331 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 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.

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.

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.

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.

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 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.

The invention relates to the technical field of medical auxiliary diagnosis, and discloses an ASD auxiliary diagnosis method fusing dynamic low-order and high-order dynamic central moment networks, and the method comprises the steps: obtaining resting state functional magnetic resonance imaging data of a subject, and carrying out the preprocessing; constructing a low-order dynamic function connection network and a high-order dynamic function connection network to respectively describe direct connection and connection collaboration of the brain region; extracting multi-order central moment characteristics of the two types of networks to obtain stable statistics; generating a low-high order dynamic central moment network by adopting a puzzle fusion strategy; inputting the fusion features into a visual Transform model for classification, and outputting an auxiliary diagnosis result of the infantile autism spectrum disorder; and the key brain region is identified by analyzing the attention weight of the model so as to enhance the interpretability. According to the method, multi-level dynamic connection information is effectively fused, the influence of time asynchronism is overcome, the method has the advantages of being high in classification accuracy, high in generalization ability and good in biological interpretation, and a reliable solution is provided for ASD auxiliary diagnosis.

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 the technical field of medical artificial intelligence, and discloses a brain disease prediction method based on double encoders and a diffusion model, and the method comprises the steps: carrying out the preprocessing of a functional magnetic resonance image, and constructing a brain function network; data enhancement of semantic preservation is achieved through a diffusion model, a dual random matrix and a cosine scheduling strategy are adopted in the noise adding process, and a GraphTransform neural network containing global topological features is utilized in the denoising process; the spatial features of the brain network and the time dynamic features of the BOLD signals are respectively extracted by using double encoders; designing a triple contrast learning mechanism to optimize cross-dimension feature interaction; and finally migrating to a downstream classification task to realize disease prediction. Small sample overfitting is relieved through diffusion enhancement, and the diagnosis reliability is improved; fusing spatial-temporal characteristics to assist multi-dimensional pathological analysis; and the cross-site adaptability of the model is enhanced, and collaborative analysis of multi-center heterogeneous data is supported. The method is suitable for auxiliary diagnosis of cerebral diseases such as infantile autism.

Provided are compounds, methods, and pharmaceutical compositions for reducing the amount or activity of SCN2A RNA in a cell or subject, and in certain instances reducing the amount of SCN2A protein in a cell or subject. Such compounds, methods, and pharmaceutical compositions are useful to ameliorate at least one symptom or hallmark of a disease or disorder associated with a voltage-gated sodium channel protein, such as, for example, a Developmental and Epileptic Encephalopathy, an intellectual disability, or an autism spectrum disorder. Such symptoms and hallmarks include, but are not limited to seizures, hypotonia, sensory integration disorders, motor development delays and dysfunctions, intellectual and cognitive dysfunctions, movement and balance dysfunctions, visual dysfunctions, delayed language and speech, gastrointestinal disorders, neurodevelopmental delays, sleep problems, and sudden unexpected death in epilepsy.

Described herein are steroids of Formula (I):and pharmaceutically acceptable salts thereof, wherein R1, R2a, R2b, R3, R4, R5a, R5b, R6, and Z 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 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 discloses an SRCIN1 gene therapy vector for autism treatment and application of the SRCIN1 gene therapy vector, and relates to the field of autism treatment. The vector is a lentiviral vector and comprises an overexpression sequence of an SRCIN1 gene, the overexpression sequence is pSLentiti-CMV-Srcin1-P2A-EGFP-WPRE, and the titer of the overexpression sequence is 2.99 E + 08 TU / ml. The SRCIN1 gene therapy vector is delivered to the prefrontal cortex through brain stereotactic injection and is used for up-regulating SRCIN1 expression and improving social behaviors, cognitive functions and anxiety-like behaviors of autism model mice. The SRCIN1 gene therapy vector further comprises an EGFP (enhanced green fluorescent protein) reporter gene which is used for visualizing transfection efficiency. The vector disclosed by the invention is simple to prepare and high in targeting property, and can be used for remarkably improving the social ability and exploration ability of mice, improving infantile autism behaviors such as anxiety behaviors and repeated engraving behaviors of the mice and increasing PSD95 protein expression.

The invention relates to a method of diagnosing a subtype of autism spectrum disorder (ASD), so called ASD Phenotype 1, by assessing the energy production capacity of patient specific cell lines in presence of various carbon sources of energy and metabolic factors or by assessing the changes in energy production capacity or lack thereof after administration of an Nrf2-inhibitor.

Compositions and methods for treating autism spectrum disorder, obsessive compulsive disorder or tics with a compound of Formula (I), or a pharmaceutically acceptable salt thereof, are provided. The compositions and methods may be used to improve one or more symptoms of autism spectrum disorder, obsessive compulsive disorder or tics. (I) or pharmaceutically acceptable salts thereof, wherein R1, R2, R7 and ring A have any of the meanings herein defined in the description.

Provided herein are 19-nor C3, 3-disubstituted steroids of Formula (I); and pharmaceutically acceptable salts thereof; wherein R1, R2, R3, and R4 are as defined herein, and A is a heteroaryl ring system comprising 3 or 4 nitrogens 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.

