21 results about "Central gray matter" patented technology

The invention discloses a method, device and equipment for generating a head model, a storage medium and a program product. The method is characterized by comprising the following steps of: receiving and verifying original three-dimensional T1 weighted image data and an original three-dimensional grid model; performing tissue segmentation based on the verified three-dimensional T1 weighted image data and the original three-dimensional T1 weighted image data to obtain a cerebral grey matter mask, a cerebral white matter mask, a scalp mask and a skull mask; performing topological structure repair on the brain grey matter mask and the brain white matter mask to obtain a brain mask; respectively converting the scalp mask, the skull mask and the brain mask into a scalp mesh model, a skull mesh model and a brain mesh model; calculating and applying a spatial transformation matrix from the verified three-dimensional grid model to the scalp grid model, and performing spatial registration and fusion on the scalp grid model, the skull grid model and the brain grid model to obtain a head model; the method has the advantages that the efficiency, precision and robustness of head model generation are effectively improved, and the method has good cross-platform deployment capability.

The invention discloses a multi-mode brain dysfunction auxiliary diagnosis method based on a dynamic function connection network. A two-stage collaborative learning framework from an individual brain graph to a group relation graph is constructed. Firstly, an individual multi-modal fusion brain map is constructed, node features of the individual multi-modal fusion brain map are obtained through node regularization regression analysis of an rs-fMRI time sequence, an adjacent matrix is obtained through calculation of the brain interval grey matter volume difference of a T1 image, and individual enhancement characterization is obtained through map convolutional network fusion. And then constructing a group relationship enhancement graph, taking individual representation as node features, constructing a dual-channel adjacency relationship for distinguishing homologous / heterologous connection according to age and gender, obtaining final discriminative representation through dual-channel graph attention network aggregation, and performing classification diagnosis according to the final discriminative representation. According to the method, deep fusion of multi-modal information and explicit modeling of key biological variables are realized, and an effective tool is provided for accurate and explainable auxiliary diagnosis of brain diseases.

The invention discloses a depression-anxiety complication-oriented disease mapping brain region positioning method and device, and relates to the technical field of medical signal processing, and the method comprises the steps: reading magnetic resonance data and identity information data of a target individual, and determining the gray matter volume corresponding to the brain of the target individual based on the magnetic resonance data and the identity information data; partitioning the brain to obtain a plurality of brain regions, and determining a brain region depression vector and a brain region anxiety vector corresponding to the brain according to the grey matter volume and the identity information data; classifying the plurality of brain regions based on the brain region depression vector and the brain region anxiety vector to determine a depression significant brain region and an anxiety significant brain region included in the plurality of brain regions; generating significant brain region tags corresponding to the multiple brain regions according to the depression significant brain region and the anxiety significant brain region, and generating network topology tags corresponding to the multiple brain regions according to the identity information data and the magnetic resonance data; and determining the disease type of the target individual based on the significant brain region tag and the network topology tag.

The invention discloses a method for identifying somatic depression by using brain structure and function abnormality, which comprises the following specific steps: selecting depression patients into groups, dividing the depression patients into a somatic depression patient group and a non-somatic depression patient group according to whether the score of a health questionnaire somatic symptom group scale of the patients is greater than or equal to 10, and recruiting a health control group at the same time. The three groups of subjects are subjected to resting state structure and functional magnetic resonance imaging scanning, and the brain grey matter volume and the brain region with significant difference are analyzed through a voxel-based morphological index and functional connection method. Pearson correlation analysis is adopted in SPSS26.0, and the relevance between the gray matter volume and the abnormal function connection of the patients in the SD group and the NSD group and the PHQ-15 scale score is compared. The method has the advantages that the illness state of the depression patient can be found in time, the patient can be treated at the best treatment opportunity, and early recognition of the body symptoms of the depression is beneficial for reducing waste of medical resources and relieving the pain of the patient.

This disclosure pertains to the field of tissue regeneration to repair damaged tissue and in particular brain tissue. The scaffold (1) of the present disclosure comprises a set of horizontal layers (11, 12, 13, 14), each layer extending in a horizontal plane orthogonal to the vertical direction (Z), each layer (11, 12, 13, 14) comprising a plurality of openings (15), each opening (15) of one layer (11, 12, 13, 14) being connected to one opening (15) of the directly adjacent layer (11, 12, 13, 14) by at least one first pillar (16) extending along the vertical direction (Z). Thus, the scaffold (1) of the present disclosure is designed to promote a cell growth radially along each layer (11, 12, 13, 14) to reconstruct the grey matter portion of the damaged area and the white matter along the vertical pillars (16) extending through the different layer (11, 12, 13, 14).

