, 102, 103, or by the server 105 and the terminal devices 101, 102, 103 co-execution.
- the device for determining target points can be set in the server 105 , or in the terminal devices 101 , 102 , 103 , or partly in the server 105 and partly in the terminal devices 101 , 102 , 103 .
- the system architecture 100 may only include the server 105 , or only include the terminal devices 101 , 102 , 103 , or may include the terminal devices 101 , 102 , 103 , the network 104 and the server 105 . The present disclosure does not limit this.
- terminal devices, networks and servers in Fig. 1 are only illustrative. According to the implementation needs, there can be any number of terminal devices, networks and servers.
- FIG. 2 shows a flow 200 of an embodiment of the target determination method according to the present disclosure.
- the target determination method includes the following steps:
- Step 201 acquire the scan data of the subject.
- the scan data includes data obtained by performing magnetic resonance imaging on the subject's brain.
- the scan data includes a Blood Oxygen Level Dependency (BOLD) signal sequence corresponding to each voxel in the preset number of voxels.
- BOLD Blood Oxygen Level Dependency
- the subject of execution of the target point determination method can first obtain the Subject's scan data.
- Voxel also known as voxel, is the abbreviation of volume pixel.
- a voxel is conceptually similar to the smallest unit of two-dimensional space - a pixel, which is used in the image data of a two-dimensional computer image.
- Voxel is the smallest unit of digital data in three-dimensional space segmentation, and it is used in three-dimensional imaging, scientific data and medical imaging and other fields.
- the BOLD signal sequence corresponding to the voxel refers to the magnetic resonance scan of the subject, and then obtains a BOLD signal for each voxel every preset time unit, and finally obtains a BOLD signal for a period of time, and collects these BOLD signals according to the
- the sequence of BOLD signals corresponding to each voxel is obtained by arranging them in chronological order, and the number of BOLD signals included therein can be the integer quotient obtained by dividing the duration corresponding to the target task by the preset time unit.
- the scanning duration is 300 seconds and the preset time unit is 2 seconds
- the BOLD signal sequence corresponding to each voxel has 150 frames of data
- the BOLD signal sequence corresponding to each voxel is a 150-dimensional vector
- the BOLD signal sequence corresponding to each voxel is a matrix of order 1 ⁇ 150, which is not specifically limited in the present disclosure.
- the specific number of voxels included in the scan data can be determined according to the scanning accuracy of functional magnetic resonance imaging or magnetic resonance imaging, or can be determined according to the accuracy of imaging equipment, and the preset number here is not for the specific number of voxels Limitations: In current practical applications, the number of voxels in human brain scan data is measured in tens of thousands or hundreds of thousands. With the advancement of scanning technology, the number of voxels included in human brain scan data can be further increased.
- the above-mentioned execution subject may obtain the subject's scan data locally or remotely from other electronic devices (such as the terminal device shown in FIG. 1 ) connected to the above-mentioned execution subject network.
- the magnetic resonance imaging may include: structural magnetic resonance imaging, and/or task state functional magnetic resonance imaging, and/or resting state functional magnetic resonance imaging.
- the data obtained by functional magnetic resonance imaging contain time series information, which is equivalent to a four-dimensional image.
- a 3-dimensional image matrix Length x Width x Height, L x M x N
- 150 frames of data can be collected in 6 minutes to form a LxMxN voxel x150 fMRI data signal.
- the data obtained by structural magnetic resonance imaging is a high-resolution three-dimensional grayscale anatomical structure image, such as T1w (T1 weighted imaging --- highlighting the difference in tissue T1 relaxation (longitudinal relaxation)) and its related images, T2w (T2 weighted Imaging——highlighting tissue T2 relaxation (transverse relaxation) difference) and related images, fluid attenuated inversion recovery sequence (fluid attenuated inversion recovery, FLAIR) and related images; structural magnetic resonance imaging can also include magnetic resonance imaging Diffusion imaging, such as: diffusion-weighted imaging (DWI) and related images, diffusion tensor imaging (DTI) and related images, etc.
