TMS individualized target spot positioning method and related product

Abstract

The invention provides a TMS individualized target spot positioning method and a related product. The method comprises the following steps: acquiring an MNI standard space template, game task conditions, game task state functional magnetic resonance imaging data of a subject in an individual space and T1 weighted structure image data; according to the MNI standard space template, the game task conditions, the game task state functional magnetic resonance imaging data of the subject in the individual space and T1 weighted structure image data, detecting an activation difference brain region of the subject in the game task conditions, and generating an activation graph of the subject in a mask of a right dorsal-lateral prefrontal cortex region in the individual space; and sequencing all the active voxels in the activation graph according to the activation intensity, and positioning the space coordinates of one or more voxels with the strongest activation as TMS individualized targets. According to the invention, an accurate and individualized target spot positioning method for treating depression through TMS is realized.

Description

TECHNICAL FIELD

[0001] The present disclosure relates to the technical field of transcranial magnetic stimulation target positioning, in particular to a TMS individualized target positioning method and related products. BACKGROUND

[0002] Depression is a mental illness that seriously affects the mood and social function of individuals, and its core features include persistent low mood, loss of interest, and social dysfunction. These symptoms not only cause heavy burden to the patients themselves, but also have adverse effects on their families and social economy. It is particularly worth noting that even after the symptoms of depression are alleviated, the social dysfunction of patients may still exist for a long time and is considered a key predictor of relapse risk and functional recovery. However, there is still a lack of specific intervention means for the improvement of psychosocial function of patients with depression.

[0003] Repetitive transcranial magnetic stimulation (rTMS) as a non-invasive treatment method has been widely used in the treatment of depression. Although repetitive transcranial magnetic stimulation shows potential for the treatment of depression, the current method mostly adopts standardized targeting strategy rather than precise individualized positioning.

[0004] Therefore, it is necessary to propose a TMS (Transcranial Magnetic Stimulation) individualized target positioning method and related products to solve at least one of the above technical problems. SUMMARY

[0005] In a first aspect, the present disclosure provides a TMS individualized target positioning method, the method comprising: obtaining an MNI standard space template, a game task condition, functional magnetic resonance imaging data of a subject in an individual space in a game task state, and T1 weighted structural image data; detecting an activated difference brain region of the subject between game task conditions according to the MNI standard space template, the game task condition, the functional magnetic resonance imaging data of the subject in the individual space in the game task state, and the T1 weighted structural image data, and generating an activation map of the subject in the individual space in a right dorsolateral prefrontal cortex region mask; sorting all positive activation voxels in the activation map according to activation intensity, and positioning the spatial coordinates of one or more voxels with the strongest activation as a TMS individualized target point.

[0006] In some alternative embodiments, the detecting the activation difference brain regions of the subject between the game task conditions, generating the activation map of the subject in the right dorsolateral prefrontal cortex region mask in the individual space according to the MNI standard space template, the game task condition, the functional magnetic resonance imaging data of the subject in the individual space in the game task state, and the T1 weighted structural image data comprises: generating a forward transformation matrix mapping the T1 weighted structural image data in the individual space to the MNI standard space and an inverse transformation matrix mapping the T1 weighted structural image data in the MNI standard space to the individual space according to the correspondence between the T1 weighted structural image data in the individual space and the MNI standard space template; spatially registering the functional magnetic resonance imaging data of the subject in the individual space in the game task state and the T1 weighted structural image data to obtain the registered functional magnetic resonance imaging data of the subject in the individual space in the game task state; detecting the activation difference brain regions of the subject between the game task conditions, generating the activation map of the subject in the right dorsolateral prefrontal cortex region mask in the individual space according to the game task condition, the forward transformation matrix, the inverse transformation matrix, and the registered functional magnetic resonance imaging data of the subject in the game task state.

[0007] In some alternative embodiments, the detecting the activation difference brain regions of the subject between the game task conditions, generating the activation map of the subject in the right dorsolateral prefrontal cortex region mask in the individual space according to the game task condition, the forward transformation matrix, the inverse transformation matrix, and the registered functional magnetic resonance imaging data of the subject in the game task state comprises: mapping the registered functional magnetic resonance imaging data of the subject in the game task state to the MNI standard space using the forward transformation matrix; detecting the activation difference brain regions of the subject between the game task conditions, and extracting the activation map of the subject in the right dorsolateral prefrontal cortex region mask in the MNI standard space according to the functional magnetic resonance imaging data of the subject in the MNI standard space in the game task state and the game task condition; mapping the activation map in the MNI standard space to the individual space using the inverse transformation matrix to generate the activation map of the subject in the right dorsolateral prefrontal cortex region mask in the individual space.

[0008] In some alternative embodiments, the game task comprises ultimatum game UG task.

[0009] In some alternative embodiments, the game task condition comprises a fair allocation scheme and an unfair allocation scheme.

[0010] In some optional embodiments, the right dorsolateral prefrontal cortex region mask is generated as follows: taking the coordinate points (-36, 39, 43), (-44, 40, 29), (-41, 16, 54) and (-37, 26, 49) in the MNI standard space as the centers of spheres, generating a spherical region of interest with a radius of 20 mm for each of the coordinate points, and performing spatial superposition of each of the spherical regions of interest and the gray matter mask to generate the right dorsolateral prefrontal cortex region mask.

