Mapping methods, positioning devices, and online query systems from the cerebral cortex to the cranial surface

Abstract

This invention discloses a mapping method, localization device, and online query system from the cerebral cortex to the cranial surface. The mapping method includes the following steps: selecting the i-th cortical target point in the MNI standard brain space; transforming the cortical target point in the MNI standard brain space to the original space of the j-th participant, as the original spatial target point; accurately locating the optimal individual cranial surface position of the i-th cortical target point in the j-th participant corresponding to the original spatial target point; mapping the i-th cortical target point to the optimal individual cranial surface positions of all participants; expressing the optimal individual cranial surface positions of all participants corresponding to the i-th cortical target point in the CPC space to calculate the corresponding optimal group-level cranial surface positions; thus obtaining the optimal group-level cranial surface positions of all cortical target points corresponding to all participants. Based on a large sample population, this invention uses cranial surface point normals to establish a group probability mapping between all coordinates in the standard brain space and the cranial surface space.

Description

Technical Field

[0001] This invention relates to a mapping method from the cerebral cortex to the cranial surface, a positioning device using the mapping method, and an online query system using the mapping method, belonging to the field of brain science technology. Background Technology

[0002] In non-invasive neuromodulation techniques, population information across individuals, studies, and modalities is often used to optimize target selection. For example, task-based brain imaging population activation coordinates are frequently used as stimulation targets to study cognitive function in local brain regions; coordinate-based meta-analysis integrates results from different studies, providing more robust stimulation targets; cross-modal analysis synthesizes multimodal evidence such as efficacy, neuromodulation, and brain injury, providing more comprehensive information and helping to identify the most therapeutically promising stimulation targets for neuropsychiatric disorders. To achieve a unified description and widespread application of stimulation targets from different individuals and studies, these target coordinates are usually converted into standard brain space (e.g., MNI) coordinates.

[0003] For these stimulation targets expressed in standard brain space, accurate localization and placement of the stimulation device on the individual's cranial surface is crucial to determining the modulatory effect. One approach is to utilize MRI-guided neuronavigation technology, which enables relatively precise placement of the stimulation device. However, this method relies on individual magnetic resonance imaging (MRI) data and frameless stereotactic navigation equipment, making it complex and costly, and difficult to implement in most practical applications.

[0004] Therefore, another type is based on the cranial surface (i.e., Figure 1 Measurement and localization methods for the scalp (within the brain's cerebral cortex) are widely used in such scenarios, such as the 10-20 system localization method. This method is often used for approximate localization of the cranial surface using standard brain space coordinates. However, the correspondence between the standard brain space and cranial surface space is poor, and manually locating 10-20 system landmarks is time-consuming and laborious. Recently proposed cranial surface heuristic methods are more accurate and easier to operate; however, existing cranial surface heuristic methods do not provide error distribution information for target localization, thus limiting their clinical value. More importantly, these methods are only developed for a few specific MNI targets, therefore a more general method is needed to manually localize the stimulation site of any MNI cortical target. Summary of the Invention

[0005] The primary technical problem to be solved by this invention is to provide a mapping method from the cerebral cortex to the cranial surface.

[0006] Another technical problem to be solved by the present invention is to provide a positioning device using the above-described mapping method.

[0007] Another technical problem to be solved by the present invention is to provide an online query system using the above-described mapping method.

[0008] To achieve the above-mentioned technical objectives, the present invention adopts the following technical solution:

[0009] According to a first aspect of the present invention, a mapping method from the cerebral cortex to the cranial surface is provided, comprising the following steps:

[0010] S1: Select the i-th cortical target point in the MNI standard brain space;

[0011] S2: Transform the cortical target points in the MNI standard brain space to the original space of the j-th participant, as the original space target point c. ij ;

[0012] S3: Corresponding to the original spatial target point c ij The optimal cranial surface location of the i-th cortical target point in the j-th participant was precisely determined.

[0013] S4: Let j = j + 1, and repeat steps S2 and S3 until the i-th cortical target point is mapped to the individual optimal cranial surface location for all participants.

[0014] S5: Express the optimal individual cranial surface locations of all participants corresponding to the i-th cortical target point in the CPC space, forming... CPC spatial distribution;

[0015] S6: Based on the results obtained in step S5 Based on the CPC spatial distribution, the corresponding optimal cranial surface location s at the population level was calculated. g (c i );

[0016] S7: Repeat steps S2-S6 to obtain the optimal cranial surface location of all cortical target points for all participants.