- DWI diffusion-weighted imaging
- DTI diffusion tensor imaging
- DTI is a magnetic resonance technique used to study the diffusion anisotropy of anatomical nerve bundles in the central nervous system and to display the anatomy of white matter fibers. It probes tissue microstructure through the anisotropy of water molecule diffusion in the tissue.
- the anisotropy of the white matter is caused by the myelin sheath axonal fibers running parallel.
- the diffusion of the white matter is the largest in the direction of the parallel nerve fibers, that is, the fractional anisotropy (fractional anisotropy, FA) is the largest, which can be approximately determined as 1 (actual It can be a fraction greater than 0.9 and approaching 1).
- This feature is color-coded to reflect the spatial directionality of the white matter, ie the direction of fastest diffusion indicates the direction in which fibers travel. Tractography by DTI yields a brain connectivity matrix that reflects brain structure.
- functional magnetic resonance imaging may include: task state functional magnetic resonance imaging, and/or resting state functional magnetic resonance imaging.
- the resting state functional magnetic resonance imaging is the magnetic resonance imaging obtained by performing magnetic resonance scanning on the subject's brain when the subject does not perform any tasks during the scanning period.
- the task-state functional magnetic resonance imaging is the magnetic resonance imaging obtained by performing magnetic resonance scanning on the subject's brain when the subject performs a target task.
- various implementation methods can be used to determine the subject's brain structure map according to the subject's brain structure magnetic resonance scan data, that is, to obtain the specific information in the subject's brain. Which regions are what structural components.
- existing software for processing three-dimensional brain scan data can be used, such as the magnetic resonance data processing software Free Cortex Reconstruction (FreeSurfer).
- FreeSurfer Free Cortex Reconstruction
- the execution subject preprocesses the scan data.
- the preprocessing method is not specifically limited.
- the preprocessing may include:
- Time layer correction head movement correction, time signal filtering, noise component regression, spatial smoothing, etc.
- the fMRI signal is projected onto a structural image (if there is one), including reconstructed individual cortical images or related group averaged structural images.
- Preprocessing of structural MRI images such as deskullization, field strength correction, segmentation of individual anatomical structures, reconstruction of cerebral cortex, etc.
- Step 202 determining at least two ROIs of the subject based on the scan data.
- step 202 the present disclosure provides multiple optional implementation manners.
- FIG. 3 is a partially exploded schematic diagram of an embodiment of step 202 in the target point determination method shown in FIG. 2 .
- the above step 202 may specifically include:
- Step 202a1 determine the connection degree between every two voxels in the scan data, and form a brain connection matrix corresponding to the scan data.
- connection degree between a voxel and ROI may include the average connection degree between a voxel and each voxel in the ROI; the connection degree between two ROIs may include the voxel in each ROI in the two ROIs
- the average of the connectivity to each voxel in another ROI the connectivity of a voxel to a brain region, which can include the average of the connectivity of a voxel to each voxel in a brain region; the connectivity between two brain regions , which can include the average of the connectivity of a voxel in each brain region with each voxel in the other brain region in two brain regions.
- Connectivity characterizes the degree of connectivity of brain connections and can also be expressed as correlation.
- brain connectivity includes functional connectivity and structural connectivity.
- the functional connection can be calculated based on the BOLD time series corresponding to the voxels in the ROI through the Pearson correlation coefficient;
- the structural connection includes, for example, the structural connection between ROIs obtained from fiber tract imaging.
- the brain connection matrix corresponding to the scan data It is a 100,000x100,000-order matrix, which can represent the connectivity between every two voxels in the above scan data.
- the degree of connection between two voxels can be calculated based on the T BOLD values corresponding to the voxels through the Pearson correlation coefficient.
- the correlation coefficient is a Pearson correlation coefficient, which is a coefficient used to measure the degree of linearity between variables. Its calculation formula is:
- the formula is defined as:
- the pearson correlation coefficient ( ⁇ x,y ) of two continuous variables (X,Y) is equal to the covariance cov(X,Y) between them divided by the product of their respective standard deviations ( ⁇ X , ⁇ Y ).
- the value of the coefficient is always between -1.0 and 1.0.
- a variable equal to or approximately equal to 0 is said to have no correlation, and a variable equal to or approximately equal to close to 1 or -1 is said to have a strong correlation.
