599 results about "Autism disorder" patented technology

The disclosure provides methods for treating a subject in need thereof comprising administering to the subject a therapeutically-effective dose of psilocybin. The methods described herein may be used to treat a variety of diseases, disorders, and conditions. For example, the methods may be used to treat neurocognitive disorders (e.g., Alzheimer's disease, Parkinson's disease), ADHD, Epilepsy, Autism, Sleep-wake disorders, Chronic pain, Inflammatory Disorders, IBD, Stroke, ALS, and / or Multiple Sclerosis.

The disclosure provides methods for treating a subject in need thereof comprising administering to the subject a therapeutically-effective dose of psilocybin. The methods described herein may be used to treat a variety of diseases, disorders, and conditions. For example, the methods may be used to treat neurocognitive disorders (e.g., Alzheimer's disease, Parkinson's disease), ADHD, Epilepsy, Autism, Sleep-wake disorders, Chronic pain, Inflammatory Disorders, IBD, Stroke, ALS, and / or Multiple Sclerosis.

The invention relates to an uncertainty perception passive multi-target field adaptive image classification method, which is used for image recognition of autism spectrum disorder patients. According to the method, firstly, source domain model parameters are obtained and used for initializing a target model, then resting state functional magnetic resonance images of a plurality of imaging centers are preprocessed, and a plurality of target domains are constructed. On this basis, a current most representative target domain is selected through a minimum inter-domain difference strategy, an uncertainty modeling method based on evidence deep learning is adopted to train a target model, and class feature consistency is improved through domain contrast learning based on a class prototype in combination with a dynamically expanded auxiliary data set; and generating a pseudo tag to relieve the influence caused by tag noise. And finally, a trained target model is obtained through fine tuning optimization, and accurate classification of unknown images is realized. The method does not need to access source domain data, has the advantages of high robustness, high generalization ability and the like, and is suitable for actual cross-center medical image analysis scenes.

The invention discloses a GNN-based multi-mode brain imaging infantile autism spectrum disorder detection method, which is characterized in that a function connection network which is different from traditional characteristics and takes an edge as a center is introduced, higher-order information interaction in a brain interval is mined, and a brain function abnormity mode of an infantile autism patient is accurately captured. Meanwhile, integrating brain imaging data and clinical text information into a unified multi-modal graph by utilizing the graph structure learning ability of GNN, capturing a complex nonlinear relationship, and realizing deep combination of multi-modal features; according to the method, the accuracy and robustness of autism detection are effectively improved, technical support is provided for personalized diagnosis and treatment, key technical problems in multi-modal data fusion and brain network analysis are solved, and the accuracy and reliability of ASD early diagnosis are improved.

The invention discloses an intelligent intervention system for children with autism spectrum disorder based on multi-modal neural feedback, which comprises a multi-modal physiological signal acquisition module for acquiring electroencephalogram signals of a target child in real time and generating an original physiological signal set; the multi-modal fusion analysis module receives the original physiological signal set and generates a comprehensive evaluation signal representing the current neurocognitive state and the emotion awakening level of the child; the self-adaptive decision control module receives the comprehensive evaluation signal and generates a self-adaptive control signal containing a neural feedback parameter adjustment instruction and a game interaction strategy instruction; the neural feedback intervention module receives the neural feedback parameter adjustment instruction and generates an adaptive neural feedback stimulation signal to act on the child; and the intelligent interactive game module receives a game interactive strategy instruction and dynamically adjusts game scene contents. The intelligent intervention system for children with autism spectrum disorder based on multi-modal neural feedback can solve the problems of poor individual adaptation of autism intervention, multi-modal data splitting and lack of dynamic adjustment.

The invention discloses an older child infantile autism evaluation system based on multi-modal data analysis. The older child infantile autism evaluation system comprises a multi-modal signal acquisition module and a multi-modal feature extraction and fusion module. The multi-modal signal acquisition module is used for acquiring physiological signals, behavior signals and scale data; and the multi-modal feature extraction and fusion module is used for extracting and fusing the features of the modal signals, generating unified feature representation, and providing an analysis result including autism evaluation in combination with scale data so as to realize the autism evaluation of the older children. According to the invention, early detection of older children can be realized.