[0017] Preferably, in step S3, locating the optimal cranial surface position of the i-th cortical target point in the j-th participant includes the following sub-steps:

[0018] Generate an individual CPC system for the j-th participant;

[0019] Calculate the three-dimensional spatial coordinates of cranial surface points in the CPC system;

[0020] Within the local cranial surface search space of the individual CPC system of the j-th participant, find the point with the minimum vertical distance h, which serves as the optimal cranial surface location of the i-th cortical target point in the individual j-th participant.

[0021] Based on the individual's optimal cranial surface location, the three-dimensional coordinates of the j-th participant's optimal cranial surface location in the original space are obtained.

[0022] Preferably, the mapping method further includes calculating the depth of the original spatial target point based on the individual's optimal cranial surface location.

[0023] Preferably, the depth is based on the original spatial target point c. ij The three-dimensional coordinates in the CPC system, and the individual optimal cranial surface site corresponding to the original spatial target point cij. The three-dimensional coordinates are obtained from calculations within the CPC system.

[0024] Preferably, the The CPC spatial distribution is the individual optimal cranial surface location corresponding to the i-th cortical target point. CPC spatial distribution.

[0025] According to a second aspect of the present invention, another method for mapping from the cerebral cortex to the cranial surface is provided, comprising the following steps:

[0026] S1: Select the i-th cortical target point in the MNI standard brain space;

[0027] S2: Transform the cortical target points in the MNI standard brain space to the original space of the j-th participant, as the original space target point c. ij ;

[0028] S3: Corresponds to the original spatial target point c obtained in step S2 ij The optimal cranial surface position of the j-th participant was precisely located, denoted as .

[0029] S4: Let i = i + 1, repeat steps S2 and S3 until all cortical target points are mapped to the individual optimal cranial surface location of the j-th participant.

[0030] S15: Let j = j + 1, and repeat steps S2-S4 until the individual optimal cranial surface location of all participants corresponding to all cortical target points is found.

[0031] S16: Express the individual optimal cranial surface location of all participants corresponding to all cortical target points obtained in step S15 in CPC space;

[0032] S17: For each cortical target point, calculate the individual best cranial surface location for all participants, then calculate the group best cranial surface location, and finally calculate the center value as the group-level best cranial surface location.

[0033] S18: Calculate the population mean depth of the optimal cranial surface location at the population level;

[0034] S19: Repeat steps S17-S18 to obtain the three-dimensional coordinates and depth of the optimal cranial surface location of all cortical target points for all participants.

[0035] Preferably, the The CPC spatial distribution corresponds to the individual's optimal cranial surface location for the i-th cortical target point. CPC spatial distribution.

[0036] Preferably, in step S3, locating the optimal cranial surface position of the i-th cortical target point in the j-th participant includes the following sub-steps:

[0037] Generate an individual CPC system for the j-th participant;

[0038] Calculate the three-dimensional spatial coordinates of cranial surface points in the CPC system;

[0039] Within the local cranial surface search space of the individual CPC system of the j-th participant, find the point with the minimum vertical distance h, which serves as the optimal cranial surface location of the i-th cortical target point in the individual j-th participant.

[0040] Based on the individual's optimal cranial surface location, the three-dimensional coordinates of the j-th participant's optimal cranial surface location in the original space are obtained.

[0041] According to a third aspect of the present invention, a positioning device is provided, comprising at least one processor and at least one memory coupled to the processor for implementing the mapping method from the cerebral cortex to the cranial surface as described above.

[0042] According to a fourth aspect of the present invention, an online query system is provided for providing online query services based on the mapping method from the cerebral cortex to the cranial surface as described above.

[0043] Compared with existing technologies, this invention is based on a large sample population and uses a steel needle geometric model (i.e., cranial surface point normal) that conforms to the electric field of transcranial magnetic stimulation and transcranial electrical stimulation. By traversing the high-density cranial surface point search space, a population probability mapping between all coordinates of the standard brain space and the cranial surface space is established. This allows the cerebral cortex and deep brain regions deep within the cortex to be mapped onto the cranial surface, enabling neuromodulation technology to stimulate or treat functional areas deep within the cortex and brain injury areas on the cranial surface. Therefore, it is beneficial for the treatment of brain diseases. Attached Figure Description

[0044] Figure 1 This is a schematic diagram showing the positional relationship between the scalp and cerebral cortex of the human brain.