- being approximately equal can be understood as the difference between the target value and the target value within the allowable range of error.
- 0.01 can be approximately equal to 0, or 0.99 can be approximately equal to 1. This is just an example, and it can be used in practical applications according to Calculate the required precision to determine the error tolerance that is approximately equal to .
- step 202a2 at least two ROIs are formed based on the brain region template and the brain connection matrix of the standard brain.
- pattern recognition or machine learning methods can be used to establish a brain atlas containing more than two brain regions for a subject based on a standard brain region template.
- Methods may include but are not limited to: Independent Component Correlation Algorithm (ICA), Principal Component Analysis (PCA), various types of clustering methods, factor analysis (factor analysis), linear discriminant analysis (linear discriminant analysis) , LDA), various matrix decomposition methods, etc.
- ICA Independent Component Correlation Algorithm
- PCA Principal Component Analysis
- various types of clustering methods include factor analysis (factor analysis), linear discriminant analysis (linear discriminant analysis) , LDA), various matrix decomposition methods, etc.
- the finally obtained brain functional network may have different voxel positions in each functional division for different subjects, but each voxel belongs to a specific ROI. That is, each ROI of the subject may be a set of voxels composed of voxels in fMRI with the same function.
- the brain regions may include brain functional partitions and/or brain structural partitions.
- FIG. 4 is an exploded schematic diagram of another embodiment of step 202 in the target point determination method shown in FIG. 2 .
- the above step 202 may specifically include:
- Step 202b1 determine the connectivity between every two voxels in the scan data.
- Step 202b2 dividing the scan data corresponding to the brain anatomy of the subject into multiple large regions, and dividing the multiple large regions (for example, each large region) into multiple brain regions, wherein, in the multiple brain regions Each brain region includes at least one voxel.
- Step 202b3 merging brain regions whose voxel connectivity between brain regions is higher than a preset threshold of voxel connectivity of brain regions among multiple brain regions to form at least two ROIs.
- the subject's brain For example, first divide the subject's brain into multiple large regions according to the main anatomical structure boundaries; then divide each large region using functional connectivity, and each large region is divided according to reliability (test-retest reliability) To determine the connectivity of voxels. After subdividing each large region, multiple brain regions are obtained, and then these brain regions are fused according to the connectivity of the voxels they contain, and the brain regions with highly correlated voxels are combined into one ROI. For example, finally At least two ROIs can be identified across the whole brain.
- the initial individual brain atlas can be divided into ten regions by dividing the left and right cortex of the brain into five regions: frontal, parietal, occipital, temporal, and pancentral sulcus regions.
- the left and right brain can be divided into 4 regions according to the higher cortex and lower cortex of the brain.
- the above step 202 may specifically include:
- a group brain atlas is pre-selected or generated as a brain atlas template, and boundaries corresponding to at least two brain regions in the brain atlas template are projected to the subject's brain scan data.
- the boundaries of at least two brain regions are adjusted based on the subject's brain scan data, so that the adjusted brain region boundaries match at least the subject's brain scan data to form at least two ROIs.
- the group brain atlas is directly projected to the subject's brain, and then a recursive algorithm is used to gradually adjust the boundaries of the brain regions projected by these group brain atlases according to the subject's anatomical brain atlas until the brain region's Boundaries tend to stabilize.
- the recursive process will use the subject's individual difference distribution of brain connections and the subject's own brain image signal-to-noise ratio to determine the magnitude of the boundary adjustment of the brain region.
- the brain regions are fused according to the correlation of voxels, thereby obtaining at least two ROIs.
- the above step 202 may specifically include:
- a standard ROI library which contains ROIs related to various test scenarios and their potential brain connection patterns.
- the corresponding ROI can be obtained by screening the test scene type, and then at least two ROIs of the subject can be obtained on this basis.
- the above step 202 may specifically include:
- At least two ROIs of the subject are determined from the scan data based on a volumetric standard brain structure template. Extract the white matter and ventricle area in the volume standard brain structure template, construct the binary mask Mask of the volume standard brain structure template, remove the white matter and ventricle area from the Mask, and obtain the Mask without white matter and ventricle area. Resampling with the Mask of the ventricle area to obtain at least two ROIs. Alternatively, construct a volumetric standard brain structure template binary Mask, and perform resampling to obtain at least two ROIs.
