Target identification method, apparatus, electronic device, storage medium, and neural modulation device

Abstract

The present disclosure provides a target identification method, an apparatus, an electronic device, a storage medium, and a neuromodulation device. The target identification method includes: acquiring scan data of a subject, the scan data including data obtained from a magnetic resonance image of the brain of the subject; identifying at least two brain regions of the subject based on the scan data, each brain region including at least one voxel; identifying at least one target brain region corresponding to a disease type of the subject among the at least two brain regions according to the disease type of the subject; and identifying a target located in the at least one target brain region according to a preset target identification rule.

Description

Technical Field

[0001] The present disclosure relates to the field of computer technology, and particularly to a target identification method, apparatus, electronic device, storage medium, and neuromodulation device.

Background Art

[0002] Many neurological and mental diseases often do not have clear lesions and appear as abnormal functions of the nervous system. Using neuromodulation means such as electricity, magnetism, light, and ultrasonic waves to directly or indirectly regulate abnormal functional networks is an important means to improve the symptoms of patients. How to select neuromodulation targets in the human brain is a difficult problem. According to research, for many neurological and mental diseases, even if only a single brain region is focused on, ideal regulation and therapeutic effects cannot be obtained, and the connectivity of target regions with distributed brain networks and high individual specificity has been shown to be highly correlated with the improvement of therapeutic effects. Therefore, clinically, there is a need for an objective, accurate, and quantifiable auxiliary means to help doctors select individual neuromodulation targets. Existing methods for identifying neuromodulation targets cannot meet this requirement.

Summary of the Invention

[0003] The present disclosure provides a target identification method, apparatus, electronic device, storage medium, and target regulation device for selecting individualized neuromodulation targets.

[0004] In a first aspect, the present disclosure provides a target identification method including: obtaining scan data of a subject including data obtained from a magnetic resonance image of the subject's brain; identifying at least two brain regions of the subject (each brain region including at least one voxel) based on the scan data; identifying at least one target brain region corresponding to the disease type among the at least two brain regions according to the disease type of the subject; and identifying a target located in the at least one target brain region according to a preset target identification rule. In some selectable embodiments, identifying at least two brain regions of the subject based on the aforementioned scan data includes the following: Based on a volumetric standard brain template, at least two brain regions of the subject are identified based on the scan data. In some selectable embodiments, identifying at least two brain regions of the subject based on the aforementioned scan data includes the following: Based on a standard cortical brain template, at least two brain regions of the subject are identified based on the scan data.

[0005] In some selectable embodiments, identifying at least two brain regions of the subject (each brain region containing at least one voxel) based on the aforementioned scan data includes the following: The degree of connectivity between any two voxels in the scan data is determined, and a brain connectivity matrix corresponding to the scan data is formed. Based on a standard brain brain region template and the brain connectivity matrix, the at least two brain regions are formed.

[0006] In some selectable embodiments, identifying at least two brain regions of the subject (each brain region containing at least one voxel) based on the aforementioned scan data includes the following: For all pairs of voxels in the scan data, the degree of connection between them is determined.

[0007] The anatomical structure of the subject's brain corresponding to the scan data is divided into multiple large regions, and each of these large regions is divided into multiple brain regions (each brain region containing at least one voxel). From among the aforementioned multiple brain regions, brain regions whose degree of connectivity between them is higher than a predetermined brain region connectivity threshold are fused to form at least two brain regions.

[0008] In some selectable embodiments, identifying a target located in the brain region of the at least one target according to the pre-defined target identification rules described above includes the following: The central position of at least one target brain region is identified as the target.

[0009] In some selectable embodiments, identifying a target located in the brain region of the at least one target according to the pre-defined target identification rules described above includes the following:

[0010] The central position of at least one target brain region is defined as the center of the sphere, the region within a predetermined target radius is defined as the region of interest (ROI), and the location of the region of interest is identified as the target.

[0011] In some optional embodiments, identifying a target located in the brain region of the at least one target according to the pre-defined target identification rules described above includes the following: Based on the type of disease, the brain structural region in which the target is located is identified. The intersection of at least one target brain region and the brain structural division is determined. Identify the target from the aforementioned intersection.

[0012] In some selectable embodiments, the magnetic resonance images include structural magnetic resonance images and / or task-state functional magnetic resonance images and / or resting-state functional magnetic resonance images.

[0013] In a second aspect, the Disclosure provides a target identification device comprising: a data acquisition unit arranged to acquire scan data of a subject, including data obtained from magnetic resonance imaging of the subject's brain; a processing unit arranged to identify at least two brain regions of the subject (each brain region including at least one voxel) based on the scan data, and further arranged to identify at least one target brain region among the at least two brain regions corresponding to the disease type, depending on the disease type of the subject; and a target identification unit arranged to identify a target located in the at least one target brain region according to a pre-configured target identification rule. In some optional embodiments, the processing unit is further arranged as follows: The degree of connectivity between any two voxels in the scan data is determined, and a brain connectivity matrix corresponding to the scan data is formed. Based on a standard brain brain region template and brain connectivity matrix, the at least two brain regions are formed. In some optional embodiments, the processing unit is further arranged as follows: For all pairs of voxels in the scan data, the degree of connection between them is determined.

[0014] The anatomical structure of the subject's brain corresponding to the scan data is divided into multiple large regions, each of the multiple large regions is divided into multiple brain regions, and each of the multiple brain regions contains at least one voxel.

[0015] The voxel connections between each of the aforementioned multiple brain regions merge brain regions that have a higher voxel connection threshold than a predetermined brain region, thereby forming the at least two brain regions. In some selectable embodiments, the target identification unit is further arranged as follows: The central position of at least one target brain region is identified as the target. In some selectable embodiments, the target identification unit is further arranged as follows.

