A technique for evaluating human cognitive and motor functions by a plurality of hand movement tasks is disclosed. A task execution method determines the execution order of a plurality of tasks which a test subject is caused to execute to acquire a characteristic quantity. A test subject group task database includes scores given in advance and characteristic quantities obtained from a plurality of tasks stored as past data corresponding to each of a plurality of test subjects. In a storage device, (1) a differentiation precision database for a case in which test subjects are divided into two groups by predetermined threshold value scores differentiated by the characteristic quantities, or (2) an estimation precision database for a case in which a score is estimated using the characteristic quantity for a predetermined score value is prepared for each of the tasks on the basis of the test subject group task database.

The method of the present invention quantifies the severity of a subject's neurodegenerative disorder. The subject answers a questionnaire which results in a patient-reported outcome dataset. Benchmark tests are carried out by the subject performing one or more tasks resulting in a task result dataset. Continuous sensors collect data resulting in a sensor dataset. Short assessment tests of the subject are conducted resulting in a short assessment dataset. The patient-reported outcome dataset, task result dataset, sensor dataset, and short assessment dataset are aggregated into an output dataset that includes non-motor outcome measures and motor outcome measures. A single score is generated that quantifies the severity of a neurodegenerative disorder of the subject based on the output dataset.

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.

Described herein are methods, devices, systems, software, and platforms used to evaluate individuals such as children for behavioral disorders, developmental delays, and neurologic impairments. Specifically, described herein are methods, devices, systems, software, and platforms that are used to analyze video and/or audio recordings of individuals having one or more behavioral disorders, developmental delays, and neurologic impairments.

The present invention is directed to a method for determining a paroxysmal slow waves event (PSWE) so as to determine blood-brain barrier dysfunction (BBBD) or increased risk of developing a neurological disease or disorder in a subject.

Embodiments of the instant invention relate to applying motor learning to promote remyelination following demyelination in a subject having a condition or disease. In certain embodiments, applying motor learning alone or in combination with vagus nerve stimulation (VNS) induces the production of new and preserves surviving oligodendrocytes. In accordance with certain embodiments of the disclosure, motor learning, when properly timed, enhances oligodendrogenesis after injury and recruits mature oligodendrocytes to participate in remyelination through the generation of new myelin sheaths. In other aspects of the disclosure, VNS paired with motor learning enhances remyelination following demyelination.

An apparatus for determining a non-apparent attribute of an object having a sensor portion with which the object makes contact and to which the object applies pressure. The apparatus has a computer in communication with the sensor portion that receives signals from the sensor portion corresponding to the contact and pressure applied to the sensor portion, and determines from the signals the non-apparent attribute. The apparatus has an output in communication with the computer that identifies the non-apparent attribute determined by the computer. A method for determining a non-apparent attribute of an object.

Apparatus, systems, and methods are provided for generating a quantified indicator of cognitive skills in an individual. In certain configurations, the apparatus, systems, and methods can be implemented for enhancing cognitive skills in an individual.

Embodiments of the present systems and methods may provide a non-invasive system to measure and integrate behavioral and cognitive features enabling early detection and progression tracking of degenerative disease. For example, a method of detecting neurodegenerative disease may comprise measuring functioning of at least one of the motor system, cognitive function, and brain activity of a subject during everyday life and analyzing the gathered at least one motor system data, cognitive function data, and brain activity data of the subject.

A method for obtaining a near-infrared spectroscopy (fNIRS) cerebral signal in a subject includes: placing a near-infrared emitter and respective proximal and distal near-infrared detectors on a skin of a head of a subject; during a baseline recording stage with the subject in resting-state, record near-infrared signals, the recorded signals including a baseline deep-signal and a baseline shallow-signal; calculate a scaling factor between amplitudes of the baseline deep-signal and the baseline shallow-signal at a given task-frequency; with the subject undergoing a cyclic cerebral stimulation at the task-frequency during a stimulation recording stage, record near-infrared signals, the recorded signals comprising a shallow-signal and a deep-signal; and applying the scaling factor to the shallow-signal, calculating the cerebral signal at the task-frequency as a difference between the deep-signal and the scaled shallow-signal, at the task-frequency.