- the above step 202 may specifically include:
- At least two ROIs of the subject are determined from the scan data based on a cortical standard brain structure template. Resampling of cortical standard brain structure templates generates at least two ROIs. For example, at least two ROIs (for example: fsaverage6 (fs6)) of the high-resolution template are generated based on the coarse-resolution cortical surface template (for example: fsaverage3 (fs3), fsaverage4 (fs4)). Specifically, sequentially assign values (1, 2, 3%) to the left and right brain vertices of the coarse resolution template, and then resample (nearest neighbor method interpolation) to the fs6 template space, and count the values corresponding to each value according to the order of assignment.
- fsaverage6 fs6
- the index numbers of all vertices in the fs6surface which are at least two ROIs in the fs6 space, for example: the 13th vertex in the fs4surface template of the left brain is assigned a value of 13, and after resampling to the fs6 template space, 30 values are all 13. vertices, then the 13th ROI of the left brain fs6surface template is composed of these 30 vertices.
- Step 203 Determine at least one abnormal ROI among at least two ROIs according to preset abnormality detection rules.
- the above step 203 may specifically include:
- the number of acquired group samples may be more than 300 persons. This is not a specific limitation on the number of group samples, but an example.
- Preprocessing is performed on the acquired population brain MRI data.
- the calculation of the population brain connectivity matrix may include the following steps:
- Step S11 for the data of each subject in the group brain magnetic resonance data, calculate the average time series of all vertices/voxels in each ROI.
- the ROI may be obtained through the method for determining the ROI in the embodiments of the present disclosure, or may be obtained through other existing methods for determining the ROI.
- Step S12 sequentially calculate the correlation coefficient between ROI and ROI time series (for example: Pearson correlation coefficient), and finally obtain the brain connectivity matrix of each subject
- the brain connectivity matrix can include functional connectivity matrix and structural connectivity matrix, to Take the functional connectivity (FC) matrix as an example, that is, indi_fc (the matrix is a diagonal matrix, and the value in row i and column j in the matrix represents the correlation between the i-th ROI and the j-th ROI time series, i ⁇ N , j ⁇ N, N is the number of ROIs, and the size of indi_fc is N x N).
- FC functional connectivity
- step 203 may specifically include Solution 1 or Solution 2. in:
- Anomaly detection algorithm A may specifically include the following steps SA1 to SA3. in:
- Sub-step SA12 data preprocessing: preprocessing the data according to the preprocessing method in the embodiment in FIG. 2 .
- Sub-step SA13 brain connection matrix calculation.
- the brain connection matrix is not limited to the functional connection matrix and the structural connection matrix.
- FC matrix as an example, that is, indi_fc (the matrix is a diagonal matrix, the i-th row and j-th column value in the matrix represent the correlation between the i-th ROI and the j-th ROI time series, i ⁇ N, j ⁇ N, N is the number of ROIs, and the size of indi_fc is N x N).
- Substep SA14 baseline generation.
- step SA2 the subject FC is generated (subject brain connection matrix).
- the brain connection matrix can include functional connection matrix and structural connection matrix, with functional connection ( Functional connectivity (FC) matrix is taken as an example, set as p_fc (its size and meaning are the same as indi_fc in step SA1).
- FC Functional connectivity
- Step SA3 an abnormality detection algorithm step.
- the matrix p_z (size N x 1) is the final anomaly detection result.
- Anomaly detection algorithm B may specifically include the following steps SB1 to SB3. in:
- Step SB1 baseline generation (group brain connection matrix). Specifically, the following substeps SB11 to SB14 may be included. in:
- Sub-step SB12 data preprocessing: preprocessing the data according to the preprocessing method in the embodiment in FIG. 2 .
- Sub-step SB13 brain connection matrix calculation.
- Substep SB14 baseline generation.
- Step S2 subject FC generates (subject brain connection matrix). Specifically, the following substeps SB21 to SB22 may be included. Wherein: sub-step SB21, using the preprocessed data of the subject (for example: P), to calculate the average time series of all vertices/voxels in each ROI (generated by step 1).
