434 results about "Mental disease" patented technology

The invention relates to an animal behaviour testing platform for optogenetics regulation and control, used for carrying out a behaviour test on tested animals. The testing platform comprises a central control system, an environmental control system, a behaviour monitoring system, a photoelectric detection, regulation and control system and a case device used for fixing the systems. A standard and integrated behaviour testing platform used for testing animal optogenetics regulation and control is established by combining an optogenetics neural regulation and control technology and electrophysiology and behaviour tests and the like; by virtue of the platform, optogenetics regulation and control as well as behaviour record on the tested animals in certain environment can be realized, thus being easy to understand relation between the animal optogenetics regulation and control and the behaviour and meeting requirements of research on pathogenesis and treatment mechanism of a nervous system and mental diseases.

The invention provides a compound of formula I:

wherein R¹-R⁶, X, Y, and B have any of the values described herein, as well as salts of such compounds, compositions comprising such compounds, and therapeutic methods that comprise the administration of such compounds. The compounds are inhibitors of MAO-B enzyme function and are useful for improving cognitive function and for treating psychiatric disorders in animals.

The invention provides an electroencephalogram signal characteristic extracting method. Network average route lengths and clustering coefficients are calculated through wavelet reconstruction, windowing horizontal visibility map complex network conversion and complex network analysis. The average route lengths and clustering coefficients composed of electroencephalogram signals are calculated to achieve characteristic analysis of electroencephalogram signals and chaotic time sequence signals of the electroencephalogram signals of different rhythms. The electroencephalogram signal characteristic extracting method has the advantages that one-dimensional chaotic time sequences are converted into complex networks, according to analysis of network characteristic parameters, fractal characters of the electroencephalogram signals are revealed, the complex non-linearity signals of the electroencephalogram signals are depicted from a brand new angle. The electroencephalogram signal characteristics can be applied to automatic diagnosis of mental disease and a characteristic identifying module of a brain-machine port system. The electroencephalogram signal characteristic extracting method can effectively distinguish the electroencephalogram signals of an epilepsia attach stage and an epilepsia non-attach stage, the equation p<0.1 is met after Mann-Whitney detection, and the electroencephalogram signal characteristic extracting method can be applied to epilepsia electroencephalogram automatic identification.

A user monitoring device system includes a user monitoring device with a microphone and sensors to determine air quality, sound level/quality, light quality and ambient temperature near the user. A movement detection device detects a user's movement information. The movement detection device and the monitoring system assist to determine user sleep information and sleep behavior information. The microphone records user movement sounds detected by the movement detection device. The movement detection device is configured to cause the microphone to stop recording user movement sounds when the movement sounds are not directed to a sleep related parameter. In response to determining user sleep information or sleep behavior information the system is used for treatment of sleep or psychiatric disorders.

The invention discloses a magnetic resonance imaging (MRI) based brain disease individual prediction method and a magnetic resonance imaging (MRI) based brain disease individual prediction system. The method comprises the following steps: 1: obtaining the MRI of the brain of a patient with mental diseases; 2: carrying out denoising and dimension reduction treatment on the MRI of the brain of the patient; 3: carrying out feature selection by utilizing a ReliefF algorithm; 4: adaptively obtaining a spatial brain area by using a spatial cluster analysis method; 5: removing redundant features by utilizing a correlation-based feature selection algorithm, thus obtaining an optimal feature subset; 6: carrying out multiple linear regression analysis based on the optimal feature subset to recognize potential biomarkers. The method has the beneficial effects that the embodiment of the invention integrates various machine learning methods and can rapidly and conveniently achieve quantitative and individual accurate prediction of the interest features of mental diseases, such as clinical indexes, based on various image data in different mode types, thus being beneficial to understanding the brain structures, function abnormity and potential pathogenesis of the diseases.

The present invention relates to compounds and methods for inducing neuronal differentiation in normal neural stem cells and brain cancer stem cells. The methods may take place in vitro, such as in isolates from the adult mammalian brain, or in vivo. Compounds and methods described herein may find use in the treatment of neurodegenerative and psychiatric diseases, the repair and regeneration of the nervous system, and in treatment of neurologic malignancy.