[0045] Figure 2 This is a schematic diagram of the cortical target points in the first embodiment of the present invention;

[0046] Figure 3 This is a schematic diagram of the location mapped from the cortical target point to the body space in the first embodiment of the present invention;

[0047] Figure 4 This is a logical diagram illustrating the calculation of the optimal cranial surface position for an individual in the first embodiment of the present invention;

[0048] Figure 5 This is a schematic diagram illustrating the calculation of the optimal cranial surface location for a population in the first embodiment of the present invention.

[0049] Figure 6 This is a schematic diagram of the optimal cranial surface position for a group in the first embodiment of the present invention;

[0050] Figure 7 This is a schematic diagram of the external structure of the third embodiment of the present invention;

[0051] Figure 8 This is a schematic diagram of the internal structure of the third embodiment of the present invention;

[0052] Figure 9 This is an example of the MNI2CPC mapping results for a specific cortical target obtained in an embodiment of the present invention;

[0053] Figure 10 The distribution of optimal scalp positions for individuals and the average value of scalp positions for the group in the 2D and 3D projections of the CPC system obtained in the embodiments of the present invention;

[0054] Figure 11 This is a verification result of the scalp depth distribution of the MNI cortical target in an embodiment of the present invention. Detailed Implementation

[0055] The technical content of the present invention will now be described in detail with reference to the accompanying drawings and specific embodiments.

[0056] As is well known, non-invasive neuromodulation involves a class of techniques that transmit physical energy (electric, magnetic, sound, light) from the cranial surface (i.e., Figure 1 The signal is transmitted from the scalp to the cerebral cortex (see...). Figure 1This invention relates to techniques for modulating neural activity. Typical neuromodulation techniques include transcranial magnetic stimulation (TMS) and transcranial electrical stimulation (tES). TMS generates a magnetic field through coils placed on the skull surface, inducing currents in the underlying cerebral cortex. tES, on the other hand, applies a weak current through electrodes placed directly on the skull surface, influencing neural activity by altering neuronal membrane potentials. This invention uses TMS or tES as examples, but this does not constitute a limitation of the invention. For example, this invention can also be used for applications such as mechanical stimulation of the skull surface.

[0057] In existing technologies, cranial population mapping methods all employ point model projection to map from the cranial surface to the cerebral cortex, such as the nearest neighbor method and the balloon inflation method. Since the cranial surface is a two-dimensional space, the projection points can only fall on the surface of the cerebral cortex, resulting in only a small number of cortical points being mapped to the cranial surface. Many functional areas and brain injury areas are located deep within the cortex, and currently there is no method to establish a mapping between deep brain regions and the cranial surface, limiting the application of neuromodulation techniques. This invention, based on a large sample population, uses a steel needle geometric model (i.e., cranial surface point normals) that conforms to the electric fields of transcranial magnetic stimulation and transcranial electrical stimulation. By traversing a high-density cranial surface point search space, it establishes a population probability mapping between all coordinates of the standard brain space and the cranial surface space. This allows the cerebral cortex and deep brain regions deep within it to be mapped to the cranial surface, enabling neuromodulation techniques to stimulate or treat functional areas and brain injury areas deep within the cortex from the cranial surface, thus benefiting the treatment of brain diseases.

[0058] First Embodiment

[0059] The first embodiment of the present invention provides a mapping method from the cerebral cortex to the cranial surface (also known as a "probabilistic mapping method from the cerebral cortex to the cranial surface"), comprising the following steps:

[0060] S1: Select the i-th cortical target point in the MNI standard brain space, where i is a positive integer.

[0061] As the distance between the cranial surface and the cortex increases, the electric field strength generated by TMS and tES decreases, and the stimulation depth is limited. Therefore, in this embodiment, only cortical target points in the MNI space within 2.5 cm of the cranial surface are considered.

[0062] This embodiment uses a subset of the Southwest University Adult Life Cycle Dataset (SALD). This dataset includes 114 young adult participants from China (63 women and 51 men, mean age 20.13 ± 1.29 years). Other datasets can also be used to apply the probabilistic mapping method from the cerebral cortex to the cranial surface provided in this embodiment of the invention.