The invention discloses a system for analyzing double-task cost and brain micro-injury of a Parkinson type multi-system atrophy patient, and relates to the technical field of medical diagnosis, and the system comprises a gait collection module which is used for collecting single-task and double-task gait data of a participant, and processing the gait data to obtain gait feature data of the participant; the gait analysis module is used for analyzing the difference of the participants in the single-task gait aspect and the double-task gait aspect according to the gait feature data and calculating the double-task cost; the nuclear magnetic resonance imaging module is used for collecting nuclear magnetic resonance imaging data of participants; the morphological analysis module is used for performing voxel-based morphological analysis on the nuclear magnetic resonance imaging data and analyzing the correlation between the double-task cost of the participant and the grey matter under the brain curtain; and the diffusion tensor imaging analysis module is used for performing diffusion tensor imaging analysis on the nuclear magnetic resonance imaging data and analyzing the correlation between the double-task cost of the participant and the white matter under the brain screen.

The invention discloses a cerebral cortex penetrator small blood vessel magnetic resonance imaging method based on an inflow enhancement effect. The method comprises the following steps: modeling a penetrator small blood vessel to generate a blood vessel TOF (Time of Flight) image; calculating the blood vessel-grey matter contrast ratio of the blood vessel TOF image under different FAs and different TRs, and optimizing the FAs and the TRs according to the blood vessel-tissue contrast ratio; obtaining a structural image of the in-vivo brain tissue, performing cortex reconstruction, and calculating an angle of a surface normal of each cortex vertex relative to the B0 field to obtain a cortex angle graph; adjusting a single-layer blood vessel scanning plane angle according to the cortex angle diagram; and according to the optimized FA and TR and the single-layer blood vessel scanning plane angle determined in the step S4, carrying out magnetic resonance imaging on the cerebral cortex perforation small blood vessel. The invention further discloses a cerebral cortex puncturing small blood vessel magnetic resonance imaging device based on the inflow enhancement effect. According to the method and the device, magnetic resonance imaging of the cerebral cortex perforation small blood vessel can be realized under the non-invasive and contrast-agent-free conditions.

The invention provides a method, a system, a medium and equipment for regulating and controlling a brain function through vibration stimulation. The method comprises the following steps: step 1, designing and developing a vibration stimulation module suitable for a head by utilizing an electromagnetic induction principle; 2, a vibration stimulation module is placed at a preset position and started, and the vibration amplitude and the vibration frequency are adjusted; step 3, setting vibration duration and performing vibration to obtain functional magnetic resonance data; and 4, evaluating the vibration efficiency, scanning by using a functional magnetic resonance sequence before and after vibration, and processing and analyzing functional magnetic resonance data to obtain a low-frequency fluctuation amplitude image representing spontaneous brain activity and coupling of a whole-brain grey matter signal and a cerebrospinal fluid signal. According to the transcranial vibration stimulation system, the purpose of implementing accurate vibration stimulation on the transcranial is achieved, the transcranial vibration stimulation with high amplitude precision and high frequency precision is achieved in a mode of combining functional magnetic resonance with transcranial vibration stimulation based on the vibration stimulation module, and the influence of vibration on brain functions can be detected.

The application discloses a kind of whole brain segmentation method, system, equipment and medium, method includes obtaining CT image data, and standard image data is obtained by preprocessing;Standard image data is input into whole brain segmentation model, and whole brain segmentation result is obtained.Wherein, whole brain segmentation model is obtained based on sample annotation data training, and whole brain segmentation model includes CNN encoder, Transformer encoder, multiple cross-domain fusion modules and the feature enhancement module corresponding to cross-domain fusion module one by one, and decoder.The application has the advantages that the soft tissue contrast of CT image data is low, it is difficult to distinguish cerebral grey matter, cerebral white matter, cerebrospinal fluid and brainstem brain tissue problem, by CNN encoder extraction local feature and Transformer encoder extraction global semantic feature, and local feature and global semantic feature dynamic interaction and fusion, accurately segmented advantage is carried out to whole brain cerebral grey matter, cerebral white matter, cerebrospinal fluid and brainstem brain tissue.