- Sub-step SB22 sequentially calculate the correlation coefficient between ROI and ROI time series (for example, using Pearson correlation coefficient), and finally obtain the brain connection matrix (functional connectivity, FC) of each subject, namely: p_fc (size is N x N).
- Sub-step SB3 an abnormality detection algorithm step. Specifically, it may include:
- p_fc, Big_mean_fc, Big_std_fc have the same meaning as the variable names in steps SB1 and SB2.
- At least one abnormal ROI may be an abnormal ROI of a brain region, or an abnormal ROI of a cerebellum region.
- Step 204 determining a target based on at least one abnormal ROI.
- the above step 204 may specifically include:
- determining the target based on at least one abnormal ROI includes:
- At least one abnormal ROI is located in an adjustable brain region, determine the center of at least one abnormal ROI as the target point, or determine the area with the center of at least one abnormal ROI as the center of the sphere and the radius of the preset target point as the first target Point ROI, determine the target point according to the position of the first target point ROI.
- At least one abnormal ROI is not located in an adjustable brain region, determine the degree of connectivity between at least one abnormal ROI and other ROIs in at least two ROIs, and determine the degree of connectivity between other ROIs and at least one abnormal ROI that exceeds a preset connectivity threshold and is located in an adjustable brain region.
- the ROI of the regulatory region is determined as the second target candidate region;
- determining the target based on at least one abnormal ROI includes:
- the disease type includes the disease type diagnosed for the subject or the disease type corresponding to the symptom to be treated for the subject.
- the corresponding relationship of brain regions of disease types can be queried according to the existing corresponding relationship of brain regions of determined disease types, and can also be set according to actual needs.
- the method of obtaining brain regions of disease types is only an example, rather than a specific limitation.
- the target brain region is the brain region corresponding to the target point, and there is a neural connection between the target point and the target point brain region, and the target brain region can be neurally regulated by stimulating the target point.
- the target point can include the coordinates corresponding to a single voxel, or it can be a set of regions composed of some voxels.
- the above step 204 may specifically include:
- the central position of at least one target brain region is determined as the target.
- the above step 204 may specifically include:
- the present disclosure does not specifically limit the length of the radius of the preset target point.
- the radius of the preset target point can be set according to the actual needs of neuromodulation, for example, the radius of the preset target point can be 3 mm.
- the above step 204 may specifically include:
- the target point needs to meet the following conditions: the target point cannot be located on the inner side and bottom of the brain, the target point can be located in the gyrus, and the target point cannot be located in the brain sulcus.
- the target points determined according to the above method are relatively accurate.
- scientific researchers or medical personnel can use optical navigation equipment or electromagnetic navigation equipment to perform neural regulation and navigation on subjects based on the target points determined by the above method, which can improve neuromodulation. efficiency.
- the present disclosure provides a neuromodulation device configured to perform neuromodulation on a subject's target according to a preset neuromodulation scheme, wherein the subject's target is a target according to any of the above-mentioned embodiments of the present disclosure. Click to confirm the method.
- Neuromodulation devices may include implantable neuromodulation devices and non-implantable neuromodulation devices, such as event-related potential analysis systems, electroencephalogram systems, brain-computer interface devices, and the like.
- implantable neuromodulation devices and non-implantable neuromodulation devices, such as event-related potential analysis systems, electroencephalogram systems, brain-computer interface devices, and the like.
- non-implantable neuromodulation devices such as event-related potential analysis systems, electroencephalogram systems, brain-computer interface devices, and the like.
- the present disclosure does not limit the specific form of the neuromodulation device, which is only illustrated here.
- the neuromodulation of the subject's target can be performed by the operator after connecting the neuromodulation device according to the target, or it can be obtained by the neuromodulation device according to the operator's input or according to the neuromodulation device's active acquisition.
- Regulatory targets This is just an example, rather than a specific limitation on the neuromodulation of the subject's target, and technicians can operate according to the actual neuromodulation device usage.