[0011] In a second aspect, the present disclosure provides a TMS individualized target positioning device, which comprises: An acquisition unit configured to acquire an MNI standard space template, a game task condition, functional magnetic resonance imaging data of a subject in an individual space in a game task state, and T1-weighted structural image data; A generation unit configured to detect a difference in activation of the subject between game task conditions according to the MNI standard space template, the game task condition, the functional magnetic resonance imaging data of the subject in the individual space in the game task state, and the T1-weighted structural image data, and generate an activation map of the subject in the individual space in a right dorsolateral prefrontal cortex region mask; A positioning unit configured to sort all positive activation voxels in the activation map according to activation intensity, and position the spatial coordinates of one or more voxels with the strongest activation as a TMS individualized target.

[0012] In a third aspect, the present disclosure provides an electronic device, which comprises: One or more processors; A storage device having one or more programs stored thereon, When the one or more programs are executed by the one or more processors, the one or more processors implement the method described in any of the embodiments of the first aspect of the present disclosure.

[0013] In a fourth aspect, the present disclosure provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program is executed by one or more processors to implement the method described in any of the embodiments of the first aspect of the present disclosure.

[0014] In a fifth aspect, the present disclosure provides a computer program product comprising computer programs / instructions, which are executed by a processor to implement the method described in any of the embodiments of the first aspect of the present disclosure.

[0015] Embodiments of the present disclosure provide a TMS individualized target positioning method and related products. An MNI standard space template, a game task condition, functional magnetic resonance imaging data of a subject in an individual space in a game task state, and T1-weighted structural image data are obtained. According to the MNI standard space template, the game task condition, the functional magnetic resonance imaging data of the subject in the individual space in the game task state, and the T1-weighted structural image data, activated difference brain regions of the subject between the game task conditions are detected, and an activation map of the subject in the individual space in a right dorsolateral prefrontal cortex region mask is generated. All positive activation voxels in the activation map are sorted according to activation intensity, and the spatial coordinates of one or more voxels with the strongest activation are located as TMS individualized targets. The present disclosure realizes a precise and individualized TMS target positioning method for treating depression by using functional magnetic resonance imaging data of a subject in a game task state, combining brain region activation differences of the subject between the game task conditions, and the functional response distribution in the right dorsolateral prefrontal cortex region mask, A precise and individualized TMS target positioning method for treating depression is realized. BRIEF DESCRIPTION OF DRAWINGS

[0016] Other features, objects, and advantages of the present disclosure will become more apparent from the following detailed description of non-limiting embodiments made with reference to the accompanying drawings. The drawings are for purposes of illustrating the specific embodiments of the present disclosure and are not intended to limit the present disclosure. In the drawings: Figure 1 is a system architecture diagram to which an embodiment of a TMS individualized target positioning method according to the present disclosure can be applied; Figure 2 is a flowchart of an embodiment of a TMS individualized target positioning method according to the present disclosure; Figure 3 is a flowchart of an experiment according to the present disclosure; Figure 4 is a diagram showing conversion of an activation map in an MNI standard space in a right dorsolateral prefrontal cortex region mask to an individual space according to the present disclosure; Figure 5 is a diagram showing results of a randomized controlled trial according to the present disclosure; Figure 6 is a structural diagram of an embodiment of a TMS individualized target positioning device according to the present disclosure; Figure 7 is a structural diagram of a computer system of an electronic device suitable for implementing embodiments of the present disclosure. DETAILED DESCRIPTION

[0017] The present disclosure will be further described below in conjunction with the drawings and embodiments. It can be understood that the specific embodiments described herein are only used to explain the related application, and not to limit the application. In addition, it should be noted that, for the sake of description, only the parts related to the application are shown in the drawings.

[0018] It should be noted that the embodiments in the present disclosure and the features in the embodiments can be combined with each other without conflict. The present disclosure will be described in detail below with reference to the drawings and in conjunction with the embodiments.

[0019] Figure 1 An exemplary system architecture 100 of the embodiments of the TMS individualized target positioning method to which the present disclosure can be applied is shown.

[0020] As shown in Figure 1 The system architecture 100 can include terminal devices 101, 102, 103, a network 104, and a server 105. The network 104 is used to provide a communication link between the terminal devices 101, 102, 103 and the server 105. The network 104 can include various communication connection types, such as wired communication links, wireless communication links, and the like.

[0021] A user can use the terminal devices 101, 102, 103 to interact with the server 105 through the network 104 to receive or send messages, etc. Various communication client applications can be installed on the terminal devices 101, 102, 103, such as TMS individualized target positioning applications, voice interaction applications, video conference applications, short video social applications, web browser applications, shopping applications, search applications, instant messaging tools, email clients, social platform software, etc.

[0022] The terminal device 101, 102, 103 can be hardware or software. When the terminal device 101, 102, 103 is hardware, it can be various electronic devices with a microphone and a speaker, including but not limited to a smart phone, a tablet computer, an e-book reader, an MP3 player (Moving Picture Experts Group Audio Layer III), an MP4 player (Moving Picture Experts Group Audio Layer IV), a portable computer, a desktop computer, and the like. When the terminal device 101, 102, 103 is software, it can be installed in the above-mentioned electronic devices. It can be implemented as multiple software or software modules (for example, acquiring an MNI standard space template, a gambling task condition, gambling task state functional magnetic resonance imaging data of a subject in individual space, and T1-weighted structural image data), or as a single software or software module. This is not specifically limited here.

[0023] The server 105 can be a server that provides various services, such as a background server that processes the MNI standard space template, the gambling task condition, the gambling task state functional magnetic resonance imaging data of the subject in individual space, and the T1-weighted structural image data acquired on the terminal device 101, 102, 103. The background server can perform corresponding processing based on the MNI standard space template, the gambling task condition, the gambling task state functional magnetic resonance imaging data of the subject in individual space, and the T1-weighted structural image data acquired by the terminal device.

