Disclosed is a method of identifying dementia by at least one processor of a device. The method includes performing a first task that causes a first object to be displayed on a first region of a screen displayed on a user terminal; and when a preset condition is satisfied, performing a second task that causes at least one object, which induces the user's gaze, to be displayed instead of the first object on the screen of the user terminal.

The present invention relates to a digital content-based method for providing therapeutics information, the method comprising: a first step of performing stimulation on a brain of a user to obtain fNIRS (functional near-infrared spectroscopy) data of the user; a second step of extracting a first brain activation area from a plurality of brain areas of the user using the obtained fNIRS data; a third step of determining a first brain state of the user, based on the first brain activation area; a fourth step of providing the user with a content determined corresponding to the first brain state determined in the third step under an XR (Extended Reality) environment; a fifth step in which the user performs a mission corresponding to the content; a sixth step of extracting a second brain activation area from the plurality of brain areas with reference to the fNIRS data of the user following performing the mission; and a seventh step of determining a second brain state of the user, based on the second brain activation area; an eighth step of determining information related to amelioration of the brain state of the user.

Disclosed is a method of identifying dementia by at least one processor of a device according to some embodiments of the present disclosure. The method may include obtaining a plurality of result data of a user obtained by performing a plurality of tests through a user terminal, calculating a score value by inputting the plurality of result data to a dementia identification model, and determining whether the user has dementia based on whether the score value is greater than a first threshold value.

Provided herein are methods for using hippocampal volume and/or cortical thickness in human subjects as a predictor of Alzheimer's disease (AD) and treating subjects who are determined to be at risk for AD or a decline in cognitive impairment.

The present invention relates to a method and system for identifying treatable and remediable factors of Dementia and aging cognitive changes, to provide recommendations for aiding in the diagnosis of dementia or predementia symptoms in a subject. According to an embodiment of the invention, the method comprising: receiving data relative to medical history and examinations, processing said received data by applying an algorithm(s) relative to the Intensive Neuropsychogeriatric Evaluation, Treatment and Prevention (INETAP) method, and verifying whether said processed data is sufficient for indicating of advanced Dementia Potential Remediable Conditions (PRCs), and outputting data for aiding in the diagnosis of one of the following: dementia PRCs, pre-dementia PRCs, no dementia/pre-dementia, or Dementia without treatment horizon.

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.

There is provided a method for determining dementia risk factors by a server using deep learning. In this instance, the method for determining dementia risk factors includes acquiring biometric information from each subject corresponding to a first control group through a wearable device, acquiring measurement information for each subject corresponding to the first control group, deriving a first dementia risk factor based on the biometric information and the measurement information for each subject, and deriving a second dementia risk factor related to the first dementia risk factor via deep learning performed based on the biometric information related to the first dementia risk factor and control group information.

In accordance with some embodiments, systems, methods, and media for predicting the conversion time of Mild Cognitive Impairment (MCI) to Alzheimer’s Disease (AD) in a patient are provided. In some embodiments, a system include a memory and a processor coupled to the memory. The processor is configured to: receive a plurality of risk factor indications and a plurality of interaction indications of a patient. Each interaction indication is an indication of interaction between two risk factor indications of the plurality of risk factor indications. The processor is further configured to obtain a trained machine learning model; apply the plurality of risk factor indications and the plurality of interaction indications to the trained machine learning model; and output a result based on the trained machine learning model.

A portable wireless neuromonitoring device can be used to diagnose and/or treat conditions of the brain and vision system. The device includes a sensor unit mountable on the head of a human subject and capable of recording signals from the brain in EEG and/or EFEG (electric field encephalography) mode, and the device can be used for simultaneous stimulus display and recording with latency of less than 1 millisecond. The device also includes electrodes that allow rapid set-up and measurement with low impedance contact with the scalp. The device can also be used in conjunction with virtual reality or alternate reality environments.

Systems and methods for detecting complex networks in MRI image data in accordance with embodiments of the invention are illustrated. One embodiment includes an image processing system, including a processor, a display device connected to the processor, an image capture device connected to the processor, and a memory connected to the processor, the memory containing an image processing application, wherein the image processing application directs the processor to obtain a time-series sequence of image data from the image capture device, identify complex networks within the time-series sequence of image data, and provide the identified complex networks using the display device.