This invention relates to the field of pharmaceutical technology, and discovers that the natural products boldinine and norboldinine can be used as NTSR1 (Neurotensin receptor-1, NTSR1) receptor antagonists and their applications. The compounds are boldinine and norboldinine ((+)-Laurolitsine), which have NTSR1 receptor antagonistic activity and are NTSR1 receptor antagonists that can prevent and treat alcohol poisoning, addiction, schizophrenia, autism spectrum disorder, and mood disorders. Accordingly, this invention can provide novel NTSR1 receptor antagonist prospective compounds with clear targets for the above-mentioned related diseases.

The application discloses an ADHD multi-feature extraction and fusion classification method based on original videos, and comprises the following steps: S1, a network camera is used to collect video records of a subject watching videos, the videos are preprocessed, and preprocessed image frames are obtained; S2, the preprocessed image frames are analyzed to obtain behavior modes including facial actions, eye movements and head movements; S3, feature components of the behavior modes are extracted and fused; and S4, a deep learning network is constructed to classify the fused features. The application further discloses an ADHD multi-feature extraction and fusion classification system based on original videos. The application classifies ADHD patients based on video sequences, avoids invasive influence, reduces cost and is easy to popularize. Through multi-modal feature fusion, the limitation of single modal data is reduced, better accuracy and effectiveness are achieved, and in addition, the application can also be used for classifying autism cases.

The present invention relates to an apparatus and method for screening for autism on the basis of user gaze monitoring. According to the present invention, the apparatus for screening for autism on the basis of user gaze monitoring, comprises: a user monitoring unit that monitors the gaze and movement of a user through a camera while image content is output on a display, and obtains real-time gaze coordinates; a layer analysis unit that extracts a plurality of elements of interest including the entire area and a partial area of each object related to an object and a character appearing in an image of the display for each hour, and divides the image into a plurality of types of layers according to the type of the element of interest; a time-series data deriving unit that derives, on the basis of the plurality of types of layers and the real-time gaze coordinates, time-series response data in which a time interval, during which the user gazed at the element of interest on each type of layer while watching the image, is recorded for each time; and a determination unit for determining autism by applying the time-series response data to a pre-constructed autism screening model.

A method for treating a mental disorder in a subject, comprising the step of normalizing the expression of both DRD1 and DRD5 belonging to the DRD1-like family of the subject to a preset standard level. The DRD1 / 5 level and expression can be increased or decreased bidirectionally by targeting the upstream promoter sequence of DRD1 / 5. The promoter activity of DRD1 / 5 can be attenuated by using antipsychotic drugs that directly target the DRD1 / 5 promoter. The antipsychotic drugs used are typical and / or atypical antipsychotic drugs such as haloperidol, risperidone, clozapine, and cariprazine. This treatment is also useful for subjects in whom the constitutive expression of the DRD1 / 5 gene is impaired. This method can be used to treat schizophrenia, autism, depression, mania, and bipolar disorder by normalizing the DRD1 / 5 expression level. The promoter gene sequencing of DRD1 and / or DRD5 can also be used for quantitative mental health screening by comparing with a standard sequence database.

This invention provides compounds for the treatment, prevention, or suppression of various pathological conditions (such as proliferative disorders like cancer, β-globin disorders, fibrosis, pain, neurodegenerative diseases, Prader-Willi syndrome, malaria, viral infections, myopathy, and autism) by inhibiting G9a. [Solution] The following general formula (I) TIFF2026062914000401.tif2449 A compound represented by or a pharmacoposly acceptable salt thereof is provided.

The invention discloses an intelligent early-stage autism screening system based on multi-modal fusion, and belongs to the field of medical auxiliary diagnosis. The intelligent early-stage autism screening system comprises a data acquisition and preprocessing module which is used for synchronously acquiring multi-modal original data and carrying out preliminary noise reduction and standardization processing; the feature extraction module is used for extracting specialized features from the original data of each modal; the single-modal analysis module is used for analyzing each modal feature and outputting an independent risk probability; the multi-modal fusion module is used for integrating modal results and generating final risk assessment; the result output module is used for generating a visual report and supporting clinical decisions; the data acquisition and preprocessing module, the feature extraction module, the single-mode analysis module, the multi-mode fusion module and the result output module are electrically connected in sequence. The core breakthrough of the method is that the limitation of single-modal data is broken through, four types of heterogeneous data including acoustic features, gene information, eye movement tracks and behavior features are effectively integrated through a multi-modal fusion method, and the screening accuracy is improved.

Described are 6-aryl isoindolin-1-ones as negative allosteric modulators of metabotropic glutamate receptor 2 (mGlu2), pharmaceutical compositions including the compounds, and methods of using the compounds and compositions for treating depression, anxiety, obsessive-compulsive disorder, cognitive disorders, Alzheimer's disease, or autism spectrum disorders in a subject.

A plurality of circular RNAs (circRNAs) the expression of which is correlated with brain disorders. The circRNAs are useful for compositions, kits, assays, and methods for the identification, diagnosis, screening, treatment and / or monitoring of brain disorders including psychiatric disorders such as bipolar disorder (BD), schizophrenia (SCZ), depression, Attention-Deficit / Hyperactivity Disorder (ADHD), Obsessive-compulsive disorder (OCD), Anxiety Disorders, etc., and neurodevelopmental disorders such as Autism, Asperger's Syndrome, and other Autism Spectrum Disorders (ASD), pervasive developmental disorders not otherwise specified (PDD-NOS), etc.