[0024] In some cases, the TMS individualized target positioning method provided by the present disclosure can be jointly performed by the terminal device 101, 102, 103 and the server 105, for example, the step of "acquiring an MNI standard space template, a gambling task condition, gambling task state functional magnetic resonance imaging data of a subject in individual space, and T1-weighted structural image data" can be performed by the terminal device 101, 102, 103, and the step of "detecting activated difference brain regions of a subject between gambling task conditions according to the MNI standard space template, the gambling task condition, the gambling task state functional magnetic resonance imaging data of the subject in individual space, and the T1-weighted structural image data, and generating an activation map in the right dorsolateral prefrontal cortex region mask of the subject in individual space" can be performed by the server 105. The present disclosure does not limit this. Accordingly, the TMS individualized target positioning apparatus can also be provided in the terminal device 101, 102, 103 and the server 105, respectively.

[0025] In some cases, the TMS individualized target positioning method provided by the present disclosure can be executed by the server 105, and accordingly, the TMS individualized target positioning device can also be arranged in the server 105. In this case, the system architecture 100 can also not include the terminal devices 101, 102, and 103.

[0026] In some cases, the TMS individualized target positioning method provided by the present disclosure can be executed by the terminal devices 101, 102, and 103, and accordingly, the TMS individualized target positioning device can also be arranged in the terminal devices 101, 102, and 103. In this case, the system architecture 100 can also not include the server 105.

[0027] It should be noted that the server 105 can be hardware or software. When the server 105 is hardware, it can be implemented as a distributed server cluster composed of multiple servers or as a single server. When the server 105 is software, it can be implemented as multiple software or software modules (for example, to provide distributed services) or as a single software or software module. No specific limitation is made herein.

[0028] It should be understood that Figure 1 The number of terminal devices, networks, and servers in the system architecture 100 is only illustrative. According to the needs of implementation, there can be any number of terminal devices, networks, and servers.

[0029] The information, data, and signals involved in the present disclosure are all authorized by the user or fully authorized by all parties, and the collection, use, and processing of relevant data comply with relevant laws, regulations, and standards of relevant countries and regions.

[0030] Depression is a mental illness that seriously affects the mood and social function of individuals. Its core features include persistent low mood, loss of interest, and social dysfunction. These symptoms not only cause heavy burden to the patients themselves, but also have adverse effects on their families and the economy. It is particularly noteworthy that even after the symptoms of depression are alleviated, the social dysfunction of patients can still exist for a long time and is considered a key predictor of relapse risk and functional recovery. However, there is still a lack of specific intervention means for the improvement of psychosocial function of patients with depression.

[0031] Repetitive transcranial magnetic stimulation (rTMS), as a non-invasive treatment method, has been widely used in the treatment of depression, usually targeting the left and right dorsolateral prefrontal cortex. Studies have shown that low-frequency stimulation of the right dorsolateral prefrontal cortex can not only provide similar antidepressant effects compared to high-frequency stimulation of the left dorsolateral prefrontal cortex, but also show higher tolerance and lower incidence of adverse reactions. In addition, studies have shown that stimulation of the right dorsolateral prefrontal cortex can regulate social interaction behavior and related brain activity in healthy people.

[0032] In particular, through the ultimatum game (UG) task, a classic paradigm, it was found that low-frequency stimulation of the right dorsolateral prefrontal cortex can significantly improve cooperative behavior, and this stimulation can also simultaneously adjust the functional connectivity with the ventromedial prefrontal cortex. This paradigm also detects abnormal social interaction behavior in depression research, and the behavior parameters based on the computational model can predict the severity of depression, confirming that the ultimatum game (UG) task paradigm can objectively quantify social interaction behavior and reveal the underlying neuropsychological mechanisms.

[0033] Although repetitive transcranial magnetic stimulation shows potential for the treatment of depression, current methods mostly use standardized targeting strategies rather than precise individualized targeting, especially when targeting the right dorsolateral prefrontal cortex. Although studies have shown that individualized targeting based on resting-state or task-state activation guidance can improve the efficacy of repetitive transcranial magnetic stimulation, the potential of this precise individualized targeting has not been fully explored in the field of depression, especially in the application of the right dorsolateral prefrontal cortex as the key center for regulating social interaction behavior.

[0034] Continuing to refer to Figure 2 , Figure 2 shows a flow 200 of one embodiment of a TMS individualized target positioning method according to the present disclosure, Figure 2 The TMS individualized target positioning method shown can be applied to Figure 1 the terminal device or server shown in . The flow 200 at least includes the following steps 201-203.

[0035] Step 201, obtaining an MNI standard space template, a game task condition, game task state functional magnetic resonance imaging data of a subject in individual space, and T1-weighted structural image data.

[0036] In this embodiment, the MNI (Montreal Neurological Institute) standard space template is a public reference brain atlas for brain image space standardization, used to realize spatial mapping between individual brain data and population standard space.

[0037] The game task can include an ultimatum game (UG) task, and the corresponding game task condition includes a fair allocation scheme and an unfair allocation scheme.

[0038] The fair allocation scheme refers to a subject who usually tends to accept a more balanced resource allocation ratio (such as 50:50 or 40:60) proposed by the proposer. The unfair allocation scheme refers to a subject who often refuses to act and related cognitive conflict processing caused by the proposer proposing an obviously biased allocation ratio (such as 80:20 or 90:10).

[0039] Reference Figure 3 , Figure 3 A flowchart of the experimental procedure for the game task according to the present disclosure is shown.

[0040] Figure 3 From left to right, five stages in a complete trial cycle are shown, each stage containing specific screen display content and duration.

[0041] The first figure shows the fixation point stage: before the experiment starts, a white "+" sign is displayed in the center of a black background on the screen, lasting 2000 milliseconds.

