178 results about "Spectrum disorder" patented technology

The invention provides an autism assessment system and method based on multi-modal time sequence data fusion, and relates to the technical field of children autism spectrum disorder assessment calculation processing. The invention innovatively provides a dual time sequence alignment method based on dynamic time warping (DTW) and LSTM prediction, the problem of time sequence dislocation of cross-modal data (heart rate / eye movement / limb movement) caused by acquisition frequency difference is solved, millisecond-level synchronization precision is realized, the technical problem of cross-modal data time sequence dislocation in an existing autism assessment system is solved, and the accuracy of time sequence alignment of the cross-modal data in the autism assessment system is improved. The small-range time deviation caused by acquisition equipment delay or physiological response difference is eliminated, and the robustness and reliability of the autism evaluation system are improved.

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 a data classification method and system based on a deep multi-path attention adaptive graph convolutional network, and relates to the technical field of medical image processing. Rs-fMRI data is acquired and preprocessed to obtain a BOLD sequence, a function connection feature vector is constructed and input into a DMAGCN model, and finally an optimal model is acquired through five-fold cross validation for classification. The data quality is guaranteed through data preprocessing, and a foundation is laid for accurate analysis. Functional connection feature vectors effectively represent data, after a model is input, a Transform backbone network and an MLP branch network can extract multi-source domain features, non-imaging data are utilized in combination with a graph network, the model can learn rich features, and generalization and adaptability are enhanced. According to the method, the classification accuracy is improved, the performance is excellent when uncertain data classification such as autism spectrum disorder is processed, related disease research is promoted, and an efficient and reliable method is provided for medical data classification.

The invention relates to the field of artificial intelligence, in particular to an autism spectrum disorder knowledge graph construction method and system. The method comprises the following steps: acquiring multi-source heterogeneous autism spectrum disorder information; constructing an autism spectrum disorder original corpus; performing information extraction by using a hybrid convolutional neural network-long and short-term memory network-hidden Markov model; designing a credibility dynamic evolution mechanism; constructing and optimizing knowledge topology self-organization; and generating an autism spectrum disorder knowledge graph. According to the method, information is efficiently and accurately extracted by constructing the hybrid model, so that the analysis efficiency of the unstructured medical text is remarkably improved; by designing a credibility dynamic evolution mechanism, dynamic iterative optimization of the triad credibility is realized; by introducing an adversarial verification mechanism, the polarity of the triple is efficiently and accurately identified, and the manual intervention requirement is greatly reduced; by constructing a multi-granularity interpretation engine, cross-level correlation analysis of gene pathway-neural circuit-behavior phenotype is realized, and the analysis accuracy is improved.

The invention provides an autism spectrum disorder classification method and device, electronic equipment and a storage medium, and relates to the field of image processing. The method comprises the following steps: acquiring a target image; the target image is an image comprising a target object; obtaining a fusion feature by capturing an internal relationship among a plurality of local regions in the target image and an interaction relationship between the local regions and a global region in the target image; classifying autism spectrum disorders according to the fusion features to obtain a classification result; the classification result is used for indicating the autism spectrum disorder type to which the target object belongs. According to the method and the apparatus, the problem of low classification accuracy of autism spectrum disorders in related technologies is solved.

The invention is suitable for the technical field of medical diagnosis, and provides a diagnosis processing method and system for autism spectrum disorders, and the method comprises the steps: obtaining electroencephalogram signal data and eye movement thermodynamic images of a to-be-diagnosed person in a plurality of diagnosis tasks; according to a preset extraction condition, feature extraction is carried out on the electroencephalogram signal data and the eye movement thermodynamic images of the multiple diagnosis tasks, and electroencephalogram feature vectors and eye movement features of the multiple diagnosis tasks are obtained; respectively carrying out cross fusion on the electroencephalogram feature vectors and the eye movement features of the plurality of diagnosis tasks to obtain multi-modal fusion attention features of the plurality of diagnosis tasks; and performing classification prediction on the multiple multi-modal fusion attention features to obtain a diagnosis result of the to-be-diagnosed person, thereby solving the problems of strong subjectivity, high misdiagnosis rate and limitation in data processing due to dependence on behavior observation and questionnaire survey in the diagnosis process of the autism spectrum disorder person in the prior art.

The invention discloses an autism spectrum disorder diagnosis method and device, equipment and a medium, and the method comprises the steps: obtaining audio and video data of a test patient, and converting the audio and video data into behavior log data; and converting the behavior log data into script description data, inputting the script description data and doctor question data into a preset large language model, and obtaining a diagnosis reason and a diagnosis result of the autism spectrum disorder of the tested patient. Compared with the prior art, the method has the advantages that massive clinical data is not needed, and only video signals and audio signals of normal form interaction between a doctor and a tested patient during diagnosis in the testing process need to be converted into script description data which can be understood by a large language model; and then, a diagnosis reason and a diagnosis result of the autism spectrum disorder of the tested patient are generated by utilizing logical reasoning and text generation capability of the large language model, so that the diagnosis result is quickly obtained, and the diagnosis credibility of a doctor is further improved.

