WO2023280003A1 - 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 PDF

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WO2023280003A1
WO2023280003A1 PCT/CN2022/101630 CN2022101630W WO2023280003A1 WO 2023280003 A1 WO2023280003 A1 WO 2023280003A1 CN 2022101630 W CN2022101630 W CN 2022101630W WO 2023280003 A1 WO2023280003 A1 WO 2023280003A1
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brain
target
subject
scan data
determining
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PCT/CN2022/101630
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English (en)
French (fr)
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魏可成
张维
张琼
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北京银河方圆科技有限公司
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Priority to EP22836757.9A priority Critical patent/EP4342372A1/en
Publication of WO2023280003A1 publication Critical patent/WO2023280003A1/zh

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    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61N—ELECTROTHERAPY; MAGNETOTHERAPY; RADIATION THERAPY; ULTRASOUND THERAPY
    • A61N2/00—Magnetotherapy
    • A61N2/004—Magnetotherapy specially adapted for a specific therapy
    • A61N2/006—Magnetotherapy specially adapted for a specific therapy for magnetic stimulation of nerve tissue
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    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/05—Detecting, measuring or recording for diagnosis by means of electric currents or magnetic fields; Measuring using microwaves or radio waves 
    • A61B5/055—Detecting, measuring or recording for diagnosis by means of electric currents or magnetic fields; Measuring using microwaves or radio waves  involving electronic [EMR] or nuclear [NMR] magnetic resonance, e.g. magnetic resonance imaging
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    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61N—ELECTROTHERAPY; MAGNETOTHERAPY; RADIATION THERAPY; ULTRASOUND THERAPY
    • A61N1/00—Electrotherapy; Circuits therefor
    • A61N1/18—Applying electric currents by contact electrodes
    • A61N1/32—Applying electric currents by contact electrodes alternating or intermittent currents
    • A61N1/36—Applying electric currents by contact electrodes alternating or intermittent currents for stimulation
    • A61N1/3605—Implantable neurostimulators for stimulating central or peripheral nerve system
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61N—ELECTROTHERAPY; MAGNETOTHERAPY; RADIATION THERAPY; ULTRASOUND THERAPY
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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 psychiatric diseases, only focusing on a single brain region usually cannot achieve ideal regulation and therapeutic effects. Distributed brain networks and the improvement of connectivity and therapeutic effects of target areas with high individual specificity show that Highly relevant. 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 point determination method, device, electronic equipment, storage medium, and target point regulation device 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 brain regions of the subject according to the scan data, each brain region comprising at least one voxel; determining the at least two brain regions in the at least two brain regions according to the subject's disease type At least one target brain region corresponding to the disease type; the target located in the at least one target brain region is determined according to preset target determination rules.
  • the determining at least two brain regions of the subject according to the scan data includes:
  • At least two brain regions of the subject are determined from the scan data based on a volumetric standard brain template.
  • the determining at least two brain regions of the subject according to the scan data includes:
  • At least two brain regions of the subject are determined from the scan data based on a cortical standard brain template.
  • the determination of at least two brain regions of the subject according to the scan data, each brain region including at least one voxel includes:
  • the at least two brain regions are formed based on a standard brain partition template and the brain connection matrix.
  • the determination of at least two brain regions of the subject according to the scan data, each brain region including at least one voxel includes:
  • the at least two brain regions are formed by fusing the brain regions whose connectivity among the multiple brain regions is higher than a preset brain region connectivity threshold.
  • the determining the target located in the at least one target brain region according to preset target determination rules includes:
  • the central position of the brain region of the at least one target point is determined as the target point.
  • the determining the target located in the at least one target brain region according to preset target determination rules includes:
  • the determining the at least one target located in the target brain region according to preset target determination rules includes:
  • the target points are determined in the intersection.
  • 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 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 brain regions of the subject according to the scan data, each brain region includes at least one voxel, and the processing unit is further configured to According to the subject's disease type, determine at least one target brain region corresponding to the disease type in the at least two brain regions; the target point determination unit is configured to determine the target point located in the at least two brain regions according to preset target point determination rules. The target of at least one target brain region.
  • processing unit is further configured to:
  • the at least two brain regions are formed based on a standard brain partition template and the brain connection matrix.
  • processing unit is further configured to:
  • the at least two brain regions are formed by merging the brain regions whose voxel connection among the brain regions is higher than the preset brain region voxel connection threshold.
  • the target determination unit is further configured to:
  • the central position of the brain region of the at least one target point is determined as the target point.
  • the target determination unit is further configured to:
  • the target determination unit is further configured to:
  • the target points are determined in the intersection.
  • 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 regulation scheme includes at least one of the following:
  • 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 device, storage medium and neuromodulation device obtained in the present disclosure obtain scan data of a subject, wherein the scan data includes data obtained by magnetic resonance imaging of the subject's brain; Determining at least two brain regions of the subject according to the scan data, each brain region including at least one voxel; determining at least one target brain region corresponding to the disease type in the at least two brain regions according to the disease type of the subject; A target located in at least one target brain region is determined according to preset target determination rules.
  • 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.
  • 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 comparison diagram of using the group result to locate the target and using the target determination method of the embodiment of the present disclosure to locate the target in practical application;
  • Fig. 6 is a schematic diagram of the target determined by the target determination method of the embodiment of the present disclosure in practical application;
  • Fig. 7 is a schematic structural diagram of an embodiment of a target point determination device according to the present disclosure.
  • FIG. 8 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 objects distinguished by “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 devices for electronically determining multiple brain regions 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 the magnetic resonance imaging, or can be determined according to the accuracy of the imaging device.
  • the preset number here is not limited to the specific number of voxels.
  • the current actual In the application 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.
  • 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.
  • 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 structural map according to the subject's brain structural magnetic resonance scanning 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.
  • MRI image preprocessing if there is a structural image), such as deskull, field strength correction, individual anatomical structure segmentation, cerebral cortex reconstruction, etc.
  • Step 202 determine at least two brain regions of the subject according to the scan data, and each brain region includes at least one voxel.
  • the brain regions may include brain functional partitions and/or brain structural partitions.
  • 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 may include 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 is a 100,000x100,000-order matrix, which can represent the connectivity between every two voxels in the above scan data; where, the connectivity between two voxels can be based on the T BOLD values corresponding to the voxels, Calculated by 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 equal to or approximately equal 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 based on the brain region template and the brain connection matrix of the standard brain, at least two brain regions are formed.
  • 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.
  • the final brain functional network may contain different voxel positions in each brain region for different subjects, but each voxel belongs to a specific brain region. That is, each brain region of the subject can be a set of voxels composed of voxels in fMRI with the same function.
  • 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 brain regions.
  • 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 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 connection of voxels, thereby obtaining at least two brain regions.
  • 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 target brain region corresponding to the disease type in at least two brain regions according to the disease type of the subject.