[0042] The second figure shows the proposer prompt stage: in the next 4000 milliseconds, the screen will display text information to inform the subject of the identity of the proposer in the current round, for example: "The proposer is P01".

[0043] The third figure shows the allocation scheme stage: in this stage, the screen will display a specific amount allocation scheme, such as: "P01 12, You 6", which lasts for 6000 milliseconds.

[0044] The fourth figure shows the response selection stage: in this stage, two option buttons will appear on the screen: "Accept" and "Reject", giving the subject up to 6000 milliseconds to decide whether to accept the above allocation scheme. If the subject chooses to accept, both parties will receive the corresponding amount according to the proposal; if they refuse, neither party will receive money.

[0045] The fifth figure shows the feedback stage: regardless of the subject's choice, the final result will be seen in the last stage, for example: "P01 gets 12, You gets 6", this feedback information will be displayed for 2000 milliseconds.

[0046] The game task-state functional magnetic resonance imaging data can refer to the time series data of whole brain dynamic blood oxygen level dependent signals collected when the subject performs a game type cognitive task (such as ultimatum game UG) in a magnetic resonance scanner.

[0047] The T1-weighted structural image data can refer to high-resolution static anatomical images obtained using magnetic resonance imaging technology.

[0048] In this embodiment, the game task-state functional magnetic resonance imaging data and T1-weighted structural image data can be collected using various known magnetic resonance scanners.

[0049] The specific scanning parameters can be as follows: the functional magnetic resonance imaging data of the game task state is axial scanning, and the parameters are as follows: TR (Repetition Time) = 2000 ms, TE (Echo Time) = 30 ms, FOV (Field of View) = 224*224 mm, FA (Flip Angle) = 90°, slice thickness = 3.5 mm, and a total of 33 layers are collected without interlayer gap.

[0050] The T1 weighted structural image data is sagittal scanning, and the parameters are as follows: TR = 2530 ms, TE = 4.21 ms, FOV = 256*256 mm, FA = 7°, slice thickness = 1 mm, and no interlayer gap.

[0051] The game task stimulus can be programmed and generated on a personal computer through E-prime 2.0 software (Psychology Software Tools, Pittsburgh, USA), and the visual stimulus picture is back projected to the screen via a liquid crystal projector, and the subject observes the stimulus content through the mirror installed in the magnetic resonance head coil. The scanner is connected with the E-prime software through a synchronous trigger signal to ensure that the starting time of each whole brain volume collection is accurately aligned with the presentation time of the first visual stimulus.

[0052] In step 202, the activated difference brain region of the subject between the game task conditions is detected according to the MNI standard space template, the game task condition, the game task state functional magnetic resonance imaging data of the subject in the individual space and the T1 weighted structural image data, and the activated map of the subject in the individual space in the right dorsolateral prefrontal cortex region mask is generated.

[0053] In this embodiment, the activated difference brain region of the subject between the game task conditions refers to the region in which the blood oxygen level dependent signal of a specific region of the brain appears statistically significant change when the game task is performed.

[0054] For example, when facing the "unfair allocation scheme" and "fair allocation scheme" of the ultimatum game UG task, some brain regions of the subject show stronger neural activity, indicating that they participate in cognitive processes such as perception of social injustice, emotional regulation or decision conflict processing.

[0055] The activated map of the subject in the individual space in the right dorsolateral prefrontal cortex region mask can be a spatial distribution map composed of the activation values of all voxels in the pre-defined right dorsolateral prefrontal cortex anatomical region, which is limited in the pre-defined right dorsolateral prefrontal cortex anatomical region based on the activated analysis result of the difference brain region. The spatial distribution map is located in the individual space of the subject itself, and reflects the task-related neural response pattern in the key brain region.

[0056] In some alternative embodiments, the right dorsolateral prefrontal cortex region mask is generated by selecting the coordinate points (-36, 39, 43), (-44, 40, 29), (-41, 16, 54) and (-37, 26, 49) in the MNI standard space, taking each coordinate point in the MNI standard space as the center of a sphere, generating a spherical region of interest with a radius of 20 mm, and performing spatial superposition of each spherical region of interest with the gray matter mask to generate the right dorsolateral prefrontal cortex region mask.

[0057] In some alternative embodiments, the right dorsolateral prefrontal cortex region mask is generated by selecting the coordinate points (-36, 39, 43), (-44, 40, 29), (-41, 16, 54) and (-37, 26, 49) in the MNI standard space, taking each coordinate point in the MNI standard space as the center of a sphere, generating a spherical region of interest with a radius of 20 mm, and performing spatial superposition of each spherical region of interest with the gray matter mask to generate the right dorsolateral prefrontal cortex region mask.

[0058] Here, the four coordinate points on the left hemisphere can be mirror-inverted about the mid-sagittal plane (X = 0 plane) of the brain to obtain the corresponding coordinate points on the right hemisphere: (36, 39, 43), (44, 40, 29), (41, 16, 54) and (37, 26, 49). Taking each mirror-inverted coordinate point as the center of a sphere, a spherical region of interest with a radius of 20 mm is generated to cover the possible functional response area of the right dorsolateral prefrontal cortex region. Subsequently, the four spherical regions of interest are spatially combined (union) to form a joint functional region. To ensure that the defined region is located in brain tissue with neuron cell body distribution, the joint region is further subjected to spatial intersection operation with the gray matter probability mask in the MNI standard space to remove the parts located in white matter and cerebrospinal fluid, and finally the right dorsolateral prefrontal cortex region mask for functional activation analysis is generated.

[0059] In some alternative embodiments, step 202 can be implemented by A1-A3 as follows.

