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.

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.

The devices and methods described below provide for more convenient stereo-electro-encephalography, which may allow the patient to move freely during the days-long monitoring period. The system includes a number of depth SEEG electrodes. In one version of the system, each SEEG electrodes are wirelessly connected to an EEG console, through a subcutaneous electrode which is connected to the SEEG electrode through a conductor. The subcutaneous electrode is in turn wirelessly connected to a supra-cutaneous appliance operable to obtain SEEG signals, generated by the SEEG electrode, through the subcutaneous electrode. The method of use entails implantation of the depth SEEG electrodes deep in the brain, implantation of the subcutaneous electrodes under the scalp. The patient need not be physically connected to a console or control system, and may be ambulatory for the SEEG protocol period.

Provided are a test method and apparatus for evaluating cognitive function decline. The test method for evaluating cognitive function decline includes performing a testing stage of testing how many missions presented for each of a plurality of test items a user performs, and calculating a test score for each of the plurality of test items, performing a category classifying stage of classifying at least one test item from among the plurality of test items as one neurocognitive category from among a plurality of neurocognitive categories, and performing an evaluating stage of evaluating whether there is cognitive function decline in the one neurocognitive category, based on a test score of the at least one test item classified as the one neurocognitive category.

Provided are a test method and apparatus for evaluating cognitive function decline. The test method for evaluating cognitive function decline includes performing a testing stage of testing how many missions presented for each of a plurality of test items a user performs, and calculating a test score for each of the plurality of test items, performing a category classifying stage of classifying at least one test item from among the plurality of test items as one neurocognitive category from among a plurality of neurocognitive categories, and performing an evaluating stage of evaluating whether there is cognitive function decline in the one neurocognitive category, based on a test score of the at least one test item classified as the one neurocognitive category.

Neurological abnormalities are often discovered through observation by health care providers, and/or parent report. Many neurodevelopmental disorders such as ASD are purely identified through behavioral analysis, and cannot be screened for using a biomarker or quantitative stimulus-response test. Current screening tools contain subjective components based on parent report and clinician observation, vary in consistency of use across providers, and demands resources, knowledge, and access to skilled expertise. As a result, the only tests used today require lengthy and subjective behavioral analysis and often, miss or misidentify neurodevelopmental disorders contributing to a delayed diagnosis. The technology disclosed herein allow for a solution to this systemic problem.

An olfactory testing platform for testing for olfactory performance and for prompting olfactory training. The kit contains a plurality of inhaler sticks, each of which includes a first indicia that is computer readable, and a second indicia that is human readable. Each inhaler stick provides a scent that is different than every other inhaler stick in smell or concentration. A computer application running on a smart device having a camera is used to identify each inhaler stick in the kit using the first indicia and to prompt a user to select a detected scent from a plurality of options in response to presentation of the first indicia to the camera of the smart device. The smart devise also captures cognitive functions and integrates performance of various cognitive domains with olfactory performance.

A movement-assessment device and methods for using the testing device includes a peg board device having a plurality of apertures on a top surface, and a plurality of photo-optical gate sensors. A computing device, comprising a touchscreen interface, communicates with the peg board device.

The present disclosure is directed to providing digital health services. In some embodiments, systems and methods for conducting virtual or remote sessions between patients and clinicians are disclosed. During the sessions, media content (e.g., images, video content, audio content, etc.) may be captured as the patient performs one or more tasks. The media content may be presented to the clinician and used to evaluate a condition of the patient or a state of the condition, adjust treatment parameters, provide therapy, or other operations to treat the patient. The analysis of the media content may be aided by one or more machine learning/artificial intelligence models that analyze various aspects of the media content, augment the media content, or other functionality to aid in the treatment of the patient.

The present disclosure is directed to providing digital health services. In some embodiments, systems and methods for conducting virtual or remote sessions between patients and clinicians are disclosed. During the sessions, media content (e.g., images, video content, audio content, etc.) may be captured as the patient performs one or more tasks. The media content may be presented to the clinician and used to evaluate a condition of the patient or a state of the condition, adjust treatment parameters, provide therapy, or other operations to treat the patient. The analysis of the media content may be aided by one or more machine learning/artificial intelligence models that analyze various aspects of the media content, augment the media content, or other functionality to aid in the treatment of the patient.