[0063] The MNI standard template is a new standard brain derived from the averaging of MRI scans from a large number of normal participants and is a commonly used template in the industry for standardizing brain images. In this embodiment, a 1mm resolution MNI 152 standard template (ICBM 2009c non-linear symmetric) is used as a brain mask, preserving approximately 640,000 cortical target points (i.e., the number of cortical target points M ≈ 640,000). Figure 2 As shown. It is understandable that MNI305 template, Colin27 template, ICBM152 template, etc. can also be used.

[0064] S2: Transform the cortical target points in the MNI standard brain space to the original space of the j-th participant, as the original space target point c. ij Where j is a positive integer.

[0065] The i-th cortical target obtained in step S1 is transformed into the original space of the j-th participant (je0, 1, ..., N) and used as the original space target c. ij Where i = 0, 1, 2...M, M is the number of cortical target points; j = 0, 1, 2...N, N represents the number of participants. For example... Figure 3 As shown, the same cortical target in the MNI standard brain space can be transferred to N participants, corresponding to c i1 Point to c iN point.

[0066] The transformation employs a nonlinear transformation utilizing a deformation field. For 3D MRI images, the deformation field of the nonlinear transformation is a component image in the x, y, and z directions, used to achieve nonlinear full-head registration from the MRI space to the participant's native space. In this embodiment, the deformation field generated during the segmentation process using MATLAB's CAT12 software is used to transform cortical target points to the original space, such as... Figure 3 As shown. Each original spatial target point c ij The corresponding three-dimensional coordinates of the original space are

[0067] S3: Corresponds to the original spatial target point c obtained in step S2 ijThe optimal cranial surface location of the i-th cortical target point in the j-th participant is precisely determined, denoted as .

[0068] like Figure 4 As shown, the optimal cranial surface location for an individual is determined through the following sub-steps.

[0069] S31: Generate the individual CPC system for the j-th participant.

[0070] Using the headreco command of Simnibs non-invasive neuromodulation, a high-density CPC (Cranial Proportional Coordinates System) is created by reconstructing the head surface based on an individual's T1 MRI structural image. The process first uses SPM12 and CAT to segment the individual's T1 MRI structural image into different tissues such as gray matter, white matter, cerebrospinal fluid, and scalp. Then, based on the marching cubes algorithm, the scalp structural image is reconstructed into an original scalp surface model. This model is then processed with denoising and smoothing to obtain the final scalp surface model. A CPC700 high-density scalp coordinate system is calculated based on this model. This is existing technology and will not be elaborated upon here.

[0071] The CPC system is built upon five skull landmarks and is a continuous coordinate system that can represent any location on the skull surface using a pair of coordinates (pNZ, pAL).

[0072] S32: Calculate the three-dimensional spatial coordinates of cranial surface points in the CPC system.

[0073] In this embodiment, the CPC system is uniformly resampled into 1mm isotropic points using the Fibonacci sequence, resulting in a total of 72,000 cranial surface points. Each cranial surface point has corresponding three-dimensional spatial coordinates (x, y, z) in the CPC system.

[0074] S33: Within the local cranial surface search space of the individual CPC system of the j-th participant, find the point with the minimum vertical distance h, which serves as the optimal cranial surface location of the i-th cortical target point in the j-th participant.

[0075] Local cranial surface search space S(c ij ), is defined as the original spatial target point c. ij The k nearest cranial surface points above ( Figure 4 The set of ). In this embodiment, k is a preset value (preset to 700 in this embodiment), corresponding to a circular radius of approximately 1 cm on the surface of the skull. The value of k can be set according to actual needs and is not limited thereto.

[0076] The objective function is the original target point c. ij The minimum perpendicular distance *h* between each point in the local cranial surface search space and the direction line of the normal vector perpendicular to the cranial surface is minimized. This method is similar to the optimization strategy commonly used in TMS navigation systems. The point with the minimum perpendicular distance *h* (whose three-dimensional coordinates in the original space are...) is... ), is defined as the optimal cranial surface site, denoted as This can be expressed mathematically as follows:

[0077]

[0078] S34: Based on the individual's optimal cranial surface location, obtain the three-dimensional coordinates of the j-th participant's optimal cranial surface location in the original space, and calculate the depth of the corresponding target point in the original space.

