CN117094987B - Method for optimizing direction of nerve regulation physical field - Google Patents
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- 238000002598 diffusion tensor imaging Methods 0.000 abstract description 20
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- 238000011491 transcranial magnetic stimulation Methods 0.000 description 7
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Abstract
The invention relates to the technical field of noninvasive brain stimulation, and discloses a method for optimizing the direction of a nerve regulation physical field, which comprises the steps of collecting image data of a T1W image and a DTI image, preprocessing, calculating the dispersion tensor of each voxel of the whole brain and the direction of a target area, finally obtaining the three-dimensional coordinates of face feature points on a tested head profile image by using a depth camera, registering the three-dimensional coordinates and a T1W structural image obtained after preprocessing to an MNI space coordinate system, calculating the included angle between the direction of a target point at the moment and the direction of an electric signal generated by the central position of a stimulator obtained by the depth camera, and visualizing; during stimulation, the angle of the stimulator is adjusted to enable the direction of the electric signal generated by the stimulator to be tangential to the direction of the target point. After the personalized stimulation target point is determined, the characteristic direction of the stimulation target point is determined based on diffusion tensor imaging, and the placement direction of the stimulator is guided during noninvasive brain stimulation, so that the brain stimulation can generate wider induced electric field, the action effect of the brain stimulation can be effectively improved, and the method has obvious advantages in preventing stimulation from off-target in clinical treatment.
Description
Technical Field
The invention relates to the technical field of noninvasive brain stimulation, in particular to a method for optimizing the direction of a nerve regulation physical field.
Background
Non-invasive brain stimulation techniques, such as TMS (transcranial magnetic stimulation ), tDCS (transcranial direct current stimulation, transcranial direct current stimulation), TUS (transcranial ultrasoundstimulation, transcranial ultrasonic stimulation), etc., can directly enter the human brain, achieving the possibility of targeted treatment of mental disorders through modulation of the brain circuit. Brain stimulation techniques have proven to be an effective alternative to drugs for the treatment of psychotic disorders, especially TMS, and are widely used clinically. However, at present, the related research of brain stimulation regulation and control nerve circuits mainly focuses on the decision of the treatment target position, and the directionality of the physical field generated by the brain stimulation technology has not been paid much attention. In recent years, published documents at home and abroad show that the TMS action effect is also influenced by the current direction caused by the stimulation coil, and similar phenomena are also found in the studies of tDCS and TUS, and many scholars consider that only an electric field along the neuron can excite the nerve fiber, so that the neuron on the circuit is excited.
At present, the application of DTI (diffusion tensor imaging diffusion tensor imaging) in the field of nerve regulation is mainly used for determining the position of a stimulation target point, determining the fiber bundle connection from a preset seed point under a cortex to the cortex part through a white matter fiber bundle tracking technology, and finally determining the position of the stimulation target point according to the obtained cellulose connection.
Although the current personalized accurate target stimulation can significantly improve the efficacy of non-invasive brain stimulation compared to the rough estimate of the early therapeutic target, a significant portion of patients are not effective in brain stimulation treatment. Taking the most widely applied TMS as an example, the existing TMS navigation technology mostly determines the specific position of a stimulation target spot on the cortex based on functional connection or white matter fiber bundle tracking, after obtaining position coordinates, an operator places a coil on a response position to perform stimulation, and the direction of the coil relative to the cortex is not concerned in the stimulation process, so that the effect of TMS treatment cannot be fully exerted.
