Target identification method, apparatus, electronic device, storage medium and neuromodulation 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 regions of interest of the subject based on the scan data, identifying at least one abnormal region of interest among the at least two regions of interest according to a preset anomaly detection rule, and identifying a target based on the at least one abnormal region of interest.

Description

[Technical Field]

[0001] The present disclosure relates to the field of computer technology, and in particular to target identification methods, apparatus, electronic devices, storage media, and neuromodulation devices. [Background technology]

[0002] Many neurological and psychiatric disorders often manifest as abnormal nervous system function without a clear lesion. Directly or indirectly modulating abnormal functional networks using neuromodulatory methods such as electricity, magnetism, light, and ultrasound is an important means of improving patients' symptoms. How to select neuromodulation targets in the human brain is a challenging task. Research has shown that for many neurological and psychiatric disorders, the etiology cannot be directly analyzed from structural images and the lesion cannot be located, resulting in the failure to achieve the desired modulation and therapeutic effects. Therefore, clinical practice requires objective, accurate, and quantifiable aids to help physicians select neuromodulation targets for individuals. Existing methods for identifying neuromodulation targets cannot meet this demand. Summary of the Invention

[0003] The present disclosure provides target identification methods, apparatus, electronic devices, storage media, and neuromodulation devices for selecting individualized neuromodulation targets.

[0004] In a first aspect, the present disclosure provides a method for target identification, including acquiring scan data of a subject, the scan data including data obtained from a magnetic resonance image of the subject's brain; identifying at least two regions of interest (ROIs) of the subject based on the scan data; identifying at least one abnormal region of interest among the at least two ROIs according to a predetermined anomaly detection rule; and identifying a target based on the at least one abnormal region of interest. In some alternative embodiments, identifying at least two regions of interest in the subject based on the scan data as described above includes: For every pair of two voxels in the scan data, the connectivity between them is determined to form a brain connectivity matrix corresponding to the scan data. The at least two regions of interest are formed based on a brain region template and a brain connectivity matrix of a standard brain. In some alternative embodiments, identifying at least two regions of interest in the subject based on the scan data as described above includes: For every pair of two voxels in the scan data, the connectivity between them is determined.

[0005] The anatomical structure of the subject's brain corresponding to the scan data is divided into a plurality of large regions, and each large region of the plurality of large regions is divided into a plurality of brain regions, each brain region in the plurality of brain regions including at least one voxel.

[0006] The at least two regions of interest are formed by fusing brain regions in which the voxel connectivity between each brain region in the plurality of brain regions is higher than a predetermined threshold value of voxel connectivity between brain regions. In some alternative embodiments, identifying at least two regions of interest in the subject based on the scan data as described above includes: At least two regions of interest are identified for the subject based on the scan data based on a volumetric standard brain structure template. In some alternative embodiments, identifying at least two regions of interest in the subject based on the scan data as described above includes: At least two regions of interest of the subject are identified based on the scan data based on a cortical standard brain structure template.

[0007] In some alternative embodiments, identifying at least one anomalous region of interest among the at least two regions of interest according to the aforementioned preset anomaly detection rules includes: Collective brain magnetic resonance data is acquired. A brain connectivity matrix of the population is determined based on the brain magnetic resonance data of the population. For every pair of two voxels in the scan data, the connectivity between them is determined to form a brain connectivity matrix corresponding to the scan data. The at least one abnormal region of interest is identified based on the brain connectivity matrix of the population and the brain connectivity matrix. In some alternative embodiments, identifying a target based on the at least one abnormal region of interest includes: Determining whether the at least one abnormal region of interest is located in an adjustable brain region.

[0008] If the at least one abnormal region of interest is located in an adjustable brain region, the center of the at least one abnormal region of interest is identified as the target, or the center of the at least one abnormal region of interest is set as the center of a sphere and an area of ​​a predetermined target radius is identified as a first target region of interest, and the target is identified based on the position of the first target region of interest.

[0009] If the at least one abnormal region of interest is not located in an adjustable brain region, determine the connectivity between the at least one abnormal region of interest and other regions of interest in the at least two regions of interest, and identify the region of interest in the other regions of interest whose connectivity with the at least one abnormal region of interest exceeds a predetermined connectivity threshold and is located in an adjustable region as a second target candidate region.

[0010] The center of the second target candidate region is identified as the target, or the center of the second target candidate region is set as the center of a sphere and a region of a predetermined target radius is identified as a second target region of interest, and the target is identified based on the position of the second target region of interest. In some alternative embodiments, identifying a target based on the at least one abnormal region of interest includes: The brain structural compartment in which the target is located is identified based on the subject's disease type.

[0011] The intersection of the brain structural division with the at least one abnormal region of interest or a region of interest whose connectivity with the abnormal region of interest satisfies a preset connectivity threshold condition is identified as a candidate target region.

[0012] The center of the target candidate region is identified as the target, or the center of the target candidate region is set as the center of a sphere, and a region of a predetermined target radius is identified as a target region of interest, and the target is identified based on the position of the target region of interest.

[0013] In some alternative embodiments, the functional magnetic resonance images include structural magnetic resonance images, and / or task-state functional magnetic resonance images, and / or resting-state functional magnetic resonance images.

[0014] In a second aspect, the present disclosure provides a target identification apparatus including: a data acquisition unit arranged to acquire scan data of a subject, the data acquisition unit including data obtained from a magnetic resonance image of the subject's brain; a processing unit arranged to identify at least two regions of interest of the subject based on the scan data; an anomaly detection unit arranged to identify at least one abnormal region of interest among the at least two regions of interest according to a preset anomaly detection rule; and a target identification unit arranged to identify a target based on the at least one abnormal region of interest. In some alternative embodiments, the processing unit is further configured as follows: For every pair of two voxels in the scan data, the connectivity between them is determined to form a brain connectivity matrix corresponding to the scan data. The at least two regions of interest are formed based on a brain region template and a brain connectivity matrix of a standard brain. In some alternative embodiments, the processing unit is further configured as follows: For every pair of two voxels in the scan data, the connectivity between them is determined.

[0015] The anatomical structure of the subject's brain corresponding to the scan data is divided into a plurality of large regions, and each large region in the plurality of large regions is divided into a plurality of brain regions, each brain region in the plurality of brain regions including at least one voxel.

[0016] The at least two regions of interest are formed by fusing brain regions in which the voxel connectivity between each brain region in the plurality of brain regions is higher than a predetermined threshold value of voxel connectivity between brain regions. In some alternative embodiments, the processing unit is further configured as follows: Based on a volumetric standard brain structure template, at least two regions of interest are identified in the subject based on the scan data. In some alternative embodiments, the processing unit is further configured as follows: At least two regions of interest of the subject are identified based on the scan data based on a cortical standard brain structure template. In some alternative embodiments, the anomaly detection unit is further configured as follows: Collective brain magnetic resonance data is acquired. A brain connectivity matrix of the population is determined based on the brain magnetic resonance data of the population. For every pair of two voxels in the scan data, the connectivity between them is determined to form a brain connectivity matrix for the subject corresponding to the scan data. The at least one abnormal region of interest is identified based on the brain connectivity matrix of the population and the brain connectivity matrix of the subject. In some alternative embodiments, identifying a target based on the at least one abnormal region of interest includes: Determining whether the at least one abnormal region of interest is located in an adjustable brain region.