[0016] Taking the center position of the at least one target brain region as the center, an area within a preset target radius range is identified as the target region of interest, and the position of the target region of interest is identified as the target. In some selectable embodiments, the target identification unit is further arranged as follows. Based on the type of the disease, the brain structure division where the target is located is identified. The intersection between the at least one target brain region and the brain structure division is determined. The target is identified from the intersection.

[0017] In some selectable embodiments, the magnetic resonance image includes a structural magnetic resonance image, and / or a task-state functional magnetic resonance image, and / or a resting-state functional magnetic resonance image.

[0018] In a third aspect, the present disclosure provides an electronic device including at least one processor and a storage device storing at least one program, wherein when the at least one program is executed by the at least one processor, the at least one processor is caused to implement the method described in any implementation form of the first aspect.

[0019] In a fourth aspect, the present disclosure provides a computer-readable storage medium storing a computer program, wherein when the computer program is executed by at least one processor, the method described in any one implementation form of the first aspect is implemented.

[0020] In a fifth aspect, the present disclosure provides a neuromodulation device arranged to neuromodulate a target of a subject according to a preset neuromodulation plan, wherein the target is identified according to the method described in any implementation form of the first aspect. In some optional embodiments, the preset adjustment plan includes at least one of the following. Deep brain electrical stimulation, Transcranial electrical stimulation, Electroconvulsive therapy, Electrical stimulation by cortical electroencephalogram electrodes, Transcranial magnetic stimulation, Focused ultrasound neuromodulation, Magnetic resonance-induced high-energy focused ultrasound treatment regulation, Optical stimulation regulation. To achieve the determination of neuromodulation targets, the commonly used technical means currently include the following.

[0021] 1. Based on group-level task-state functional magnetic resonance imaging (fMRI), identify the neuromodulation target. The disadvantages of this method include that the signal-to-noise ratio of task-state fMRI is low, the reproducibility is not high, it requires a certain cognitive level for the subject, the results of the functional areas of task-state fMRI are greatly affected by task design, and it is difficult to determine the baseline level of the functional areas.

[0022] 2. Based on clinical experience based on brain anatomical structure, find the overall position of the body surface projection of specific functional areas on the scalp surface of the patient and identify the neuromodulation target. For example, the method for locating the left dorsolateral prefrontal cortex (DLPFC) (also called the "5 cm" location method) for treating resistant depression by repetitive transcranial magnetic stimulation (rTMS) approved by the US Food and Drug Administration (FDA). The disadvantages of this method include ignoring the differences in individual anatomical structures, having low location accuracy, resulting in inaccurate location of the neuromodulation target, ignoring the differences in individual functional networks, and the possibility that the target location is in other brain functional areas.

[0023] 3. Identify neural regulatory targets using electrode caps. For example, the international 10-20 electrode cap method. Disadvantages of this method include ignoring differences in individual anatomical structures, having low localization accuracy, resulting in inaccurate localization of neural regulatory targets, and ignoring differences in individual functional networks.

[0024] 4. Identify neuromodulatory targets based on ROIs defined by anatomical structures or population-average fMRI studies. Disadvantages of this method include the fact that many neurological and psychiatric disorders often do not have clear lesions and only exhibit dysfunction of the nervous system, that simple anatomical structures cannot reflect disease characteristics, and that the etiology of neurological and psychiatric disorders is complex, and combined with individual differences, the effectiveness of treatment planning based on population-average fMRI is low.

[0025] 5. Identify neuromodulatory targets based on the state of tissue structure and metabolism reflected in PET scan data. Disadvantages of this method include the high cost of PET scans, which increases the medical burden; the presence of some radiation during the scanning process; the limitations on the neurological and psychiatric disorders to which PET scans can be applied; and the low signal-to-noise ratio and unclear boundaries of anatomical structures, which affect the accuracy of target identification and result in low clinical efficacy.

[0026] The target identification method, apparatus, electronic device, storage medium, and neural modulation device provided in this disclosure acquire scan data of a subject, including data obtained from magnetic resonance imaging of the subject's brain; identify at least two brain regions of the subject (each brain region containing at least one voxel) based on the scan data; identify at least one target brain region corresponding to the disease type among the at least two brain regions depending on the subject's disease type; and identify a target located in at least one target brain region according to a pre-set target identification rule. Embodiments of this disclosure provide scan data of the subject's brain using functional magnetic resonance imaging, identify the subject's brain regions, and, while fully considering individual differences, effectively solve the problem of inaccurate neural modulation targets due to the failure to consider structural or functional differences of individuals in conventional methods, thereby achieving the localization of individualized neural modulation targets for the subject.

[0027] The drawings schematically illustrate, not limiting, each embodiment described herein. [Brief explanation of the drawing]

[0028] [Figure 1] Figure 1 is an exemplary system configuration diagram to which one embodiment of the present disclosure may be applied. [Figure 2] Figure 2 is a flowchart of one embodiment relating to the target identification method of this disclosure. [Figure 3] Figure 3 is an exploded view of one embodiment of step 202 in the target identification method shown in Figure 2. [Figure 4] Figure 4 is an exploded view of yet another embodiment of step 202 in the target identification method shown in Figure 2. [Figure 5] Figure 5 is a comparison diagram of target location identification using group results and target location identification using the target location identification method of the embodiment of this disclosure. [Figure 6] Figure 6 is a schematic diagram of a target identified using the target identification method of the embodiment of this disclosure in an application of the invention. [Figure 7] Figure 7 is a structural diagram of one embodiment of the target identification device according to this disclosure. [Figure 8] Figure 8 is a structural diagram of a computer system suitable for implementing the terminal device or server of this disclosure. [Modes for carrying out the invention]

[0029] To enable a more detailed understanding of the features and technical content of the embodiments of this disclosure, the implementation of the embodiments of this disclosure will be described in detail below with reference to the attached drawings. However, the attached drawings are for illustrative purposes only and do not limit the embodiments of this disclosure.