The invention relates to the technical field of brain function network research and more particularly relates to a brain function network construction method based on a micro-state. Starting from theconstruction of an EEG brain function network, according to the theory of EEG micro-state related aspects, namely that the EEG micro-state is the most basic state which reflects the brain activity, proposing that the EEG micro-state can also be applied to construction of the brain function network. The method provided by the invention solves the problem that the time consumed for construction of the traditional brain function network is too long and reduces a large quantity of redundant information in an original EEG time sequence by using the time sequence after micro-state reconstruction. Byapplying the method to an EEG data set of mental disease working memory collected in a hospital, the EEG brain function network for schizophrenia patients and normal persons is constructed; and compared with the brain function network constructed by using an original EEG signal, the brain function network constructed by the time sequence after micro-state reconstruction has the advantage that thecharacteristics of the human brain network can be more accurately reflected.

The compositions and methods described are directed to microRNA chips having a plurality of different oligonucleotides with specificity for genes associated with autism spectrum disorders. The invention further provides methods of identifying microRNA profiles for neurological and psychiatric conditions including autism spectrum disorders, methods of treating such conditions, and methods of identifying therapeutics for the treatment of such neurological and psychiatric conditions.

The invention discloses a dynamic brain function connection mode dividing method based on PAC and K-means clustering. The method divides the dynamic function into basic connection modes. The method includes the following steps: conducting pre-processing on resting state magnetic resonance data, extracting the time sequences of brain zones, then adopting the sliding window method in calculating related coefficients between each two of the brain zones, constructing corresponding function connection matrixes, and finally conducting PCA and K-means clustering on the function connection matrixes so as to obtain basic connection modes. According to the invention, the dynamic function connection mode dividing algorithm can effectively and accurately obtain the divided basic function connection modes, not only can capture transition among different basic connection modes, and can provide strategies for further research and prevention and treatment of clinic neurological diseases, such as Schizophrenia, Alzheimer's disease, depression, and etc.

The invention belongs to emotional state recognition technology, and provides an electroencephalogram emotional state feature extraction method based on adaptive tracking in different frequency bands, which is a more objective emotional state recognition method and is a more objective evaluation method for therapy evaluation to mental diseases. The technical scheme includes that the electroencephalogram emotional state feature extraction method based on adaptive tracking in different frequency bands comprises following steps: data acquisition and preprocessing, time frequency feature extraction, adaptive frequency band selection and emotional state recognition; the specific data acquisition and preprocessing step comprises emotionally inducing a testee by the aid of emotional images and preprocessing acquired original electroencephalogram signals, and preprocessing comprises changing reference potential, reducing sampling, realizing bandpass filtering and removing electrooculogram; time-frequency change is realized for processed electroencephalogram signals by the aid of short time Fourier transform; and adaptive frequency band selection is realized by a frequency band variable adaptive tracking method, and an SVM (support vector machine) is used for realizing classification recognition for feature frequency bands. The electroencephalogram emotional state feature extraction method is mainly applied to recognizing emotional states.

The invention provides methods for the treatment of major depressive disorder in male subjects and methods for the treatment of psychiatric disorders at high altitudes by administering one or more creatine-containing compounds.

The invention provides a neural fingerprint extraction and classification method. The method comprises an image acquisition step, a brain structure annotation step and a neural fingerprint extractionstep in sequence. The method has the advantages that information mining is performed on magnetic resonance images in different sequences, a unified fingerprint extraction and establishment method forall brain areas is formed through unified structure annotation establishment and positioning, a comprehensive neural fingerprint system is constructed, neural fingerprint standard libraries for different sexes and different ages are established, and therefore brain health assessment, disease classification and parting and disease prediction can be performed on a to-be-tested target; through the establishment of the neural fingerprint standard libraries, quantitative assessment and classification can be performed on brain development, brain senescence, neural diseases and mental diseases of a to-be-tested object, and talent assessment, advantage assessment, senescence assessment, rehabilitation assessment, assistant diagnosis, disease prediction and other technologies can be formed; and a standard technical foundation is laid for advantage cultivation, brain healthcare and diagnosis and prevention of brain diseases of human talents.