[0017] If the at least one abnormal region of interest is located in an adjustable brain region, the center of the at least one abnormal region of interest is identified as the target, or the center of the at least one abnormal region of interest is set as the center of a sphere, and an area of ​​a predetermined target radius is identified as a first target region of interest, and the target is identified based on the position of the first target region of interest.

[0018] If the at least one abnormal region of interest is not located in an adjustable brain region, determine the connectivity between the at least one abnormal region of interest and other regions of interest in the at least two regions of interest, and identify a region of interest whose connectivity with the at least one abnormal region of interest in the other regions of interest exceeds a predetermined connectivity threshold and is located in an adjustable region as a second target candidate region.

[0019] The center of the second target candidate region is identified as the target, or the center of the second target candidate region is set as the center of a sphere and a region of a predetermined target radius is identified as a second target region of interest, and the target is identified based on the position of the second target region of interest. In some alternative embodiments, identifying a target based on the at least one abnormal region of interest includes: The brain structural compartment in which the target is located is identified based on the subject's disease type.

[0020] The at least one abnormal region of interest or an intersection of the brain structural division with a region of interest whose connectivity with the abnormal region of interest satisfies a preset connectivity threshold condition is identified as a candidate target region.

[0021] The center of the target candidate region is identified as the target, or the center of the target candidate region is set as the center of a sphere, and a region of a predetermined target radius is identified as a target region of interest, and the target is identified based on the position of the target region of interest.

[0022] In some alternative 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.

[0023] In a third aspect, the present disclosure provides an electronic device including at least one processor and a storage device having stored thereon at least one program that, when executed by the at least one processor, causes the at least one processor to perform a method according to any of the implementations of the first aspect.

[0024] In a fourth aspect, the present disclosure provides a computer-readable storage medium having stored thereon a computer program that, when executed by at least one processor, performs a method according to any one of the implementations of the first aspect.

[0025] In a fifth aspect, the present disclosure provides a neuromodulation device arranged to neuromodulate targets of a subject according to a preset neuromodulation plan, the targets identified according to a method described in any implementation of the first aspect.

[0026] In some alternative embodiments, the preset modulation plan includes at least one of deep brain stimulation, transcranial electrical stimulation, electroconvulsive therapy, electrical stimulation with cortical brain electrodes, transcranial magnetic stimulation, ultrasound focused neuromodulation, magnetic resonance guided high-energy ultrasound focused therapy modulation, and light stimulation modulation. To achieve neuromodulatory target identification, currently commonly used technical means include:

[0027] 1. We identify neuromodulatory targets based on group-level task-state functional magnetic resonance imaging (fMRI). The drawbacks of this method include the low signal-to-noise ratio and poor reproducibility of task-state fMRI, the requirement for a certain cognitive level from subjects, the fact that task-state fMRI results in functional areas are significantly affected by task design, and the difficulty of determining baseline levels of functional areas.

[0028] 2. Clinical experience based on brain anatomical structure allows for the global location of the body surface projection of specific functional areas on the patient's scalp to identify neuromodulation targets. For example, the left dorsal lateral prefrontal cortex (DLPFC) localization method (also known as the "5cm" localization method) is approved by the U.S. Food and Drug Administration (FDA) for the treatment of resistant depression using repetitive transcranial magnetic stimulation (rTMS). The drawbacks of this method include ignoring individual anatomical differences and low localization accuracy, resulting in inaccurate localization of neuromodulation targets. It also ignores individual differences in functional networks, meaning that the target may be located in other brain functional areas.

[0029] 3. Identifying neuromodulation targets using electrode caps, such as the International 10-20 electrode cap localization method. The drawbacks of this method include ignoring individual differences in anatomical structure, low localization accuracy, inaccurate localization of neuromodulation targets, and ignoring differences in individual functional networks.

[0030] 4. Identifying neuromodulation targets based on ROIs defined by anatomical structures or population-averaged fMRI studies. The drawbacks of this method include the fact that many neurological and psychiatric disorders often lack clear lesions and merely demonstrate functional abnormalities in the nervous system, meaning that simple anatomical structures cannot accurately reflect disease characteristics. Furthermore, the etiology of neurological and psychiatric disorders is complex, with individual differences, making treatment planning based on population-averaged fMRI less effective.

[0031] 5. Identifying neuromodulatory targets based on the state of tissue structure and metabolism reflected by PET scan data. The drawbacks of this method include the high cost of PET scans, which increases the medical burden, the presence of certain radiation during the scanning process, the limited application of PET scans to neurological and psychiatric disorders, the low image signal-to-noise ratio, and the unclear boundaries of anatomical structures, which affect the accuracy of target identification and reduce the effectiveness of clinical treatment.

[0032] The target identification method, apparatus, electronic device, storage medium, and neuromodulation device provided by the present disclosure include acquiring scan data of a subject, including data obtained from magnetic resonance imaging of the subject's brain, identifying at least two regions of interest of the subject based on the scan data, identifying at least one abnormal region of interest among the at least two regions of interest according to a preset anomaly detection rule, and identifying a target based on the at least one abnormal region of interest. Examples of the present disclosure provide brain scan data of the subject using functional magnetic resonance imaging to identify brain regions of the subject, fully taking into account individual differences, effectively solving the problem of inaccurate neuromodulation target identification in conventional methods due to a failure to consider individual structural or functional differences, and realizing the location of individualized neuromodulation targets for the subject. The individualized anomaly detection method combines structural and functional information of the brain to efficiently detect brain regions with abnormal brain connectivity patterns compared to normal subjects, effectively solving the problem of inaccurate neuromodulation target identification in conventional methods due to a failure to consider individual structural or functional differences. [Brief explanation of the drawings]

[0033] [Figure 1] FIG. 1 is a diagram illustrating an exemplary system configuration to which an embodiment of the present disclosure can be applied. [Figure 2] FIG. 2 is a flow diagram of one embodiment of the target identification method of the present disclosure. [Figure 3] FIG. 3 is an exploded view of one embodiment of step 202 in the target identification method shown in FIG. [Figure 4] FIG. 4 is an exploded view of yet another embodiment of step 202 in the target identification method shown in FIG. [Figure 5] FIG. 5 is a structural diagram of an embodiment of a target identification device according to the present disclosure. [Figure 6] FIG. 6 is a structural diagram of a computer system suitable for implementing a terminal device or server of the present disclosure. DETAILED DESCRIPTION OF THE INVENTION

[0034] In order to allow a more detailed understanding of the features and technical contents of the embodiments of the present disclosure, the implementation of the embodiments of the present disclosure will be described in detail below with reference to the accompanying drawings. However, the accompanying drawings are for illustrative purposes only and do not limit the embodiments of the present disclosure.

[0035] In describing the embodiments of the present invention, unless otherwise specified or limited, the term "connection" should be understood broadly, and may refer to, for example, an electrical connection, internal communication 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.