Disclosure of Invention
In view of the above problems, the present invention aims to provide a method for optimizing the direction of a physical field of nerve regulation, which determines the optimal characteristic direction of a target point of stimulation based on diffusion tensor imaging after determining an individual target point of stimulation, and ensures that an electric signal caused by the generated physical field can be tangent to the target point direction to the greatest extent to obtain the maximum action effect when the regulation treatment is implemented by an image navigation technology. The technical proposal is as follows: a method of optimizing the direction of a neuromodulation physical field, comprising the steps of:
step 1: collecting image data
Collecting a T1W structural image covering the whole head, and collecting DTI images with set number of gradient directions, wherein the DTI images are b0 images with AP and PA coding directions;
step 2: image data preprocessing
Preprocessing the T1W structural image by using FreeSterfer to obtain a precise individual brain segmentation result; preprocessing the DTI image, removing noise and artifacts, and registering to a T1W structural image space;
step 3: calculating a diffusion tensor
Calculating the dispersion tensor of each voxel of the whole brain according to the preprocessed DTI image; calculating tensor matrix of each voxel by combining corresponding gradient direction and b value through corrected diffusion image dataD：
；
Wherein,D xy =D yx 、D xz =D zx 、D yz =D zy 、D xx 、D yy 、D zz diffusion coefficients in six directions;
step 4: calculating target direction
Tensor matrix for voxelsDAnd (3) performing eigenvalue decomposition:
；
wherein,v 1 、v 2 andv 3 as a feature vector of the object set,l 1 、l 2 andl 3 is the corresponding characteristic value;
reservation ofIs used to generate a whole brain tensor pattern, and a first eigenvector of the voxels in the region of interest in the tensor pattern is extractedv 1 Performing cluster analysis on voxels of the region of interest, and performing first eigenvector of central voxel of each classv 1 As class feature directions of the class; selecting the class characteristic direction of the optimal class as a target point direction;
step 5: image navigation
Registering the T1W structural image to an MNI space coordinate system, then obtaining three-dimensional coordinates of face feature points on a tested head outline image by using a depth camera, registering to the MNI space coordinate system, calculating an included angle between a target point direction calculated by the DTI image and an electric signal direction generated by a central position of a stimulator acquired by the depth camera, and visualizing; during stimulation, the angle of the stimulator is adjusted to enable the direction of the electric signal generated by the stimulator to be tangential to the direction of the target point.
Further, in the step 2, the preprocessing of the T1W structural image specifically includes: adopting a freeform's recycle-all command to sequentially perform head motion correction, nonuniform intensity standardization, talairach transformation, intensity standardization, brain peeling, linear mention registration, CA intensity standardization, CA nonlinear volume registration, neck removal, cephalic registration, CA label and statistics, intensity normalization, white matter segmentation, white matter editing, filling shearing, surface subdivision, original surface smoothing, expansion, quasi-homoembryo spherical surface, automatic topology restoration, final surface generation, secondary smoothing, secondary expansion, sphere mapping, sphere registration, homolateral and contralateral surface registration, curvature mapping, cortical partition and partition statistics, cortical band masking, and finally mapping cortical partition to Aseg, and finally obtaining a precise individual brain segmentation result; the intensity is normalized to: gray scale normalization is carried out on all voxel intensities, so that the average intensity of white matter is 110;
the cortical tape mask is as follows: a cortical binarization mask is created with voxels at the cortex of 1, otherwise 0.
Furthermore, in the step 2, fsl software is adopted when preprocessing the DTI image, which specifically includes:
1) Correcting magnetic field inhomogeneity: estimating a bias field caused by magnetic sensitivity using diffusion weighted imaging of a pair of opposite encoding directions when no gradient magnetic field is applied;
2) Extracting brain tissue;
3) Vortex field and head motion effects: correcting the magnetic susceptibility of each diffusion-weighted imaging and the current estimation of eddy current and motion parameters, and then loading the current estimation into a Gaussian process; once all the images are loaded, the super parameters of the Gaussian process can be estimated and predicted;
4) Registering: registering the diffuse image of the individual to the T1W structural image space.
Further, in the step 4, the selection of the optimal class considers three factors: class sizeC s Spatial concentration of voxels in classC i Directional consistency of classC c The method comprises the steps of carrying out a first treatment on the surface of the Spatial concentration of voxels in classC i Represented by the average value of the three-dimensional Euclidean distance of voxels in a class divided by the class size, the directional consistency of the classC c Represented by calculating the mean of cosine similarities between voxels of the group; finally, the characteristic direction of the optimal class is selected together according to three factors, namely the size of the class, the space aggregation degree of voxels in the class and the direction consistency of the class, and is taken as the target point direction; wherein, the class when the following formula takes the maximum value is the optimal class;
；
wherein,Cand judging the value of the formula for the optimal class.
