CN115831368B - Rehabilitation analysis management system based on cerebral imaging stroke patient data - Google Patents

Abstract

The invention discloses a rehabilitation analysis management system based on brain imaging apoplexy patient data, which relates to the technical field of rehabilitation data analysis, solves the technical problems that vision and hearing information sensing and analysis capability are weakened, movement driving and action command execution are difficult to complete, the upper limb function recovery process of a cerebral apoplexy patient is seriously hindered, and the conventional rehabilitation curative effect is weakened.

Description

Rehabilitation analysis management system based on cerebral imaging stroke patient data

Technical Field

The invention belongs to the technical field of rehabilitation data analysis, and particularly relates to a rehabilitation analysis management system based on brain imaging stroke patient data.

Background

Cerebral apoplexy has the characteristics of high morbidity, high mortality, high disability rate and the like, and hemiplegic side limb movement dysfunction is the most common functional defect, wherein the incidence rate of hand and upper limb movement dysfunction is up to 70% -80%, and the upper limb and hand functions are recovered more slowly than the lower limb, even practical functions cannot be recovered, and the loss of single side hand functions can lead to the loss of overall functions up to 27%, so that the life quality of patients is seriously influenced.

Visual and auditory information is two important sources for human perception of the external world, and visual and auditory information perception and analysis capability are weakened, so that movement driving and action command execution are difficult to complete, the upper limb function recovery process of a cerebral apoplexy patient is seriously hindered, and the conventional rehabilitation curative effect is weakened.

Disclosure of Invention

The present invention aims to solve at least one of the technical problems existing in the prior art; therefore, the invention provides a rehabilitation analysis management system based on brain imaging stroke patient data, which is used for solving the technical problems that vision and hearing information perception and analysis capability are weakened, movement driving and action command execution are difficult to complete, the upper limb function recovery process of the stroke patient is seriously hindered, and the conventional rehabilitation treatment effect is weakened.

To achieve the above objective, an embodiment according to a first aspect of the present invention proposes a rehabilitation analysis management system based on brain imaging stroke patient data, including an experimental data input end, a gray matter volume analysis end, a fiber bundle analysis end, a resting state analysis end, a task state analysis end, a comprehensive evaluation end, and a data acquisition end;

the experimental data input end is used for acquiring experimental data generated in the experimental process and sequentially transmitting the acquired experimental data into the gray matter volume analysis end, the fiber bundle analysis end and the resting state analysis end, wherein the experimental data comprises three-dimensional high-spatial resolution image data, DTI fiber bundle imaging data, resting state fMRI data and task state fMRI data;

the gray matter volume analysis end analyzes and processes experimental data, and the relationship between the gray matter volume of the ROI and the motor sensory function is known to represent the reduction degree of the structural integrity of the gray matter on the damaged side after cerebral apoplexy, so that the change of the gray matter integrity after training is reflected;

the fiber bundle analysis end analyzes the damaged/undamaged ratio of FA and MD of the fiber bundles among 13 ROIs of cortex, subcortical and cerebellum by processing experimental data, and reflects the property and function change of the important movement fiber bundles before and after training;

the resting state analysis end analyzes and processes experimental data and counts functional connection differences between cerebellum ROIs and between the ROIs and the whole brain;

the data acquisition end is used for acquiring training data of different cerebral apoplexy persons and transmitting the acquired training data into the task state analysis end, wherein the training data comprises action observation group data, action vocabulary processing group data and vision-hearing integration group data;

the task state analysis end receives the obtained training data, and analyzes three groups of task blocks corresponding to the three groups of training data to obtain execution degree parameters of different cerebral apoplexy persons;

the comprehensive evaluation terminal receives a plurality of groups of different basic parameters, displays the rehabilitation state of the corresponding cerebral apoplexy personnel according to the corresponding parameter values, and simultaneously transmits the plurality of groups of different basic parameters to the display terminal for display.

