A system and method for evaluation, detection, conditioning, and treatment of neurological functioning and conditions which uses data obtained while a person is engaged in simultaneously in a range of primary physical tasks combined with defined types of associative activity, such as listening, reading, speaking, mathematics, logic puzzles, navigation of a virtual environment, recall of past stimuli, etc. The data from the physical and associative activities are combined to generate a composite functioning score visualization indicating the relative functioning of areas aspects of neurological functioning; including those in which deficiencies may be present, which are early indicators of possible neurological conditions. Through algorithmic recommendations combined with expert and user input, a conditioning regimen targeting neurological aspects of interest paired with periodic testing allows the user to track their progress in these areas over time.

Exemplary quantitative susceptibility mapping methods, systems and computer-accessible medium can be provided to generate images of tissue magnetism property from complex magnetic resonance imaging data using the Bayesian inference approach, which minimizes a cost function consisting of a data fidelity term and two regularization terms. The data fidelity term is constructed directly from the complex magnetic resonance imaging data. The first prior is constructed from matching structures or information content in known morphology. The second prior is constructed from a region having an approximately homogenous and known susceptibility value and a characteristic feature on anatomic images. The quantitative susceptibility map can be determined by minimizing the cost function. Thus, according to the exemplary embodiment, system, method and computer-accessible medium can be provided for determining magnetic susceptibility information associated with at least one structure.

A system for non-invasive estimation of dementia progression. The system includes a computer device and a server. The computer device obtains an image of a subject's head from at least one angle. The server and/or computer device includes a plurality of machine learning models configured to: analyze the image for patterns related to dementia symptoms; and estimate progress of said dementia symptoms of said subject based on the analysis. The server and/or computer device pre-processes the image by performing a plurality of pre-processing steps comprising: importing the image; detecting eyes and shape of the head based on a previously trained machine learning model; rotating the image based on detection of the eyes and shape of the head; normalizing the image to one standard.

An information processor according to an aspect of the present disclosure includes a deriving unit that derives a cognitive capacity of a user on a basis of dispersion of reaction times of the user for a plurality of requests.

The present disclosure provides an analyte sensor for use in detecting glutamate. In certain embodiments, a glutamate-responsive active site of a presently disclosed analyte sensor includes a glutamate oxidase and a redox mediator disposed upon a surface of a working electrode. The present disclosure further provides methods for detecting glutamate using the disclosed analyte sensors.

Apparatus and methods are described including driving a coil to apply a magnetic stimulation to a subject's brain, and receiving a magnetic-stimulation-evoked-potential signal from electrodes that are placed in contact with the subject's head. A slope of a late portion of a curve of the magnetic-stimulation-evoked-potential signal is measured. At least partially in response to detecting an indication that the slope of the late portion of the curve of the magnetic-stimulation-evoked-potential signal is below a given threshold, an output is generated that is indicative of the subject being at risk of dementia. Other applications are also described.

Provided is a pathological condition analysis system using voice, the pathological condition analysis system allowing anyone to perform measurement and estimate a disease anywhere, in a short time, non-invasively, and without being known to others. A pathological condition analysis system according to the present invention analyzes a pathological condition of a subject, and includes: an input means that acquires voice data of the subject; an estimation means that estimates a disease of the subject based on a voice feature amount extracted from the voice data acquired by the input means; and a display means that displays an estimation result by the estimation means, in which the voice feature amount includes the intensity of voice.

A system for improving cognitive ability includes a content providing module configured to provide a cognitive ability improving content; an input module configured to receive learning data of the user for the cognitive ability improving content; an analyzing module configured to calculate performance information of each of a plurality of cognitive areas based on the learning data; and a machine learning module configured to store a learning result by machine learning using a preset training data set and determine a characteristic of a cognitive ability improving content to be provided to a user by using the performance information of each of the plurality of cognitive areas and the learning result. According to the system for improving cognitive ability, it is possible to provide a content in the form of a comprehensive program including multi-area intervention important for dementia prevention and perform a customized dementia prevention function by reflecting individual characteristics.

The present invention relates to an apparatus for predicting a region-specific cerebral cortical contraction rate on the basis of a CT image. The present invention may comprise: a deep learning step of a deep learning network learning, by selecting and using CT images of a plurality of patients and segmentation information thereof, a correlation between the CT images and the segmentation information; a feature extraction step of extracting, on the basis of each piece of the segmentation information, semantic feature information corresponding to the CT images; a machine learning step of a machine learning model learning, after a plurality of region-specific cerebral cortical contraction rates corresponding to each piece of the semantic feature information are additionally acquired, a correlation between the semantic feature information and the region-specific cerebral cortical contraction rates; a segmentation step of, when an image to be analyzed is input, acquiring segmentation information corresponding to the image to be analyzed, through the deep learning network; and a prediction step of predicting and reporting, after semantic feature information corresponding to the image to be analyzed is extracted on the basis of the segmentation information, a region-specific cerebral cortical contraction rate corresponding to the semantic feature information through the machine learning model.

An information processing apparatus according to the present invention includes: a calculating unit configured to calculate, based on subject information including a first assessment value representing an assessment of a subject at a predetermined moment for each of a plurality of items set in FIM (Functional Independence Measure), a prediction value representing an assessment of the subject predicted after the predetermined moment; and a control unit configured to set the prediction value as a provisional assessment value representing a provisional assessment of the subject for a predetermined item of the FIM, and control to output so as to display in a correctable manner on an information processing device operated by an assessor.