Disclosed herein is a method for stereotyped behavior detection for supporting diagnosis of Autism Spectrum Disorder (ASD). The method includes detecting a target object to be assessed in an input video, detecting a section in which a periodic behavior occurs using an image sequence of the target object, and classifying a stereotyped behavior in the section in which the periodic behavior occurs.

The invention belongs to the field of autism spectrum disorder recognition, and particularly discloses an autism spectrum disorder recognition method based on a multi-mode graph convolutional network, and the method comprises the steps: collecting patient data, preprocessing the data, and constructing the graph convolutional network. The MPPCA method based on the threshold value is designed, prior information related to noise variance and the PCA denoising threshold value are combined, signals with dominant noise can be better distinguished, the function of the prior information related to the noise variance and the like is fully played, and a better data basis is provided for follow-up phenotype prediction; according to the method, a multi-modal graph convolutional network is used, phenotypes are predicted according to brain imaging data, behavior data and gene data of a patient, advantage complementation among different data types is utilized, and specific parameterization design is performed on the weight of a filter, so that the recognition accuracy of the autism spectrum disorder is improved.

The invention relates to 2 '-fucosyllactose, in particular to application of the 2'-fucosyllactose to preparation of immune balance drugs or food. The immune balance comprises regulation and control of the ratio of Th17 cells to Treg cells of Th17 / Treg immune imbalance offspring activated by maternal immune or induced by virus infection; according to the application disclosed by the invention, the fact that filial generations generated after the maternal body of a mammal generates immune reaction activation can regulate and control the proportion of Th17 and Treg cells through the influence of 2 '-fucosyllactose on intestinal flora is found, so that Th17 and Treg immune balance is realized; in addition, social and cognitive disorders and repetitive behaviors related to filial generation autism induced by Th17 / Treg immune imbalance can be improved.

Disclosed is a method and an oral composition for treating CNS disorders. An embodiment of the invention comprises orally administering an allopregnanolone containing composition to a subject having a CNS disorder. Exemplary CNS disorders include sleep disorders, mood disorders, dysthymic disorders, bipolar disorders, anxiety disorders, stress disorders, compulsive disorders, schizophrenia spectrum disorders, convulsive disorders, memory disorders, cognition disorders, movement disorders, personality disorders, autism spectrum disorders, substance abuse disorders, dementia, pain, traumatic brain injury (TBI), vascular disease, withdrawal syndrome, and tinnitus. The composition and methods disclosed herein exhibit effective oral absorption / bioavailability.

The invention relates to an infantile autism classification method based on a double-branch functional topological graph neural network. The infantile autism classification method can realize the classification of the infantile autism by using functional magnetic resonance imaging data. According to the provided autism classification network, long-distance connection and short-distance connection are divided based on the shortest path between brain intervals, then an exponential decay mask is introduced through a functional topological graph Transform branch to adjust attention weight and accurately extract long-distance dependency features, a graph isomorphic network in the other branch is subjected to multiple neighborhood aggregation operations, short-distance dependency features are captured, and the short-distance dependency features are extracted. According to the method, multi-scale dependence of the brain network is extracted in parallel through a double-branch structure, information redundancy is reduced by means of a topology perception attention mechanism, and the adaptive ability of the model to the heterogeneous brain network is improved by using the adaptive fusion module, so that multi-scale dependence of the heterogeneous brain network is balanced in a self-adaptive manner. The classification accuracy is remarkably superior to that of an existing mainstream method, objective and efficient technical support is provided for autism diagnosis, and high interpretability is achieved.

Systems and methods are described for treatment of neurological conditions in which transcranial illumination using infrared, near-infrared and / or red wavelengths of light are delivered into the brain of a patient using a portable head wearable device. Arrays of light emitters are simultaneously controlled to provide a selected power density and distribution during a therapeutic session to treat a condition of the patient. An external controller such as a touchscreen tablet or laptop computer can be used to select illumination parameters for each patient. The system can be used to provide treatment to children such as those having autism spectrum disorder (ASD).