Preferably, the specific way of analyzing and processing the experimental data at the ash volume analysis end is as follows:

removing scalp and skull data in combination with the MRI image, dividing the brain into gray matter, white matter and cerebrospinal fluid, and registering the tested structural image to a Montreal neurological institute template by using a nonlinear image registration tool of FMRIB;

dividing the brain volume of the tested T1 into 120 AAL areas based on deformation field information generated by a registration algorithm;

checking the image quality by a specialist and performing manual preprocessing to determine 8 ROIs of the cortex, 5 ROIs of the subcortical and cerebellum;

calculating the number of voxels in the 13 ROIs in each AAL zone and converting the number of voxels into a volume VOL, wherein vol=the number of voxels×the voxel size;

further divided into damaged VOL (VOL) _ipsi And contralateral VOL, VOL _contra ；

For M1 region corresponding to upper limb of brain region, VOL is used _ipsi Divided by VOL _contra VOL is obtained _ratio ，VOL _ratio Is a relative value, and obtains the reduction degree of the structural integrity of the gray matter on the damaged side after cerebral apoplexy and also obtains corresponding reduction parameters.

Preferably, the specific mode of the fiber bundle analysis end for processing the experimental data is as follows:

performing head motion correction and vortex correction, and taking a non-dispersion weighted graph, namely b0, as a reference graph;

each voxel is independently fitted with a diffusion tensor model to generate a partial anisotropic graph;

registering each tested b0 image with the T1 image by using algorithm-based interchange information, obtaining a b0 standardized deformation field to an MNI space by connecting the deformation field from the tested b0 image to the T1 image and the deformation field from the tested T1 image to the MNI space, reversing the deformation field from the b0 to the MNI space, obtaining the deformation field from the MNI space to the b0, and drawing a motion critical fiber bundle by registering a structure defined by white matter templates of John Hopkins university to the b0 image for each tested;

the corticospinal tract was delineated and visually inspected by a specialist for manual correction.

Obtaining the FA and MD values of the entire CST, STT, DCML and calculating the ratio of the damaged side to the undamaged side fiber bundles FA, MD to obtain ratio parameters such as CST _ratio 、STT _ratio 、DCML _ratio To reflect these important motor fiber bundle properties and functional changes before and after training.

Preferably, the specific way of analyzing and processing the experimental data by the resting state analysis end is as follows:

removing the first 10 time point data due to the instability of the initial signal;

and (3) performing head movement correction on the data, wherein the removal standard is as follows: each axis is displaced by >1mm or rotated by >1 °;

the data is spatially standardized, different brain images are required to be spatially standardized due to the difference of the tested brain in anatomical structures, T1 images of all experimenters are standardized to a T1 template, and then an average structural image is generated on average;

smoothing the data with a 4mm full width half maximum Gaussian kernel;

and removing data with excessive head movement, adopting programmed REST software, respectively using a functional connection method, a low-frequency amplitude method and a local consistency method to obtain respective results, counting the functional connection differences between the ROIs and the whole brain, and obtaining difference parameters.

Preferably, the specific way for the task state analysis end to analyze the three sets of task blocks corresponding to the three sets of training data is as follows:

when a tested patient is subjected to MRI scanning, the tested patient can watch the actions of both hands in the video through the reflectors and receive auditory stimuli, and the three task blocks are A, B and C task blocks respectively;

each chunk comprises 10 sections of video, 40s total, and each section of manual video is 4s;

when the task A is executed, analyzing the hand motions of the cerebral apoplexy personnel and hearing related motion sounds, checking the execution degree, and marking the execution degree as ZXa;

when the task B is executed, verb characters of a cerebral apoplexy person are acquired, the cerebral apoplexy person listens to sound of action words, the execution degree is checked, and the action words are marked as ZXb;

when executing the task block C, the content of the task block A and the content of the task block B are subjected to centralized execution processing, and an execution degree parameter ZXc is obtained;

and transmitting the execution degree parameters, the ratio parameters, the reduction parameters of the structural integrity of the gray matter on the damaged side after the cerebral apoplexy and the functional connection difference parameters between the ROIs and the whole brain of different cerebral apoplexy personnel into a comprehensive evaluation end.