  • 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 brain region correspondence of disease types can be queried based on the existing brain region correspondences of identified disease types, and can also be set according to actual needs; here, the method of obtaining brain regions of disease types is just an example, not specific limited.
  • 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.
  • Step 204 Determine the target located in at least one target brain region according to the preset target determination rule.
  • 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 preset target determination rules may include at least one of the following rules:
  • Rule 1 determine the target point by the center of the brain area: the voxel or coordinates located in the center of the target brain area are used as the regulatory target.
  • Rule 2 Determine the target by generating ROI in the brain area: take the center of the target brain area as the center of the sphere, and generate an ROI with a certain distance (such as 3mm) as the radius, and use it as the regulatory target ROI.
  • Rule 3 Determine the target according to the type of disease and existing prior knowledge: If the structural partition of the target is known, it is necessary to find the intersection of the functional partition and the structural partition of the target, and use this intersection as a new target candidate area. Then determine the target through Rule 1 or Rule 2.
  • 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, EEG systems, brain-computer interface devices, etc.
  • implantable neuromodulation devices and non-implantable neuromodulation devices, such as event-related potential analysis systems, EEG systems, brain-computer interface devices, etc.
  • non-implantable neuromodulation devices such as event-related potential analysis systems, EEG systems, brain-computer interface devices, etc.
  • 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:
  • 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.
  • FIG. 5 is a comparison diagram of using the group result to locate the target and using the target determination method of the embodiment of the present disclosure to locate the target.
  • 501 is the target of the target disease type located using the group brain map
  • 502, 503, and 504 are the targets determined by using the target determination method of the embodiment of the present disclosure corresponding to different subjects. Targets for disease types.
  • FIG. 6 is a schematic diagram of targets determined by using the target determination method of the embodiment of the present disclosure in practical applications.
  • 601 is the ventral target of a depressed patient determined on the brain atlas of individual 92 partitions by using the target determination method of the embodiment of the present disclosure
  • 602 is the brain atlas of individual 213 partitions by using the target determination method of the embodiment of the present disclosure
  • the embodiments of the present disclosure can efficiently and reliably obtain functional information of various parts of the brain through the method of 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 point determination device 700 of this embodiment includes: a data acquisition unit 701 , a processing unit 702 and a target point determination unit 703 . in:
  • the data acquisition unit 701 is configured to acquire scan data of the subject, wherein the scan data includes data obtained by performing magnetic resonance imaging on the subject's brain; the scan data includes blood corresponding to each voxel in the preset number of voxels Oxygen levels depend on the BOLD signal sequence.
  • the processing unit 702 is configured to determine at least two brain regions of the subject according to the scan data, each brain region comprising at least one voxel.
  • the processing unit 702 is further configured to determine at least one target brain region corresponding to the disease type in at least two brain regions according to the disease type of the subject.
  • the target point determination unit 703 is configured to determine a target point located in at least one target brain region according to preset target point determination rules.
  • processing unit 702 is further configured to:
  • the connectivity between every two voxels in the scan data is determined to form a brain connection matrix corresponding to the scan data;
  • At least two brain regions are formed based on a standard brain functional partition template and a brain connection matrix.
  • processing unit 702 is further configured to:
  • the brain regions whose voxel connections between the brain regions are higher than the preset brain region voxel connection threshold are fused to form at least two brain regions.
  • the target determination unit 703 is further configured to:
  • the central position of at least one target brain region is determined as the target.
  • the target determination unit 703 is further configured to:
  • the target determination unit 703 is further configured to:
  • Targets are identified in the intersection.
  • functional 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. 8 shows a schematic structural diagram of a computer system 800 suitable for implementing a terminal device or a server of the present disclosure.
  • the terminal device or server shown in FIG. 8 is just an example, and should not limit the functions and application scope of the present disclosure.
  • the computer system 800 includes a central processing unit (CPU, Central Processing Unit) 801, which can be stored in a program in a read-only memory (ROM, Read Only Memory) 802 or loaded into random access from a storage section 808 Various appropriate actions and processes are executed by programs in the memory (RAM, Random Access Memory) 803. In the RAM 803, various programs and data required for the operation of the system 800 are also stored.
  • the CPU 801, ROM 802, and RAM 803 are connected to each other via a bus 804.
  • An input/output (I/O, Input/Output) interface 805 is also connected to the bus 804 .
  • the following components are connected to the I/O interface 805: an input section 806 including a keyboard, a mouse, etc.; an output section 807 including a cathode ray tube (CRT, Cathode Ray Tube), a liquid crystal display (LCD, Liquid Crystal Display), etc., and a speaker ; a storage section 808 including a hard disk, etc.; and a communication section 809 including a network interface card such as a LAN (Local Area Network, Local Area Network) card, a modem, or the like.
  • the communication section 809 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 809 .
  • the central processing unit (CPU) 801 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 having at least one lead, portable computer diskettes, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable Read memory (EPROM or flash memory), fiber optics, portable compact disk read only memory (CD-ROM), optical storage devices, magnetic storage devices, 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.
  • a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code therein. Such propagated data signals may take many forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the foregoing.
  • a computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device.
  • Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
  • Computer program code for carrying out the operations of the present disclosure can be written in one or more programming languages, or combinations thereof, including object-oriented programming languages—such as Java, Smalltalk, C++, Python, and conventional A 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 may be connected to the user computer through any kind of network, including a local area network (LAN) or a wide area network (WAN), or may 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, which may be contained in the device described in the above-mentioned embodiments, or may exist independently 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 Obtained data; determine at least two brain regions of the subject according to the scan data, each brain region includes at least one voxel; determine at least one target corresponding to the disease type in the at least two brain regions according to the disease type of the subject Target brain area; determine at least one target located in the target brain area according to preset target determination rules.