Novel compounds such as DHMP maintain DHM features and pharmacological activities with increased potency and efficacy, improved water solubility, enhanced bioavailability, increased permeability to the blood brain barrier (BBB), and increased stability. Because DHMP retains the therapeutic activities of DHM and has properties that improve its bioavailability, it can be used as a therapeutic agent for treating Alzheimer's disease-related diseases (ADRD) including Alzheimer's disease (AD), Vascular Dementia, Frontotemporal Dementia (FTD), Mixed Dementia as well as neurodegeneration such as Parkinson's disease. Administration of these compounds result in beneficial pathological changes including reduction of Aꞵ and Tau, improvement of neuroinflammation, reduction of cognitive / memory deficits, as well as improvement of anxiety, depression, and aggression. The disclosed DHM derivatives can also be used to treat sleep disorders, neurological disorders such as PTSD, Autism, Epilepsy / Seizures, and Alcohol Use Disorders (AUD), fetal alcohol syndrome (FAD), substance addictions, and cancers.

Described herein are systems, methods, and other techniques for training and deploying a random forest model to predict a presence of ASD in a subject. A training dataset can be obtained including abundance measurements of a first cohort of subjects with ASD and a second cohort of subjects without ASD across the set of microbial markers. Multiple candidate random forest models are trained by generating a validation dataset comprising one of k subsets of the training dataset and a training subset comprising the remaining k-1 subsets, training a candidate random forest model of the k candidate random forest models using the training subset, and assessing the candidate random forest model using the validation dataset by computing performance metrics for the candidate random forest model. Candidate random forest models are evaluated by computing an AUC.

The invention discloses a multi-mode autism diagnosis method and system based on resting state fMRI and phenotypic text information, a medium and a product. FMRI and phenotypic text information are processed through a pre-trained heterogeneous graph neural network model to output a diagnosis result; the resting state fMRI is utilized to construct a brain function connection graph, and the phenotypic text information is coded into a two-dimensional vector; respectively extracting brain connection feature embedding of the fMRI mode and phenotype information feature embedding of the text mode; fusing the feature embedding in the two modes through a gating fusion network; and constructing a heterogeneous group diagram based on the fusion features and phenotypic information similarity, and obtaining group diagram features on the heterogeneous group diagram by using dual-channel information aggregation and adaptive feature fusion to output a diagnosis result. The invention aims to improve the accuracy of autism diagnosis by using multi-modal information, and can be applied to the fields of neuroimaging analysis, intelligent medical treatment and the like.

The present technology relates to a method of treating one or more behavioral symptoms of Autism Spectrum Disorder (ASD) in a subject by transdermally administering an effective amount of cannabidiol (CBD) to the subject wherein one or more behavioral symptoms of ASD are treated in the subject.

A system and method for stimulating the activation of neural mirroring mechanisms through personalized avatars to enhance learning and skill development. The system creates hyper-realistic or stylized digital representations of users and animates these avatars to present educational content or demonstrate skills, stimulating mirror neuron activation when users observe themselves demonstrating mastery. Applications include academic learning, language acquisition, professional training, personal development, athletic performance enhancement, and therapeutic interventions including for Autism Spectrum Disorder.

The present technology relates to a method of treating one or more behavioral symptoms of Autism Spectrum Disorder (ASD) in a subject by transdermally administering an effective amount of cannabidiol (CBD) to the subject wherein one or more behavioral symptoms of ASD are treated in the subject.

The invention provides a neural developmental disorder prediction model construction method, system and device and a medium, and the method comprises the steps: obtaining the clinical information of a child patient, the clinical information comprises severity of developmental retardation / dyspepsia, complexity of syndromic neurodevelopmental disorder, autism spectrum disorder, epilepsy, attention deficit hyperactivity disorder, head circumference deformity, brain development deformity, hearing / visual function impairment and related ear-eye development deformity; applying logistic regression analysis to screen out predictive factors significantly related to the positive diagnosis rate of third-generation whole exon genome sequencing in the clinical information; constructing a neural developmental disorder prediction model based on binary logistic regression according to the prediction factor; and verifying the neurodevelopmental disorder prediction model by applying data of the internal verification group and the external verification group so as to evaluate the prediction efficiency of the neurodevelopmental disorder prediction model, so that the probability of obtaining positive diagnosis after the child patient receives trio-WES detection can be accurately evaluated, and the prediction efficiency is remarkably improved.