- the preset neuromodulation scheme may include but not limited to:
- Deep brain stimulation i. Deep brain stimulation; ii. Transcranial electrical stimulation; iii. Electric convulsion-related therapy; iv. Electrical stimulation based on cortical EEG electrodes; v. Related derivative technologies of the above technologies.
- the target determination method of the present disclosure can also be used in future neuromodulation equipment and neuromodulation schemes to determine neuromodulation targets, which also belongs to the protection of this disclosure. category.
- the embodiments of the present disclosure can efficiently and reliably obtain functional information of various parts of the brain by establishing an accurate individual brain atlas, and improve the accuracy of brain region positioning. Functional positioning with the help of accurate individual-level brain maps improves the reliability of the results of neural regulation target positioning.
- the present disclosure provides an embodiment of a device for target determination, which corresponds to the method embodiment shown in FIG. 2 , and the device specifically It can be applied to various electronic devices.
- the target determination device 500 of this embodiment includes: a data acquisition unit 501 , a processing unit 502 , an abnormality detection unit 503 and a target determination unit 504 . in:
- the data acquisition unit 501 is configured to acquire scan data of the subject, wherein the scan data includes data obtained by performing magnetic resonance imaging on the brain of the subject, and the scan data includes blood corresponding to each voxel in the preset number of voxels.
- the oxygen level depends on the BOLD signal sequence;
- the processing unit 502 is configured to determine at least two ROIs of the subject based on the scan data;
- the abnormality detection unit 503 is configured to determine in at least two ROIs according to preset abnormality detection rules At least one abnormal ROI;
- the target determination unit 504 is configured to determine a target based on the at least one abnormal ROI.
- processing unit 502 is further configured to:
- At least two ROIs are formed.
- processing unit 502 is further configured to:
- Brain regions whose voxel connectivity between brain regions among the multiple brain regions are higher than a preset threshold of voxel connectivity of brain regions are fused to form at least two ROIs.
- processing unit 502 is further configured to:
- At least two ROIs of the subject are determined from the scan data based on a volumetric standard brain structure template.
- processing unit 502 is further configured to:
- At least two ROIs of the subject are determined from the scan data based on a cortical standard brain structure template.
- the abnormality detection unit 503 is further configured to:
- the connectivity between every two voxels in the scan data is determined to form a subject brain connection matrix corresponding to the scan data;
- At least one abnormal ROI is determined based on the population brain connectivity matrix and the subject brain connectivity matrix.
- the target determination unit 504 is further configured to:
- Determining a target based on the at least one abnormal ROI includes:
- the center of the at least one abnormal ROI is determined as the target point, or the center of the at least one abnormal ROI is used as the center of the sphere, and the preset target
- the area of the dot radius is determined as the first target ROI, and the target is determined according to the position of the first target ROI.
- the at least one abnormal ROI is not located in an adjustable brain region, determine the degree of connectivity between the at least one abnormal ROI and other ROIs in the at least two ROIs, and combine the connections between the other ROIs and the at least one abnormal ROI
- the ROI whose connectivity exceeds the preset connectivity threshold and is located in the adjustable region is determined as the second target candidate region;
- the determining the target based on the at least one abnormal ROI includes:
- the center of the target candidate area is determined as the target, or the center of the target candidate area is determined as the center of the sphere and the area with the preset target radius is determined as the target ROI, according to the target
- the position of the point ROI determines the target point.
- the magnetic resonance imaging includes: structural magnetic resonance imaging, and/or task state functional magnetic resonance imaging, and/or resting state functional magnetic resonance imaging.
- FIG. 6 it shows a schematic structural diagram of a computer system 600 suitable for implementing a terminal device or a server of the present disclosure.
- the terminal device or server shown in FIG. 6 is just an example, and should not limit the functions and application scope of the present disclosure.
- computer system 600 comprises central processing unit (CPU, Central Processing Unit) 601, and it can be stored in the program of read only memory (ROM, Read Only Memory) 602 or be loaded into random access memory from storage part 608 (RAM, Random Access Memory) 603 to execute various appropriate actions and processes.
- ROM read only memory
- RAM Random Access Memory
- various programs and data required for the operation of the system 600 are also stored.