[0036] It should be noted that the terms "first," "second," and "third" used in the embodiments of the present disclosure are merely used to distinguish between similar objects and do not represent a particular order for the objects, and "first," "second," and "third" may be used to reverse a particular order or priority, if permitted. Objects distinguished by "first," "second," and "third" may be reversed in some cases so that the embodiments of the present disclosure described herein may be implemented in an order other than that illustrated or described herein. FIG. 1 shows an exemplary system configuration 100 of an embodiment to which the target identification method or target identification device of the present disclosure can be applied.

[0037] 1, system configuration 100 includes terminal devices 101, 102, 103, a network 104, and a server 105. Network 104 serves as a medium providing a communication link between terminal devices 101, 102, 103 and server 105. Network 104 may include various connection types, such as wires, wireless communication links, fiber optic cables, etc.

[0038] Users may use the terminal devices 101, 102, 103 to interact with the server 105 via the network 104 to receive or send messages, etc. Various communication client applications may be installed on the terminal devices 101, 102, 103, such as a magnetic resonance imaging control application, a functional magnetic resonance imaging control application, a web browser application, a shopping application, a search application, an instant communication tool, a mailbox client, and social platform software.

[0039] The terminal devices 101, 102, and 103 may be hardware or software. If the terminal devices 101, 102, and 103 are hardware, they may be various electronic devices with display screens, including, but not limited to, smartphones, tablets, laptops, and desktops. If the terminal devices 101, 102, and 103 are software, they may be installed in an electronic device that identifies multiple brain regions of the subject listed above. They may be implemented as multiple software programs or software modules (e.g., processing for providing a brain atlas), or as a single software program or software module. No specific limitations are provided here.

[0040] The server 105 may be a server that provides various services, for example, a background data processing server that processes scan data sent by the terminal devices 101, 102, and 103. The background data processing server can identify multiple brain regions of the subject and voxels corresponding to each brain region based on the scan data, and feed the identified voxels back to the terminal devices.

[0041] 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 programs or software modules (e.g., to provide a distributed service) or as a single software program or software module. No specific limitations are provided here. It should be noted that the target identification method provided by the present disclosure is generally executed by the server 105, and therefore the target identification device is generally provided in the server 105.

[0042] In some cases, the target identification method provided by the present disclosure may be executed by the server 105, or may be executed by the terminal devices 101, 102, and 103, or may be executed in cooperation between the server 105 and the terminal devices 101, 102, and 103. Therefore, the target determination device may be provided in the server 105, or may be provided in the terminal devices 101, 102, and 103, or may be partially provided in the server 105 and partially provided in the terminal devices 101, 102, and 103. Therefore, the system configuration 100 may include only the server 105, or may include only the terminal devices 101, 102, and 103, or may include the terminal devices 101, 102, and 103, the network 104, and the server 105. This is not a limitation of the present disclosure.

[0043] 1 are merely exemplary, and any number of terminal devices, networks, and servers may be provided depending on the implementation needs. Continuing to refer to Figure 2, there is shown a flow chart 200 of an embodiment of a target identification method according to the present disclosure, which includes the following steps: Step 201, scan data of a subject is acquired. In an embodiment of the present disclosure, the scan data includes data obtained from a magnetic resonance image of the subject's brain.

[0044] The scan data includes a Blood Oxygen Level Dependency (BOLD) signal sequence corresponding to each voxel in a predetermined number of voxels.

[0045] In this embodiment, the executing entity of the target identification method (e.g., the server shown in FIG. 1) can first locally or remotely acquire scan data of the subject from another electronic device (e.g., the terminal device shown in FIG. 1) network-connected to the executing entity.

[0046] Voxels, also known as volume voxels, are short for volume pixel. Conceptually, voxels are similar to pixels, the smallest unit of two-dimensional space, which are used to represent image data in two-dimensional computer images. Voxels are the smallest unit of three-dimensional spatial division of digital data, and are used in fields such as three-dimensional imaging, scientific data, and medical images.

[0047] The BOLD signal sequence corresponding to a voxel may be obtained by performing a magnetic resonance scan on a subject, acquiring one BOLD signal for each voxel for each predetermined time unit, and finally acquiring a BOLD signal for a certain time period. These BOLD signals are arranged in order of acquisition time to obtain a BOLD signal sequence corresponding to each voxel, and 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 unit. For example, if the length of time corresponding to the scan is 300 seconds and the predetermined time unit is 2 seconds, the BOLD signal sequence corresponding to each voxel may be considered to have 150 BOLD values, 150 frames of data, a 150-dimensional vector, or a 1×150-dimensional matrix, although the present disclosure is not limited thereto.

[0048] The specific number of voxels included in the scan data may be determined based on the scanning accuracy of the functional magnetic resonance image or magnetic resonance image, or based on the accuracy of the imaging device. The preset number here is not a limitation on the specific number of voxels. It is understood that in current practical applications, the number of voxels in human brain scan data is measured in tens of thousands or hundreds of thousands, and with the advancement of scanning technology, the number of voxels included in human brain scan data may be further increased.

[0049] In the present disclosure, the executing entity can acquire scan data of the subject locally or remotely from another electronic device (e.g., the terminal device shown in FIG. 1) network-connected to the executing entity.

[0050] In embodiments of the present disclosure, the magnetic resonance images may include structural magnetic resonance images, and / or task-state functional magnetic resonance images, and / or resting-state functional magnetic resonance images.

[0051] Data obtained by functional magnetic resonance imaging (FMRI) contains time-series information and corresponds to a four-dimensional image. For example, if FMRI images are acquired and a three-dimensional image matrix (Length × Width × Height, L × M × N) is acquired at one frame every two seconds, 150 frames of data can be acquired in six minutes, forming a functional magnetic resonance image data signal of L × M × N voxels × 150.

[0052] Data obtained by structural magnetic resonance imaging include high-resolution, three-dimensional grayscale anatomical images, such as T1w (T1-weighted imaging—the difference between longitudinal and longitudinal relaxation of prominent tissues) and related images, T2w (T2-weighted imaging—the difference between transverse and transverse relaxation of prominent tissues) and related images, and fluid-attenuated inversion recovery (FLAIR) sequences and related images. Structural magnetic resonance images may also include magnetic resonance diffusion images, such as diffusion-weighted imaging (DWI) and related images, and diffusion tensor imaging (DTI) and related images.

[0053] DTI is a magnetic resonance technique used to study the diffusion anisotropy of anatomical nerve bundles in the central nervous system and visualize the anatomical structure of white matter fibers. It detects the microstructure of tissue through the anisotropy of water molecule diffusion within the tissue. White matter anisotropy is due to the parallel orientation of myelinated axon fibers. White matter diffusion is greatest in the direction of parallel nerve fibers; therefore, the fractional anisotropy (FA) is greatest and can be determined to be approximately 1 (in practice, a fraction greater than 0.9 and close to 1 is sufficient). This characteristic, when labeled with color, can reflect the spatial orientation of white matter; that is, the direction of fastest diffusion corresponds to the fiber orientation. Fiber bundle imaging using DTI can provide a brain connectivity matrix that reflects the brain's structure. In embodiments of the present disclosure, the functional magnetic resonance images may include task-state functional magnetic resonance images and / or resting-state functional magnetic resonance images.