[0030] In the description of embodiments of the present invention, unless otherwise stated or limited, the term "connection" should be understood broadly, and may refer to, for example, an electrical connection, an internal connection between two elements, a direct connection, or an indirect connection via an intermediate medium. Those skilled in the art will be able to understand the specific meaning of the term depending on the specific circumstances.

[0031] The terms “first,” “second,” and “third” in relation to the embodiments of this disclosure are merely for distinguishing similar subjects and do not represent a specific order of subjects. If permitted, the “first,” “second,” and “third” may be replaced in any particular order or priority. The subjects distinguished by “first,” “second,” and “third” may be replaced in any case so that the embodiments of this disclosure described herein can be carried out in an order other than that illustrated or described herein. Figure 1 shows an exemplary system configuration 100 of an embodiment to which the target identification method or target identification apparatus of this disclosure can be applied.

[0032] As shown in Figure 1, the system configuration 100 includes terminal devices 101, 102, and 103, a network 104, and a server 105. The network 104 serves as a medium providing communication links between the terminal devices 101, 102, and 103 and the server 105. The network 104 may include various connection types, such as wired, wireless, or fiber optic cables.

[0033] Users may use terminal devices 101, 102, and 103 to interact with server 105 via network 104 to receive or send messages, etc. Various communication client applications may be installed on 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 communication tools, mailbox clients, and social platform software.

[0034] Terminal devices 101, 102, and 103 may be hardware or software. If terminal devices 101, 102, and 103 are hardware, they may be various electronic devices having a display screen, including but not limited to smartphones, tablets, laptops, and desktops. If terminal devices 101, 102, and 103 are software, they may be implemented on an electronic device that identifies multiple brain regions of a subject as listed above. This may be implemented as multiple software or software modules (e.g., processing for providing a brain atlas) or as a single software or software module. This is not specifically limited here.

[0035] Server 105 may be a server that provides various services, for example, a background data processing server that processes scan data transmitted by terminal devices 101, 102, and 103. Based on the scan data, the background data processing server can identify multiple brain regions of the subject and the voxels corresponding to each brain region and provide feedback to the terminal devices.

[0036] Server 105 may be hardware or software. If Server 105 is hardware, it may be implemented as a distributed server cluster consisting of multiple servers, or as a single server. If Server 105 is software, it may be implemented as multiple software or software modules (for example, to provide a distributed service), or as a single software or software module. No specific limitations are made here. Furthermore, since the target identification method provided in this disclosure is generally executed on server 105, the target identification device is generally installed on server 105.

[0037] In some cases, the target identification method provided in this disclosure may be executed on server 105, on terminal devices 101, 102, and 103, or on server 105 and terminal devices 101, 102, and 103 in cooperation with each other. Therefore, the target identification device may be provided on server 105, on terminal devices 101, 102, and 103, or part of it may be provided on server 105 and part of it may be provided on terminal devices 101, 102, and 103. Therefore, the system configuration 100 may include only server 105, or only terminal devices 101, 102, and 103, or it may include terminal devices 101, 102, and 103, network 104, and server 105. This disclosure is not limited thereto.

[0038] Note that the number of terminal devices, networks, and servers in Figure 1 are merely illustrative. Any number of terminal devices, networks, and servers may be provided as needed for implementation. Referring to Figure 2, a flowchart 200 of one embodiment of the target identification method according to the present disclosure is shown. The target identification method includes the following steps: Step 201: Obtain the subject's scan data. In the embodiments of this disclosure, the scan data includes data obtained from magnetic resonance imaging of the subject's brain.

[0039] The scan data includes a blood oxygen level dependency (BOLD) signal sequence corresponding to each voxel in a predetermined number of voxels.

[0040] In this embodiment, the entity executing the target identification method (for example, the server shown in Figure 1) can first acquire scan data of a subject locally or remotely from other electronic devices (for example, the terminal device shown in Figure 1) that are network-connected to the entity executing the method.

[0041] A voxel, also known as a three-dimensional voxel, is an abbreviation of volume pixel. Conceptually, a voxel is similar to a pixel, the smallest unit in two-dimensional space, and pixels are used for image data in two-dimensional computer images. A voxel is the smallest unit in the three-dimensional spatial division of digital data and is used in fields such as three-dimensional imaging, scientific data, and medical imaging.

[0042] A BOLD signal sequence corresponding to a voxel is obtained by performing a magnetic resonance scan on a subject, acquiring one BOLD signal for each voxel at predetermined time intervals, and finally acquiring a BOLD signal for a certain time. These BOLD signals are then arranged in order of acquisition time to obtain a BOLD signal sequence corresponding to each voxel. The number of BOLD signals included may be an integer quotient obtained by dividing the length of time corresponding to the target task by the predetermined time interval. For example, if the length of time corresponding to the scan is 300 seconds and the predetermined time interval is 2 seconds, then the BOLD signal sequence corresponding to each voxel may be thought of as having 150 BOLD values ​​and 150 frames of data, or as a 150-dimensional vector, or as a 1 × 150-dimensional matrix, but this disclosure does not specifically limit this.

[0043] The specific number of voxels included in the scan data may be determined based on the scanning accuracy of the magnetic resonance image, or based on the accuracy of the imaging device. The predetermined number here is not a limitation on the specific number of voxels, and it is understood that in current practical applications, the number of voxels in human brain scan data is measured to be tens of thousands or hundreds of thousands, and with advances in scanning technology, the number of voxels included in human brain scan data may increase further.

[0044] In this disclosure, the implementing entity may acquire the subject's scan data locally or remotely from other electronic devices connected to the implementing entity via a network (for example, the terminal device shown in Figure 1).

[0045] In embodiments of this disclosure, 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.