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Abstract

本公开提供了一种靶点确定方法、装置、电子设备、存储介质和神经调控设备。靶点确定方法包括：获取受试者的扫描数据，其中，扫描数据包括对受试者的脑部进行磁共振成像得到的数据；根据扫描数据确定受试者的至少两个脑区，每个脑区包括至少一个体素；根据受试者的疾病类型在至少两个脑区中确定疾病类型对应的至少一个靶点脑区；根据预设靶点确定规则确定至少一个位于靶点脑区的靶点。

Description

靶点确定方法、装置、电子设备、存储介质及神经调控设备 技术领域
本公开涉及计算机技术领域，尤其涉及一种靶点确定方法、装置、电子设备、存储介质及神经调控设备。
背景技术
多种神经与精神疾病往往没有明确的致病灶，只表现为神经系统功能异常。利用电、磁、光以及超声等神经调控手段直接或间接对异常功能网络进行调整是改善患者症状的重要手段。如何在人脑中选择神经调控靶点是难题。研究表明，对于大多数的神经与精神疾病，只关注单一脑区通常不能取得理想的调控和治疗效果，分布式脑网络以及具有高度个体特异性的靶点区域联通性与治疗效果的提升表现出高度相关性。因此，临床需要一种客观、准确并且可量化的辅助手段来帮助医生筛选个体化神经调控靶点。现有的确定神经调控靶点的方法不能满足这一需求。
发明内容
本公开提出了靶点确定方法、装置、电子设备、存储介质及靶点调控设备，用以筛选个体化神经调控靶点。
第一方面，本公开提供了一种靶点确定方法，该方法包括：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；根据所述扫描数据确定所述受试者的至少两个脑区，每个脑区包括至少一个体素；根据所述受试者的疾病类型在所述至少两个脑区中确定所述疾病类型对应的至少一个靶点脑区；根据预设靶点确定规则确定位于所述至少一个靶点脑区的靶点。
在一些可选的实施方式中，所述根据所述扫描数据确定所述受试者的至少两个脑区，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脑区。
在一些可选的实施方式中，所述根据所述扫描数据确定所述受试者的至少两个脑区，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脑区。
在一些可选的实施方式中，所述根据所述扫描数据确定所述受试者的至少两个脑区，每个脑区包括至少一个体素，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；
基于标准脑的分区模板及所述脑连接矩阵，形成所述至少两个脑区。
在一些可选的实施方式中，所述根据所述扫描数据确定所述受试者的至少两个脑区，每个脑区包括至少一个体素，包括：
确定所述扫描数据中每两个体素之间的连接度；
将所述扫描数据对应所述受试者的脑部解剖结构分为多个大区，将所述多个大区中的每个大区剖分为多个脑区，其中，所述多个脑区中每个脑区包括至少一个体素；
将所述多个脑区中各脑区之间的连接度高于预设脑区连接度阈值的脑区融合，形成所述至少两个脑区。
在一些可选的实施方式中，所述根据预设靶点确定规则确定位于所述至少一个靶点脑区的靶点，包括：
将所述至少一个靶点脑区的中心位置确定为所述靶点。
在一些可选的实施方式中，所述根据预设靶点确定规则确定位于所述至少一个靶点脑区的靶点，包括：
确定以所述至少一个靶点脑区的中心位置为球心、以预设靶点半径范围内的区域为靶点感兴趣区域(region of interest，ROI)，将所述靶点感兴趣区域的位置确定为所述靶点。
在一些可选的实施方式中，所述根据预设靶点确定规则确定所述至少一个位于靶点脑区的靶点，包括：
根据所述疾病类型确定所述靶点所在的脑结构分区；
确定所述至少一个靶点脑区与所述脑结构分区的交集；
在所述交集中确定所述靶点。
在一些可选的实施方式中，所述磁共振成像包括：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。
第二方面，本公开提供了一种靶点确定装置，该装置包括：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，被配置成根据所述扫描数据确定所述受试者的至少两个脑区，每个脑区包括至少一个体素，所述处理单元还被配置成根据所述受试者的疾病类型在所述至少两个脑区中确定所述疾病类型对应的至少一个靶点脑区；靶点确定单元，被配置成根据预设靶点确定规则确定位于所述至少一个靶点脑区的靶点。
在一些可选的实施方式中，所述处理单元被进一步配置成：
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；
基于标准脑的分区模板及所述脑连接矩阵，形成所述至少两个脑区。
在一些可选的实施方式中，所述处理单元被进一步配置成：
确定所述扫描数据中每两个体素之间的连接度；
将所述扫描数据对应所述受试者的脑部解剖结构分为多个大区，将所述多个大区中的每个大区剖分为多个脑区，其中，所述多个脑区中每个脑区包括至少一个体素；
将所述多个脑区中各脑区之间的体素连接高于预设脑区体素连接阈值的脑区融合，形成所述至少两个脑区。
在一些可选的实施方式中，所述靶点确定单元被进一步配置成：
将所述至少一个靶点脑区的中心位置确定为所述靶点。
在一些可选的实施方式中，所述靶点确定单元被进一步配置成：
确定以所述至少一个靶点脑区的中心位置为球心、以预设靶点半径范围内的区域为靶点感兴趣区域，将所述靶点感兴趣区域的位置确定为所述靶点。
在一些可选的实施方式中，所述靶点确定单元被进一步配置成：
根据所述疾病类型确定所述靶点所在的脑结构分区；
确定所述至少一个靶点脑区与所述脑结构分区的交集；
在所述交集中确定所述靶点。
在一些可选的实施方式中，所述磁共振成像包括：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。
第三方面，本公开提供了一种电子设备，包括：至少一个处理器；存储装置，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第一方面中任一实现方式描述的方法。
第四方面，本公开提供了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第一方面中任一实现方式描述的方法。
第五方面，本公开提供了一种神经调控设备，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，所述靶点是根据如第一方面中任一实现方式描述的方法确定的。
在一些可选的实施方式中，所述预设的调控方案包括以下至少之一：
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。
为了实现确定神经调控靶点，当前常用的技术手段包括：
1、基于组水平任务态功能磁共振成像(functional magnetic resonance imaging，fMRI)来确定神经调控靶点；这一方式的缺陷包括：任务态fMRI信噪比低、可重复性不高，且要求受试者具备一定的认知水平；任务态fMRI的功能区结果受任务设计影响大；确定功能区的基线水平难度大。