[0079] Based on the individual's optimal cranial surface location, the individual's optimal cranial surface location is calculated using the following formula. Depth:

[0080]

[0081] Here d ij (c ij ) represents c ij depth, Represents the target point c in the original space. ij 3D coordinates; Represents the target point c in the original space. i j corresponds to the individual's optimal cranial surface site Three-dimensional coordinates. This allows us to obtain the optimal cranial surface position for each individual. like Figure 5 As shown.

[0082] S4: Let j = j + 1, and repeat steps S2 and S3 until the i-th cortical target point is mapped to the individual optimal cranial surface location for all participants.

[0083] By repeating steps S2 and S3, the i-th cortical target point is mapped to the optimal cranial surface location for each of the N participants. This completes the optimization process for all participants.

[0084] S5: Express the optimal individual cranial surface locations of all participants corresponding to the i-th cortical target point in the CPC space, forming... CPC spatial distribution.

[0085] like Figure 6 As shown, the optimal individual cranial surface location corresponding to the i-th cortical target point. The CPC spatial distribution is a set of multiple points distributed within a specific range with a certain probability. Figure 6 middle, This represents the points distributed in the graph, used to calculate the average value of each point to obtain s. g (c i ).

[0086] S6: Based on the results obtained in step S5 Based on the CPC spatial distribution, the corresponding optimal cranial surface location s at the population level was calculated. g (c i ).

[0087] Based on the i-th cortical target point in step S5 The CPC spatial distribution is used to calculate the average location, which is then used as the optimal cranial surface location at the population level. In other words, based on... population distribution ( Figure 6 ), can be calculated Expected value s g (c i ):

[0088]

[0089] Here, and yes CPC coordinates of each point.

[0090] Here, the population average depth of the i-th cortical target is denoted as d. g (c i ):

[0091]

[0092] s g (c i ) and d g (c i It can be used to guide the placement of non-invasive neuromodulation devices on the cranial surface of new individuals to stimulate cortical target c.

[0093] S7: Repeat steps S2-S6 to obtain the optimal cranial surface location s for all cortical target points across all participants. g (c) 3D coordinates and depth d g (c)).

[0094] Those skilled in the art will understand that the order of steps S2 to S6 can be appropriately adjusted and is not limited thereto. This order is used herein for ease of description and understanding only, and does not constitute a limitation on the order of steps.

[0095] Second Embodiment

[0096] This embodiment is an alternative to the first embodiment. Only the differences from the first embodiment are described here. For the contents not described, please refer to the corresponding contents in the first embodiment.

[0097] The mapping method from the cerebral cortex to the cranial surface provided in this embodiment includes the following steps:

[0098] S1: Select the i-th cortical target point in the MNI standard brain space;

[0099] S2: Transform the cortical target points in the MNI standard brain space to the original space of the j-th participant, as the original space target point c. ij ;

[0100] S3: Corresponds to the original spatial target point c obtained in step S2 ij The optimal cranial surface position of the j-th participant was precisely located, denoted as .

[0101] S4: Let i = i + 1, repeat steps S2 and S3 until all (M) cortical target points are mapped to the individual optimal cranial surface location of the j-th participant.

[0102] S15: Let j = j + 1, repeat steps S2-S4 until the optimal individual cranial surface location of all (N) participants corresponding to all cortical target points is found.

[0103] S16: Express the optimal individual cranial surface locations of the N participants corresponding to the M cortical target points obtained in step S15 in CPC space;

[0104] S17: For each cortical target point, calculate the individual optimal cranial surface location for all participants, then calculate the population optimal cranial surface location, and finally calculate the center value as the population-level optimal cranial surface location s. g (c i );

[0105] S18: Calculate the optimal cranial surface location s at the population level g (c i The average depth of the population;

[0106] S19: Repeat steps S17-S18 to obtain the optimal cranial surface location s for all cortical target points across all participants. g (c) and d g (c)

[0107] Those skilled in the art will understand that the order of the above steps can be appropriately adjusted and is not limited thereto. The use of this order in the description is merely for ease of understanding and does not constitute a limitation on the order of the steps.

[0108] Third Embodiment

[0109] The present invention also provides a positioning device using the above-described mapping method from the cerebral cortex to the cranial surface. It employs the mapping method from the cerebral cortex to the cranial surface as described in the first or second embodiment above, for mapping and positioning any location in the entire brain space to the scalp (the input is any brain space coordinates, and the output is the corresponding scalp point), thereby guiding the placement of non-invasive neuromodulation devices on the cranial surface of new participants for treatment or detection.