Furthermore, the step 4 is replaced by determining the trend of the fiber bundle, which specifically includes:
to observe dispersion signalsSExpressed as a signal response function of individual fibersRAnd nerve fiber direction distribution functionF(v) Is a spherical convolution of:
；
wherein,s 2 representing a unit sphere;
the response function is estimated by the tensor matrix of step 3, assuming thatl 2 =l 3 =β：
；
Wherein,v 1 、v 2 andv 3 as a feature vector of the object set,l 1 、l 2 andl 3 for the corresponding characteristic value(s),βnamely, isl 2 Or (b)l 3 Is a value of (2); then
；
Wherein,bin order to have a diffusion coefficient of sensitivity,S 0 for a signal to which no gradient pulse is added,as a dispersive signalSA corresponding gradient direction; obtaining a nerve fiber orientation density function through deconvolution, and searching the extremum of the nerve fiber orientation density function to obtain the direction of the nerve fiber;
then, connecting the preset seed points, namely subcutaneous nucleuses, to the target area in a consistent direction end to obtain the corresponding fiber bundle trend;
correspondingly, the included angle in step 5 is replaced by: the fiber bundle trend calculated by the DTI image at the moment and the included angle of the electric signal direction generated by the center position of the stimulator acquired by the depth camera.
Compared with the prior art, the invention has the beneficial effects that: after the personalized stimulation target point is determined, the characteristic direction of the stimulation target point is determined based on diffusion tensor imaging, and the placement direction of the stimulator is guided during noninvasive brain stimulation, so that the brain stimulation can generate wider induced electric field, the action effect of the brain stimulation can be effectively improved, and the method has obvious advantages in preventing stimulation from off-target in clinical treatment.
Drawings
FIG. 1 is a flow chart of a method of optimizing neuromodulation physical field orientation in accordance with the present invention.
Detailed Description
The invention will now be described in further detail with reference to the drawings and to specific examples.
After an individual stimulation target point is determined, the characteristic direction of the stimulation target point is determined based on diffusion tensor imaging, the generated inductive current is ensured to be tangent with the characteristic direction to the greatest extent when a coil is placed through an image navigation technology to obtain the maximum action effect (a flow chart is shown in figure 1), and the specific process is as follows:
1. acquisition of image data (T1W structural image, DTI image)
The T1W structure is like to cover the whole head (including chin); DTI image: 100 gradient directions, there are b0 images of the AP and PA encoding directions.
2. Preprocessing of image data
T1W: preprocessing by adopting a freeform-all command of freresurfer, wherein preprocessing comprises head motion correction, nonuniform intensity standardization (deviation field correction), talairach transformation, intensity standardization, brain peeling, linear mention registration, CA intensity standardization, CA nonlinear volume registration, neck removal, cephalic registration, CA label and statistics, intensity normalization, white matter segmentation, white matter editing, filling shearing, surface subdivision, original surface smoothing, expansion, quasi-embryogenic sphere, automatic topology restoration, final surface generation, secondary smoothing, secondary expansion, sphere mapping, sphere registration, homolateral and contralateral surface registration, curvature mapping, cortical partition and partition statistics, cortical tape mask, cortical partition mapping to Aseg, and finally obtaining accurate individual brain segmentation results; wherein, the intensity is normalized as follows: gray scale normalization is carried out on all voxel intensities, so that the average intensity of white matter is 110; the cortical tape mask is: a cortical binarization mask is created with voxels at the cortex of 1, otherwise 0.
DTI: preprocessing DTI data by adopting fsl software, wherein the preprocessing comprises the following steps: (1) Magnetic field inhomogeneity correction (topup) for estimating a bias field due to magnetic susceptibility using a pair of diffusion weighted imaging of opposite encoding directions when no gradient magnetic field is applied; (2) brain tissue extraction (bet); (3) Vortex and head correction (eddy), i.e. eliminating the influence of vortex fields and head movements, is specifically: correcting the magnetic susceptibility of each diffusion weighted image and the current estimation of eddy current and motion parameters, and then loading the current estimation into a Gaussian process; once all images are loaded, the hyper-parameters of the gaussian process can be estimated and predicted. (4) registration: registering the diffuse image of the individual to the T1W structural image space.