[0046] The data obtained from functional magnetic resonance imaging (FMRI) includes time-series information and corresponds to a four-dimensional image. For example, if functional magnetic resonance imaging is collected and one frame of a three-dimensional image matrix (Length × Width × Height, L × M × N) is collected every two seconds, 150 frames of data can be collected in six minutes, forming a functional magnetic resonance image data signal of L × M × N voxels × 150.

[0047] The data obtained by structural magnetic resonance imaging (MSR) are high-resolution three-dimensional grayscale anatomical structural images, such as T1w (T1-weighted image - difference in T1 relaxation (longitudinal relaxation) of protruding tissue) and related images, T2w (T2-weighted image - difference in T2 relaxation (transverse relaxation) of protruding tissue) and related images, and fluid attenuated inversion recovery (FLAIR) sequences and related images. Structural MMR images may also include MMR diffusion images, such as diffusion-weighted imaging (DWI) and related images, diffusion tensor imaging (DTI) and related images, etc.

[0048] DTI is a magnetic resonance (DTI) technique used to study the diffusion anisotropy of anatomical nerve bundles in the central nervous system and to visualize the anatomical structure of white matter fibers. It detects the fine structure of tissues by detecting the anisotropy of water molecule diffusion within the tissue. White matter anisotropy is due to myelin sheath axonal fibers running parallel to each other, and white matter diffusion is greatest in the direction of parallel nerve fibers, meaning the diffusion anisotropy fraction (FA) is greatest and can be approximately determined to be 1 (in practice, a fraction greater than 0.9 and close to 1 is acceptable). This property can be represented by color to reflect the spatial orientation of white matter, meaning that the direction of fastest diffusion indicates the direction of fiber movement. Fiber bundle images obtained using the DTI method can yield a brain connectivity matrix that reflects the structure of the brain.

[0049] Resting-state functional magnetic resonance imaging (MSI) is understood to be a magnetic resonance image obtained by performing a MSI brain scan while the subject is not performing a task during the scan. Task-state functional MSI is a magnetic resonance image obtained by performing a MSI brain scan while the subject is performing a target task.

[0050] After acquiring brain structure magnetic resonance (MRL) scan data from a subject, it is possible to determine the subject's brain structure based on the MRL data using various implementation methods. In other words, it is possible to determine which components constitute specific regions of the subject's brain. For example, this can be achieved using existing software that processes three-dimensional brain scan data, such as FreeSurfer, a magnetic resonance data processing software. Alternatively, for example, a deep learning model may be trained in advance based on a large amount of brain structure image scan sample data and corresponding brain component labeling. The subject's brain structure MRL data can then be input into the trained deep learning model to obtain the corresponding brain structure diagram. In some selectable embodiments, the implementing entity acquires scan data of the subject and then preprocesses the scan data. In the present invention, the pretreatment method is not specifically limited, and for example, the pretreatment may include the following: Preprocessing for magnetic resonance imaging images, for example, (1) Time layer correction, head motion correction, time signal filtering, noise component regression, spatial smoothing, etc. (2) Registration of functional magnetic resonance imaging images and structural images (if structural images exist),

[0051] (3) Project the functional magnetic resonance imaging signal onto a reconstructed individual cerebral cortex image or a structural image (if a structural image exists) that includes an associated group-mean level structural image.

[0052] For magnetic resonance imaging images (if structural images exist), preprocessing is performed, such as skull removal, electric field intensity correction, segmentation of the individual's anatomical structure, and reconstruction of the cerebral cortex. Step 202, based on the scan data, identify at least two brain regions of the subject, each brain region containing at least one voxel. In the embodiments of this disclosure, the brain region may include brain functional regions and / or brain structural regions. With respect to step 202 described above, the present disclosure provides various alternative embodiments.

[0053] Figure 3 is a partially exploded view of one embodiment of step 202 in the target identification method shown in Figure 2. In some optional embodiments, as shown in Figure 3, step 202 may specifically include the following: Step 202a1: Determine the degree of connectivity between any pair of voxels in the scan data and form a brain connectivity matrix corresponding to the scan data.

[0054] In this disclosure, the degree of connectivity between a voxel and an ROI may include the average value of the degree of connectivity between a voxel and each voxel in the ROI; the degree of connectivity between two ROIs may include the average value of the degree of connectivity between a voxel in one ROI and each voxel in the other ROI; the degree of connectivity between a voxel and a brain region may include the average value of the degree of connectivity between a voxel and each voxel in the brain region; and the degree of connectivity between two brain regions may include the average value of the degree of connectivity between a voxel in one brain region and each voxel in the other brain region.

[0055] The degree of connectivity characterizes the degree of connectivity in brain connections and can also be expressed as a correlation coefficient. Here, brain connectivity can include functional connectivity and structural connectivity. Functional connectivity is obtained by calculating the Pearson correlation coefficient based on bold time series corresponding to voxels in ROIs, while structural connectivity includes structural connectivity between ROIs obtained by tractography, etc.

[0056] For example, assuming that the number of voxels in the scan data is 100,000, and that the BOLD signal sequence corresponding to each voxel contains T BOLD values, where T is the number of samples in the time dimension corresponding to the scan time, then the brain connectivity matrix corresponding to the scan data is a 100,000 × 10¹⁶ matrix of order 10¹⁶, and this brain connectivity matrix can characterize the degree of connectivity between all pairs of voxels in the scan data, of which the degree of connectivity between two voxels can be calculated by the Pearson correlation coefficient based on the T BOLD values ​​corresponding to the voxels. In this disclosure, the correlation coefficient is the Pearson correlation coefficient, which is a coefficient used to measure the degree of linearity between variables. The formula for calculating it is as follows:

number

[0057] The formula defines the Pearson correlation coefficient (ρx,y) of two continuous variables (X,Y) as being equal to the covariance between them, cov(X,Y), divided by the product of their respective standard deviations (σX,σY). The coefficient value is always between -1.0 and 1.0; variables equal to or approximately equal to 0 are said to be uncorrelated, and variables equal to or approximately equal to 1 or -1 are said to be strongly correlated. Here, it can be understood that the difference between the approximation and the target value is within the acceptable margin of error. For example, in this invention, 0.01 can be approximated to 0, or 0.99 can be approximated to 1, but this is merely illustrative, and in actual applications, an approximately equal margin of error can be determined based on the precision required for the calculation. Step 202a2: Form at least two brain regions based on a standard brain brain region template and brain connectivity matrix.