[0110] like Figure 7 and Figure 8 As shown, the positioning device includes a headgear 10, multiple electrodes 20 mounted on the headgear, one or more processors 21, and at least one memory 22. The memory 22 is coupled to the processors 21 and stores one or more programs. When executed by the processors 21, these programs enable the processors to perform the mapping method from the cerebral cortex to the cranial surface as described in the above embodiment. With an external power source or battery, the processors control the electrodes to emit stimulation signals to the participant's scalp, thereby implementing the probabilistic mapping method from the cerebral cortex to the cranial surface as described above. This allows for the localization of stimulation sites for any MNI cortical target for neuromodulation, providing effective treatment for neuropsychiatric disorders such as depression, obsessive-compulsive disorder, and stroke rehabilitation.

[0111] The processor 21 controls the overall operation of the positioning device to complete all or part of the steps of the mapping method from the cerebral cortex to the cranial surface. The processor 21 can be a central processing unit (CPU), graphics processing unit (GPU), field-programmable gate array (FPGA), application-specific integrated circuit (ASIC), digital signal processing (DSP) chip, etc. The memory 22 stores various types of data to support the operation of the positioning device. This data may include, for example, instructions for any application or method operating on the positioning device, and application-related data. The memory 22 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, etc.

[0112] In one exemplary embodiment, the positioning device may be implemented by a computer chip or physical entity, or by a product with a certain function, for performing the above-described mapping method from the cerebral cortex to the cranial surface and achieving the same technical effect as the above method.

[0113] In another exemplary embodiment, the present invention also provides a computer-readable storage medium including program instructions that, when executed by a processor, implement the steps of the mapping method from the cerebral cortex to the cranial surface in any of the above embodiments.

[0114] Fourth embodiment

[0115] The fourth embodiment of the present invention provides an online query system (online query tool) for querying CPC coordinates corresponding to any MNI cortical target according to the probability mapping method from the cerebral cortex to the cranial surface as described above.

[0116] Next, leave-one-out cross-validation is used to evaluate the localization error of the probabilistic mapping method from the cerebral cortex to the cranial surface (MNI2CPC) provided in the embodiments of the present invention.

[0117] This invention was validated using two independent cross-ethnicity (n=27) and cross-patient (n=58) datasets. The cross-ethnicity dataset was used to measure cross-ethnicity error. The cross-patient dataset was used to measure the error between healthy and patient populations. The cross-ethnicity dataset included 27 participants from a specific ethnic group (9 women and 18 men, mean age 24.59 ± 4.76 years). The patient dataset included 58 patients with major depressive disorder from two centers (43 women and 15 men, mean age 34.33 ± 11.90 years).

[0118] Cross-participant validation showed that the localization error on the cranial surface was 4.03 ± 0.69 mm, and the localization error in the cortex was 3.30 ± 0.59 mm. Furthermore, approximately 99.9% of cortical targets had a localization error of less than 6 mm on the cranial surface and less than 5 mm intracortical. Considering the effective stimulation range of common non-invasive neuromodulation techniques such as TMS and tES, this localization accuracy is acceptable. Moreover, validation showed that the consistency of localization errors across two independent cohorts indicates good generalization ability.

[0119] Figure 9 MNI2CPC mapping results for some specific cortical targets are provided as an example to show the query interface of the MNI2CPC online tool in the fourth embodiment of the present invention, as well as specific cortical targets from clinical and cognitive studies. Figure 10 The distribution of optimal scalp positions for individuals and the average scalp positions for the population are displayed in 2D and 3D projections of the CPC system. Different colors represent different studies, with light dots representing optimal individual positions and dark dots representing average population positions. Figure 11 The scalp depth distribution of MNI cortical targets is shown by a probabilistic mapping method from the cerebral cortex to the cranial surface.

[0120] It should be noted that the order of steps in the above embodiments can be adjusted according to actual needs, and other steps can be inserted or added, such as preprocessing the volume data.

[0121] The foregoing has provided a detailed description of the mapping method, positioning device, and online query system from the cerebral cortex to the cranial surface provided by this invention. Any obvious modifications made by those skilled in the art without departing from the essence of this invention will constitute an infringement of the patent rights of this invention and will incur corresponding legal liability.