Compared with the prior art, the invention has the beneficial effects that: the method is characterized in that by means of virtual reality and image capturing conversion technology, the healthy hand is led to be a bilateral symmetry hand action, so that a patient sees that the virtual hand is synchronous in time and close in space to the own hand action, and accordingly 'movement' of the patient hand is generated, and brain representation and activation similar to actual hand movement are formed;

advanced software is utilized to determine focus, movement region and activation region as ROI, so as to ensure the accuracy of key brain region and functional connection analysis; positioning according to international standard coordinates after spatial standardization, combining with positioning according to anatomical marks of an original image, and combining commonality and personality standards; the sensitivity and the specificity of fiber bundle tracking are improved, the starting point and the dead point of the fiber bundle are exchanged to be used as a seed ROI, and DTI analysis such as three-dimensional analysis, two-dimensional analysis, targeting analysis, avoidance analysis and the like are flexibly applied;

for each group of patients participating in brain imaging and electrophysiological research, a method of combining group analysis and a personal control design mode of cognitive neurophysiology is adopted for analysis, pearson correlation analysis is adopted for determining the relation between continuous variables and upper limb movement functions, and a hierarchical multiple linear regression model is also used for determining brain mechanisms of upper limb function changes of a plurality of trained patients.

Drawings

FIG. 1 is a schematic diagram of a principal frame of the present invention;

FIG. 2 is a schematic illustration of the present invention.

Detailed Description

The technical solutions of the present invention will be clearly and completely described in connection with the embodiments, and it is obvious that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.

Referring to fig. 1, the application provides a rehabilitation analysis management system based on brain imaging stroke patient data, which comprises an experimental data input end, a gray matter volume analysis end, a fiber bundle analysis end, a resting state analysis end, a task state analysis end, a comprehensive evaluation end and a data acquisition end;

the experimental data input end is used for acquiring experimental data generated in the experimental process, wherein a patient to be tested lies on an examination bed in the experimental process, the head is comfortably fixed by adopting a foam cushion and a binding belt to prevent head movement, and earplugs are provided to reduce the influence of scanning noise on the hearing to be tested, and the experimental data comprises three-dimensional high-spatial resolution image data, DTI fiber bundle imaging data, resting fMRI data and task fMRI data;

in particular, when three-dimensional high-spatial resolution image data is acquired, a 3D-MPRAGE sequence is adopted, tr=190ms, te=4.6 ms, reverse angle=9°, layer thickness = 1mm, interval = 0mm, voxel 1 x 1mm ³ ，FOV＝256×256mm ² Matrix=256×256, number of layers=160 layers;

when acquiring DTI fiber bundle imaging data, SE-EPI sequence is adoptedTr=15000 ms, te=68 ms. Matrix=128×128, fov=256×256mm ² Nex=2, layer thickness=2 mm, spacing=0, voxel = 2 x 2mm ³ 。b＝1000s/mm ² Collecting 5 b0 graphs and 75 layers in 30 directions;

when resting fMRI data is acquired: with the GE-EPI/GRE-EPI sequence, tr=2000 ms, te=24 ms, matrix=64×64, fov=230×230mm ² Layer thickness = 4mm, spacing = 0. Scanning time 456 seconds, and the number of layers is 33;

when task state fMRI data is acquired: using single shot T2 sensitive to changes in blood oxygen levels ^* The weighted GRE-EPI sequence recorded BOLD signal changes, tr=2000 ms, te=40 ms, flip angle 90 °, layer thickness 4mm, no gap, fov=230×230mm ² Matrix=64×64.