- the CPU 601, ROM 602, and RAM 603 are connected to each other through a bus 604.
- An input/output (I/O, Input/Output) interface 605 is also connected to the bus 604 .
- the following components are connected to the I/O interface 605: an input section 606 including a keyboard, a mouse, and the like. It includes an output section 607 such as a cathode ray tube (CRT, Cathode Ray Tube), a liquid crystal display (LCD, Liquid Crystal Display) and a speaker.
- a storage section 608 including a hard disk or the like.
- a communication section 609 including a network interface card such as a LAN (Local Area Network) card, a modem, or the like. The communication section 609 performs communication processing via a network such as the Internet.
- embodiments of the present disclosure include a computer program product, which includes a computer program carried on a computer-readable medium, where the computer program includes program codes for executing the methods shown in the flowcharts.
- the computer program can be downloaded and installed from the network through the communication part 609 .
- CPU central processing unit
- the above-mentioned functions defined in the method of the present disclosure are performed.
- the computer-readable medium in the present disclosure may be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two.
- a computer readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to, electrical connections with one or more wires, portable computer diskettes, hard disks, random access memory (RAM), read-only memory (ROM), erasable Programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination of the above.
- a computer-readable storage medium may be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.
- Computer program code for carrying out the operations of the present disclosure can be written in at least one programming language, or a combination thereof, including object-oriented programming languages—such as Java, Smalltalk, C++, Python, and conventional procedural programming language—such as "C" or a similar programming language.
- the program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server.
- the remote computer can be connected to the user computer through any kind of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computer (such as through an Internet service provider). Internet connection).
- LAN local area network
- WAN wide area network
- Internet service provider such as AT&T, MCI, Sprint, EarthLink, MSN, GTE, etc.
- each block in the flowchart or block diagram may represent a module, program segment, or part of code that contains at least one programmable logic function for implementing the specified logical function.
- Execute instructions may also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or they may sometimes be executed in the reverse order, depending upon the functionality involved.
- each block of the block diagrams and/or flowchart illustrations, and combinations of blocks in the block diagrams and/or flowchart illustrations can be implemented by a dedicated hardware-based system that performs the specified functions or operations , or may be implemented by a combination of dedicated hardware and computer instructions.
- the units involved in the description in the present disclosure may be realized by software or by hardware.
- the described units may also be set in a processor, for example, it may be described as: a processor includes a scan data acquisition unit, a setting unit, a processing unit and a target determination unit. Wherein, the names of these units do not constitute a limitation of the unit itself under certain circumstances.
- the present disclosure also provides a computer-readable medium, and the computer-readable medium may be included in the devices described in the above-mentioned embodiments. It can also exist alone without being assembled into the device.
- the above-mentioned computer-readable medium carries at least one program, and when the above-mentioned at least one program is executed by the device, the device: acquires scan data of the subject, wherein the scan data includes magnetic resonance imaging of the subject's brain The obtained data, the scan data includes the blood oxygen level-dependent BOLD signal sequence corresponding to each voxel in the preset number of voxels; determine at least two ROIs of the subject based on the scan data; Determine at least one abnormal ROI in at least two ROIs; determine the target based on the at least one abnormal ROI.