[0054] A resting-state functional magnetic resonance image is understood to be a magnetic resonance image obtained by performing a magnetic resonance scan of a subject's brain while the subject is not performing a task during the scan. Correspondingly, a task-state functional magnetic resonance image is a magnetic resonance image obtained by performing a magnetic resonance scan of a subject's brain while the subject is performing a task of interest.

[0055] After obtaining a subject's structural brain magnetic resonance scan data, various methods can be used to determine the subject's structural brain map based on the subject's structural brain magnetic resonance scan data, i.e., to identify which components each specific region of the subject's brain is. For example, this can be achieved using existing software for processing three-dimensional brain scan data, such as FreeSurfer, a magnetic resonance data processing software. Another example is to train a deep learning model based on a large amount of sample brain structural scan data and corresponding brain component labeling, and then input the subject's structural brain magnetic resonance scan data into the trained deep learning model to obtain the corresponding structural brain map. In some alternative embodiments, the executing entity pre-processes the scan data after acquiring the scan data of the subject. In the present invention, the method of pretreatment is not specifically limited, and for example, the pretreatment may include the following: Preprocessing for functional magnetic resonance imaging images, e.g. (1) Temporal layer correction, head motion correction, temporal signal filtering, noise component regression, spatial smoothing, etc. (2) Registration of functional magnetic resonance imaging images with structural images (if structural images exist);

[0056] (3) Projecting the functional magnetic resonance imaging signals onto a structural image, including a reconstructed individual brain cortical image or a relevant group-average level structural image (if a structural image exists).

[0057] Structural magnetic resonance imaging images (if structural images exist) are preprocessed, for example by skull removal, field intensity correction, segmentation of individual anatomical structures, and brain cortical reconstruction. Step 202, identify at least two ROIs of the subject based on the scan data. With respect to step 202 above, the present disclosure provides various alternative implementations.

[0058] Figure 3 is a partial exploded view of one embodiment of step 202 in the target identification method shown in Figure 2. In some alternative embodiments, as shown in Figure 3, the step 202 may specifically include: Step 202a1: Determine the connectivity between every pair of two voxels in the scan data to form a brain connectivity matrix corresponding to the scan data.

[0059] In the present disclosure, the connectivity between a voxel and an ROI may include an average value of the connectivity between the voxel and each voxel in the ROI; the connectivity between two ROIs may include an average value of the connectivity between a voxel in each ROI and each voxel in the other ROI, in the two ROIs; the connectivity between a voxel and a brain region may include an average value of the connectivity between a voxel and each voxel in a brain region; and the connectivity between two brain regions may include an average value of the connectivity between a voxel in each brain region and each voxel in the other brain region, in the two brain regions.

[0060] The connectivity characterizes the connectivity of brain connections and can also be expressed as a correlation. Here, the brain connectivity includes functional connectivity and structural connectivity. The functional connectivity is obtained by Pearson correlation coefficient calculation based on the BOLD time series corresponding to voxels in ROIs, and the structural connectivity includes the structural connectivity between ROIs obtained by tractography.

[0061] For example, suppose the number of voxels in the scan data is 100,000, the BOLD signal sequence corresponding to each voxel contains T BOLD values, and T is the number of samples in the time dimension corresponding to the scan time. The brain connectivity matrix corresponding to the scan data is a 100,000 x 10 matrix of order 100,000. The brain connectivity matrix can characterize the connectivity between every combination of two voxels in the scan data, and the connectivity between the 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 for measuring the degree of linearity between variables. The calculation formula is as follows:

number

[0062] The formula is the Pearson correlation coefficient (ρ X,Y) is the covariance between them, cov(X,Y), multiplied by the product of their respective standard deviations (σ X ,σ Y ) divided by the coefficient. The coefficient value is always between -1.0 and 1.0, and variables equal to or approximately equal to 0 are said to be uncorrelated, while variables equal to or approximately equal to 1 or a value close to -1 are said to have a strong correlation. Here, it can be understood that the difference between the approximate value and the target value is within an error tolerance. For example, in the present invention, 0.01 can be approximated to 0, or 0.99 can be approximated to 1. However, this is merely an example, and in actual applications, an approximately equal error tolerance can be determined based on the accuracy required for calculation. Step 202a2: Form at least two ROIs based on the brain region template and brain connectivity matrix of a standard brain.

[0063] For example, a brain atlas including two or more brain regions can be generated for a subject using pattern recognition or machine learning methods based on a brain region template of a standard brain. Methods may include, but are not limited to, independent component correlation algorithm (ICA), principal component analysis (PCA), various clustering methods, factor analysis, linear discriminant analysis (LDA), and various matrix decomposition methods. Although the voxel locations included in each functional partition of the resulting brain functional network may differ for different subjects, each voxel belongs to a specific ROI. That is, each ROI for a subject may be a collection of voxels consisting of voxels in fMRI that have the same function. In embodiments of the present disclosure, the brain regions may include functional brain regions and / or structural brain regions.

[0064] Figure 4 is an exploded view of yet another example of step 202 in the target identification method shown in Figure 2. In some alternative embodiments, as shown in Figure 4, step 202 may specifically include the following steps: Step 202b1: For every pair of two voxels in the scan data, determine the connectivity between them.

[0065] Step 202b2 divides the subject's brain anatomical structure corresponding to the scan data into a plurality of large regions, and divides the plurality of large regions (e.g., each large region) into a plurality of brain regions, each of the plurality of brain regions including at least one voxel.

[0066] Step 202b3: Fusing the brain regions in which the voxel connectivity between each of the plurality of brain regions is higher than a preset voxel connectivity threshold for the brain regions to form at least two brain regions.

[0067] For example, first, the subject's brain is divided into multiple large regions according to major anatomical boundaries, and then each large region is segmented according to functional connectivity, and the connectivity of voxels 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 connectivity of the voxels contained therein, and brain regions with highly connected voxels are integrated into one ROI, for example, and finally at least two brain region ROIs can be identified in the whole brain.

[0068] For example, by dividing the left and right cortices of the brain into five large regions, namely the frontal lobe, parietal lobe, occipital lobe, temporal lobe, and the entire central sulcus region, the initial individual brain map can be divided into 10 large regions. Also, for example, the brain can be divided into the upper cortex and the lower cortex, and the left and right hemispheres can be divided into a total of four regions. In some alternative embodiments, step 202 may specifically include:

[0069] A brain atlas of the population is pre-selected or pre-created as a brain atlas template, and boundaries corresponding to at least two brain regions in the brain atlas template are projected onto the subject's brain scan data.

[0070] Boundaries of at least two brain regions are adjusted based on the brain scan data of the subject, and the adjusted brain region boundaries are matched with at least the brain scan data of the subject to form at least two ROIs.