The gray matter volume analysis end is used for analyzing and processing experimental data, and the reduction degree of the structural integrity of the gray matter on the damaged side after cerebral apoplexy is represented by knowing the relation between the gray matter volume of the ROI and the motor feel function, so that the change of the structural integrity of the gray matter after training is reflected, wherein the specific mode for analyzing and processing is as follows:

removing scalp and skull data in combination with the MRI image and segmenting the brain into grey matter, white matter and cerebrospinal fluid, registering the test structural image to a Montreal Neurological Institute (MNI) template using a non-linear image registration tool (FNIRT) of FMRIB;

dividing the brain volume of the tested T1 into 120 AAL areas based on deformation field information generated by a registration algorithm;

image quality was examined by a specialist and manually pre-processed (blinded, i.e. without knowledge of patient grouping and relation to motor sensory functions), with cortical 8 ROIs (primary motor cortex, anterior motor cortex dorsal side, anterior motor cortex ventral side, auxiliary motor area, dorsal lateral frontal lobe), subcortical and cerebellar 5 ROIs (putamen, caudate nucleus, cerebellum anterior lobe and cerebellum posterior lobe) were determined, with increased and motor sensory ROI emphasis ROIs comprising 5 cortical motor areas: primary motor cortex (M1), anterior motor cortex dorsal side (dorsal premotor cortex, PMd), anterior motor cortex ventral side (ventral premotor cortex, PMv), auxiliary motor zone (supplementary motor area, SMA), dorsal lateral forehead lobe (dorsal lateral prefrontal cortex, DLPFC); cortical sensory area 3 ROIs: primary secondary sensory cortex (primary/secondarysomatosensory cortex, S1/S2), anterior parietal sulcus (anterior intraparietal sulcus, aaps), posterior parietal sulcus (posterior intraparietal sulcus, pIPS). Subcortical and cerebellum 5 ROIs: caudate nucleus (CAU), putamen (PUT), thalamus (THA), anterior cerebellum leaflet (anterior lobe of cerebellum, CBAL) and posterior leaflet (posterior lobe of cerebellum, CBPL). Namely, 13 ROIs on the left and right cortex and under the cortex;

calculating the number of voxels in each AAL region, especially the 13 ROIs, converting the number of voxels into volume (VOL, the number of voxels multiplied by the size of voxels), and knowing the relation between gray matter volume and motor feeling function of the ROIs;

further divided into damaged VOL (VOL) _ipsi And contralateral VOL, VOL _contra ；

For a brain region such as M1 region corresponding to upper limb, VOL is used _ipsi Divided by VOL _contra VOL is obtained _ratio ，VOL _ratio Is a relative value, obtains the reduction degree of the gray matter structural integrity of the damaged side after cerebral apoplexy and also obtains corresponding reduction parameters, and can reflect the change of the gray matter integrity after 4 weeks of training.

The fiber bundle analysis end analyzes the damage/undamaged ratio of FA and MD of the fiber bundles among 13 ROIs of cortex, subcortical and cerebellum by processing experimental data, and reflects the property and function change of the important movement fiber bundles before and after training, wherein the specific mode for analysis is as follows:

performing head motion correction and vortex correction, and taking a non-dispersion weighted graph, namely b0, as a reference graph;

each voxel is independently fitted with a diffusion tensor model to generate a partial anisotropy (FA) map;

registering each tested b0 image with the T1 image using algorithm-based interchange information, obtaining a b0 normalized to MNI space deformation field by connecting the deformation field from the tested b0 image to the T1 image and the deformation field from the tested T1 to MNI space, inverting the deformation field from b0 to MNI space, obtaining a deformation field from MNI space to b0, and for each tested, depicting the motion critical fiber bundle (in MNI space) by registering the structure defined by the John Hopkins University (JHU) white matter template to the b0 image.

The corticospinal tracts (corticospinal tract, CST) are delineated and visually inspected (blind, i.e. without knowledge of grouping and relation to motor function) by experienced specialists, with manual correction if necessary.

Obtaining the FA and MD values of the entire CST, STT, DCML and calculating the ratio of the damaged side to the undamaged side fiber bundles FA, MD to obtain ratio parameters such as CST _ratio 、STT _ratio 、DCML _ratio To reflect these important motor fiber bundle properties and functional changes before and after training.