[0058] For example, a brain map containing two or more brain regions can be created for a subject based on a standard brain region template using pattern recognition or machine learning methods. These methods may include, but are not limited to, independent component correlation algorithms (ICA), principal component analysis (PCA), various clustering methods, factor analysis, linear discriminant analysis (LDA), and various matrix decomposition methods. The resulting brain functional network may have different voxel locations within each brain region for different subjects, but each voxel belongs to a specific brain region. That is, each brain region of a subject may be a collection of voxels composed of fMRI voxels with the same function.

[0059] Figure 4 is an exploded view of yet another embodiment of step 202 in the target identification method shown in Figure 2. In some optional embodiments, as shown in Figure 4, step 202 may specifically include the following steps: Step 202b1: Determine the degree of connectivity between any pair of voxels in the scan data.

[0060] Step 202b2: The anatomical structure of the subject's brain corresponding to the scan data is divided into multiple large regions, and each of these large regions (e.g., each large region) is divided into multiple brain regions, each of which contains at least one voxel.

[0061] Step 202b3: Fuse brain regions in which the degree of voxel connectivity between each of the multiple brain regions is higher than a predetermined voxel connectivity threshold for the brain region, to form at least two brain regions.

[0062] For example, first, the subject's brain is divided into multiple large regions according to major anatomical boundaries. Then, each large region is segmented based on functional connectivity, and the degree of voxel connectivity in each large region is determined according to test-retest reliability. Each large region is then divided to obtain multiple brain regions, and these brain regions are further fused based on the degree of voxel connectivity they contain. Brain regions with highly connected voxels are integrated into a single brain region, and for example, at least two brain regions can ultimately be identified in the entire brain.

[0063] For example, by dividing the left and right cortices of the brain into five major regions—the frontal lobe, parietal lobe, occipital lobe, temporal lobe, and the entire central sulcus—the initial individual brain map can be divided into 10 major regions. Alternatively, for example, the brain can be divided into an upper cortex and a lower cortex, dividing the left and right hemispheres into a total of four regions. In some optional embodiments, step 202 may specifically include the following:

[0064] A group brain map is pre-selected or created as a brain map template, and the boundaries corresponding to at least two brain regions in the brain map template are projected onto the subject's brain scan data.

[0065] Based on the subject's brain scan data, the boundaries of at least two brain regions are adjusted, and the adjusted brain region boundaries are matched with at least the subject's brain scan data to form at least two brain regions.

[0066] For example, after projecting a collective brain map directly onto a subject's brain, a recursive algorithm is used to incrementally adjust the boundaries of the brain regions onto which the collective brain map is projected, according to the subject's anatomical brain map, until the boundaries of the brain regions stabilize. The recursive process utilizes the individual differences in the subject's brain connectivity distribution and the signal-to-noise ratio of the subject's own brain image to determine the range of brain region boundary adjustment. Finally, at least two brain regions are obtained by fusing the brain regions based on voxel connectivity. In some optional embodiments, step 202 may specifically include the following:

[0067] Based on a volumetric brain structure template and scan data, determine at least two ROIs for the subject. Extract the white matter and ventricular regions from the volumetric brain structure template, construct a binary mask of the volumetric brain structure template, remove the white matter and ventricular regions from the mask to obtain a mask without white matter and ventricular regions, and resample the mask without white matter and ventricular regions to obtain at least two ROIs. Alternatively, construct a binary mask of the volumetric brain structure template, perform resampling, and obtain at least two ROIs. In some optional embodiments, step 202 may specifically include the following:

[0068] Based on a standard cortical brain structure template and scan data, at least two ROIs for a subject are determined. The standard cortical brain structure template is resampled to generate at least two ROIs. For example, based on a coarse-resolution cortical surface template (e.g., fsaverage3 (fs3), fsaverage4 (fs4)), at least two ROIs of a high-resolution template (e.g., fsaverage6 (fs6)) are generated. Specifically, the left and right brain vertices of the coarse-resolution template are assigned sequentially (1, 2, 3...), and then these are resampled into the fs6 template space (proximity interpolation). In the order they were assigned, all vertex index numbers on the fs6 surface corresponding to each number are statistically determined sequentially, which are at least two ROIs in the fs6 space. For example, if the 13th vertex in the left brain fs4 surface template is assigned to 13, and after resampling into the fs6 template space, 30 vertices can be found where all 13 numbers are 13, the 13th ROI of the left brain fs6 surface template consists of these 30 vertices. Based on all vertices in the surface template, at least two ROIs are generated, and for any surface template, each vertex is assigned one ROI (for example, the fsaverage6 left brain template contains 40,962 vertices, and correspondingly, 40,962 ROIs can be generated). Step 203: Depending on the subject's disease type, identify at least one target brain region corresponding to the disease type from at least two brain regions. The disease type includes the disease type identified in the diagnosis of the subject, or the disease type corresponding to the symptoms the subject is trying to treat.

[0069] The brain region correspondences for disease types can be queried based on existing, already identified brain region correspondences for disease types, or they can be established according to actual needs. Here, the method for obtaining brain regions for disease types is merely illustrative and not specifically limited.

[0070] Here, the target brain region is the brain region corresponding to the target, and there is a neural relationship between the target and the target brain region, allowing for neural modulation of the target brain region through stimulation of the target. Step 204: Identify targets located in at least one target brain region according to pre-defined target identification rules. The target may include coordinates corresponding to a single voxel, or it may be a set of regions consisting of several voxels. Here, the pre-configured target identification rules may include at least one of the following rules: Rule 1. Identify the target from the center of the brain region sphere. The voxel or coordinates located at the center of the target brain region are used as the modal target.