[0071] For example, after directly projecting a population brain map onto the subject's brain, a recursive algorithm is used to gradually adjust the boundaries of the brain regions onto which the population brain map is projected according to the subject's anatomical brain map until the boundaries are stable. The recursive process utilizes the subject's individual differences in brain connectivity and the signal-to-noise ratio of the subject's own brain image to determine the width of the brain region boundary adjustment. Finally, at least two ROIs are obtained by fusing the brain regions of the brain based on voxel correlation. In some alternative embodiments, step 202 may specifically include:

[0072] A standard ROI library is constructed, which contains multiple test scenario-related ROIs and their potential brain connectivity patterns. With the standard ROI library, we can select corresponding ROIs according to the test scenario type, and then obtain at least two ROIs for each subject based on the selected ROIs. In some optional embodiments, step 202 may specifically include:

[0073] Based on the volumetric standard brain structure template and scan data, at least two ROIs are determined for the subject. White matter and ventricular regions are extracted from the volumetric standard brain structure template, a binary mask of the volumetric standard brain structure template is constructed, the white matter and ventricular regions are removed from the mask to obtain a mask without white matter and ventricular regions, and the mask without white matter and ventricular regions is resampled to obtain at least two ROIs. Alternatively, a binary mask of the volumetric standard brain structure template is constructed and resampled to obtain at least two ROIs. In some alternative embodiments, step 202 may specifically include:

[0074] Based on the cortical standard brain structure template and the scan data, at least two ROIs are determined for the subject. The cortical standard 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) and fsaverage4 (fs4)), at least two ROIs (e.g., fsaverage6 (fs6)) are generated for a high-resolution template. Specifically, the left and right brain vertices of the coarse-resolution template are assigned sequentially (1, 2, 3, etc.), and then resampled (by nearest neighbor interpolation) into the fs6 template space. All vertex index numbers in the fs6 surface corresponding to each number are counted in order according to the assigned order, resulting in at least two ROIs in the fs6 space. For example, the 13th vertex in the left brain fs4 surface template is assigned the number 13. After resampling into the fs6 template space, 30 vertices, all of which are 13, are found. The 13th ROI in the left brain fs6 surface template consists of these 30 vertices. Generate at least two ROIs based on all vertices in the surface template, with each vertex representing one ROI for any given surface template (e.g., the fsaverage6 left brain template contains 40962 vertices, corresponding to 40962 ROIs that can be generated). Step 203: Identify at least one abnormal ROI among the at least two ROIs according to a preset abnormality detection rule. In some alternative embodiments, step 203 may specifically include: Collective brain magnetic resonance data is acquired. For example, the number of population samples obtained may be 300 or more. Here, the number of population samples is not specifically limited, but is merely an illustrative example. Preprocessing of acquired population brain magnetic resonance data. A brain connectivity matrix of the population is determined based on the brain magnetic resonance data of the population. Here, the calculation of the population brain connectivity matrix may include the following steps:

[0075] Step S11: For each subject in the brain magnetic resonance data of the population, calculate the average time series of all vertices / voxels in each ROI, where the ROI may be obtained by the ROI identification method in the embodiment of the present disclosure or by other conventional ROI identification methods.

[0076] Step S12: Sequentially calculate the correlation coefficients (e.g., Pearson correlation coefficients) between the ROIs and the ROI time series, and finally obtain the brain connectivity matrix for each subject. The brain connectivity matrix may include a functional connectivity matrix and a structural connectivity matrix. Take functional connectivity (FC) as an example, it is indi_fc (this matrix is ​​a diagonal matrix, and the value in the i-th row and j-th column in the matrix represents the correlation between the i-th ROI and the j-th ROI time series, where i≦N, j≦N, N is the number of ROIs, and the size of indi_fc is N×N). A BOLD signal sequence corresponding to each voxel in the scan data is acquired.

[0077] Based on the BOLD signal sequence corresponding to each voxel, the connectivity between all pairs of voxels in the scan data is determined, forming a brain connectivity matrix for the subject corresponding to the scan data. At least one abnormal ROI is identified based on the population brain connectivity matrix and the subject brain connectivity matrix. In some embodiments, step 203 may specifically include Plan 1 or Plan 2. Plan 1: Anomaly detection algorithm A may specifically include the following steps SA1 to SA3. Step SA1: baseline generation (population brain connectivity matrix). Specifically, the following sub-steps SA11 to SA14 may be included. Sub-step SA11, Data Collection: Collect large sample fMRI data (typically >= 300 individuals). Sub-step SA12, data pre-processing: Pre-process the data according to the pre-processing method of the embodiment of FIG. Substep SA13, brain connectivity matrix calculation.

[0078] For each subject's data in the large sample data, calculate the average time series of all vertices / voxels in each ROI (generated from step SA1, assuming there are N ROIs).

[0079] The correlation coefficients (e.g., Pearson correlation coefficients) between ROIs and ROI time series are calculated sequentially, and finally a brain connectivity matrix for each sample is obtained. The brain connectivity matrix is ​​not limited to functional connectivity matrix and structural connectivity matrix. Take the functional connectivity (FC) matrix as an example, indi_fc (this matrix is ​​a diagonal matrix, and the value in the ith row and jth column in the matrix represents the correlation between the ith ROI and the jth ROI time series, where i≦N, j≦N, N is the number of ROIs, and the size of indi_fc is N×N). Substep SA14, baseline generation. The indi_fc of all subjects is averaged to obtain a mean matrix, i.e., Big_mean_fc (size N×N).

[0080] The correlation coefficient of each row of indi_fc with the corresponding row of Big_mean_fc is calculated sequentially to obtain a one-dimensional matrix (size N × 1). Finally, the mean and standard deviation, i.e., Corr_mean (size N × 1) and Corr_std (size N × 1), are calculated for this one-dimensional matrix for all subjects. Step SA2, subject FC generation (subject brain connectivity matrix). Using the preprocessed data of a subject (P), calculate the average time series of all vertices / voxels in each ROI (generated from step 1).

[0081] The correlation coefficients (e.g., using the Pearson correlation coefficient) between the ROIs and the time series of the ROIs are calculated sequentially, and finally, the brain connectivity matrix of subject P is obtained. The brain connectivity matrix may include a functional connectivity matrix and a structural connectivity matrix. For example, the functional connectivity (FC) matrix is ​​denoted as p_fc (its size and meaning are the same as indi_fc in step SA1).

[0082] The correlation coefficient between each row of p_fc and the corresponding row of Big_mean_fc (generated in step SA1) is calculated in sequence to obtain an N×1 one-dimensional matrix, for example, p_corr. Step SA3, anomaly detection algorithm procedure. The following calculation is performed for the nth (n≦N, N is the number of ROIs) ROI of p_corr.

number

[0083] Here, sub-steps SB11 to SB13 have the same contents and effects as sub-steps SA11 to SA13 in step SA1 of Plan 1, and therefore a description thereof will be omitted here.Sub-step SB14: baseline generation. Calculate the mean and standard deviation matrices of indi_fc for all sample data, i.e., Big_mean_fc and Big_std_fc.

[0084] Step S2, subject FC generation (subject brain connectivity matrix), which may specifically include the following substeps SB21 to SB22: Substep SB21, using the preprocessed data of the subject (e.g., P), calculate the average time series of all vertices / voxels in each ROI (generated from step 1).

