A neuromodulation system for treatment of physiological disorders. The system includes one or more stimulators for stimulating one or more cranial nerves; one or more detectors configured for detecting a predetermined physiological state; and a control unit that controls nerve stimulation by the one or more stimulators so that it is synchronized with the at least one predetermined physiological state detected by the one or more detectors. A method of neuromodulating a patient for treatment of physiological disorder. The method includes the steps of detecting a predetermined physiological state and applying stimulation to one of the cranial nerves during the predetermined physiological state by one or more stimulators of a neuromodulation system.

Methods and systems for determining whether brain tissue is indicative of a disorder, such as a neurodegenerative disorder, are provided. The methods and systems generally utilize data processing techniques to assess a level of congruence between measured parameters obtained from magnetic resonance imaging (MRI) data and simulated parameters obtained from computational modeling of brain tissues.

In one example in accordance with the present disclosure, an emitter system is described. The emitter system includes an array of emitters. Each emitter emits waves towards a target surface. The emitter system also includes a control system. The control system includes a sensing system to determine whether emitters in the array are properly positioned relative to the target surface. A controller of the control system adjusts emitter sets of the array of emitters based on an output of the sensing system.

Systems and methods described herein provide techniques for detecting subject behavior by processing video data using one or more trained models configured to detect subject behavior. The described system processes sets of frames from the video data using different trained models. The system further processes different orientations of the sets of frames. The various outputs from the different trained models and from processing the different orientations of the sets of frames may be combined to then make a final determination as to whether the subject is exhibiting a particular behavior during a particular frame.

Apparatuses, systems, methods, and computer program products are disclosed for medical assessment based on voice. A query module is configured to audibly question a user from an electronic display screen and/or a speaker of a computing device with one or more open ended questions. A response module is configured to receive a conversational verbal response of a user from a microphone of a computing device in response to one or more open ended questions. A detection module is configured to provide a machine learning assessment for a user of a medical condition based on a machine learning analysis of a received conversational verbal response of the user.

There is provided a computer implemented method of diagnosing a medical state associated with a neuro-psychiatric disorder in a subject, comprising: receiving a plurality of EEG datasets, each respective EEG dataset from a respective EEG electrode of a plurality of EEG electrodes monitoring a head of the subject, clustering the plurality of EEG datasets into a plurality of clusters, computing a p-adic representation of the plurality of clusters, extracting a quantum potential value from p-adic representation of the plurality of clusters, and diagnosing the medical state associated with the neuro-psychiatric disorder according to the quantum potential relative to a threshold that separates between presence of the medical state and non-presence of the medical state.

A system and a method for Alzheimer's disease prediction using a neural network, a computer-readable recording medium with a stored program, and a computer program product are provided. The processor obtains a first brain MRI data, a second brain MRI data, a first neuropsychological assessment score, and a second neuropsychological assessment score. The processor obtains a plurality of image feature data according to the first brain MRI data and the second brain MRI data. Each image feature data is selected from a group consisting of a plurality of hippocampal subfield geometric change data. The processor obtains a neuropsychological change data according to the first neuropsychological assessment score and the second neuropsychological assessment score. The processor obtains an Alzheimer's disease prediction result according to the neural network module, the image feature data, and the neuropsychological change data.

Methods and systems useful for detecting neurological disorders and for measuring general cognitive performance, in particular by measuring eye movements and/or pupil diameter during eye-movement tasks.

Example systems, methods, and apparatus, including cognitive platforms, are provided for computing performance metrics of an individual based at least in part on user interaction(s) with computerized tasks and/or interference and at least one physiological measure of the individual, where the performance metric provides an indication of the cognitive abilities of the individual. The apparatus can be coupled to at least one physiological component to perform the physiological measurement of the individual. The apparatus also can be configured to adapt the tasks and/or interferences to enhance the individual’s cognitive abilities.

Systems and methods evaluate eye movements in Multiple Sclerosis (MS) patients who receive different drugs (e.g., Dimethyl fumarate, Fingolimod, Cladribine, Ofatumumab) or treatments that (a) decrease inflammation and prevent nerve damage that can cause symptoms of multiple sclerosis); (b) test Sphingosine-1-phosphate receptor modulator, which sequesters lymphocytes in the lymphocytes nodes, preventing them from contributing to an autoimmune reaction); (c) check an Immune suppressor agent that works on the lymphocyte's pathway) and (d) analyze the effect of Monoclonal Antibodies for inhibiting the activation of lymphocyte B.