The invention discloses an autism diagnosis method for performing cross-domain facial expression recognition based on clustering. The method comprises the following steps: S1 to S4 are source domain model training parts; s1, acquiring a source domain facial expression image; s2, using a large model CLIP to initialize a pre-training model, and preprocessing an input face image; s3, initializing deep learning training conditions, freezing large model parameters, and only training Adapter and a classification module; s4, after the model is trained, facial expression images can be classified, and a trained source domain model weight is also obtained; s5-S10 are target domain model training and autism diagnosis parts. S5, inputting a face video (image sequence) of each child; s6, using the trained source domain model weight to initialize the model, and preprocessing a non-label face image; s7, training a deep learning model, freezing large model parameters, and only training Adapter and a classification module; and S8, according to expressions and facial features classified by the model, domain self-adaption is carried out through a clustering-based dynamic pseudo tag generation module. S9, classifying a facial expression sequence by using the model subjected to domain self-adaption, and performing autism classification by using a long-short-term memory network; and S10, obtaining a diagnosis result.

A system for recognizing autism based on hybrid deep learning includes a data acquisition module, a skeleton keypoint extraction module and a recognition and classification module. The data acquisition module is configured for obtaining a dataset based on a parent-child dyad block game protocol. The skeleton keypoint extraction module is configured for identifying a plurality of skeleton keypoints of a target and a position of each of the plurality of skeleton keypoints in the video data based on a high-resolution network to generate a skeleton sequence. The recognition and classification module is configured for classifying the child into autism spectrum disorder (ASD) children and typically developing (TD) children by inputting the skeleton sequence in a graph form into a Two-stream Graph Attention Long Short-Term Memory (2sG-ALSTM) network architecture.

Aspects of the disclosure relate to methods and compositions for diagnosing and / or treating subjects having one or more symptoms of Autism Spectrum Disorder (ASD) or subjects at risk of developing ASD. In some embodiments, methods comprise determining and / or modulating levels of chorismate and / or a molecule within a chorismate metabolic pathway in a subject. In some embodiments, methods comprise determining and / or modulating levels of a microbial feature (e.g., a Sarcina bacterium) in a subject. In some embodiments, methods comprise determining and / or modulating levels of proton flux in a gastrointestinal tract in a subject.

The invention relates to the technical field of infantile autism intervention training, and discloses an active intervention training method and system for infantile autism children, and the method comprises the following steps: employing a visual music intervention method to guide the attention of the infantile autism children; guiding the autistic child to review the content of the previous intervention training; guiding the autistic children to learn and consolidate social gestures; and guiding the autistic children to learn and consolidate emotional expression. The robot has multiple intervention training means, the requirement for different children with the autism is met, in the intervention training process, the robot can autonomously interact with the children with the autism, and the emotion of the children with the autism is fully aroused.

The invention discloses an infantile autism patient hand-eye coordination training device which comprises a base, a height adjusting part and a marble mechanism, and the marble mechanism is installed at the upper end of the base through the height adjusting part; the marble mechanism comprises a cantilever, marbles and a ball receiving seat, the ball receiving seat is arranged at the upper end of the height adjusting part, the cantilever is arranged in the front-back direction, the middle of the cantilever protrudes upwards to be in a bow shape, the rear end of the cantilever is connected with the rear upper end of the ball receiving seat, and a plurality of ball receiving grooves are formed in the front upper end of the ball receiving seat at intervals in the left-right direction. A sliding way is arranged on the cantilever in the front-back direction, a sliding piece matched with the sliding way is arranged on the cantilever, the lower portion of the sliding piece is connected with a marble through a suspension cable, and the height adjusting piece is arranged, so that the height of the marble mechanism can be flexibly adjusted according to the height of the autistic child; the autistic child can aim at any one ball receiving groove backwards for ball throwing training, and therefore the hand-eye coordination ability of the autistic child can be trained while the autistic child is entertained.

The present invention includes compositions and methods of treating CNS-related conditions, comprising: administering an effective amount of a compound that lowers the levels of one or more branched-chain amino acids (BCAAs) or metabolites thereof, or any pharmaceutically acceptable salt thereof, wherein the CNS-related conditions is selected from the group consisting of Alzheimer's disease and any related forms of dementia including vascular dementia, Lewy body dementia, frontotemporal dementia, Creutzfeldt-Jacob disease, Wernicke-Korsakoff disease, and Huntington's disease, Multiple sclerosis, Parkinson's disease, autism, Amyotrophic lateral sclerosis (ALS), Hereditary diffuse leukoencephalopathy with spheroids (HDLS) and epilepsy.