[0085] In substep SB22, we sequentially calculate the correlation coefficients between the ROIs and the time series of the ROIs (e.g., using the Pearson correlation coefficient), and finally obtain the brain connectivity matrix (functional connectivity, FC) for each subject, i.e., p_fc (size N × N). Sub-step SB, anomaly detection algorithm procedure, which may specifically include: The following calculation is performed on the data in the i-th row and j-th column (i≦N, j≦N, N is the number of ROIs) in the p_fc matrix.

number

[0086] By summing up the values ​​in each row of p_z (size N × 1) that are greater than a threshold k (k is an integer greater than or equal to 2), we finally obtain a one-dimensional N × 1 matrix called p_z_sum (size N × 1), which is the final anomaly detection result. Here, the abnormality detection result is localized to m (m>=0) brain regions, that is, m ROIs, where the subject has abnormalities compared to normal subjects. In step 203, the at least one abnormal ROI may be an abnormal ROI in the cerebral region or an abnormal ROI in the cerebellar region. In step 204, a target is identified based on at least one abnormal ROI. In some alternative embodiments, the above step 204 may specifically include: In some alternative embodiments, identifying a target based on at least one abnormal ROI includes: Determine whether at least one abnormal ROI is located in a modulatable brain region.

[0087] If at least one abnormal ROI is located in an adjustable brain region, the center of the at least one abnormal ROI is identified as a target, or the center of the at least one abnormal ROI is set as the center of a sphere and an area of ​​a predetermined target radius is identified as a first target ROI, and the target is identified based on the position of the first target ROI.

[0088] If at least one abnormal ROI is not located in the adjustable brain region, the connectivity of the at least one abnormal ROI with other ROIs in at least two ROIs is determined, and an ROI whose connectivity with the at least one abnormal ROI in the other ROI exceeds a predetermined connectivity threshold and is located in the adjustable region is identified as a second target candidate region.

[0089] The center of the second target candidate region is identified as the target, or the center of the second target candidate region is set as the center of a sphere and an area of ​​a predetermined target radius is identified as a second target ROI, and the target is identified based on the position of the second target ROI. In some alternative embodiments, identifying a target based on at least one abnormal ROI includes: The brain structural section in which the target is located is identified depending on the type of disease of the subject.

[0090] At least one abnormal ROI or an intersection of an ROI and a brain structural partition whose connectivity with the abnormal ROI satisfies a preset connectivity threshold condition is identified as a target candidate region.

[0091] The center of the target candidate region is identified as the target, or an area having a preset target radius centered on the center of the target candidate region is identified as the target ROI, and the target is identified based on the position of the target ROI. The disease type includes a disease type determined by diagnosing the subject, or a disease type corresponding to the condition for which the subject is to be treated.

[0092] The brain region correspondence relationship between disease types can be obtained based on existing brain region correspondence relationships between disease types that have already been identified, or can be established according to actual needs. Here, the method for obtaining the brain region correspondence relationship between disease types is merely an example and is not specifically limited.

[0093] Here, the target brain region is a brain region corresponding to a target, there is a neural connection between the target and the target brain region, and stimulation of the target can neuromodulate the target brain region. A target may include coordinates corresponding to a single voxel, or it may be a collection of regions consisting of several voxels. In some alternative embodiments, the above step 204 may specifically include: The central location of at least one target brain region is identified as a target. In some alternative embodiments, the above step 204 may specifically include:

[0094] The center position of at least one target brain region is set as the center of a sphere, a region of a predetermined target radius is identified as a target region of interest (ROI), and the position of the target ROI is identified as a target.

[0095] The present disclosure is not particularly limited on the length of the preset target radius, and the preset target radius may be set according to the actual needs of neuromodulation, for example, the preset target radius may be 3 mm. In some alternative embodiments, the above step 204 may specifically include: A brain structural section in which a target exists is identified according to a disease type, an intersection between at least one target brain region and the brain structural section is determined, and the target is identified within the intersection.

[0096] In some alternative embodiments, the target must meet the following conditions: the target is not located on the inside and bottom of the brain, the target may be located in a gyrus, but the target is not located in a sulcus.

[0097] The targets identified by the method are relatively accurate, and in practical applications, scientists or medical personnel can use optical navigation devices or electromagnetic navigation devices to perform neuromodulation navigation of subjects based on the targets identified by the method, thereby improving the efficiency of neuromodulation.

[0098] The present disclosure provides a neuromodulation device arranged to neuromodulate a target of a subject according to a pre-determined neuromodulation plan, the target of the subject having been identified according to the target identification method of any one of the above-mentioned embodiments of the present disclosure.

[0099] Neuromodulation devices may include implanted and non-implanted neuromodulation devices, such as event-related potential analysis systems, electroencephalogram systems, brain interface devices, etc. The present disclosure is not limited to the specific form of the neuromodulation device, which is provided here for illustrative purposes only.

[0100] Neuromodulation of a subject's target may be performed by an operator connecting a neuromodulation device to the target and performing the adjustment, or by the neuromodulation device performing the adjustment based on operator input or actively acquired subject targets from the neuromodulation device. This is merely exemplary and is not intended to be a specific limitation on performing neuromodulation of a subject's target; those skilled in the art can manipulate the neuromodulation device according to the actual method of use. For example, pre-set neuromodulation plans include, but are not limited to, the following: a. Neuromodulation plan based on electrical pulse sequences i. Deep brain stimulation, ii. Transcranial electrical stimulation, iii. Electroconvulsive therapy, iv. Electrical stimulation using cortical brain electrodes, v. Related derivative technologies of the above technologies. b. Neuromodulation plan based on magnetic pulse sequences i. Transcranial magnetic stimulation and related technologies; ii. Related derivative technologies of the above technologies c. Ultrasound-based neuromodulation plan i. Ultrasound focused neuromodulation schemes; ii. Magnetic resonance guided high energy ultrasound focused therapy systems and related modulation schemes; iii. Related derivative technologies of the above technologies. d. Light-based neuromodulation strategies i. Light stimulation in different wavelength bands and related plans; ii. Related derivative technologies of the above technologies

[0101] With the development of new neuromodulation devices and neuromodulation technologies, future neuromodulation devices and neuromodulation plans can also use the target identification method of the present disclosure to identify neuromodulation targets, which also fall within the scope of protection of the present disclosure.

[0102] The embodiments of the present disclosure provide a method for establishing an accurate individual brain atlas, which can efficiently and reliably obtain functional information of various brain regions, thereby improving the accuracy of brain region localization. By performing functional localization using an accurate individual-level brain atlas, the reliability of the localization results of neuromodulation targets can be improved.

[0103] Further, referring to FIG. 5, as an implementation of the methods shown in the above figures, the present disclosure provides an embodiment of a target identification apparatus corresponding to the embodiment of the method shown in FIG. 2, which can be applied to various electronic devices.

[0104] As shown in FIG. 5, the target identification device 500 of this embodiment includes a data acquisition unit 501, a processing unit 502, an anomaly detection unit 503, and a target identification unit 504.