[0060] A1, according to the correspondence between the T1-weighted structural image data in the individual space and the MNI standard space template, generating a positive transformation matrix for mapping the T1-weighted structural image data in the individual space to the MNI standard space, and an inverse transformation matrix for mapping the T1-weighted structural image data in the MNI standard space to the individual space.

[0061] In this embodiment, SPM (Statistical Parametric Mapping) software can be used to preprocess the T1-weighted structural image data. The specific process is as follows: First, the acquired T1-weighted structural image data is manually quality checked to ensure that the T1-weighted structural image data meets the image quality requirements for subsequent analysis. Then, SPM software can be used to perform tissue classification and segmentation on the T1-weighted image data that meets the quality standards. This process includes: skull dissection, cerebrospinal fluid, white matter, and gray matter brain tissue segmentation. Finally, the nonlinear spatial deformation field between the subject's individual space T1-weighted structural image data and the MNI standard space template is evaluated to generate a forward transformation matrix that maps the individual space T1-weighted structural image data to the MNI standard space, and an inverse transformation matrix that maps the MNI standard space T1-weighted structural image data to the individual space.

[0062] The forward transformation matrix is ​​used to register the T1-weighted structural image data in the individual space to the MNI standard space to achieve spatial standardization, while the inverse transformation matrix is ​​used to accurately reverse map the T1-weighted structural image data located in the MNI standard space back to the original individual space.

[0063] A2, spatial registration is performed on the game-task-state functional magnetic resonance imaging data of the individual space and the T1-weighted structural image data to obtain the registered game-task-state functional magnetic resonance imaging data.

[0064] In this embodiment, spatial registration of the game-theoretic task-state functional magnetic resonance imaging (fMRI) data and T1-weighted structural image data in individual space can achieve geometric alignment between the game-theoretic task-state fMRI data and the T1-weighted structural image data.

[0065] Specifically, a combined DPABI (Data Processing & Analysis for Brain Imaging) and SPM processing workflow can be used to preprocess the game-task-oriented functional magnetic resonance imaging (fMRI) data. The specific workflow is as follows: First, the acquired game-task-oriented fMRI data undergoes manual quality checks to ensure that it meets the image quality requirements for subsequent analysis. Then, acquisition time points irrelevant to the game task in the initial scanning phase (such as the first six time points) can be deleted to ensure magnetic field homogeneity and subject adaptation to scanning conditions. Next, time-level correction and head motion correction can be performed using DPABI software. Finally, SPM software can be used to register the corrected fMRI data sequence with the subject's T1-weighted structural images to obtain the registered game-task-oriented fMRI data.

[0066] A3, based on the game task conditions, the forward transformation matrix, the inverse transformation matrix, and the registered game task-state functional magnetic resonance imaging data, detects the brain regions with different activations in the subjects under the game task conditions, and generates an activation map of the subjects within the mask of the right dorsolateral prefrontal cortex region in the individual space.

[0067] In some alternative implementations, A3 can be implemented by the following A31-A33.

[0068] A31 uses a positive transformation matrix to map the registered game-theoretic functional magnetic resonance imaging data to the MNI standard space.

[0069] In this embodiment, SPM software and a pre-determined positive transformation matrix can be used to standardize the game-themed functional magnetic resonance imaging data that has undergone spatial registration.

[0070] Specifically, by using a positive transformation matrix, the functional images of the subjects in the individual space can be transformed to the MNI standard space. The standard space parameters used can be: bounding box [-90, -126, -72; 90, 90, 108], and voxel size [3, 3, 3].

[0071] Subsequently, to further improve the quality and reliability of data analysis, SPM software can be used to perform spatial smoothing on game-task-state functional magnetic resonance imaging data mapped to the MNI standard space.

[0072] This smoothing process can use a Gaussian kernel function with a full width at half maximum (FWHM) of 6 mm to convolve the image, thereby reducing image noise while preserving important signal features and improving the sensitivity and accuracy of subsequent statistical analysis steps.

[0073] A32, based on the game-task state functional magnetic resonance imaging data and game-task conditions in the MNI standard space, detected the brain regions with different activations in the subjects under game-task conditions, and extracted the activation map within the mask of the right dorsolateral prefrontal cortex region of the subjects in the MNI standard space.

[0074] In this embodiment, under the game-theoretic task-state functional magnetic resonance imaging data in the MNI standard space, the categories of each experimental condition in the game-theoretic task (such as "fair allocation scheme" and "unfair allocation scheme") can be determined, and the presentation time point and presentation duration of each trial can be recorded to construct an accurate task temporal model. Then, a general linear model (GLM) at the individual subject level can be established using SPM software. Furthermore, different task conditions are incorporated as factors into the model to estimate the blood oxygen response amplitude under each condition. Specifically, "fair allocation scheme" and "unfair allocation scheme" can be used as independent regression variables, and by calculating the difference between the [unfair allocation - fair allocation] conditions, the significantly activated region within the mask of the right dorsolateral prefrontal cortex region of the subject when dealing with unfair situations can be identified, generating an activation map within the mask of the right dorsolateral prefrontal cortex region of the subject in the MNI standard space.

[0075] A33 uses an inverse transformation matrix to map the activation map of the MNI standard space to the individual space, generating the activation map of the subject within the mask of the right dorsolateral prefrontal cortex region in the individual space.

[0076] In this embodiment, in order to analyze the brain function activation of a specific subject in their individual space, it is necessary to use an inverse transformation matrix to map the activation map based on the MNI standard space back to the individual space.