3. Calculation of diffusion tensor and target area direction
Calculation of the diffusion tensor for each voxel of the whole brain (dtifit): calculating tensor matrix of each voxel by combining corresponding gradient direction and b value through corrected diffusion image dataD：
；
Wherein,D xy =D yx 、D xz =D zx 、D yz =D zy 、D xx 、D yy 、D zz is the diffusion coefficient in six directions.
4. Determination of target direction or fiber bundle orientation
4a) Determining target direction
The process is performed in MATLAB software, tensor matrix for voxelsDAnd (3) performing eigenvalue decomposition:
；
wherein,v 1 、v 2 andv 3 as a feature vector of the object set,l 1 、l 2 andl 3 is the corresponding characteristic value.
Reservation ofIs used to generate a whole brain tensor pattern, and a first eigenvector of the voxels in the region of interest in the tensor pattern is extractedv 1 Performing direction-based cluster analysis on voxels of the region of interest, the central voxel of each classv 1 As class feature directions of this class.
The selection of the optimal class takes into account three factors, the size of the classC s Spatial concentration of voxels in classC i Directional consistency of classC c The method comprises the steps of carrying out a first treatment on the surface of the Spatial concentration of voxels in classC i Represented by the average value of the three-dimensional Euclidean distance of voxels in a class divided by the class size, the directional consistency of the classC c Represented by calculating the mean of cosine similarities between voxels of the group; finally, the characteristic direction of the class, which is commonly selected by the three factors of the size of the class, the space aggregation degree of the class and the direction consistency, is the target direction; wherein, the class when the following formula takes the maximum value is the optimal class;
；
wherein,Cand judging the value of the formula for the optimal class.
4b) Determining fibre bundle orientation
This process is done using MRtrix3 software.
Solving fiber direction using constrained sphere deconvolution algorithm that uses observed dispersion signalsSExpressed as a signal response function of individual fibersRAnd fiber direction distribution functionF(v) Is a spherical convolution of:
；
wherein,representing a unit sphere.
The response function is estimated by a tensor matrix assuming thatl 2 =l 3 =β：
；
Then；
Wherein,bin order to have a diffusion coefficient of sensitivity,S 0 for a signal to which no gradient pulse is added,is thatSThe gradient direction corresponding to the signal. And obtaining a fiber orientation density function through deconvolution, and searching for an extremum of the fiber orientation density function to obtain a fiber direction. Finally, the preset seed points (subcutaneous nuclei) are connected end to end in a consistent direction (the included angle is not more than 45 degrees) on the path from the seed points to the target point area, and the corresponding fiber bundle trend is obtained.
5. Image navigation
Registering the T1W structural image to an MNI space, then obtaining three-dimensional coordinates of face feature points on a tested head profile by using a depth camera, registering the three-dimensional coordinates to an MNI space coordinate system, calculating and visualizing an included angle between a target point direction (or a fiber bundle trend obtained by 4 b) calculated by the DTI at the moment and an electric signal caused by the central position of a stimulator obtained by the depth camera. During stimulation, the angle of the stimulator is adjusted to enable the direction of the physiological electric signal caused by the stimulator to be tangential to the direction of the target point.
Wherein the MNI space is a coordinate system established by the Montreal neurological institute (Montreal Neurological Institute) from a series of magnetic resonance images of normal human brain.