The invention discloses a medicine for treating autism, application of the medicine for treating autism in preparation of a medicine for improving social competence damage of autism animals or people, and application of the medicine for treating autism in preparation of a medicine for improving anxiety behaviors of autism animals or people. When the high-dose vitamin D is used for intervening autism mice, the method has the advantages of being economical, convenient to take and good in effect, animal experiment evidence is provided for supplementing large-dose VD to intervene autism spectrum disorders in the early stage of clinical experiments, and a new thought is provided for early prevention and treatment of neurodevelopment disorders of clinical children.

The invention relates to an autism spectrum disorder screening application system which comprises a front-end user interaction device and a background data processing server. The front-end user interaction device comprises a child information management module; the scale screening module is used for providing at least one autism screening scale for the user to select and fill, and generating a scale screening score after receiving a scale answer filled by the user; the photo screening module is used for receiving at least one child front face image uploaded by the user, preprocessing the face image and then calling a photo analysis model to generate a photo screening score; the screening process of the front-end user interaction device comprises the steps that after a user selects a target child through the child information management module, operation of the scale screening module and the photo screening module is completed in sequence, and the background data processing server generates a comprehensive score and a risk level for scale screening scores and photo screening scores through a weighted voting mechanism. Through the application system, accurate ASD pre-diagnosis classification can be quickly provided.

The invention relates to the technical field of neuroscience and artificial intelligence, in particular to an autism spectrum disorder prediction method based on EEG machine learning, which comprises the following steps: step 1, collecting EEG data for children with autism spectrum disorder and healthy control children; step 2, preprocessing the collected EEG data, then extracting sleep spindle wave signals in the EEG data, and analyzing the sleep spindle wave signals to obtain feature information data; step 3, performing standardization processing on the obtained feature information data to obtain a data set, and then dividing the data set into a training set and a test set; 4, training the training set through different machine learning algorithms to obtain corresponding training models, and selecting an optimal model from the training models through a five-fold cross validation strategy; and 5, inputting the test set into each optimal model for evaluation, and comparing evaluation results to obtain a target model. The method provides a scientific basis for early intervention of ASD.

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.

Provided are methods for clinical treatment of neuromyelitis optica spectrum disorder (NMOSD) using an anti-C5 antibody, or antigen binding fragment thereof.

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.

The invention belongs to the technical field of behavior recognition, and provides a rapid recognition method for abnormal behaviors of children with autism spectrum disorder, and the method comprises the steps: obtaining the position information and face orientation of all children in each frame of image of a free activity video of the children; based on the position information, analyzing the position movement and the activity range of each child in different frames of images to obtain the activity tendency of each child; performing clustering analysis on the position information of all children in each frame of image, and adjusting clustering parameters in combination with activity tendencies to obtain an optimal comparison range of each child; analyzing active and passive interaction conditions between each child and other children in the corresponding optimal comparison range based on the face orientation to obtain an interaction tendency and an interaction weight of each child; and obtaining the abnormal behavior degree of each child in combination with the activity tendency, the interaction tendency and the interaction weight, and completing the recognition of the abnormal behavior of the child. The method provided by the invention can quickly and accurately identify children with abnormal behaviors based on the adjusted optimal comparison range.

The invention relates to the technical field of autism detection, in particular to an autism spectrum disorder detection system based on eye movement recognition, which comprises a video acquisition module, a data preprocessing module, a sight line drop point analysis module, a data correction module, a recognition model module and a month age association evaluation module, the data preprocessing module is used for determining a target user, the sight falling point analysis module is used for forming a sight falling point time sequence, the data correction module is used for correcting the sight falling point time sequence, and the recognition model module is used for outputting a result. According to the system and the method, the user is guided to watch various types of interactive videos through the video acquisition module to obtain rich eye movement related data, and visual performance in different scenes is integrated, so that the detection basis is more comprehensive, the autism spectrum disorder detection accuracy is improved, and autism spectrum disorder and typical development conditions are distinguished more accurately.

The embodiment of the invention discloses an autism spectrum disorder subtype division method and device, a medium and a program product. The method comprises the following steps: constructing a connection brain map based on neuroimaging data and a functional brain region division template of an ASD individual; constructing a brain age regression model, predicting the social brain age of the ASD individual based on the connection brain map and the brain age regression model, and obtaining the brain age difference of the ASD individual in combination with the actual brain age of the ASD individual; obtaining an ADOS social score of the ASD individual, and carrying out clustering analysis on the ADOS social score and the brain age difference to obtain a clustering subtype; and carrying out behavioral verification and neural dimension verification on the clustering subtypes, and constructing a combined portrait among the subtypes, the behavior features and the neural features based on a verification result. According to the method, the social brain age can be predicted by constructing the connection brain map, the clustering subtypes are divided, the combined portrait is constructed, a doctor can be accurately assisted to detect ASD subtype neural development differences, and discovery and application of subtype specific biomarkers are assisted.