The resting state analysis end analyzes and processes experimental data, and counts functional connection differences between cerebellum ROIs and between the ROIs and the whole brain, wherein the specific mode of the analysis and processing is as follows:

1. the first 10 time point data are removed in order to avoid the influence of the tested machine due to unstable initial signals;

2. and (3) performing head movement correction on the data, wherein the removal standard is as follows: each axis is displaced by >1mm or rotated by >1 °;

3. the data is spatially standardized, different brain images are required to be spatially standardized due to the difference of the tested brain in anatomical structures, T1 images of all experimenters are standardized to a T1 template, and then an average structural image is generated on average;

4. smoothing the data with a 4mm full width half maximum Gaussian kernel;

5. after the pretreatment, the data with excessive head movement is removed, the programmed REST software is adopted, the respective results are obtained by respectively using the functional connection (function connectivity, FC), the low-frequency amplitude (amplitudeof low frequency fluctuation, ALFF) and the local consistency method, the functional connection differences between the ROIs and the whole brain are counted, and the difference parameters are obtained.

The data acquisition end is used for acquiring training data of different cerebral apoplexy persons and transmitting the acquired training data into the task state analysis end, wherein the training data comprises action observation group data, action vocabulary processing group data and vision-hearing integration group data;

the task state analysis end receives the obtained training data, and analyzes three groups of task blocks corresponding to the three groups of training data to obtain execution degree parameters of different cerebral apoplexy persons, wherein the specific mode of analysis is as follows:

when a tested patient is subjected to MRI scanning, the tested patient can watch the actions of both hands in the video through the reflectors and receive auditory stimuli, and the three task blocks are A, B and C task blocks respectively;

each chunk comprises 10 sections of video, 40s total, and each section of manual video is 4s;

when the task A is executed, analyzing the hand motions of the cerebral apoplexy personnel and hearing related motion sounds, checking the execution degree, and marking the execution degree as ZXa;

when the task B is executed, verb characters of a cerebral apoplexy person are acquired, the cerebral apoplexy person listens to sound of action words, the execution degree is checked, and the action words are marked as ZXb;

when executing the task block C, the content of the task block A and the content of the task block B are subjected to centralized execution processing, and an execution degree parameter ZXc is obtained;

and transmitting the execution degree parameters, the ratio parameters, the reduction parameters of the structural integrity of the gray matter on the damaged side after the cerebral apoplexy and the functional connection difference parameters between the ROIs and the whole brain of different cerebral apoplexy personnel into a comprehensive evaluation end.

The comprehensive evaluation terminal receives a plurality of groups of different basic parameters, displays the rehabilitation state of the corresponding cerebral apoplexy personnel according to the corresponding parameter values, and simultaneously transmits the plurality of groups of different basic parameters to the display terminal for display.

Specifically, as shown in FIG. 2, an example is

Inclusion criteria: 1. meets the diagnosis standard of cerebrovascular diseases and confirms cerebral infarction through craniocerebral CT or MRI examination; 2. primary onset, involvement of unilateral hemispheres, right hand, age 40-75 years, course of disease 3 months-1 year, hierarchical brunstrom II-V stage of hemiplegia function; 3. the primary school and above, no aphasia, no obvious cognitive impairment (the score of the simple intelligent state examination scale is above the corresponding critical value of different cultural levels), and no other serious somatic diseases; 4. the binocular vision or corrected vision is more than or equal to 1.0, and the hearing is free from obvious disorder; 5. a test of 0.5 to 1h tolerance; 6. the informed consent is signed. Exclusion criteria: 1. patients with severe mass, liver and kidney diseases or infectious diseases, parkinsonism, epilepsy or idiopathic epilepsy history in orthotics and using epilepsy-causing drugs; 2. skull defects, pacemaker wear, metal implants in the body, etc. are unsuitable for MRI and TMS inspectors; 3. those with impaired motor systems prior to onset; 4. is not willing to participate in the study or does not complete the entire investigator.

Training grouping: patients meeting the above group entry criteria were randomly divided into 3 groups of action observation groups (i.e., action observation+action sound+imitation), action vocabulary processing training groups (i.e., word watching+word listening+imitation), action observation+action vocabulary processing training groups ("visual-auditory-dynamic" integration), and 30 cases of stroke patients were selected in each group according to the prior art, taking into account 10% shedding rate. Clinical medication received by all patients is basically consistent with conventional rehabilitation;

on an AOT basis, the motion of the hands is mirrored (derived as symmetrical hands) and projected together by a specific camera and computer system. The patient can see the virtual healthy side and the affected side limbs to do the same action, so as to generate the illusion that the affected side limbs can normally move.