Claims (5)
1. A method for optimizing the direction of a neuromodulation physical field, comprising the steps of:
step 1: collecting image data
Collecting a T1W structural image covering the whole head, and collecting DTI images with set number of gradient directions, wherein the DTI images are b0 images with AP and PA coding directions;
step 2: image data preprocessing
Preprocessing the T1W structural image by using FreeSterfer to obtain a precise individual brain segmentation result; preprocessing the DTI image, removing noise and artifacts, and registering to a T1W structural image space;
step 3: calculating a diffusion tensor
Calculating the dispersion tensor of each voxel of the whole brain according to the preprocessed DTI image; calculating tensor matrix of each voxel by combining corresponding gradient direction and b value through corrected diffusion image dataD：
；
Wherein,D xy =D yx 、D xz =D zx 、D yz =D zy 、D xx 、D yy 、D zz diffusion coefficients in six directions;
step 4: determining target direction
Tensor matrix for voxelsDAnd (3) performing eigenvalue decomposition:
；
wherein,v 1 、v 2 andv 3 as a feature vector of the object set,l 1 、l 2 andl 3 is the corresponding characteristic value;
reservation ofIs used to generate a whole brain tensor pattern, and a first eigenvector of the voxels in the region of interest in the tensor pattern is extractedv 1 Performing cluster analysis on voxels of the region of interest, and performing first eigenvector of central voxel of each classv 1 As class feature directions of the class; selecting the class characteristic direction of the optimal class as a target point direction;
step 5: image navigation
Registering the T1W structural image to an MNI space coordinate system, then obtaining three-dimensional coordinates of face feature points on a tested head outline image by using a depth camera, registering to the MNI space coordinate system, calculating an included angle between a target point direction calculated by the DTI image and an electric signal direction generated by a central position of a stimulator acquired by the depth camera, and visualizing; during stimulation, the angle of the stimulator is adjusted to enable the direction of the electric signal generated by the stimulator to be tangential to the direction of the target point.
2. The method for optimizing the direction of a neuromodulation physical field according to claim 1, wherein the preprocessing of the T1W structural image in step 2 is specifically: adopting a freeform's recycle-all command to sequentially perform head motion correction, nonuniform intensity standardization, talairach transformation, intensity standardization, brain peeling, linear volume registration, CA intensity standardization, CA nonlinear volume registration, neck removal, cephalic registration, CA label and statistics, intensity normalization, white matter segmentation, white matter editing, filling shearing, surface subdivision, original surface smoothing, expansion, quasi-homoembryo spherical surface, automatic topology restoration, final surface generation, secondary smoothing, secondary expansion, sphere mapping, sphere registration, homolateral and contralateral surface registration, curvature mapping, cortical partition and partition statistics, cortical band masking, and finally mapping cortical partition to Aseg, and finally obtaining a precise individual brain segmentation result;
the intensity is normalized to: gray scale normalization is carried out on all voxel intensities, so that the average intensity of white matter is 110;
the cortical tape mask is as follows: a cortical binarization mask is created with voxels at the cortex of 1, otherwise 0.
3. The method for optimizing the direction of a neuromodulation physical field according to claim 1, wherein the preprocessing of the DTI image in step 2 uses fsl software, and specifically comprises:
1) Correcting magnetic field inhomogeneity: estimating a bias field caused by magnetic sensitivity using diffusion weighted imaging of a pair of opposite encoding directions when no gradient magnetic field is applied;
2) Extracting brain tissue;
3) Vortex field and head motion effects: correcting the magnetic susceptibility of each diffusion-weighted imaging and the current estimation of eddy current and motion parameters, and then loading the current estimation into a Gaussian process; once all the images are loaded, the super parameters of the Gaussian process can be estimated and predicted;
4) Registering: registering the diffuse image of the individual to the T1W structural image space.
4. The method according to claim 1, wherein in the step 4, the selection of the optimal class considers three factors: class sizeC s Spatial concentration of voxels in classC i Directional consistency of classC c The method comprises the steps of carrying out a first treatment on the surface of the Spatial concentration of voxels in classC i Represented by the average value of the three-dimensional Euclidean distance of voxels in a class divided by the class size, the directional consistency of the classC c Represented by calculating the mean of cosine similarities between voxels of the group; finally, spatial aggregation of voxels in a class according to the size of the classThe characteristic direction of the optimal class selected by the three factors of degree and class direction consistency is taken as the target direction; wherein, the class when the following formula takes the maximum value is the optimal class;
；
wherein,Cand judging the value of the formula for the optimal class.