[0077] Specifically, the activation map in the MNI standard space can be mapped to the individual space using an inverse transformation matrix, generating an activation map of the subject within a mask in the right dorsolateral prefrontal cortex region of the individual space. This activation map only contains the activation information originally located within the mask of the right DLPFC region in the MNI standard space, but has been adapted according to the individual's unique brain morphology.

[0078] refer to Figure 4 , Figure 4 This is a schematic diagram of the conversion of the activation map of the MNI standard space within the mask of the right dorsolateral prefrontal cortex region according to this disclosure to the individual space.

[0079] Figure 4 The image on the left shows the activation map in the MNI standard space. This activation region is located in the right dorsolateral prefrontal cortex, with coordinates (21, 36, 57). Figure 4 The image on the right shows the result of the same activation map being inversely transformed into the subject's individual space. The position of the activation area of ​​the activation map has been precisely adjusted according to the subject's individual spatial morphology, and the corresponding coordinates become (12, 24, 72), which is the spatial location of the activation point in the subject's real brain.

[0080] Step 203: Sort all positively activated voxels in the activation map according to their activation intensity, and locate the spatial coordinates of one or more voxels with the strongest activation as TMS individualized target points.

[0081] In this embodiment, all positively activated voxels (i.e., those voxels whose activity levels under unfair allocation conditions are significantly higher than those under fair allocation conditions) can be extracted from the activation map first. Then, for all positively activated voxels, their respective activation intensity values ​​can be calculated. All positively activated voxels are then sorted in descending order according to their activation intensity values ​​to identify one or more voxels with the highest activation intensity. Thus, the spatial coordinates of one or more voxels with the strongest activation can be located as TMS personalized target points.

[0082] This disclosure further validates the effectiveness and safety of TMS treatment for patients with depression using the described target localization method through randomized controlled trials.

[0083] Specifically, 70 patients diagnosed with major depressive disorder were randomly assigned to receive either a two-week regimen of real stimulation (n=37) or spurious stimulation (n=33) accelerated continuous theta-pulse stimulation (cTBS), consisting of 1800 pulses per session, three times daily (i.e., 5400 pulses), for a total of 54,000 pulses over ten days. The therapeutic targets were determined based on individualized right dorsolateral prefrontal cortex activity induced by social interaction tasks (such as the Ultimatum Game UG task).

[0084] Before and after treatment, we assessed the patients' symptoms of depression and anxiety, overall social functioning, behavioral indicators of the Ultimatum Game UG task, and computational modeling parameters (such as learning rate).

[0085] refer to Figure 5 , Figure 5 This is a diagram illustrating the results of a randomized controlled trial based on this disclosure.

[0086] Figure 5 The results showed that, compared with the sham stimulation group, the real stimulation group showed significant improvements in the following aspects: First, symptoms of depression and anxiety were alleviated; second, overall social functioning was improved; third, cooperative behavior increased in the ultimatum game task; and fourth, the learning rate was improved.

[0087] in, Figure 5 Figure A in the figure is the Hamilton Depression Rating Scale-17 (HAMD-17), which is used to quantify the severity of depressive symptoms.

[0088] The results showed that the HAMD-17 score decreased significantly after treatment in the true stimulation group (p<0.001), indicating a significant relief of depressive symptoms; the sham stimulation group also showed improvement (p<0.001), but the magnitude was smaller than that in the true stimulation group.

[0089] Figure 5 Figure B in the diagram is the Hamilton Anxiety Rating Scale (HAMA), which is used to reflect the level of anxiety symptoms.

[0090] The results showed that the anxiety score was significantly reduced in the real stimulation group after treatment (p<0.001); the sham stimulation group also showed improvement (p =0.003), but the magnitude was smaller than that in the real stimulation group.

[0091] Figure 5 Figure C in the figure represents the Global Assessment of Functioning (GAF), which measures an individual's social functioning and daily adaptability.

[0092] The results showed that the GAF score was significantly improved in the true stimulation group after treatment (p<0.001); the sham stimulation group showed a slight increase (p =0.033), but the improvement was limited.

[0093] Figure 5 The D-plot in the diagram represents the change in the acceptance rate, which is derived from behavioral data from the Ultimatum Game (UG) task and represents the proportion of participants who accept the unfair allocation scheme.

[0094] The results showed that the acceptance rate was significantly higher in the true stimulation group after treatment (p = 0.002), suggesting that they were more inclined to cooperate and tolerate in social interactions; there was no significant change in the sham stimulation group.

[0095] Figure 5 The E-plot in the diagram represents the change in the learning rate and is often used to measure cognitive flexibility.

[0096] The results showed that the learning rate distribution in the true stimulation group shifted significantly to the right (i.e., the value increased) before and after treatment, indicating that the learning efficiency was improved. No significant change was observed in the learning rate distribution in the sham stimulation group.

[0097] Furthermore, the clinical remission rate in the true stimulation group reached 64.9%, and the cure rate was 37.8%. Notably, no serious adverse events were reported in either group.

[0098] In summary, accelerated cTBS treatment targeting the right dorsolateral prefrontal cortex based on task-oriented brain activation localization not only demonstrated its effectiveness in improving clinical symptoms and social dysfunction in patients with depression, but also showed good safety.

[0099] The TMS personalized target localization method provided in this disclosure acquires a standard spatial template of the MNI (Minimum Intensive Neuromuscular Organization), game task conditions, functional magnetic resonance imaging (fMRI) data of the subject in the game task state within the individual space, and T1-weighted structural image data. Based on the MNI standard spatial template, game task conditions, and the subject's game task state fMRI data and T1-weighted structural image data within the individual space, it detects brain regions with activation differences between game task conditions and generates an activation map of the subject within a mask in the right dorsolateral prefrontal cortex region within the individual space. All positively activated voxels within the activation map are sorted according to activation intensity, and the spatial coordinates of one or more voxels with the strongest activation are located as TMS personalized target points. This disclosure, by combining functional magnetic resonance imaging data of depressed subjects in the game task state with differences in brain region activation between game task conditions and the functional response distribution within a mask in the right dorsolateral prefrontal cortex region, achieves a precise and personalized TMS target localization method for treating depression.