Group A: motion observation group (motion observation/view + motion sound/hearing + imitation)

The patient can watch virtual double-hand actions (such as 'writing', 'ball beating', and the like) through the VR glasses and simultaneously hear action-related sounds (such as 'writing' brushing sound, 'ball beating' slamming, and the like), and the healthy hand simulates actions on a screen (such as 'writing', 'ball beating', and the like), and the sick hand strives to simulate the healthy hand actions symmetrically, which meets the requirement of AOT training.

Group B: action vocabulary processing group (watch action vocabulary/watch+listen action vocabulary/listen+imitate)

The patient can see the continuously presented action vocabulary word pictures (such as 'writing', 'racket', etc.) through the VR glasses, and can hear the corresponding verbs (such as 'writing', 'racket', etc.). Imagine movements, hand-building movements (e.g. "write", "racket", etc.), while affected hands strive to simulate the hand-building movements symmetrically, as required by AOT training.

Group C: visual-audio integration group (action observation + action vocabulary processing + imitation)

The patient firstly looks at virtual double-hand actions (such as 'writing', 'ball beating', and the like) through the VR glasses, and simultaneously hears action-related sounds (such as 'writing' brushing sound, 'ball beating' slamming, and the like); and then looking at the text and picture corresponding to the action (such as 'writing', 'racket', etc.), and hearing the corresponding verb (such as 'writing', 'racket', etc.). The hands are involved in actions (such as "writing", "racket ball", etc.), while the affected hands strive to simulate the hand actions symmetrically, as required by AOT training.

Each group had 10 actions, each action was trained for 3min, and the total time per training was 30min. The 3 groups of patients received 1 time 30min training every morning and afternoon, 10 times per week for 5 days, and 4 weeks.

3) Exercise function assessment method

FMA-UE:33 total 66 points including reflex activity, flexor coaction, extensor coaction, activity accompanied by coaction, separation, hyperreflexia, wrist stability, wrist activity, finger activity, coordination and speed.

WMFT: the motor ability of the patient's upper limb was quantitatively assessed by timing single joint movement, multiple joint movement and functional activity and assessing the quality of movement. Including 17 tasks such as putting the forearm on a lateral table, extending the elbow laterally, lifting a pop can, etc.

Each task was rated at a minimum of 0, at a maximum of 5, and at a total of 75.

BBT: the number of 2.5cm square pieces of wood in the box were moved from one side across the shelf to the other within 1min with the fastest speed. The ability of the patient to grasp, transfer and release the wood pieces can be assessed.

The partial data in the formula are all obtained by removing dimension and taking the numerical value for calculation, and the formula is a formula closest to the real situation obtained by simulating a large amount of collected data through software; the preset parameters and the preset threshold values in the formula are set by those skilled in the art according to actual conditions or are obtained through mass data simulation.

The working principle of the invention is as follows: the method is characterized in that by means of virtual reality and image capturing conversion technology, the healthy hand is led to be a bilateral symmetry hand action, so that a patient sees that the virtual hand is synchronous in time and close in space to the own hand action, and accordingly 'movement' of the patient hand is generated, and brain representation and activation similar to actual hand movement are formed;

advanced software is utilized to determine focus, movement region and activation region as ROI, so as to ensure the accuracy of key brain region and functional connection analysis; positioning according to international standard coordinates after spatial standardization, combining with positioning according to anatomical marks of an original image, and combining commonality and personality standards; the sensitivity and the specificity of fiber bundle tracking are improved, the starting point and the dead point of the fiber bundle are exchanged to be used as a seed ROI, and DTI analysis such as three-dimensional analysis, two-dimensional analysis, targeting analysis, avoidance analysis and the like are flexibly applied;

for each group of patients participating in brain imaging (multi-modality MRI), electrophysiological (TMS-MEP) studies, a method of combining group analysis and individual case control design of cognitive neurophysiology was used to determine the relationship between continuous variables (e.g. gray matter volume and VOL ratio of fiber bundles, fiber bundles FA ratio, MD ratio) and upper limb motor function using Pearson correlation analysis, and several brain mechanisms of upper limb functional changes of the patient after training were also determined using a hierarchical multiple linear regression model.