2、借助基于大脑解剖结构的临床经验，在患者头皮表面找到特定功能区的体表投影大致位置确定神经调控靶点，如美国食品和药物管理局FDA(Food And Drug Administration)批准的重复经颅磁刺激(repetitive transcranial magnetic stimulation，rTMS)治疗难治性抑郁症的左侧背外侧前额叶(Dorsal Lateral Prefrontal Cortex，DLPFC)定位方法(常称为“5cm”定位法)；这一方式的缺陷包括：忽略了个体解剖结构差异且定位精度低，导致神经调控靶点定位不精准；忽略个体功能网络差异，靶点位置可能位于其他大脑功能区。
3、根据电极帽确定神经调控靶点，如国际10-20电极帽定位方法；这一方式的缺陷包括：忽略了个体解剖结构差异且定位精度低，导致神经调控靶点定位不精准；忽略个体功能网络差异。
4、基于解剖结构或人群平均fMRI研究定义的ROI来确定神经调控靶点；这一方式的缺陷包括：多种神经与精神疾病往往没有明确的致病灶、只表现为神经系统功能异常，单纯解剖结构无法反应疾病特征；神经和精神类疾病病因复杂，加之个体差异，基于人群平均fMRI的治疗方案有效率低。
5、根据PET扫描数据反映的组织结构代谢情况来确定神经调控靶点；这一方式的缺陷包括：PET扫描价格昂贵，从而加重医疗负担；扫描过程存在一定辐射；PET扫描适用的神经与精神疾病有限；图像信噪比低，解剖结构的边界不清晰影响确定靶点的准确性，临床治疗有效性低。
本公开提供的靶点确定方法、装置、电子设备、存储介质和神经调控设备，通过获取受 试者的扫描数据，其中，扫描数据包括对受试者的脑部进行磁共振成像得到的数据；根据扫描数据确定受试者的至少两个脑区，每个脑区包括至少一个体素；根据受试者的疾病类型在至少两个脑区中确定疾病类型对应的至少一个靶点脑区；根据预设靶点确定规则确定位于至少一个靶点脑区的靶点。本公开的实施例利用功能磁共振成像提供受试者大脑扫描数据，确定受试者的脑区，在充分考虑个体差异性的基础上，可以有效解决传统方法中未考虑个体结构或功能差异导致的神经调控靶点不准确的问题，实现了对受试者的个体化神经调控靶点的定位。
附图说明
附图以示例而非限制的方式大体示出了本文中所讨论的各个实施例。
图1是本公开的一个实施例可以应用于其中的示例性系统架构图；
图2是根据本公开的靶点确定方法的一个实施例的流程示意图；
图3是图2所示的靶点确定方法中步骤202一个实施例的分解示意图；
图4是图2所示的靶点确定方法中步骤202又一实施例的分解示意图；
图5是实际应用中采用群组结果定位靶点与采用本公开实施例靶点确定方法定位靶点的对比图；
图6是实际应用中采用本公开实施例靶点确定方法确定的靶点示意图；
图7是根据本公开的靶点确定装置的一个实施例的结构示意图；
图8是适于用来实现本公开的终端设备或服务器的计算机系统的结构示意图。
具体实施方式
为了能够更加详尽地了解本公开实施例的特点与技术内容，下面结合附图对本公开实施例的实现进行详细阐述，所附附图仅供参考说明之用，并非用来限定本公开实施例。
在本公开实施例记载中，需要说明的是，除非另有说明和限定，术语“连接”应做广义理解，例如，可以是电连接，也可以是两个元件内部的连通，可以是直接相连，也可以通过中间媒介间接相连，对于本领域的普通技术人员而言，可以根据具体情况理解上述术语的具体含义。
需要说明的是，本公开实施例所涉及的术语“第一\第二\第三”仅仅是是区别类似的对象，不代表针对对象的特定排序，可以理解地，“第一\第二\第三”在允许的情况下可以互换特定的顺序或先后次序。应该理解“第一\第二\第三”区分的对象在适当情况下可以互换， 以使这里描述的本公开的实施例可以除了在这里图示或描述的那些以外的顺序实施。
图1示出了可以应用本公开的靶点确定方法或靶点确定装置的实施例的示例性系统架构100。
如图1所示，系统架构100可以包括终端设备101、102、103，网络104和服务器105。网络104用以在终端设备101、102、103和服务器105之间提供通信链路的介质。网络104可以包括各种连接类型，例如有线、无线通信链路或者光纤电缆等等。
用户可以使用终端设备101、102、103通过网络104与服务器105交互，以接收或发送消息等。终端设备101、102、103上可以安装有各种通讯客户端应用，例如磁共振成像控制应用、功能磁共振成像控制应用、网页浏览器应用、购物类应用、搜索类应用、即时通信工具、邮箱客户端、社交平台软件等。
终端设备101、102、103可以是硬件，也可以是软件。当终端设备101、102、103为硬件时，可以是具有显示屏的各种电子设备，包括但不限于智能手机、平板电脑、膝上型便携计算机和台式计算机等等。当终端设备101、102、103为软件时，可以安装在上述所列举的电子确定受试者的多个脑区设备中。其可以实现成多个软件或软件模块(例如用来提供脑图谱的处理)，也可以实现成单个软件或软件模块。在此不做具体限定。
服务器105可以是提供各种服务的服务器，例如对终端设备101、102、103发送的扫描数据进行处理的后台数据处理服务器。后台数据处理服务器可以根据扫描数据，确定受试者的多个脑区及每个脑区对应的体素反馈给终端设备。
需要说明的是，服务器105可以是硬件，也可以是软件。当服务器105为硬件时，可以实现成多个服务器组成的分布式服务器集群，也可以实现成单个服务器。当服务器105为软件时，可以实现成多个软件或软件模块(例如用来提供分布式服务)，也可以实现成单个软件或软件模块。在此不做具体限定。
需要说明的是，本公开所提供的靶点确定方法一般由服务器105执行，相应地，靶点确定装置一般设置于服务器105中。
需要说明的是，在一些情况下，本公开所提供的靶点确定方法可以通过服务器105执行，也可以通过终端设备101、102、103执行，还可以通过服务器105和终端设备101、102、103共同执行。相应地，靶点确定装置可以设置于服务器105中，也可以设置于终端设备101、102、103中，还可以部分设置于服务器105中、部分设置于终端设备101、102、103中。以及相应地，系统架构100可以只包括服务器105，或者只包括终端设备101、102、103，或者可以包括终端设备101、102、103，网络104和服务器105。本公开对此不做限定。
应该理解，图1中的终端设备、网络和服务器的数目仅仅是示意性的。根据实现需要，可以具有任意数目的终端设备、网络和服务器。
继续参考图2，其示出了根据本公开的靶点确定方法的一个实施例的流程200。该靶点确定方法，包括以下步骤：
步骤201，获取受试者的扫描数据。
本公开实施例中，扫描数据包括对受试者的脑部进行磁共振成像得到的数据。
扫描数据包括预设数目个体素中每个体素对应的血氧水平依赖(Blood Oxygen Level Dependency，BOLD)信号序列。
在本实施例中，靶点确定方法的执行主体(例如图1所示的服务器)可以首先本地或者远程地从与上述执行主体网络连接的其他电子设备(例如图1所示的终端设备)获取受试者的扫描数据。