[0100] Further reference Figure 6 As an implementation of the methods shown in the above figures, this disclosure provides an embodiment of a TMS personalized target localization device, which is similar to... Figure 2 Corresponding to the method embodiments shown, the device can be specifically applied to various terminal devices or servers.

[0101] like Figure 6 As shown, the TMS personalized target localization device of this embodiment includes: device 600. Acquisition unit 601, generation unit 602 and positioning unit 603.

[0102] The acquisition unit 601 is used to acquire the MNI standard spatial template, game task conditions, and the subject's game task-state functional magnetic resonance imaging data and T1-weighted structural image data in the individual space; the generation unit 602 is used to detect the brain regions with activation differences between the game task conditions based on the MNI standard spatial template, game task conditions, and the subject's game task-state functional magnetic resonance imaging data and T1-weighted structural image data in the individual space, and generate an activation map of the subject within the mask of the right dorsolateral prefrontal cortex region in the individual space; the localization unit 603 is used to sort all positively activated voxels in the activation map according to their activation intensity, and locate the spatial coordinates of one or more voxels with the strongest activation as TMS personalized target points.

[0103] In this embodiment, the specific processing of the acquisition unit 601, the generation unit 602, and the positioning unit 603, and the resulting technical effects, can be found by referring to [the relevant documentation]. Figure 2 The relevant descriptions of steps 201 to 203 in the corresponding embodiments will not be repeated here.

[0104] In some alternative implementations, the generation unit 602 may be further used for: Based on the correspondence between the T1-weighted structural image data of the individual space and the MNI standard space template, a forward transformation matrix is ​​generated to map the T1-weighted structural image data of the individual space to the MNI standard space, and an inverse transformation matrix is ​​generated to map the T1-weighted structural image data of the MIN standard space to the individual space. Spatial registration was performed on individual spatial game-task-state functional magnetic resonance imaging data and T1-weighted structural image data to obtain registered game-task-state functional magnetic resonance imaging data. Based on the game task conditions, the forward transformation matrix, the inverse transformation matrix, and the registered game task-state functional magnetic resonance imaging data, the brain regions with different activations in the subjects were detected between game task conditions, and activation maps of the subjects within the mask of the right dorsolateral prefrontal cortex region in individual space were generated.

[0105] In some alternative implementations, the generation unit 602 may be further used for: Using a positive transformation matrix, the registered game-themed functional magnetic resonance imaging data are mapped to the MNI standard space; Based on the game task state functional magnetic resonance imaging data and game task conditions in the MNI standard space, the brain regions with different activations in the subjects were detected between game task conditions, and the activation map in the mask of the right dorsolateral prefrontal cortex region of the subjects was extracted in the MNI standard space. Using an inverse transformation matrix, the activation map in the MNI standard space is mapped to the individual space, generating the activation map of the subject within the mask of the right dorsolateral prefrontal cortex region in the individual space.

[0106] In some alternative implementations, the game task includes an ultimatum game (UG) task.

[0107] In some alternative implementations, the game task conditions include fair allocation schemes and unfair allocation schemes.

[0108] In some optional implementations, the mask for the right dorsolateral prefrontal cortex region is as follows: select coordinate points (-36,39,43), (-44,40,29), (-41,16,54), and (-37,26,49) in the MNI standard space, use the mirror coordinate point of each coordinate point in the MNI standard space as the center of the sphere, generate a spherical region of interest with a radius of 20 mm, and spatially superimpose each spherical region of interest with the gray matter mask to generate the mask for the right dorsolateral prefrontal cortex region.

[0109] It should be noted that the implementation details and technical effects of each unit in the TMS personalized target localization device provided in the embodiments of this disclosure can be referred to the descriptions of other embodiments in this disclosure, and will not be repeated here.

[0110] The following is for reference. Figure 7 It shows a schematic diagram of the structure of a computer system 700 suitable for implementing the terminal device of this disclosure. Figure 7 The computer system 700 shown is merely an example and should not be construed as limiting the functionality and scope of the embodiments of this disclosure.

[0111] like Figure 7 As shown, the computer system 700 may include a processing device (e.g., a central processing unit, a graphics processing unit, etc.) 701, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 702 or a program loaded from a storage device 708 into a random access memory (RAM) 703. The RAM 703 also stores various programs and data required for the operation of the computer system 700. The processing device 701, ROM 702, and RAM 703 are interconnected via a bus 704. An input / output (I / O) interface 705 is also connected to the bus 704.

[0112] Typically, the following devices can be connected to I / O interface 705: input devices 706 including, for example, touchscreens, touchpads, keyboards, mice, cameras, microphones, etc.; output devices 707 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; storage devices 708 including, for example, magnetic tapes, hard disks, etc.; and communication devices 709. Communication device 709 allows computer system 700 to communicate wirelessly or wiredly with other devices to exchange data. Although Figure 7 A computer system 700 with various electronic devices is shown; however, it should be understood that it is not required to implement or possess all of the devices shown. More or fewer devices may be implemented or possessed alternatively.

[0113] In particular, according to embodiments of this disclosure, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of this disclosure include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication device 709, or installed from storage device 708, or installed from ROM 702. When the computer program is executed by processing device 701, it performs the functions defined above in the methods of embodiments of this disclosure.