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 present application relates to the use of a composition for the preparation of a medicament for the treatment of autism spectrum disorders, wherein the composition comprises taurine and a beta-receptor blocker.

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.

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.

The invention discloses a rehabilitation nursing system for treating autism spectrum disorder children based on AI intelligent accompanying. The rehabilitation nursing system comprises an AI intelligent accompanying robot, a multi-mode interaction module, a personalized intervention scheme generation module, a real-time monitoring module and a cloud data platform. The system obtains facial expressions, voices, actions and electroencephalogram signals of a child patient in real time through a camera, a sensor, electroencephalogram equipment and other multi-mode data acquisition modules, emotion recognition is conducted in combination with a convolutional neural network, the emotion state is analyzed through an emotion calculation model, and an autism diagnosis and treatment knowledge base is constructed through a knowledge graph. The system generates a personalized intervention scheme based on a deep learning algorithm, and dynamically adjusts the training difficulty through real-time intervention means such as virtual coaches, action correction, dialogue guidance and the like in combination with reinforcement learning. The system provides an efficient and personalized home rehabilitation solution for children with autism, the social ability is improved, and the rehabilitation period is shortened.

The invention discloses an autism child attention prediction method based on a Mamb-Unet structure, and the method comprises the steps: constructing a multi-modal data set for autism child attention prediction, constructing an autism child attention prediction model based on the Mamb-Unet structure, and training the model; and performing atypical visual attention prediction on a to-be-detected image by using the trained model to obtain a prediction result. The model comprises a preprocessing module, an encoder part, a bottleneck part, a decoder part and an output part which are connected in sequence, wherein an efficient self-adaptive visual state space block, a frequency domain attention module, a conditional visual state space block and a conditional attention fusion module are arranged; the output of the encoder portion is also propagated through a hopping connection to the decoder portion. According to the method, the autism spectrum disorder atypical visual attention prediction accuracy is remarkably improved.

The application relates to the technical field of image recognition, in particular to a children autism spectrum disorder stereotyped behavior recognition system based on video images. The system screens target pixel points from pixel points; determines a motion intensity index according to a trajectory point sequence of the target pixel points; converts the trajectory point sequence of the target pixel points into a motion vector sequence; determines direction similarity according to direction similarity between vectors; clusters the vectors based on the direction similarity, and identifies a target cluster; determines a motion regularity index according to the number of vectors in the target cluster and the direction similarity; determines a saliency index in combination with the motion intensity index; clusters the target pixel points based on the saliency index, and sets a region of interest; determines an enhancement weight according to the density of the target pixel points in the region and the saliency index; and determines a total enhancement demand value in combination with the gray scale and gradient features of the pixel points in the region; and reversely maps the total enhancement demand value into a gamma correction parameter, so that the accuracy of recognition is improved.

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.

The invention provides a compound used as a CaV1.22 activating agent. In particular, a compound according to formula (I) or a pharmaceutically acceptable salt thereof is provided as a CaV1.22 activator for the treatment of schizophrenia, bipolar disorder, severe depressive disorder, substance use disorder, ADHD, Ferren-Mecdemedes syndrome, autism spectrum disorder, multiple sclerosis, frontotemporal dementia, Alzheimer's disease, Bruggea syndrome, short QT syndrome, and the like. Or early repolarization syndrome. # imgabs0 #

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.

A method for early diagnosis of autism spectrum disorder (ASD) including determining an ASD risk index, determining an ASD brain index that includes administering at least one auditory brainstem response (ABR) test to an ear canal of a patient, producing a plurality of biomarkers and identifying at least one biomarker of the plurality of biomarkers as a potential ASD biomarker, and determining an ASD likelihood responsive to each of the ASD risk index and ASD brain index.

Embodiments of the instant disclosure relate to intervention and / or treatment of autism spectrum disorder (ASD). Certain embodiments relate to treating a subject having ASD with one or more proinflammatory cytokines. In some embodiments, the one or more proinflammatory cytokines include, but are not limited to, granulocyte-macrophage colony-stimulating factor (GM-CSF) or derivative thereof, or fusion polypeptide thereof, or recombinant thereof. Other embodiments include a mouse model for assessing efficacy of treatment of ASD in humans and other mammals.

Provided is an AAV vector capable of expressing a human Mef2c gene in the brain. Specifically, provided are an adeno-associated virus vector capable of expressing a full-length MEF2C protein, and the use thereof. The adeno-associated virus vector can significantly ameliorate the social deficit symptoms of mice with Mef2c gene deletion.