体素又称立体像素(voxel)，是体积像素(volume pixel)的简称。体素从概念上类似二维空间的最小单位——像素，像素用在二维电脑图像的影像数据上。体素是数字数据于三维空间分割上的最小单位，应用于三维成像、科学数据与医学影像等领域。
体素对应的BOLD信号序列是指，对受试者进行磁共振扫描，进而对每个体素每隔预设时间单位得到一个BOLD信号，并最终得到一段时间的BOLD信号，把这些BOLD信号按照采集时间先后顺序排列即得到每个体素对应的BOLD信号序列，其中所包括的BOLD信号数目可以为目标任务对应的时长除以预设时间单位所得到的整数商。例如，扫描对应的时长300秒，预设时间单位为2秒，则每个体素对应的BOLD信号序列中有150个BOLD值，也可以认为每个体素对应的BOLD信号序列有150帧数据，或者也可以认为每个体素对应的BOLD信号序列为维度为150维的向量，或者也可以认为每个体素对应的BOLD信号序列为1×150阶矩阵，本公开对此不做具体限定。
可以理解的是，扫描数据所包括体素的具体数目可以根据磁共振成像的扫描精度确定，也可以根据成像设备的精度确定，这里的预设数目并非对于体素的具体数量限定，目前的实际应用中，人脑扫描数据的体素数量是以万或十万来衡量的，随着扫描技术的进步，人脑扫描数据所包括的体素数量还能够进一步提高。
在本公开中，上述执行主体可以从本地或者远程地从与上述执行主体网络连接的其他电子设备(例如图1所示的终端设备)获取受试者的扫描数据。
本公开的实施例中，磁共振成像可包括：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。
功能磁共振成像得到的数据含有时间序列信息，相当于四维图像。例如：采集功能磁共振成像图像，3维的图像矩阵(Length x Width x Height，L x M x N)，每2秒采集一帧，则6分钟可采集150帧数据，形成LxMxN个体素x150的功能磁共振成像数据信号。
结构磁共振成像得到的数据是一个高分辨率的三维灰度解剖结构图像，例如T1w(T1加权成像---突出组织T1弛豫(纵向弛豫)差别)及其相关影像，T2w(T2加权成像----突出组织T2弛豫(横向弛豫)差别)及其相关影像，液体衰减反转恢复序列(fluid attenuated inversion recovery，FLAIR)及其相关影像；结构磁共振成像还可包括磁共振弥散成像，如：弥散加权成像(diffusion-weighted imaging，DWI)及其相关影像，弥散张量成像(diffusion tensor imaging，DTI)及其相关影像等。
DTI是一种用于研究中枢神经系统解剖神经束弥散各向异性和显示白质纤维解剖的磁共振技术，通过组织中水分子弥散的各向异性(anisotropy)来探测组织微观结构。脑白质的各向异性是由于平行走行的髓鞘轴索纤维所致，脑白质的弥散在平行神经纤维方向最大，即弥散各向异性分数(fractionalanisotropy，FA)最大，可近似确定为1(实际可为大于0.9并趋近于1的分数)。这一特性用彩色标记可反映出脑白质的空间方向性，即弥散最快的方向指示纤维走行的方向。通过DTI进行纤维束成像可得到反映大脑结构的脑连接矩阵。
可以理解的是，静息态功能磁共振成像为受试者在扫描期间不执行任何任务时对受试者脑部进行磁共振扫描所得到的磁共振成像。任务态功能磁共振成像为在受试者执行目标任务时对受试者脑部进行磁共振扫描所得到的磁共振成像。
在获取受试者的脑结构磁共振扫描数据后，可以采用各种实现方式根据受试者的脑结构磁共振扫描数据确定受试者的脑结构图，即，得到受试者的大脑中具体哪些区域是什么结构部件。例如，可以采用现有的处理三维脑扫描数据的软件来实现，比如磁共振数据处理软件自由皮层重建(FreeSurfer)。又例如，也可以预先基于大量的脑结构影像扫描样本数据和对应的脑结构部件的标注对深度学习模型进行训练，再将受试者的脑结构磁共振扫描数据输入训练得到的深度学习模型，并得到相应的脑结构图。
在一些可选的实施方式中，上述执行主体在获取到受试者的扫描数据后，对扫描数据进行预处理。
本公开中，对于预处理的处理方法不做具体限定，示例性地，预处理可包括：
对磁共振成像影像预处理，例如，
(1)时间层校正、头动校正、时间信号滤波、噪声成分回归、空间平滑等；
(2)功能磁共振成像影像与结构像配准(如果有结构像)；
(3)功能磁共振成像信号投影到结构像(如果有结构像)，包括重建的个体脑皮层影像或者相关组平均水平的结构影像。
对磁共振成像影像预处理(如果有结构像)，例如去头骨、场强校正、个体解剖结构分割、脑皮层重建等。
步骤202，根据扫描数据确定受试者的至少两个脑区，每个脑区包括至少一个体素。
本公开实施例中，脑区可包括脑功能分区和/或脑结构分区。
对于上述步骤202，本公开提供了多种可选的实现方式。
图3是图2所示的靶点确定方法中步骤202一个实施例的局部分解示意图。在一些可选的实施方案中，如图3所示，上述步骤202可具体包括：
步骤202a1，确定扫描数据中每两个体素之间的连接度，形成扫描数据对应的脑连接矩阵。
本公开中，体素与ROI的连接度，可包括体素与ROI中每个体素连接度的平均值；两个ROI之间的连接度，可包括两个ROI中，每个ROI中体素与另一个ROI中每个体素的连接度的平均值；体素与脑区的连接度，可包括体素与脑区中每个体素连接度的平均值；两个脑区之间的连接度，可包括两个脑区中，每个脑区中体素与另一个脑区中每个体素的连接度的平均值。
连接度表征脑连接的连接程度，也可以表示为相关度。这里，脑连接可包括功能连接和结构连接。功能连接可基于ROI内体素对应的BOLD时间序列，通过皮尔逊相关系数计算得到；结构连接包括如根据纤维束成像获得的ROI间的结构连接等。
示例地，假设扫描数据中体素的数量为10万个，每个体素对应的BOLD信号序列包括T个BOLD值，T为扫描时间对应的时间维度的采样数，则扫描数据对应的脑连接矩阵则为10万x10万阶矩阵，该脑连接矩阵能够表征上述扫描数据中每两个体素之间的连接度；其中，两个体素之间的连接度可基于体素对应的T个BOLD值，通过皮尔逊相关系数计算得到。
本公开中，相关性系数为皮尔逊(pearson)相关系数，是用来衡量变量间的线性程度的系数。其计算公式为：
Figure PCTCN2022101630-appb-000001
公式定义为：两个连续变量(X,Y)的pearson相关性系数(ρ x,y)等于它们之间的协方差cov(X,Y)除以它们各自标准差的乘积(σ X,σ Y)。系数的取值总是在-1.0到1.0之间，等于或近似等于0的变量被称为无相关性，等于或近似等于1或者-1被称为具有强相关性。这里，近 似等于可以理解为与目标值的差值在误差允许的范围内，例如，本公开中，0.01可近似等于0，或者，0.99可近似等于1，这里只是举例说明，实际应用中可根据计算所需的精度来确定近似等于的误差允许范围。
步骤202a2，基于标准脑的脑区模板及脑连接矩阵，形成至少两个脑区。