[0114] It should be noted that the computer-readable medium described in this disclosure can be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. A computer-readable storage medium can be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this disclosure, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In this disclosure, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium can be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to: wires, optical fibers, RF (radio frequency), etc., or any suitable combination thereof.

[0115] The aforementioned computer-readable medium may be included in the aforementioned electronic device; or it may exist independently and not assembled into the electronic device.

[0116] The aforementioned computer-readable medium carries one or more programs, which, when executed by the electronic device, cause the electronic device to perform the following functions: Figure 2 The embodiments shown and their alternative implementations illustrate a TMS personalized target localization method.

[0117] 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 conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0118] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0119] The units described in the embodiments of this disclosure can be implemented in software or in hardware. The names of the units are not necessarily limiting in certain circumstances; for example, a generation unit can also be described as a "unit for generating an activation map".

[0120] The above description is merely a preferred embodiment of this disclosure and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of this disclosure is not limited to technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the above-described concept. For example, technical solutions formed by substituting the above features with (but not limited to) technical features disclosed in this disclosure that have similar functions.

Claims

1. A TMS personalized target localization method, characterized in that, The method includes: Acquire the MNI standard spatial template, game task conditions, functional magnetic resonance imaging data of subjects in the game task state in individual space, and T1-weighted structural image data; Based on the MNI standard spatial template, the game task conditions, the subject's game task state functional magnetic resonance imaging data in the individual space, and the T1-weighted structural image data, the brain regions with activation differences between the subject's game task conditions are detected, and an activation map of the subject in the mask of the right dorsolateral prefrontal cortex region in the individual space is generated. All positively activated voxels in the activation map are sorted according to their activation intensity, and the spatial coordinates of one or more voxels with the strongest activation are located as TMS individualized target points.

2. The method according to claim 1, characterized in that, The method involves detecting differentially activated brain regions in the subject under different game task conditions based on the MNI standard spatial template, the game task conditions, the subject's game task-state functional magnetic resonance imaging data in the individual space, and the T1-weighted structural image data, and generating an activation map of the subject within a mask in the right dorsolateral prefrontal cortex region in the individual space, including: Based on the correspondence between the T1-weighted structural image data in the individual space and the MNI standard space template, a forward transformation matrix is ​​generated to map the T1-weighted structural image data in the individual space to the MNI standard space, and an inverse transformation matrix is ​​generated to map the T1-weighted structural image data in the MIN standard space to the individual space. Spatial registration is performed on the game-task-state functional magnetic resonance imaging data and the T1-weighted structural image data in the individual space to obtain the registered game-task-state functional magnetic resonance imaging data. Based on the game task conditions, the forward transformation matrix, the inverse transformation matrix, and the registered game task-state functional magnetic resonance imaging data, the activation difference brain regions of the subject between game task conditions are detected, and an activation map of the subject within the mask of the right dorsolateral prefrontal cortex region in individual space is generated.

3. The method according to claim 2, characterized in that, The step involves detecting differentially activated brain regions in the subject under different game task conditions based on the game task conditions, the forward transformation matrix, the inverse transformation matrix, and the registered game task-state functional magnetic resonance imaging data, and generating an activation map of the subject within a mask in the right dorsolateral prefrontal cortex region in individual space, including: Using the positive transformation matrix, the registered game-themed functional magnetic resonance imaging data is mapped to the MNI standard space; Based on the game task state functional magnetic resonance imaging data and game task conditions in the MNI standard space, the brain regions with activation differences in the subject between game task conditions were detected, and the activation map in the mask of the right dorsolateral prefrontal cortex region of the subject was extracted in the MNI standard space. Using the inverse transformation matrix, the activation map in the MNI standard space is mapped to the individual space to generate the activation map of the subject within the mask of the right dorsolateral prefrontal cortex region in the individual space.

4. The method according to claim 1, characterized in that, The game-themed tasks include the Ultimatum Game (UG) task.

5. The method according to claim 4, characterized in that, The game task conditions include fair allocation schemes and unfair allocation schemes.

6. The method according to claim 1, characterized in that, The mask for the right dorsolateral prefrontal cortex region is created by selecting coordinate points (-36,39,43), (-44,40,29), (-41,16,54), and (-37,26,49) in the MNI standard space, using the mirror coordinate point of each coordinate point in the MNI standard space as the center of a sphere, generating spherical regions of interest with a radius of 20 mm, and spatially superimposing each spherical region of interest with a gray matter mask to generate the mask for the right dorsolateral prefrontal cortex region.

7. A TMS personalized target localization device, characterized in that, The device includes: The acquisition unit is used to acquire the MNI standard spatial template, game task conditions, game task state functional magnetic resonance imaging data of the subject in the individual space, and T1-weighted structural image data. The generation unit is used to detect the brain regions with activation differences between the game task conditions of the subject based on the MNI standard spatial template, the game task conditions, the game task state functional magnetic resonance imaging data of the subject in the individual space and the T1-weighted structural image data, and generate the activation map of the subject in the mask of the right dorsolateral prefrontal cortex region in the individual space. The positioning unit is used to sort all positively activated voxels in the activation map according to their activation intensity, and to locate the spatial coordinates of one or more voxels with the strongest activation as TMS individualized target points.

8. An electronic device, characterized in that, include: One or more processors; Storage device, on which one or more programs are stored, When one or more programs are executed by one or more processors, the one or more processors implement the method as claimed in any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, It stores a computer program thereon, wherein the computer program, when executed by one or more processors, implements the method as claimed in any one of claims 1-6.

10. A computer program product, characterized in that, Includes a computer program / instruction that, when executed by a processor, implements the method as described in any one of claims 1-6.

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