The embodiments of the invention disclose a brain stimulation system and method based on artificial intelligence, equipment and a storage medium. The system comprises a plurality of brain stimulationterminals and a cloud platform; the cloud platform is used for adopting an artificial intelligence technology to generate multidirectional psychological big data by learning physiological data and psychological state evaluation parameters from the multiple brain stimulation terminals and combining a preset diagnosis treatment model for various diseases; and the brain stimulation terminals are usedfor adopting the artificial intelligence technology to analyze the collected physiological data and psychological state evaluation parameters of a target object based on the multidirectional psychological big data, the mental state of the target object is determined, brain stimulation parameters required for the target object are obtained according to the mental state, and corresponding noninvasive bran stimulation is generated to the target object according to the brain stimulation parameters. The brain stimulation system and method based on the artificial intelligence, the equipment and thestorage medium are suitable for maintaining mental health of ordinary people and regulating unhealthy emotions of ordinary people and at least have the effects of early prediction, professional prevention and timely interference on certain serious mental diseases.

The invention provide a ultra-performance liquid chromatogram-mass spectrum detection method for 23 types of drugs and mental disease resistant drugs in blood. The method comprises the following steps: 1) sample processing: employing an organic solvent for processing a sample through protein sediment; 2) primary screening detection: employing a ultra-performance liquid chromatogram-tandem mass spectrum (UPLC-MS/MS) multi-reaction monitoring (MRM) mode for primary screening detection; and 3) employing the ultra-performance liquid chromatogram-tandem mass spectrum (UPLC-MS/MS) multi-reaction monitoring (MRM) mode for reinspection of the positive sample through the primary screening in the step 2). According to physical and chemical properties of 9 types of drugs and 14 types of mental disease resistant drugs, during reinspection, the UPLC-MS/MS detection condition which is different with the primary screening detection is employed, different sample pretreatment methods are employed, so that the detection method can satisfy the rapid, sensitive and accurate requirements of primary screening detection, and misjudgement of false positive results can be reduced.

The invention discloses lactobacillus plantarum DP189 and application thereof, and relates to the field of functional food microorganisms. The strain is preserved in China Center for Type Culture Collection on March 25 , 2019 with the preservation number of CCTCC NO:M2019199. By means of the strain, the learning and memory capability of a depressed rat can be improved; the level of brain-derived neurotrophic factors of the depressed rat can be improved; the injury to hippocampal neurons and apoptosis of neuron cells of the depressed rat are reduced; the content of apoptosis proteins JUK2 and Bcl-2 of brain tissue of the depressed rat is lowered; the abundance of verrucomicrobiaceae of the intestinal tract is lowered; the abundance of bacterium lacticum of the intestinal tract is improved, the alpha-diversity of the intestinal floras is improved, and the intestinal flora disturbance of the depressed rat is alleviated. In this way, the lactobacillus plantarum DP189 has good effects of preventing and treating depression/anxiety, infantile autism and inflammatory enteritis. The strain has the anti-depression function, and can serve as a functional probiotic bacterium for preventingand treating the depression, infantile autism and inflammatory enteritis, and a new idea is provided for clinical intervention with and control over depression and other relevant mental diseases.

The invention discloses mental stress assessment and high-risk population intervention system oriented to undergraduates. The mental stress assessment and high-risk population intervention system comprises a multi-mode brain function detecting device, a mobile terminal, a network server and a monitoring client-side, wherein the multi-mode brain function detecting device is connected with the mobile terminal through a Bluetooth signal, the mobile terminal is connected with the network server through a mobile internet, and the monitoring client-side is connected with the network server through the internet. The mental stress assessment and high-risk population intervention system is based on a mobile internet technology, adopts the multi-mode brain function detecting device to acquire brain blood oxygen signals and electroencephalogram signals of the undergraduates, utilizes the mobile terminal to receive the brain blood oxygen signals and electroencephalogram signals and input family mental disease histories and earlier-stage diagnosis information of the undergraduates, then uploads the data to the network server for data processing and analysis, generates corresponding diagnosis results, feeds the results back to the mobile terminal and the monitoring client-side, achieves early finding and early treatment of the mental diseases of the undergraduates and is beneficial to early warning and intervention of the mental diseases of the undergraduates.

The invention discloses an intelligent identification system for mental diseases. The intelligent identification system comprises a control and communication module, a trigger module, an intelligent behavior sensing module, a behavior data analysis module, a physiological signal acquisition module, a physiological signal analysis module and a disease identification output module. The intelligent identification system can be applied to the fields of children autism identification and the like, multi-modal signal autism identification is developed, and mental diseases such as autism and the likecan be comprehensively, quantitatively and timely identified from two dimensions of external behavior observation and internal physiological indexes.