示例地，可利用模式识别或机器学习方法，基于标准脑的脑区模板对受试者建立包含2个以上脑区的脑图谱。方法可包括但不限于：独立成分分析(Independent Component Correlation Algorithm，ICA)，主成分分析(Principal Component Analysis，PCA)，各类型聚类方法，因子分析(factor analysis)，线性判别分析(linear discriminant analysis，LDA)，各种矩阵分解方法等。最终得到的脑功能网络，针对不同被试可能各个脑区包括的体素位置不同，但每个体素都归属于某一个特定的脑区。即受试者的每个脑区，都可以是由具有相同功能的fMRI中的体素构成的体素集合。
图4是图2所示的靶点确定方法中步骤202又一实施例的分解示意图。在一些可选的实施方式中，如图4所示，上述步骤202可具体包括：
步骤202b1，确定扫描数据中每两个体素之间的连接度。
步骤202b2，将扫描数据对应受试者的脑部解剖结构分为多个大区，将多个大区(例如，每个大区)剖分为多个脑区，其中，多个脑区中每个脑区包括至少一个体素。
步骤202b3，将多个脑区中各脑区之间的体素连接度高于预设脑区体素连接度阈值的脑区融合，形成至少两个脑区。
示例地，首先把受试者的大脑先按照主要的解剖结构边界分成多个大区；之后在每个大区利用功能连接进行剖分，每个大区都按可靠性(test-retest reliability)来确定体素的连接度。把每个大区进行剖分之后得到多个脑区，再把这些脑区根据其所包含的体素的连接度进行融合，将体素高度连接的脑区合为一个脑区，示例地，最终可在全脑确定至少两个脑区。
例如，可通过将大脑的左、右皮层各分为额叶、顶叶、枕叶、颞叶和泛中央沟区域五个大区，将初始的个体脑图谱分为十个大区。再例如，可按大脑的高级皮层和低级皮层分区，左右脑共分4个区域。
在一些可选的实施方式中，上述步骤202可具体包括：
预先选择或生成一个群体脑图谱作为脑图谱模板，将脑图谱模板中对应至少两个脑区的边界投射到受试者的脑部扫描数据。
基于受试者的脑部扫描数据对至少两个脑脑区域的边界调整，以使调整后的脑脑区域边 界与至少受试者的脑部扫描数据相匹配，形成至少两个脑区。
示例地，先将群体脑图谱直接投射到受试者的大脑，之后再采用递归算法，根据受试者的解剖脑图谱对这些群体脑图谱投射的脑区的边界进行逐步调整，直到脑区的边界趋于稳定。递归过程将利用受试者的脑连接个体差异分布，以及受试者自身的脑影像信噪比，来确定脑脑区域的边界调整的幅度。最后，将脑脑区域按照体素的连接进行融合，由此得到至少两个脑区。
在一些可选的实施方式中，上述步骤202可具体包括：
基于体积标准脑结构模板根据扫描数据确定受试者的至少两个ROI。将体积标准脑结构模板中的白质和脑室区域提取出来，构建体积标准脑结构模板二值掩膜Mask，将白质和脑室区域从该Mask中去掉，得到无白质和脑室区域的Mask，将无白质和脑室区域的Mask重采样，得到至少两个ROI。或者，构建体积标准脑结构模板二值Mask，并进行重采样，得到至少两个ROI。
在一些可选的实施方式中，上述步骤202可具体包括：
基于皮层标准脑结构模板根据扫描数据确定受试者的至少两个ROI。对皮层标准脑结构模板重采样生成至少两个ROI。例如，基于粗分辨率皮层surface模板(比如：fsaverage3(fs3)、fsaverage4(fs4))生成高分辨率模板的至少两个ROI(比如：fsaverage6(fs6))。具体地，分别对粗分辨率模板左右脑顶点进行顺序赋值(1,2,3...),再将其重采样(近邻法插值)至fs6模板空间,根据赋值顺序依次统计各数值所对应的fs6surface中所有顶点索引号,此为fs6空间的至少两个ROI,例如:左脑fs4surface模板中第13个顶点被赋值为13,重采样至fs6模板空间后可以找到30个数值都为13的顶点,那么左脑fs6surface模板第13个ROI就是由这30个顶点组成的。基于surface模板中所有顶点生成至少两个ROI，对任意surface模板，将其中每个顶点作为1个ROI，比如(fsaverage6左脑模板包括40962个顶点，可对应生成40962个ROI)。
步骤203，根据受试者的疾病类型在至少两个脑区中确定疾病类型对应的至少一个靶点脑区。
疾病类型包括对受试者诊断确定的疾病类型或欲对受试者进行治疗的症状对应的疾病类型。
疾病类型的脑区对应关系可以根据现有的已确定的疾病类型的脑区对应关系查询，也可根据实际需求进行设置；这里对于疾病类型的脑区的获取方式只是举例说明，而非具体的限定。
这里，靶点脑区即与靶点相对应的脑区，靶点与靶点脑区之间具有神经关联，能够通过对靶点的刺激对靶点脑区进行神经调控。
步骤204，根据预设靶点确定规则确定位于至少一个靶点脑区的靶点。
靶点可以包括单一体素对应的坐标，也可以是一些体素构成的一个区域集合。
这里，预设靶点确定规则可包括以下规则中的至少一个：
规则一，通过脑区中心点确定靶点：将位于靶点脑区中心位置的体素或坐标作为调控靶点。
规则二，通过脑区生成ROI确定靶点：以位于靶点脑区中心位置为球心，某一距离(如3mm)为半径生成ROI，作为调控靶点ROI。
规则三，根据疾病类型和已有的先验知识确定靶点：如已知靶点所在结构分区，则需要找到靶点功能分区与结构分区的交集，将此交集作为新的靶点候选区，再通过规则一或规则二确定靶点。
在一些可选的实施方式中，上述步骤204可具体包括：
将至少一个靶点脑区的中心位置确定为靶点。
在一些可选的实施方式中，上述步骤204可具体包括：
确定以至少一个靶点脑区的中心位置为球心、以预设靶点半径范围内的区域为靶点ROI，将靶点ROI的位置确定为靶点。
本公开对于预设靶点半径的长度不做具体限定，预设靶点半径可根据神经调控的实际需要进行设置，例如预设靶点半径可以是3mm。
在一些可选的实施方式中，上述步骤204可具体包括：
根据疾病类型确定靶点所在的脑结构分区，确定至少一个靶点脑区与脑结构分区的交集，在该交集中确定靶点。
在一些可选的实施方式中，靶点需要满足以下条件：靶点不可位于大脑内侧面和底部，靶点可位于脑回，靶点不可位于脑沟。
按照上述方法确定的靶点比较准确，在实际应用中，科研人员或医护人员可根据上述方法确定的靶点，利用光学导航设备或电磁导航设备对受试者进行神经调控导航，能够提高神经调控的有效率。
本公开提供了一种神经调控设备，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，受试者的靶点是根据本公开上述任一实施例中靶点确定方法确定的。
神经调控设备可包括植入性神经调控设备和非植入性神经调控设备，例如：事件相关电 位分析系统、脑电图系统、脑机接口设备等。本公开对于神经调控设备的具体形式不做限定，这里只是举例说明。
对受试者的靶点进行神经调控，可以是由操作人员按照靶点对神经调控设备进行连接后调控，也可以是由神经调控设备根据操作人员输入或根据神经调控设备主动获取得到的受试者的靶点进行调控。这里只是举例说明，而非具体的对受试者的靶点进行神经调控的限定，技术人员可以根据实际的神经调控设备使用方式进行操作。示例地，预设的神经调控方案可以包括但不限于：
a.基于电脉冲序列的神经调控方案
i.脑深部电刺激
ii.经颅电刺激