The above embodiments are only for illustrating the technical method of the present invention and not for limiting the same, and it should be understood by those skilled in the art that the technical method of the present invention may be modified or substituted without departing from the spirit and scope of the technical method of the present invention.

Claims (5)

1. The rehabilitation analysis management system based on brain imaging stroke patient data is characterized by comprising an experimental data input end, a gray matter volume analysis end, a fiber bundle analysis end, a resting state analysis end, a task state analysis end, a comprehensive evaluation end and a data acquisition end;

the experimental data input end is used for acquiring experimental data generated in the experimental process and sequentially transmitting the acquired experimental data into the gray matter volume analysis end, the fiber bundle analysis end and the resting state analysis end, wherein the experimental data comprises three-dimensional high-spatial resolution image data, DTI fiber bundle imaging data, resting state fMRI data and task state fMRI data;

the gray matter volume analysis end analyzes and processes experimental data, and the relationship between the gray matter volume of the ROI and the motor sensory function is known to represent the reduction degree of the structural integrity of the gray matter on the damaged side after cerebral apoplexy, so that the change of the gray matter integrity after training is reflected;

the fiber bundle analysis end analyzes the damaged/undamaged ratio of FA and MD of the fiber bundles among 13 ROIs of cortex, subcortical and cerebellum by processing experimental data, and reflects the property and function change of the important movement fiber bundles before and after training;

the resting state analysis end analyzes and processes the experimental data to know the local consistency and low-frequency wave amplitude condition of 13 ROIs of the cortex, the subcortical and the cerebellum and the functional connection change among the ROIs and between the ROIs and the whole brain voxels of a patient under the resting state, namely, under the condition of waking and quietly closing eyes, and reflects the brain mechanism of the motion function change of a more constant skeleton;

the data acquisition end is used for acquiring training data of different cerebral apoplexy persons and transmitting the acquired training data into the task state analysis end, wherein the training data comprises action observation group data, action vocabulary processing group data and vision-hearing integration group data;

the task state analysis end receives the obtained training data, and analyzes three groups of task blocks corresponding to the three groups of training data to obtain execution degree parameters of different cerebral apoplexy persons;

the comprehensive evaluation terminal receives a plurality of groups of different basic parameters, displays the rehabilitation state of the corresponding cerebral apoplexy personnel according to the corresponding parameter values, and simultaneously transmits the plurality of groups of different basic parameters to the display terminal for display.

2. The rehabilitation analysis management system based on cerebral imaging stroke patient data according to claim 1, wherein the specific way of analyzing experimental data by the gray matter volume analysis end is as follows:

removing scalp and skull data in combination with the MRI image, dividing the brain into gray matter, white matter and cerebrospinal fluid, and registering the tested structural image to a Montreal neurological institute template by using a nonlinear image registration tool of FMRIB;

dividing the brain volume of the tested T1 into 120 AAL areas based on deformation field information generated by a registration algorithm;

checking the image quality by a specialist and performing manual preprocessing to determine 8 ROIs of the cortex, 5 ROIs of the subcortical and cerebellum;

calculating the number of voxels in the 13 ROIs in each AAL zone and converting the number of voxels into a volume VOL, wherein vol=the number of voxels×the voxel size;

further divided into damaged VOL (VOL) _ipsi And contralateral VOL, VOL _contra ；

For M1 region corresponding to upper limb of brain region, VOL is used _ipsi Divided by VOL _contra VOL is obtained _ratio ，VOL _ratio Is a relative value, and obtains the reduction degree of the structural integrity of the gray matter on the damaged side after cerebral apoplexy and also obtains corresponding reduction parameters.