The invention relates to the technical field of virtual reality, in particular to a virtual reality-combined neurosurgery preoperative simulation training method, which comprises the following steps of: acquiring a grey matter boundary and analyzing a nerve fiber trend, delimiting a path to form a training path, screening out a path range in contact with a functional area, loading track comparison and extracting an overlapping path, and performing simulation training on the overlapping path. Identifying the corresponding relation between the path abnormity and the area, and recombining the action sequence and the execution range according to the problem distribution. According to the invention, by identifying the spatial continuity of nodes in an image, combining the contact condition of a path and a functional region, screening a response region which can be included, comparing the spatial correspondence between an operation track and the nodes, mining offset and fracture points in the path, and determining the spatial continuity of the nodes. The action sequence is rearranged according to the frequent occurrence positions of the actions in the path and the continuation relation of the nodes, so that the training path has continuity and fluency, the action connection between the regions is optimized, and the coordination and consistency of the path and the operation content are improved.

The present application relates to neuromodulation and brain-computer interface technical field, propose a kind of robust optimization method and system under the condition of parameter uncertainty, robust optimization method includes constructing five-layer head tissue model including skin, skull, cerebrospinal fluid, grey matter and white matter based on the magnetic resonance image of patient, and the independent conductivity uncertainty radius of each layer of tissue is set, multi-dimensional ellipsoid uncertainty set is constructed;Min-Max worst case cost function is constructed and is solved, guarantee in ellipsoid set under the worst case, electrode current weight meets safety constraint;Worst case cost function is converted into second-order cone programming form using dual principle;Pseudo-trace perception sliding window mechanism is introduced to monitor the continuity of physiological signal or impedance data, when detecting non-physiological mutation, trigger re-optimization by calling historical stable parameters and amplifying uncertainty radius;When re-optimization has no solution, trigger safety derating mechanism, and the current instruction of previous period is scaled proportionally.

The invention discloses an oxygen uptake cardiopulmonary endurance testing method based on load turn-back walking and a human brain structure, and relates to the technical field of exercise physiology and biomedicine detection.The method comprises the steps that progressive load turn-back walking exercise data and a high-resolution brain structure image of a subject are obtained; extracting key brain region parameters such as the thickness of the anterior cinerary cortex, the grey matter density of island leaves and the signal intensity of brainstem respiratory center; normalizing the motion features and brain structure parameters and then constructing a 12-dimensional multi-modal fusion feature vector; and inputting a deep feedforward neural network model to predict the maximum oxygen uptake, and performing five-level cardiopulmonary endurance evaluation according to an age and gender correction result. According to the method, quantitative association between peripheral exercise performance and a central nervous anatomy basis can be realized, the heart and lung endurance evaluation precision of old people and individuals with abnormal neurological functions is remarkably improved, and rapid screening and personalized exercise prescription making are supported.

The invention relates to the field of neuroanatomy research, and discloses a human craniocerebral specimen grey matter dyeing method which comprises the following steps: S100, putting a fresh craniocerebral specimen into a neutral formalin solution to fix the specimen; s200, the craniocerebral specimen is soaked in a solution containing 2% of phenol, so that white matter in the craniocerebral specimen generates resistance to dye used subsequently, and a pretreated specimen is obtained; s300, transferring the pretreated specimen into a staining solution containing 0.5% of copper sulfate to perform staining treatment so as to obtain a stained specimen; and S400, washing the stained specimen, and carrying out multi-step dehydration treatment to obtain the stained brain section. According to the method, the grey matter dyeing effect in the brain specimen section is remarkably improved, the contrast of the white matter dyeing effect and the grey matter dyeing effect is strong, observation is easy, and the method is simple, easy to implement and suitable for large-scale neuroanatomy research.

The invention belongs to the technical field of intelligent diagnosis of mental diseases, and particularly relates to a social function prediction system for depression patients. The system comprises an input module used for inputting indexes of a patient; the feature extraction module is used for extracting an attention-related grey matter feature ICSAGMV of the brain network from the input indexes; the prediction module is integrated with a depression patient social function prediction model, and the indexes are calculated to obtain a patient social function prediction result; and the output module is used for outputting the prediction result. The system provided by the invention can accurately predict the social function of the depression patient, and has a good application prospect.

A minimally invasive method of reducing hypertension using focused ultrasound is provided. Upon injection of ultrasound responsive nanodroplets loaded with a therapeutic agent, such as a barbital drug, focused ultrasound is delivered to the noradrenaline producing area, such as the area of grey matter around an aqueduct, thereby locally releasing the therapeutic agent payload from the nanodroplets and achieving a therapeutic reduction of blood pressure. For example, acoustic emissions from vaporized droplets may be employed to infer one or more treatment parameters from a pre-established correlation. For example, plasma hormone content and / or blood pressure changes may be inferred, thereby providing a method of real-time treatment monitoring. The example methods are useful for effecting blood pressure reduction in subjects exhibiting drug-resistant hypertension.