The invention discloses an infantile autism diagnosis method based on brain function correlation structure modeling and a default mode network, which comprises the following steps of: firstly, preprocessing acquired infantile autism data, and extracting average time sequence data from the acquired infantile autism data to construct a whole brain function connection matrix and a DMN (default mode network) function connection matrix; a fusion weight parameter is initialized and is used for fusing the two constructed matrixes; constructing a cross-dimension dual adaptive attention module to extract features from a time dimension and a space dimension; a product-based embedded graph generation module is defined to generate a graph structure, the nodes correspond to brain regions, and the weights of the edges correspond to the similarity between the nodes; and high-order features are extracted from the generated graph structure by defining a graph convolutional network predictor, and node features are mapped to classification tags for subsequent diagnosis of autism. The method disclosed by the invention has the beneficial effects that the brain function association relationship of the autism patient is disclosed, the interpretability of clinical application is enhanced, and the autism diagnosis accuracy is improved.

Provided herein are 19-nor C3,3-disubstituted C21-pyrazolyl steroids of Formula (I):and pharmaceutically acceptable salts thereof; wherein , R1, R2, R3a, R3b, R4a, R4b, R5, R6, and R7 are as defined herein. Such compounds are contemplated useful for the prevention and treatment of a variety of CNS-related conditions, for example, treatment of sleep disorders, mood disorders, schizophrenia spectrum disorders, convulsive disorders, disorders of memory and / or cognition, movement disorders, personality disorders, autism spectrum disorders, pain, traumatic brain injury, vascular diseases, substance abuse disorders and / or withdrawal syndromes, and tinnitus.

The invention discloses an autism diagnosis method based on ARLSTM-GCNs, and relates to a medical mode recognition diagnosis method.The autism spectrum disorder diagnosis method based on an improved AR-LSTM and a fused attention graph convolutional network-ARLSTM-GCNs comprises the steps that collected fMRI images are segmented into a plurality of ROIs through AAL, and BOLD sequence groups of all the ROIs are extracted; carrying out linear normalization processing on a time-frequency graph generated by carrying out wavelet transformation on the BOLD sequence, and estimating FCNs between sample ROIs for obtained two-dimensional sequence data by using Pearson's correlation; dividing the processed data set into a training set, a verification set and a test set; building an ARLSTM-GCNs diagnosis model, wherein the ARLSTM-GCNs diagnosis model comprises an improved AR-LSTM embedded model and an improved GCN-SAGPool model; and based on the constructed ARLSTM-GCNs model, executing a staged training task. According to the method, the autism can be accurately predicted.

The invention discloses pediococcus acidilactici for improving offspring social behaviors of a parent and application thereof, and belongs to the technical field of microorganisms. According to the pediococcus acidilactici CCFM1344 provided by the invention, by intervening neurodysplasia caused by high-fat diet of a parent body, the improvement of hyperactivity and social behaviors of the offspring is specifically shown as follows: (1) hyperactivity disorder, induced by high-fat diet of the parent body, of the offspring in juvenile and adult periods is recovered; (2) restoring parent high-fat diet induced social contact disorders of offspring juveniles and adult periods; (3) reducing the gene expression of the small glial cell M1 type differentiation marker CD86 in the offspring hippocampus; and (4) reducing the gene expression of the macrophage activation marker F4 / 80 in the offspring hippocampus. Through the probiotic preparation containing the pediococcus acidilactici CCFM1344, a new thought and method can be provided for early intervention of the autism spectrum disorder.

The invention discloses an infantile autism spectrum disorder identification system based on a graph neural network, and the system specifically comprises a data processing module which is used for extracting time sequence information from fMRI data and constructing a brain function connection graph; the model training module is configured with an unsupervised graph auto-encoder GAE and comprises a three-layer graph convolutional network GCN as an encoder and an inner product decoder; the biomarker recognition module is used for executing a replacement test and screening brain regions with significant differences; and the performance evaluation module is used for evaluating the diagnostic performance of the system and the effectiveness of biomarker discovery on the ABIDE data set. The invention provides an ASD identification solution which does not need manual annotation, is high in interpretability and excellent in performance, and has important significance for promoting the development of ASD early diagnosis and personalized treatment.