[0071] Rule 2. Identify targets by generating ROIs from brain regions. The central position of the target brain region is used as the center of the sphere, and an ROI is generated with a radius of a certain distance (e.g., 3 mm) to be used as the modulated target ROI.

[0072] Rule 3. Identify the target based on the disease type and prior knowledge. If the structural segmentation where the target is located is known, it is necessary to find the intersection of the target functional segmentation and the structural segmentation. This intersection is then identified as a new target candidate region, and the target is further identified according to Rule 1 or Rule 2. In some optional embodiments, step 204 may specifically include the following: Identify the central location of at least one target brain region as the target. In some optional embodiments, step 204 may specifically include the following: The center of at least one target brain region is defined as the center of the sphere, the region within a predetermined radius of the target is defined as the target ROI, and the location of the target ROI is identified as the target.

[0073] This disclosure does not specifically limit the length of the pre-set target radius, which may be set according to the actual need for neuromodulation. For example, the pre-set target radius may be 3 mm. In some optional embodiments, step 204 may specifically include the following: Identify brain structural regions where targets exist according to the disease type, determine the intersection of at least one target brain region and the brain structural region, and identify the target within the said intersection.

[0074] In some selectable embodiments, the target must satisfy the following conditions: the target is not located in the medial or basal part of the brain; the target may be located in the gyri, but not in the sulci.

[0075] The targets identified by the above method are relatively accurate, and in practical applications, scientists or medical professionals can use optical or electromagnetic navigation devices to perform neuromodulation navigation of subjects based on the targets identified by the above method, thereby improving the efficiency of neuromodulation.

[0076] This disclosure provides a neuromodulatory device configured to neuromodulate a subject's target according to a pre-configured neuromodulatory plan, wherein the subject's target is identified according to a target identification method in any one of the above-described embodiments of this disclosure.

[0077] Neuromodulatory devices may include implantable and non-implantable neuromodulatory devices, such as event-related potential analysis systems, electroencephalography systems, and brain interface devices. This disclosure does not limit the specific forms of neuromodulatory devices and is merely illustrative.

[0078] Neuromodulation of a subject's target may be performed by an operator connecting a neuromodulatory device to the target and performing the modulation accordingly, or by the neuromodulatory device performing the modulation based on operator input or based on the subject's target actively obtained from the neuromodulatory device. This is merely illustrative and does not specifically limit the neuromodulation of a subject's target, and those skilled in the art can operate according to the actual use of the neuromodulatory device. For example, pre-set neuromodulation plans include, but are not limited to, the following: a. Neural control plans based on electrical pulse sequences i. Deep brain electrical stimulation ii. Transcranial electrical stimulation iii. Electroconvulsive therapy iv. Electrical stimulation using cortical-brain electroelectrodes v. Related derivative technologies of the above technology b. Neural control plans based on magnetic pulse sequences i. Transcranial magnetic stimulation and related plans ii. Related derivative technologies of the above technology c. Neuromodulation plans based on ultrasound i. Ultrasound-focused nerve control plan ii. Magnetic resonance-induced high-energy ultrasound focused therapy system and associated adjustment plan iii. Related derivative technologies of the above technology d. Light-based neural control plans i. Light stimulation and associated plans in different wavelength bands ii. Related derivative technologies of the above technology

[0079] With the development of new neuromodulatory devices and technologies, future neuromodulatory devices and plans can also use the target identification method of this disclosure to identify neuromodulatory targets, and this also falls within the scope of protection of this disclosure.

[0080] To more clearly illustrate the effectiveness of the method in the above-described embodiment, Figure 5 is illustratively a comparison of target location using cluster results in an actual application and target location using the target location method according to the embodiment of this disclosure. As shown in Figure 5, 501 is a target of the target disease type identified using the cluster brain map, and 502, 503, and 504 are targets of the same target disease type identified for different subjects using the target location method of the embodiment of this disclosure.

[0081] Figure 6 is a schematic diagram of targets identified using the target identification method of the embodiment of this disclosure in an actual application. As shown in Figure 6, 601 is a ventral target of a depressed patient identified on a brain map of 92 individual divisions using the target identification method of the embodiment of this disclosure, and 602 is a ventral target of an aphasic patient identified on a brain map of 213 individual divisions using the target identification method of the embodiment of this disclosure.

[0082] The embodiments of this disclosure enable the efficient and reliable acquisition of functional information from various brain regions by establishing an accurate brain map of an individual, thereby improving the accuracy of brain region localization. By performing functional localization using an accurate individual-level brain map, the reliability of localization results for neurally modulated targets is improved.

[0083] Furthermore, referring to Figure 7, as an implementation of the methods shown in each of the above figures, the present disclosure provides an embodiment of a target identification device corresponding to an embodiment of the method shown in Figure 2, which can be applied to various electronic devices. As shown in Figure 7, the target identification device 700 of this embodiment includes a data acquisition unit 701, a processing unit 702, and a target identification unit 703.

[0084] The data acquisition unit 701 is configured to acquire scan data of the subject, which includes data obtained by magnetic resonance imaging of the subject's brain, and the scan data includes a sequence of blood oxygen concentration-dependent bold signals corresponding to each of a predetermined number of voxels. The processing unit 702 is configured to identify at least two brain regions of the subject based on the scan data, with each brain region containing at least one voxel.

[0085] The processing unit 702 is further configured to identify at least one target brain region corresponding to the disease type from at least two brain regions, depending on the subject's disease type. The target identification unit 703 is positioned to identify a target located in at least one target brain region, according to a pre-configured target identification rule. In some selectable embodiments, the processing unit 702 is further arranged as follows: The BOLD signal sequence corresponding to each voxel in the scan data is obtained.