[0105] The data acquisition unit 501 is configured to acquire scan data of the subject, including data obtained by magnetic resonance imaging of the subject's brain, the scan data including a sequence of blood oxygen level-dependent BOLD signals corresponding to each of a predetermined number of voxels. The processing unit 502 is configured to identify at least two regions of interest (ROIs) of the subject based on the scan data. The anomaly detection unit 503 identifies at least one abnormal ROI among the at least two ROIs according to a predetermined anomaly detection rule. The target identification unit 504 is configured to identify a target based on the at least one abnormal ROI. In some alternative embodiments, the processing unit 502 is further configured as follows. A BOLD signal sequence corresponding to each voxel in the scan data is acquired.

[0106] Based on the BOLD signal sequence corresponding to each voxel, the connectivity between all pairs of voxels in the scan data is determined, forming a brain connectivity matrix for the subject corresponding to the scan data. Create at least two ROIs based on the brain region template of a standard brain and the subject's brain connectivity matrix. In some alternative embodiments, the processing unit 502 is further configured as follows. A BOLD signal sequence corresponding to each voxel in the scan data is acquired. Based on the BOLD signal sequence corresponding to each voxel, the connectivity between all pairs of voxels in the scan data is determined.

[0107] The subject's brain anatomy corresponding to the scan data is divided into a plurality of large regions, and the plurality of large regions are divided into a plurality of brain regions, each of which includes at least one voxel.

[0108] Brain regions in which the voxel connectivity between each brain region in the plurality of brain regions is higher than a predetermined voxel connectivity threshold for the brain region are fused to form at least two ROIs. In some alternative embodiments, the processing unit 502 is further configured as follows. Based on a volumetric standard brain structure template, identify at least two ROIs for the subject based on the scan data. In some alternative embodiments, the processing unit 502 is further configured as follows. Based on a cortical standard brain structure template, identify at least two ROIs for the subject based on the scan data. In an alternative embodiment, the anomaly detection unit 503 is further arranged as follows. Collective brain magnetic resonance data is acquired. A population brain connectivity matrix is ​​determined from population brain magnetic resonance data. A BOLD signal sequence corresponding to each voxel in the scan data is acquired.

[0109] Based on the BOLD signal sequence corresponding to each voxel, the connectivity between all pairs of voxels in the scan data is determined, forming a brain connectivity matrix for the subject corresponding to the scan data. At least one abnormal ROI is identified based on the population brain connectivity matrix and the subject brain connectivity matrix. In some alternative embodiments, the target identification unit 504 is further configured as follows. Identifying a target based on the at least one abnormal ROI includes: Determine whether the at least one abnormal ROI is located in a regulatable brain region.

[0110] If it is determined that the at least one ROI is located in an adjustable brain region, the center of the at least one abnormal ROI is identified as the target, or the center of the at least one abnormal ROI is set as the center of a sphere and an area with a predetermined target radius is identified as a first target ROI, and the target is identified based on the position of the first target ROI.

[0111] If it is determined that the at least one abnormal ROI is not located in an adjustable brain region, the connectivity between the at least one abnormal ROI and other ROIs in the at least two ROIs is determined, and an ROI whose connectivity with the at least one abnormal ROI in the other ROI exceeds a predetermined connectivity threshold and is located in an adjustable region is identified as a second target candidate region.

[0112] The center of the second target candidate region is identified as the target, or the center of the second target candidate region is set as the center of a sphere and an area of ​​a predetermined target radius is identified as a second target ROI, and the target is identified based on the position of the second target ROI. In some alternative embodiments, identifying a target based on the at least one abnormal ROI includes: The brain structural compartment in which the target is located is identified based on the subject's disease type.

[0113] The at least one abnormal ROI, or an intersection of an ROI whose connectivity with the abnormal ROI satisfies a preset connectivity threshold condition, with the brain structural division is identified as a target candidate region.

[0114] The center of the target candidate region is identified as the target, or the center of the target candidate region is set as the center of a sphere and an area of ​​a predetermined target radius is identified as a target ROI, and the target is identified based on the position of the target ROI.

[0115] In some alternative 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.

[0116] Furthermore, the implementation details and technical effects of each unit in the target identification device provided by the present disclosure may refer to other embodiments in the present disclosure, and the description thereof will be omitted here.

[0117] 6, there is shown a structural schematic diagram of a computer system 600 of a terminal device or server suitable for implementing the present disclosure. The terminal device or server shown in FIG. 6 is merely an example and does not limit the functionality or scope of application of the present disclosure in any way.

[0118] 6, computer system 600 includes a central processing unit (CPU) 601 capable of performing various appropriate operations and processes in accordance with a program stored in read-only memory (ROM) 602 or a program loaded from storage unit 608 into random access memory (RAM) 603. RAM 603 also stores various programs and data necessary for the operation of system 600. CPU 601, ROM 602, and RAM 603 are interconnected via bus 604. An input / output (I / O) interface 605 is also connected to bus 604.

[0119] An input unit 606 including a keyboard, a mouse, etc., an output unit 607 including a cathode ray tube (CRT), a liquid crystal display (LCD), etc. and a speaker, etc., a storage unit 608 including a hard disk, etc., and a communication unit 609 including a network interface card such as a LAN (Local Area Network) card and a modem, are connected to the I / O interface 605. The communication unit 609 performs communication processing via a network such as the Internet.

[0120] In particular, according to an embodiment of the present disclosure, the processes described with reference to the flowcharts above may be implemented as a computer software program. For example, an embodiment of the present disclosure includes a computer program product including a computer program loaded onto a computer-readable medium, the computer program including program code for executing the methods illustrated in the flowcharts. In such an embodiment, the computer program may be downloaded and installed from a network via the communication unit 609. When the computer program is executed by the central processing unit (CPU) 601, the functions defined in the methods of the present disclosure are performed. Note that the computer-readable medium of the present disclosure may be a computer-readable signal medium, a computer-readable storage medium, or any combination thereof. The computer-readable storage medium may be, for example, but is not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to, an electrical connection having one or more conductors, a portable computer magnetic disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present disclosure, a computer-readable storage medium may be any tangible medium that contains or stores a program, which may be used by or in connection with an instruction execution system, apparatus, or device. In the present disclosure, a computer-readable signal medium may include a data signal transmitted in baseband or as part of a carrier wave, loaded with computer-readable program code. Such propagated data signals may take various forms, including, but 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 that can transmit, propagate, or transmit a program for use by or in combination with an instruction 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 cable, RF, etc., or any suitable combination of the foregoing.

[0121] Computer program code for carrying out operations of the present disclosure can be written in at least one programming language, or combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, Python, and further including conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may run entirely on the user computer, partially on the user computer, as a separate software package, partially on the user computer and partially on a remote computer, or entirely on a remote computer or server. When a remote computer is involved, the remote computer may be connected to the user computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., connected via the Internet using an Internet Service Provider).

[0122] The flowcharts and block diagrams in the drawings illustrate possible system configurations, functions, and operations of systems, methods, and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowcharts or block diagrams may represent a module, block, or portion of code, including one or more executable instructions for implementing a predetermined logical function. It should be noted that the functions described in the blocks may alternatively occur in a different order from the order described in the drawings. For example, two blocks shown in succession may actually be executed essentially in parallel, or in some cases, they may be executed in the reverse order, as determined by such functionality. It should be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, may be implemented in a system using dedicated hardware that performs a predetermined function or operation, or may be implemented using a combination of dedicated hardware and computer instructions.