3. The rehabilitation analysis management system based on cerebral imaging stroke patient data according to claim 2, wherein the specific mode of processing experimental data by the fiber bundle analysis end is as follows:

performing head motion correction and vortex correction, and taking a non-dispersion weighted graph, namely b0, as a reference graph;

each voxel is independently fitted with a diffusion tensor model to generate a partial anisotropic graph;

registering each tested b0 image with the T1 image by using algorithm-based interchange information, obtaining a b0 standardized deformation field to an MNI space by connecting the deformation field from the tested b0 image to the T1 image and the deformation field from the tested T1 image to the MNI space, reversing the deformation field from the b0 to the MNI space, obtaining the deformation field from the MNI space to the b0, and drawing a motion critical fiber bundle by registering a structure defined by white matter templates of John Hopkins university to the b0 image for each tested;

the special person performs drawing and visual inspection on the corticospinal cord bundle, and performs manual correction;

obtaining the FA and MD values of the whole CST, STT, DCML, calculating the ratio of the damaged side and undamaged side fiber bundles FA and MD to obtain ratio parameters, CST _ratio 、STT _ratio 、DCML _ratio To reflect these important motor fiber bundle properties and functional changes before and after training.

4. The rehabilitation analysis management system based on brain imaging stroke patient data according to claim 3, wherein the specific way of analyzing and processing the experimental data by the resting state analysis end is as follows:

removing the first 10 time point data due to the instability of the initial signal;

and (3) performing head movement correction on the data, wherein the removal standard is as follows: each axis is displaced by >1mm or rotated by >1 °;

the data is spatially standardized, different brain images are required to be spatially standardized due to the difference of the tested brain in anatomical structures, T1 images of all experimenters are standardized to a T1 template, and then an average structural image is generated on average;

smoothing the data with a 4mm full width half maximum Gaussian kernel;

and removing data with excessive head movement, adopting programmed REST software, respectively using a functional connection method, a low-frequency amplitude method and a local consistency method to obtain respective results, counting the functional connection differences between the ROIs and the whole brain, and obtaining difference parameters.

5. The rehabilitation analysis management system based on cerebral imaging stroke patient data according to claim 4, wherein the specific way for the task state analysis end to analyze three sets of training data corresponding to three sets of task blocks is as follows:

when a tested patient is subjected to MRI scanning, the tested patient can watch the actions of both hands in the video through the reflectors and receive auditory stimuli, and the three task blocks are A, B and C task blocks respectively;

the A task block is an action observation group, wherein the action observation group refers to that a patient observes actions and simultaneously hears action sounds and imitates actions, the patient looks at virtual double-hand actions through VR glasses and simultaneously hears action related sounds, and the hand is healthy to imitate actions on a screen;

the task B module is an action vocabulary processing group, wherein the action vocabulary processing group refers to that a patient looks at vocabularies and simultaneously hears corresponding verbs and imitates actions, and the patient looks at continuously presented action vocabulary text pictures through VR glasses and simultaneously hears the corresponding verbs;

the C task block is a 'vision-hearing-moving' multichannel integration group, and a patient firstly looks at virtual double-hand actions through VR glasses and simultaneously hears action-related sounds; then looking at the text picture corresponding to the action, and hearing the corresponding verb at the same time;

each chunk comprises 10 sections of video, 40s total, and each section of manual video is 4s;

when the task A is executed, analyzing the hand motions of the cerebral apoplexy personnel and hearing related motion sounds, checking the execution degree, and marking the execution degree as ZXa;

when the task B is executed, verb characters of a cerebral apoplexy person are acquired, the cerebral apoplexy person listens to sound of action words, the execution degree is checked, and the action words are marked as ZXb;

when executing the task block C, the content of the task block A and the content of the task block B are subjected to centralized execution processing, and an execution degree parameter ZXc is obtained;

and transmitting the execution degree parameters, the ratio parameters, the reduction parameters of the structural integrity of the gray matter on the damaged side after the cerebral apoplexy and the functional connection difference parameters between the ROIs and the whole brain of different cerebral apoplexy personnel into a comprehensive evaluation end.

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