iii.电抽搐相关疗法
iv.基于皮层脑电电极的电刺激
v.以上技术的相关衍生技术
b.基于磁脉冲序列的神经调控方案
i.经颅磁刺激及相关方案
ii.以上技术的相关衍生技术
c.基于超声的神经调控方案
i.超声聚焦神经调控方案
ii.磁共振引导高能超声聚焦治疗系统及相关调控方案
iii.以上技术的相关衍生技术
d.基于光的神经调控方案
i.不同波段的光刺激及相关方案
ii.以上技术的相关衍生技术
随着新型神经调控设备和神经调控技术的逐渐发展，在将来的神经调控设备及神经调控方案中也均可采用本公开的靶点确定方法确定神经调控的靶点，这也属于本公开的保护范畴。
为直观展示上述实施例中方法的效果，示例地，图5为实际应用中采用群组结果定位靶点与采用本公开实施例靶点确定方法定位靶点的对比图。如图5所示，其中，501为利用群组脑图谱定位的目标疾病种类的靶点，502、503和504为分别对应不同的受试者利用本公开实施例靶点确定方法确定的该目标疾病种类的靶点。
图6为实际应用中采用本公开实施例靶点确定方法确定的靶点示意图。如图6所示，601 为利用本公开实施例靶点确定方法在个体92分区脑图谱上确定的抑郁病人腹侧靶点，602为利用本公开实施例靶点确定方法在个体213分区脑图谱上确定的失语病人腹侧靶点。
本公开实施例通过建立精准个体脑图谱的方法能高效、可靠地获取大脑各个部位的功能信息，提升了脑区定位的准确性。借助精准个体水平的脑图谱进行功能定位，提升了神经调控靶点定位结果的可靠性。
进一步参考图7，作为对上述各图所示方法的实现，本公开提供了一种靶点确定装置的一个实施例，该装置实施例与图2所示的方法实施例相对应，该装置具体可以应用于各种电子设备中。
如图7所示，本实施例的靶点确定装置700包括：数据获取单元701、处理单元702及靶点确定单元703。其中：
数据获取单元701被配置成获取受试者的扫描数据，其中，扫描数据包括对受试者的脑部进行磁共振成像得到的数据；扫描数据包括预设数目个体素中每个体素对应的血氧水平依赖BOLD信号序列。
处理单元702被配置成根据扫描数据确定受试者的至少两个脑区，每个脑区包括至少一个体素。
处理单元702还被配置成根据受试者的疾病类型在至少两个脑区中确定疾病类型对应的至少一个靶点脑区。
靶点确定单元703被配置成根据预设靶点确定规则确定位于至少一个靶点脑区的靶点。
在一些可选的实施方式中，处理单元702被进一步配置成：
获取扫描数据中每个体素对应的BOLD信号序列；
基于每个体素对应的BOLD信号序列，确定扫描数据中每两个体素之间的连接度，形成扫描数据对应的脑连接矩阵；
基于标准脑的功能分区模板及脑连接矩阵，形成至少两个脑区。
在一些可选的实施方式中，处理单元702被进一步配置成：
确定扫描数据中每两个体素之间的连接度；
将扫描数据对应受试者的脑部解剖结构分为多个大区，将多个大区中的每个大区剖分为多个脑区，其中，多个脑区中每个脑区包括至少一个体素；
将多个脑区中各脑区之间的体素连接高于预设脑区体素连接阈值的脑区融合，形成至少两个脑区。
在一些可选的实施方式中，靶点确定单元703被进一步配置成：
将至少一个靶点脑区的中心位置确定为靶点。
在一些可选的实施方式中，靶点确定单元703被进一步配置成：
确定以至少一个靶点脑区的中心位置为球心、以预设靶点半径范围内的区域为靶点ROI，将靶点ROI的位置确定为靶点。
在一些可选的实施方式中，靶点确定单元703被进一步配置成：
根据疾病类型确定靶点所在的脑结构分区；
确定至少一个靶点脑区与脑结构分区的交集；
在交集中确定靶点。
在一些可选的实施方式中，功能磁共振成像包括：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。
需要说明的是，本公开提供的靶点确定装置中各单元的实现细节和技术效果可以参考本公开中其它实施例的，在此不再赘述。
下面参考图8，其示出了适于用来实现本公开的终端设备或服务器的计算机系统800的结构示意图。图8示出的终端设备或服务器仅仅是一个示例，不应对本公开的功能和使用范围带来任何限制。
如图8所示，计算机系统800包括中央处理单元(CPU，Central Processing Unit)801，其可以根据存储在只读存储器(ROM，Read Only Memory)802中的程序或者从存储部分808加载到随机访问存储器(RAM，Random Access Memory)803中的程序而执行各种适当的动作和处理。在RAM 803中，还存储有系统800操作所需的各种程序和数据。CPU 801、ROM 802以及RAM 803通过总线804彼此相连。输入/输出(I/O，Input/Output)接口805也连接至总线804。
以下部件连接至I/O接口805：包括键盘、鼠标等的输入部分806；包括诸如阴极射线管(CRT，Cathode Ray Tube)、液晶显示器(LCD，Liquid Crystal Display)等以及扬声器等的输出部分807；包括硬盘等的存储部分808；以及包括诸如LAN(局域网，Local Area Network)卡、调制解调器等的网络接口卡的通信部分809。通信部分809经由诸如因特网的网络执行通信处理。
特别地，根据本公开的实施例，上文参考流程图描述的过程可以被实现为计算机软件程序。例如，本公开的实施例包括一种计算机程序产品，其包括承载在计算机可读介质上的计算机程序，该计算机程序包含用于执行流程图所示的方法的程序代码。在这样的实施例中，该计算机程序可以通过通信部分809从网络上被下载和安装。在该计算机程序被中央处理单 元(CPU)801执行时，执行本公开的方法中限定的上述功能。需要说明的是，本公开的计算机可读介质可以是计算机可读信号介质或者计算机可读存储介质或者是上述两者的任意组合。计算机可读存储介质例如可以是——但不限于——电、磁、光、电磁、红外线、或半导体的系统、装置或器件，或者任意以上的组合。计算机可读存储介质的更具体的例子可以包括但不限于：具有至少一个导线的电连接、便携式计算机磁盘、硬盘、随机访问存储器(RAM)、只读存储器(ROM)、可擦式可编程只读存储器(EPROM或闪存)、光纤、便携式紧凑磁盘只读存储器(CD-ROM)、光存储器件、磁存储器件、或者上述的任意合适的组合。在本公开中，计算机可读存储介质可以是任何包含或存储程序的有形介质，该程序可以被指令执行系统、装置或者器件使用或者与其结合使用。而在本公开中，计算机可读的信号介质可以包括在基带中或者作为载波一部分传播的数据信号，其中承载了计算机可读的程序代码。这种传播的数据信号可以采用多种形式，包括但不限于电磁信号、光信号或上述的任意合适的组合。计算机可读的信号介质还可以是计算机可读存储介质以外的任何计算机可读介质，该计算机可读介质可以发送、传播或者传输用于由指令执行系统、装置或者器件使用或者与其结合使用的程序。计算机可读介质上包含的程序代码可以用任何适当的介质传输，包括但不限于：无线、电线、光缆、RF等等，或者上述的任意合适的组合。