5. The method of claim 1, wherein the step 4 is replaced by determining the fiber bundle trend, and the method specifically comprises:
to observe dispersion signalsSExpressed as a signal response function of individual fibersRAnd nerve fiber direction distribution functionF(v) Is a spherical convolution of:
；
wherein,s 2 representing a unit sphere;
the response function is estimated by the tensor matrix of step 3, assuming thatl 2 =l 3 =β：
；
Wherein,v 1 、v 2 andv 3 as a feature vector of the object set,l 1 、l 2 andl 3 for the corresponding characteristic value(s),βnamely, isl 2 Or (b)l 3 Is a value of (2); then
；
Wherein,bin order to have a diffusion coefficient of sensitivity,S 0 for a signal to which no gradient pulse is added,as a dispersive signalSA corresponding gradient direction; obtaining a nerve fiber orientation density function through deconvolution, and searching the extremum of the nerve fiber orientation density function to obtain the direction of the nerve fiber;
then, connecting the preset seed points, namely subcutaneous nucleuses, to the target area in a consistent direction end to obtain the corresponding fiber bundle trend;
correspondingly, the included angle in step 5 is replaced by: the fiber bundle trend calculated by the DTI image at the moment and the included angle of the electric signal direction generated by the center position of the stimulator acquired by the depth camera.
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Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110121293A (en) * | 2017-11-24 | 2019-08-13 | 佳能医疗系统株式会社 | Medical data processing equipment, MR imaging apparatus and study finish model generating method |
| CN110473206A (en) * | 2019-07-24 | 2019-11-19 | 东南大学 | It is a kind of based on super voxel and the dispersion tensor image partition method for estimating study |
| CN111583281A (en) * | 2020-05-15 | 2020-08-25 | 江南大学 | Brain region dividing method in multi-mode connection mode |
| CN114847922A (en) * | 2022-05-05 | 2022-08-05 | 浙江工业大学 | Brain age prediction method based on automatic fiber bundle identification |
| CN115670429A (en) * | 2022-11-17 | 2023-02-03 | 电子科技大学 | Positioning method for transcranial magnetic stimulation individual structure target based on diffusion weighted imaging |
| WO2023134062A1 (en) * | 2022-01-11 | 2023-07-20 | 平安科技（深圳）有限公司 | Artificial intelligence-based drug-target interaction relationship determination method and apparatus |
-
2023
- 2023-10-13 CN CN202311325484.2A patent/CN117094987B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110121293A (en) * | 2017-11-24 | 2019-08-13 | 佳能医疗系统株式会社 | Medical data processing equipment, MR imaging apparatus and study finish model generating method |
| CN110473206A (en) * | 2019-07-24 | 2019-11-19 | 东南大学 | It is a kind of based on super voxel and the dispersion tensor image partition method for estimating study |
| CN111583281A (en) * | 2020-05-15 | 2020-08-25 | 江南大学 | Brain region dividing method in multi-mode connection mode |
| WO2023134062A1 (en) * | 2022-01-11 | 2023-07-20 | 平安科技（深圳）有限公司 | Artificial intelligence-based drug-target interaction relationship determination method and apparatus |
| CN114847922A (en) * | 2022-05-05 | 2022-08-05 | 浙江工业大学 | Brain age prediction method based on automatic fiber bundle identification |
| CN115670429A (en) * | 2022-11-17 | 2023-02-03 | 电子科技大学 | Positioning method for transcranial magnetic stimulation individual structure target based on diffusion weighted imaging |
Non-Patent Citations (7)
| Title |
|---|
| Automated Surface-Based Segmentation of Deep Gray Matter Regions Based on Diffusion Tensor Images Reveals Unique Age Trajectories Over the Healthy Lifespan;Little G等;《bioRxiv》;1-15 * |
| Canonical polyadic decomposition of third-order tensors: reduction to generalized eigenvalue decomposition;Domanov I等;《Siam Journal on Matrix Analysis & Applications》;第35卷(第2期);636–660 * |
| 基于VBM、HARDI技术对神经变性病、脑损伤的脑结构研究;王吉;《中国博士学位论文全文数据库 (医药卫生科技辑)》(第3期);E070-2 * |
| 弓状束弥散张量成像技术结合神经影像导航在癫外科术中的应用价值;杜飞舟等;《医疗装备》;第33卷(第1期);8-10 * |
| 磁共振扩散张量成像在神经导航中的初步应用;孙敬等;《中国CT和MRI杂志》(第5期);45-48 * |
| 神经导航的临床应用;杨勇等;《中华临床医师杂志(电子版)》(第1期);112-118 * |
| 胶质瘤脑浸润程度与病理级别的相关性研究;晏怡等;《中华神经外科疾病研究杂志》(第4期);52-56 * |
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