Claims

1. A method of mapping from the cerebral cortex to the surface of the skull, characterized in that Includes the following steps: S1: Select the first [section / part] in the MNI standard brain space. Several cortical targets, among which It is a positive integer; S2: Convert the cortical targets in the MNI standard brain space to the... The original space of each participant, as the original spatial target. ,in It is a positive integer; S3: Corresponding to the original spatial target point Accurately locate the first The first cortical target in the first Optimal cranial surface position for each participant ; S4: Order = +1, repeat steps S2 and S3 until the first... Each cortical target point corresponds to an individual optimal cranial surface location for all participants. ; S5: Will be with the first The optimal individual cranial surface location of each cortical target point for all participants is represented in CPC space, forming... CPC spatial distribution; S6: Based on the results obtained in step S5 Based on the spatial distribution of CPC, the optimal cranial surface location at the population level was calculated. ; S7: Repeat steps S2-S6 to obtain the optimal cranial surface location of all cortical target points for all participants. 3D coordinates; In step S3, the first... The first cortical target in the first The optimal cranial surface location for each participant includes the following sub-steps: Generate the first An individual CPC system with 1 participant; Calculate the three-dimensional spatial coordinates of cranial surface points in the CPC system; In the Within the local cranial surface search space of each participant's individual CPC system, search for the element with the minimum vertical distance. The point, as the first The first cortical target in the first Optimal cranial surface position for each participant ; Based on the individual's optimal cranial surface location, the first... The three-dimensional coordinates of the optimal cranial surface location for each participant in the original space.

2. The mapping method from the cerebral cortex to the cranial surface as described in claim 1, characterized in that... It also includes calculating the depth of the original spatial target point based on the individual's optimal cranial surface location.

3. The mapping method from the cerebral cortex to the cranial surface as described in claim 2, characterized in that: The depth is based on the original spatial target point. The three-dimensional coordinates in the CPC system, and the original spatial target point Corresponding individual optimal cranial surface site The three-dimensional coordinates are obtained from calculations within the CPC system.

4. The mapping method from the cerebral cortex to the cranial surface as described in any one of claims 1 to 3, characterized in that: The The CPC spatial distribution corresponds to the first Optimal individual cranial surface location for each cortical target point CPC spatial distribution.

5. A mapping method from the cerebral cortex to the cranial surface, characterized in that... Includes the following steps: S1: Select the first [section / part] in the MNI standard brain space. Several cortical targets, among which It is a positive integer; S2: Convert the cortical targets in the MNI standard brain space to the... The original space of each participant, as the original spatial target. ,in It is a positive integer; S3: Corresponds to the original spatial target point obtained in step S2 Precisely located at the first The optimal cranial surface location for each participant is denoted as . ; S4: Order = +1, repeat steps S2 and S3 until all cortical target points are mapped to the first... The optimal cranial surface position for each participant; S15: Order = +1, repeat steps S2-S4 until the individual optimal cranial surface location of all participants corresponding to all cortical target points is found. S16: Express the individual optimal cranial surface location of all participants corresponding to all cortical target points obtained in step S15 in CPC space; S17: For each cortical target point, calculate the individual best cranial surface location for all participants, then calculate the group best cranial surface location, and finally calculate the center value as the group-level best cranial surface location. S18: Calculate the population mean depth of the optimal cranial surface location at the population level; S19: Repeat steps S17-S18 to obtain the three-dimensional coordinates and depth of the optimal cranial surface location for all cortical target points across all participants. In step S3, the first... The first cortical target in the first The optimal cranial surface location for each participant includes the following sub-steps: Generate the first An individual CPC system with 1 participant; Calculate the three-dimensional spatial coordinates of cranial surface points in the CPC system; In the Within the local cranial surface search space of each participant's individual CPC system, search for the element with the minimum vertical distance. The point, as the first The first cortical target in the first Optimal cranial surface position for each participant ; Based on the individual's optimal cranial surface location, the first... The three-dimensional coordinates of the optimal cranial surface location for each participant in the original space.

6. The mapping method from the cerebral cortex to the cranial surface as described in claim 5, characterized in that: The The CPC spatial distribution corresponds to the first Optimal individual cranial surface location for each cortical target point CPC spatial distribution.

7. A positioning device, characterized in that... It includes at least one processor and at least one memory, the memory being coupled to the processor, for implementing the mapping method from the cerebral cortex to the cranial surface as described in any one of claims 1 to 6.

8. An online query system for providing online query services according to the mapping method from the cerebral cortex to the cranial surface as described in any one of claims 1 to 6.

Images