[0086] Based on the BOLD signal sequence corresponding to each voxel, the degree of connectivity between any pair of voxels in the scan data is determined, forming a brain connectivity matrix corresponding to the scan data. Based on a standard brain functional partitioning template and brain connectivity matrix, at least two brain regions are formed. In some selectable embodiments, the processing unit 702 is further arranged as follows: Determine the degree of connectivity between any pair of voxels in the scan data.

[0087] The anatomical structure of the subject's brain corresponding to the scan data is divided into multiple large regions, each of these large regions is divided into multiple brain regions, and each brain region contains at least one voxel. Brain regions where the voxel connections between each brain region are higher than a pre-defined voxel connection threshold for a given brain region are fused to form at least two brain regions. In some select embodiments, the target identification unit 703 is further arranged as follows: Identify the central location of at least one target brain region as the target. In some select embodiments, the target identification unit 703 is further arranged as follows:

[0088] The center of at least one target brain region is defined as the center of the sphere, and the region within a predetermined radius of the target is identified as the target ROI, and the location of the target ROI is identified as the target. In some select embodiments, the target identification unit 703 is further arranged as follows: Based on the type of disease, identify the brain structural region where the target is located. Identify the intersection of at least one target brain region with a brain structural division. Identify the target from the intersection.

[0089] In some selectable embodiments, 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.

[0090] Details of the implementation and technical effects of each unit in the target identification device provided in this disclosure can be found in other embodiments of this disclosure and are therefore omitted here.

[0091] Next, referring to Figure 8, a schematic diagram of a computer system 800 of a terminal device or server suitable for implementing this disclosure is shown. The terminal device and server shown in Figure 8 are merely examples and do not in any way limit the functions or scope of use of this disclosure.

[0092] As shown in Figure 8, the computer system 800 includes a central processing unit (CPU) 801 capable of performing various appropriate operations and processes according to programs stored in read-only memory (ROM) 802 or programs loaded from memory unit 808 into random access memory (RAM) 803. RAM 803 also stores various programs and data necessary for the operation of system 800. The CPU 801, ROM 802, and RAM 803 are interconnected via bus 804. An input / output (I / O) interface 805 is also connected to bus 804.

[0093] The I / O interface 805 is connected to an input unit 806 including a keyboard and mouse, an output unit 807 including a cathode ray tube (CRT), liquid crystal display (LCD), and speakers, a storage unit 808 including a hard disk, and a communication unit 809 including a LAN (Local Area Network) card and a network interface card such as a modem. The communication unit 809 performs communication processing via a network such as the Internet.

[0094] In particular, according to embodiments of the present disclosure, the process described with reference to the flowchart above may be implemented as a computer software program. For example, embodiments of the present disclosure include a computer program product which includes a computer program loaded onto a computer-readable medium, the computer program including program code for performing the method shown in the flowchart. In such embodiments, the computer program may be downloaded and installed from a network via a communication unit 809. When the computer program is executed by a central processing unit (CPU) 801, the functions limited to those described in the method of the present disclosure are performed. The computer-readable medium of the present disclosure may be a computer-readable signal medium or a computer-readable storage medium or any combination thereof. The computer-readable storage medium may be, but is not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus or device, or any combination of more than these. More specific examples of computer-readable storage media may include, but are not limited to, electrical connections having at least one conductor, portable computer magnetic disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the above. In this disclosure, computer-readable storage media may be any tangible medium containing or storing a program, the program may be used by or in combination with a command execution system, apparatus or device. In this disclosure, computer-readable signaling media may include data signals transmitted as part of a baseband or carrier wave, on which computer-readable program code is loaded. Such propagating data signals may take various forms, including, but are not limited to, electromagnetic signals, optical signals or any suitable combination of the above.The computer-readable medium may be any computer-readable medium other than a computer-readable storage medium, and such computer-readable medium may transmit, propagate, or transmit a program for use by or in combination with a command and execution system, apparatus, or device. The program code contained on the computer-readable medium may be transmitted by any suitable medium, including, but not limited to, wireless, electrical wiring, optical cables, RF, or any suitable combination thereof.

[0095] Computer program code for performing the operations of this disclosure can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and Python, and further including conventional procedural programming languages ​​such as the C language or similar programming languages. The program code may run entirely on the user's computer, partially on the user's computer, run as a standalone software package, run partially on the user's computer and partially on a remote computer, or run entirely on a remote computer or server. If a remote computer is involved, the remote computer may be connected to the user's computer via any type of network, including a local area network (LAN) or wide area network (WAN), or it may be connected to an external computer (for example, connected via the Internet using an Internet service provider).

[0096] The flowcharts and block diagrams in the drawings illustrate the feasible system configurations, functions, and operations of the systems, methods, and computer program products according to various embodiments of the present disclosure. In this regard, each block in a flowchart or block diagram may represent a module, block, or portion of code containing one or more executable instructions for realizing a predefined logical function. Alternatively, note that the functions described in a block may occur in an order different from that shown in the drawings. For example, two consecutively shown blocks may actually be executed essentially in parallel, and in some cases they may be executed in reverse order, depending on the function. Furthermore, each block in a block diagram and / or flowchart, and combinations of blocks in a block diagram and / or flowchart, may be implemented by a system with dedicated hardware that performs a predefined function or operation, or by a combination of dedicated hardware and computer instructions.

[0097] The units relating to this disclosure may be implemented in software or in hardware. The described units may be provided in a processor and may be described as a processor including, for example, a scan data acquisition unit, a processing unit, a location identification unit, and a lateral identification unit. However, the names of these units do not necessarily limit the units themselves.