[0123] The units of the present disclosure may be implemented in software or hardware. The described units may be provided in a processor, and may be described as including, for example, a scan data acquisition unit, a processing unit, a localization unit, and a laterality determination unit. However, the names of these units may not necessarily be limiting.

[0124] In another aspect, the present disclosure further provides a computer-readable medium, which may be included in the device described in the above embodiment, or may exist independently and not be integrated into the device. The computer-readable medium is loaded with at least one program, and when the at least one program is executed by the device, the device acquires scan data of a subject, the scan data including data obtained from a magnetic resonance image of the subject's brain, and the scan data including blood oxygen level-dependent BOLD signal sequences corresponding to each voxel in a predetermined number of voxels. Based on the scan data, the device identifies at least two regions of interest (ROIs) of the subject, identifies at least one abnormal ROI among the at least two ROIs according to a predetermined abnormality detection rule, and identifies a target based on the at least one abnormal ROI.

[0125] The above description merely describes the preferred embodiments and operational technical principles of the present disclosure. Those skilled in the art should understand that the scope of the present disclosure is not limited to the specific combination of the above technical features, but also includes other technical forms formed by any combination of the above technical features or their equivalent features, as long as it does not deviate from the above inventive idea. For example, technical means formed by mutually replacing the above features with technical features having similar functions (not limited to) disclosed in the present disclosure. The technical aspects described in the embodiments of the present disclosure may be arbitrarily combined if they do not conflict with each other.

[0126] As described above, the present disclosure merely provides specific embodiments, and the scope of protection of the present disclosure is not limited thereto. Those skilled in the art may easily devise modifications or substitutions within the technical scope disclosed in the present disclosure, and all such modifications or substitutions should be included in the scope of protection of the present disclosure. Therefore, the scope of protection of the present disclosure is subject to the scope of protection of the claims.

Claims

1. acquiring scan data of the subject, the scan data including data obtained from a magnetic resonance image of the subject's brain; identifying at least two regions of interest in the subject based on the scan data; identifying at least one anomalous region of interest among the at least two regions of interest according to a predetermined anomaly detection rule; identifying a target based on whether the at least one abnormal region of interest is located in a modulatable brain region; If the at least one abnormal region of interest is located in an adjustable brain region, identify the center of the at least one abnormal region of interest as the target, or use the center of the at least one abnormal region of interest as the center of a sphere and identify an area with a predetermined target radius as a first target region of interest, and identify the target based on the position of the first target region of interest; If the at least one abnormal region of interest is not located in an adjustable brain region, determining the connectivity between the at least one abnormal region of interest and other regions of interest in the at least two regions of interest, and identifying the other regions of interest whose connectivity with the at least one abnormal region of interest exceeds a preset connectivity threshold and is located in an adjustable brain region as a second target candidate region; Identifying the center of the second target candidate region as the target, or setting the center of the second target candidate region as the center of a sphere and identifying an area of ​​a predetermined target radius as a second target region of interest, and identifying the target based on the position of the second target region of interest; A target identification method comprising:

2. 2. The method of claim 1, wherein identifying at least two regions of interest in the subject based on the scan data comprises identifying at least two regions of interest in the subject based on the scan data based on a volumetric standard brain template.

3. 2. The method of claim 1, wherein identifying at least two regions of interest of the subject based on the scan data comprises identifying at least two regions of interest of the subject based on the scan data based on a cortical standard brain template.

4. Identifying at least two regions of interest of the subject based on the scan data as described above includes: determining the connectivity between every pair of two voxels in the scan data to form a brain connectivity matrix corresponding to the scan data; forming the at least two regions of interest based on a brain region template and a brain connectivity matrix of a standard brain; 10. The method of claim 1, comprising:

5. Identifying at least one abnormal region of interest among the at least two regions of interest according to the predetermined anomaly detection rule includes: acquiring brain magnetic resonance data of a group; determining a brain connectivity matrix of the population based on the brain magnetic resonance data of the population; determining the connectivity between every pair of voxels in the scan data to form a brain connectivity matrix for the subject corresponding to the scan data; identifying the at least one abnormal region of interest based on a brain connectivity matrix of the population and a brain connectivity matrix of the subject; 10. The method of claim 1, comprising:

6. Identifying a target based on the at least one abnormal region of interest as described above includes: Identifying a brain structural section in which the target is located according to the disease type of the subject; Identifying, as a target candidate region, an intersection between the at least one abnormal region of interest or a region of interest whose connectivity with the abnormal region of interest satisfies a preset connectivity threshold condition and the brain structure division; Identifying the center of the target candidate region as the target, or setting the center of the target candidate region as the center of a sphere and identifying an area of ​​a predetermined target radius as a target region of interest, and identifying the target based on the position of the target region of interest; 10. The method of claim 1, comprising:

7. The method of claim 1 , wherein the magnetic resonance images comprise structural brain magnetic resonance images, and / or task-state functional magnetic resonance images, and / or resting-state functional magnetic resonance images.

8. a data acquisition unit for acquiring scan data of the subject, the scan data including data obtained from a magnetic resonance image of the brain of the subject; a processing unit configured to identify at least two regions of interest of the subject based on the scan data; an anomaly detection unit for identifying at least one anomalous region of interest among the at least two regions of interest according to a preset anomaly detection rule; a target identification unit configured to identify a target based on whether the at least one abnormal region of interest is located in an adjustable brain region; If the at least one abnormal region of interest is located in an adjustable brain region, identify the center of the at least one abnormal region of interest as the target, or use the center of the at least one abnormal region of interest as the center of a sphere and identify an area with a predetermined target radius as a first target region of interest, and identify the target based on the position of the first target region of interest; If the at least one abnormal region of interest is not located in an adjustable brain region, determining the connectivity between the at least one abnormal region of interest and other regions of interest in the at least two regions of interest, and identifying the other regions of interest whose connectivity with the at least one abnormal region of interest exceeds a preset connectivity threshold and is located in an adjustable brain region as a second target candidate region; Identifying the center of the second target candidate region as the target, or setting the center of the second target candidate region as the center of a sphere and identifying an area of ​​a predetermined target radius as a second target region of interest, and identifying the target based on the position of the second target region of interest; A target identification device comprising:

9. at least one processor; a storage device having stored thereon at least one program, the at least one program, when executed by the at least one processor, causing the at least one processor to perform the method of any one of claims 1 to 7; Electronic devices including:

10. A computer readable storage medium having stored thereon a computer program, the computer program being adapted to perform the method of any one of claims 1 to 7 when executed by at least one processor.

11. 8. A neuromodulation device arranged to neuromodulate targets in a subject according to a pre-defined neuromodulation plan, the targets identified according to the method of any one of claims 1 to 7.

12. The preset adjustment plan comprises: Deep brain stimulation, transcranial electrical stimulation, electroconvulsive therapy, Cortical brain electrode-based electrical stimulation, transcranial magnetic stimulation, Ultrasound focused neuromodulation, Magnetic resonance guided high energy ultrasound focused therapy modulation, Light stimulation regulation 12. The device of claim 11, comprising at least one of:

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