The invention discloses a Parkinson's disease evolution distinguishing method based on brain structure texture features, and belongs to the technical field of biomedical image mode recognition. The main pathological feature of Parkinson's disease is gradual loss of dopaminergic neurons in the compact part of nigra, but due to the fact that a grey matter signal of the compact part of nigra is too small, the feature extracted according to the size of the signal does not achieve a good classification effect, and most researches also have conflict results. According to the method, the defects caused by too small signals are avoided by investigating the texture features of the compact part of the nigra. After ten-fold cross validation is carried out on test data, the average accuracy rate of classification tasks based on texture features is 71.11%, the area under a subject feature curve is 0.70, and the specificity is 80.00%. The average accuracy rate of classification tasks with the grey matter volume as the characteristic is 58.89%, the area under a subject characteristic curve is 0.53, and the specificity is 68.33%. Compared with the grey matter volume, the performance indexes of the textural features are improved, the average accuracy rate is improved by 12.22%, the area under a subject characteristic curve is improved by 0.17%, the specificity is improved by 11.67%, and the sensitivity is improved by 18.89%.

The application provides a grey / white matter partitioned biomimetic spinal cord scaffold which can be customized grey / white matter partitioned and can match the grey / white matter morphology of any segment; the grey / white matter partitioned biomimetic spinal cord scaffold is composed of an internal 'butterfly' shaped grey matter area and an external white matter area, and the whole appearance is in a cylindrical shape; the grey matter area mainly comprises a hydrogel material; the white matter area mainly comprises a hydrogel and a directional fiber material; the direction of the directional fiber material is consistent with the longitudinal direction of the scaffold; the grey matter area and the white matter area are fused into a whole through the hydrogel, and there is no obvious barrier structure between the grey matter area and the white matter area.

A device or system for delivering fluid to or removing fluid from a CSF-containing space of a brain and for recording electrical activity from white or grey matter in the brain includes a catheter including a proximal end, a distal end portion, a first lumen extending from the proximal end to the distal end portion, and one or more electrodes positioned relative to the catheter a distance from a distal end of the catheter, such that the one or more electrodes would be placed in contact with white or grey matter of the brain if the distal end of the catheter were positioned in the CSF-containing space. The catheter may include the electrodes or a lead adjacent the catheter may include the electrodes.

The invention provides a schizophrenia auxiliary diagnosis and abnormity positioning method and system, and relates to the technical field of artificial intelligence. According to the method, BOLD signal extraction is carried out on each grey matter brain region and white matter brain region in the preprocessed data set, and white matter bold signals are introduced into classification feature construction of schizophrenia for the first time, so that the dimension of brain function analysis is expanded; on this basis, a new TW3C feature is constructed to quantify collaborative activities between grey matter areas under the same white matter pathway, resonance / common collaborative information in the transmission process of grey matter and white matter neural signals is effectively extracted, brain area features with the most discriminative ability are extracted, and diagnosis and research of schizophrenia are facilitated. In addition, based on the classification feature set, an SVM classifier is used for classification, and the brain region corresponding to the feature with the high weight value is positioned.

The application discloses a kind of analysis parkinsonian multiple system atrophy patient double task cost and brain microinjury system, it is related to medical diagnosis technical field, including: gait acquisition module, for collecting the gait data of single task and double task of participant, and gait data are handled, obtain the gait characteristic data of participant;Gait analysis module is used for according to gait characteristic data, analysis participant is different in single task and double task gait and calculates double task cost;Nuclear magnetic resonance imaging module is used for collecting the nuclear magnetic resonance imaging data of participant;Morphological analysis module is used for carrying out voxel-based morphological analysis to nuclear magnetic resonance imaging data, analyzes the correlation of participant double task cost and cerebral underlay grey matter;Diffusion tensor imaging analysis module is used for carrying out diffusion tensor imaging analysis to nuclear magnetic resonance imaging data, analyzes the correlation of participant double task cost and cerebral underlay white matter.

A computer-implemented method for parceling grey matter of a human brain of a human patient comprising obtaining a tractogram including tractogram streamlines, each having a first extremity located in a first portion of grey matter of a second extremity located in a second portion of grey matter. The parceling method also comprises using a predetermined clustering algorithm to obtain tractogram streamline clusters, and, for each cluster of at least a part of the clusters, identifying a respective first region of grey matter including for each streamline of the cluster its first extremity, and a respective second region of grey matter including for each streamline of the cluster its second extremity. The parceling method also comprises determining a parcellation based on the identified regions, including an iterative merging process which includes merging pairs of regions based on a metric quantifying an overlap.