[0098] In another aspect, the Disclosure further provides a computer-readable medium which may be included in the apparatus described in the above embodiments, or which may exist independently and not be incorporated into the apparatus. When at least one program is loaded onto the computer-readable medium and the at least one program is executed by the apparatus, the apparatus acquires scan data of a subject, including data obtained from magnetic resonance imaging of the subject's brain; identifies at least two brain regions of the subject (each brain region containing at least one voxel) based on the scan data; identifies at least one target brain region corresponding to the disease type in at least two brain regions depending on the type of disease of the subject; and identifies a target located in at least one target brain region according to a pre-configured target identification rule.

[0099] The above description is merely an explanation of preferred embodiments and operational technical principles of the present disclosure. Those skilled in the art should understand that the scope of the invention as disclosed is not limited to any specific combination of the above technical features, but also includes other technical forms formed by any combination of the above technical features or equivalent features, as long as they do not deviate from the inventive spirit. For example, technical means formed by substituting the above features with similar functional technical features disclosed (but not limited to) in this disclosure. The technical embodiments described in the embodiments of this disclosure may be combined in any way, provided they do not conflict.

[0100] As described above, these are merely specific embodiments of the Disclosure, and the scope of protection of the Disclosure is not limited thereto. A person skilled in the art can easily conceive of any modifications or substitutions within the technical scope disclosed herein, and such modifications or substitutions should fall within the scope of protection of the Disclosure. Therefore, the scope of protection of the Disclosure is equivalent to the scope of protection of the claims.

Claims

1. A step of acquiring scan data of the subject, including data obtained from magnetic resonance imaging of the subject's brain, Here, the magnetic resonance image includes resting-state functional magnetic resonance images. The scan data includes a step comprising a blood oxygen level dependency (BOLD) signal sequence corresponding to each voxel in a predetermined number of voxels, A step of identifying at least two brain regions of the subject (each brain region containing at least one voxel) based on the scan data, Here, each of the two brain regions is a cortical brain region of the subject, For all pairs of voxels in the scan data, the degree of connection between them is determined. The aforementioned degree of connectivity characterizes the degree of connectivity of brain connections, The brain connections include functional connections, which are obtained by calculating the Pearson correlation coefficient based on the BOLD time series corresponding to the voxels in the ROI. A step of identifying the at least two brain regions based on a standard brain brain region template and the degree of connectivity, The steps include identifying at least one target brain region among the at least two brain regions that corresponds to the disease type of the subject, A target identification method comprising the step of identifying a target located in at least one target brain region according to a pre-set target identification rule, This target identification method does not use magnetic resonance imaging data in a task state, and therefore, when scanning the subject's brain, the subject does not perform a specific task to activate surface and deep brain regions.

2. The step of identifying at least two brain regions of the subject (each brain region containing at least one voxel) based on the aforementioned scan data is: Based on the determined degree of connectivity, a brain connectivity matrix corresponding to the scan data is formed, which is composed of the degree of connectivity between each pair of voxels in the scan data. The method according to claim 1, further comprising identifying the at least two brain regions based on the brain region template of the standard brain and the brain connectivity matrix.

3. The method according to claim 1, wherein the step of identifying a target located in the at least one target brain region according to the pre-set target identification rules described above includes identifying the central position of the at least one target brain region as the target.

4. The method according to claim 1, wherein the step of identifying a target located in the at least one target brain region according to the aforementioned pre-set target identification rules includes defining the central position of the at least one target brain region as the center of a sphere, identifying the region within a pre-set target radius range as the target region of interest, and identifying the location of the target region of interest as the target.

5. The step of identifying a target located in at least one target brain region according to the pre-set target identification rules described above is: Based on the disease type, identify the brain structural region where the target is located, To determine the intersection of at least one target brain region and the brain structural division, The method according to claim 1, comprising identifying the target from the intersection.

6. A data acquisition unit that acquires scan data of a subject, including data obtained from magnetic resonance imaging of the subject's brain, Here, the magnetic resonance image includes resting-state functional magnetic resonance images. The scan data includes a data acquisition unit that includes a blood oxygen level dependency (BOLD) signal sequence corresponding to each voxel in a preset number of voxels, A processing unit that identifies at least two brain regions of the subject (each brain region containing at least one voxel) based on the scan data, and further identifies at least one target brain region among the at least two brain regions that corresponds to the disease type of the subject, Here, each of the two brain regions is a cortical brain region of the subject, For all pairs of voxels in the scan data, the degree of connection between them is determined. The aforementioned degree of connectivity characterizes the degree of connectivity of brain connections, The brain connections include functional connections, which are obtained by calculating the Pearson correlation coefficient based on the BOLD time series corresponding to the voxels in the ROI. A processing unit that identifies the at least two brain regions based on a standard brain brain region template and the degree of connectivity, A target identification device comprising: a target identification unit that identifies a target located in at least one target brain region according to a pre-set target identification rule, This target identification device does not use magnetic resonance imaging data of a task state, and therefore, when scanning a subject's brain, the subject does not perform a specific task to activate surface and deep brain regions.

7. At least one processor, A storage device storing at least one program, wherein when the at least one program is executed by the at least one processor, the storage device causes the at least one processor to perform the method according to any one of claims 1 to 5, Electronic devices including

8. A computer-readable storage medium storing a computer program, wherein when the computer program is executed by at least one processor, the computer-readable storage medium implements the method according to any one of claims 1 to 5.

9. A neuromodulatory device configured to neuromodulate a target of a subject according to a pre-set neuromodulatory plan, wherein the target is identified according to the method described in any one of claims 1 to 5.

10. The aforementioned pre-set adjustment plan is Deep brain electrical stimulation, Transcranial electrical stimulation, Electroconvulsive therapy, Electrical stimulation based on cortical-brain electrical electrodes, Transcranial magnetic stimulation, Ultrasound-focused nerve control, Magnetic resonance-induced high-energy ultrasound focused therapy control, Light stimulation regulation The device according to claim 9, comprising at least one of the following.

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