可以以一种或多种程序设计语言或其组合来编写用于执行本公开的操作的计算机程序代码，程序设计语言包括面向对象的程序设计语言—诸如Java、Smalltalk、C++、Python，还包括常规的过程式程序设计语言—诸如“C”语言或类似的程序设计语言。程序代码可以完全地在用户计算机上执行、部分地在用户计算机上执行、作为一个独立的软件包执行、部分在用户计算机上部分在远程计算机上执行、或者完全在远程计算机或服务器上执行。在涉及远程计算机的情形中，远程计算机可以通过任意种类的网络——包括局域网(LAN)或广域网(WAN)—连接到用户计算机，或者，可以连接到外部计算机(例如利用因特网服务提供商来通过因特网连接)。
附图中的流程图和框图，图示了按照本公开各种实施例的系统、方法和计算机程序产品的可能实现的体系架构、功能和操作。在这点上，流程图或框图中的每个方框可以代表一个模块、程序段、或代码的一部分，该模块、程序段、或代码的一部分包含至少一个用于实现规定的逻辑功能的可执行指令。也应当注意，在有些作为替换的实现中，方框中所标注的功能也可以以不同于附图中所标注的顺序发生。例如，两个接连地表示的方框实际上可以基本并行地执行，它们有时也可以按相反的顺序执行，这依所涉及的功能而定。也要注意的是，框图和/或流程图中的每个方框、以及框图和/或流程图中的方框的组合，可以用执行规定的 功能或操作的专用的基于硬件的系统来实现，或者可以用专用硬件与计算机指令的组合来实现。
描述于本公开中所涉及到的单元可以通过软件的方式实现，也可以通过硬件的方式来实现。所描述的单元也可以设置在处理器中，例如，可以描述为：一种处理器包括扫描数据获取单元、设置单元、处理单元和靶点确定单元。其中，这些单元的名称在某种情况下并不构成对该单元本身的限定。
作为另一方面，本公开还提供了一种计算机可读介质，该计算机可读介质可以是上述实施例中描述的装置中所包含的；也可以是单独存在，而未装配入该装置中。上述计算机可读介质承载有至少一个程序，当上述至少一个程序被该装置执行时，使得该装置：获取受试者的扫描数据，其中，扫描数据包括对受试者的脑部进行磁共振成像得到的数据；根据扫描数据确定受试者的至少两个脑区，每个脑区包括至少一个体素；根据受试者的疾病类型在至少两个脑区中确定疾病类型对应的至少一个靶点脑区；根据预设靶点确定规则确定至少一个位于靶点脑区的靶点。
以上描述仅为本公开的较佳实施例以及对所运用技术原理的说明。本领域技术人员应当理解，本公开中所涉及的发明范围，并不限于上述技术特征的特定组合而成的技术方案，同时也应涵盖在不脱离上述发明构思的情况下，由上述技术特征或其等同特征进行任意组合而形成的其它技术方案。例如上述特征与本公开中公开的(但不限于)具有类似功能的技术特征进行互相替换而形成的技术方案。
本公开实施例所记载的技术方案之间，在不冲突的情况下，可以任意组合。
以上，仅为本公开的具体实施方式，但本公开的保护范围并不局限于此，任何熟悉本技术领域的技术人员在本公开揭露的技术范围内，可轻易想到变化或替换，都应涵盖在本公开的保护范围之内。因此，本公开的保护范围应以权利要求的保护范围为准。

Claims (14)

  1. 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；
    根据所述扫描数据确定所述受试者的至少两个脑区，每个脑区包括至少一个体素；
    根据所述受试者的疾病类型在所述至少两个脑区中确定所述疾病类型对应的至少一个靶点脑区；
    根据预设靶点确定规则确定位于所述至少一个靶点脑区的靶点。
  2. 根据权利要求1所述的方法，其中，所述根据所述扫描数据确定所述受试者的至少两个脑区，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脑区。
  3. 根据权利要求1所述的方法，其中，所述根据所述扫描数据确定所述受试者的至少两个脑区，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脑区。
  4. 根据权利要求1所述的方法，其中，所述根据所述扫描数据确定所述受试者的至少两个脑区，每个脑区包括至少一个体素，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，形成所述至少两个脑区。
  5. 根据权利要求1所述的方法，其中，所述根据所述扫描数据确定所述受试者的至少两个脑区，每个脑区包括至少一个体素，包括：
    确定所述扫描数据中每两个体素之间的连接度；
    将所述扫描数据对应所述受试者的脑部解剖结构分为多个大区，将所述多个大区中的每个大区剖分为多个脑区，其中，所述多个脑区中每个脑区包括至少一个体素；
    将所述多个脑区中各脑区之间的连接度高于预设脑区连接度阈值的脑区融合，形成所述 至少两个脑区。
  6. 根据权利要求1所述的方法，其中，所述根据预设靶点确定规则确定位于所述至少一个靶点脑区的靶点，包括：
    将所述至少一个靶点脑区的中心位置确定为所述靶点。
  7. 根据权利要求1所述的方法，其中，所述根据预设靶点确定规则确定位于所述至少一个靶点脑区的靶点，包括：
    确定以所述至少一个靶点脑区的中心位置为球心、以预设靶点半径范围内的区域为靶点感兴趣区域，将所述靶点感兴趣区域的位置确定为所述靶点。
  8. 根据权利要求1所述的方法，其中，所述根据预设靶点确定规则确定位于所述至少一个靶点脑区的靶点，包括：
    根据所述疾病类型确定所述靶点所在的脑结构分区；
    确定所述至少一个靶点脑区与所述脑结构分区的交集；
    在所述交集中确定所述靶点。
  9. 根据权利要求1所述的方法，其中，所述磁共振成像包括：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。
  10. 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，被配置成根据所述扫描数据确定所述受试者的至少两个脑区，每个脑区包括至少一个体素，
    所述处理单元还被配置成根据所述受试者的疾病类型在所述至少两个脑区中确定所述疾病类型对应的至少一个靶点脑区；
    靶点确定单元，被配置成根据预设靶点确定规则确定位于所述至少一个靶点脑区的靶点。
  11. 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1-9中任一所述的方法。
  12. 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1-9中任一所述的方法。
  13. 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1-9中任一所述的方法确定的。
  14. 根据权利要求13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。
PCT/CN2022/101630 2021-07-05 2022-06-27 靶点确定方法、装置、电子设备、存储介质及神经调控设备 WO2023280003A1 (zh)

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