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Abstract
Description
Claims (13)
- 一种靶点确定方法,包括:获取受试者的扫描数据,其中,所述扫描数据包括对所述受试者的脑部进行磁共振成像得到的数据;基于所述扫描数据确定所述受试者的至少两个感兴趣区域;根据预设的异常检测规则在所述至少两个感兴趣区域中确定至少一个异常感兴趣区域;基于所述至少一个异常感兴趣区域确定靶点。
- 根据权利要求1所述的方法,其中,所述基于所述扫描数据确定所述受试者的至少两个感兴趣区域,包括:基于体积标准脑模板根据所述扫描数据确定所述受试者的至少两个感兴趣区域。
- 根据权利要求1所述的方法,其中,所述基于所述扫描数据确定所述受试者的至少两个感兴趣区域,包括:基于皮层标准脑模板根据所述扫描数据确定所述受试者的至少两个感兴趣区域。
- 根据权利要求1所述的方法,其中,所述基于所述扫描数据确定所述受试者的至少两个感兴趣区域,包括:确定所述扫描数据中每两个体素之间的连接度,形成所述扫描数据对应的脑连接矩阵;基于标准脑的脑区模板及所述脑连接矩阵,形成所述至少两个感兴趣区域。
- 根据权利要求1所述的方法,其中,所述根据预设的异常检测规则在所述至少两个感兴趣区域中确定至少一个异常感兴趣区域,包括:获取群体脑部磁共振数据;根据所述群体脑部磁共振数据确定群体脑连接矩阵;确定所述扫描数据中每两个体素之间的连接度,形成所述扫描数据对应的受试者脑连接矩阵;根据所述群体脑连接矩阵和所述受试者脑连接矩阵确定所述至少一个异常感兴趣区域。
- 根据权利要求1所述的方法,其中,所述基于所述至少一个异常感兴趣区域确定靶点,包括:确定所述至少一个异常感兴趣区域是否位于可调控脑区域;如果所述至少一个异常感兴趣区域位于可调控脑区域,将所述至少一个异常感兴趣区域的中心确定为所述靶点,或,将以所述至少一个异常感兴趣区域的中心为球心、以预设靶点半径的区域确定为第一靶点感兴趣区域,根据所述第一靶点感兴趣区域的位置确定所述靶点;如果所述至少一个异常感兴趣区域没有位于可调控脑区域,确定所述至少一个异常感兴趣区域与所述至少两个感兴趣区域中其他感兴趣区域的连接度,将所述其他感兴趣区域中与所述至少一个异常感兴趣区域的连接度超过预设连接度阈值且位于可调控区域的感兴趣区域确定为第二靶点候选区;将所述第二靶点候选区的中心确定为所述靶点,或,将以所述第二靶点候选区的中心为球心、以预设靶点半径的区域确定为第二靶点感兴趣区域,根据所述第二靶点感兴趣区域的位置确定所述靶点。
- 根据权利要求1所述的方法,其中,所述基于所述至少一个异常感兴趣区域确定靶点,包括:根据所述受试者的疾病类型确定所述靶点所在的脑结构分区;确定所述至少一个异常感兴趣区域或与异常感兴趣区域连接度满足预设连接度阈值条件的感兴趣区域与所述脑结构分区的交集为靶点候选区;将所述靶点候选区的中心确定为所述靶点,或,将以所述靶点候选区的中心为球心、以预设靶点半径的区域确定为靶点感兴趣区域,根据所述靶点感兴趣区域的位置确定所述靶点。
- 根据权利要求1所述的方法,其中,所述磁共振成像包括:脑结构磁共振成像,和/或,任务态功能磁共振成像,和/或,静息态功能磁共振成像。
- 一种靶点确定装置,包括:数据获取单元,被配置成获取受试者的扫描数据,其中,所述扫描数据包括对所述受试者的脑部进行磁共振成像得到的数据;处理单元,被配置成基于所述扫描数据确定所述受试者的至少两个感兴趣区域;异常检测单元,被配置成根据预设的异常检测规则在所述至少两个感兴趣区域中确定至少一个异常感兴趣区域;靶点确定单元,被配置成基于所述至少一个异常感兴趣区域确定靶点。
- 一种电子设备,包括:至少一个处理器;存储装置,所述存储装置上存储有至少一个程序,当所述至少一个程序被所述至少一个处理器执行时,使得所述至少一个处理器实现如权利要求1-8中任一项所述的方法。
- 一种计算机可读存储介质,所述计算机可读存储介质上存储有计算机程序,其中,所述计算机程序被至少一个处理器执行时实现如权利要求1-8中任一项所述的方法。
- 一种神经调控设备,被配置成按照预设的神经调控方案对受试者的靶点进行神经调控;其中,所述靶点是根据权利要求1-8中任一项所述的方法确定的。
- 根据权利要求12所述的设备,其中,所述预设的调控方案包括以下中的至少一项:脑深部电刺激;经颅电刺激;电抽搐疗法;基于皮层脑电电极的电刺激;经颅磁刺激;超声聚焦神经调控;磁共振引导高能超声聚焦治疗调控;光刺激调控。
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