Preferred embodiments of the present invention provide an apparatus and method for providing dementia diagnosis assistance information. In the present invention, an MRI brain image and a PET brain image of a subject for diagnosis are received, the MRI brain image is divided into a plurality of regions and registered with the PET brain image. Then, a standardized uptake value ratio (SUVR) of each divided region is obtained from the registered image, and for each divided region, a standard value indicating the degree of proximity of a SUVR of the subject to the average value of the SUVR of Alzheimer's group and the average value of the SUVR of the normal group is obtained. Then, the standard value of the SUVR of the subject is displayed together with the average value and the range of the standard deviation of each control group for each of the divided regions as a graph.

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating a mental health prediction for a patient. One of the methods includes obtaining brain data captured by one or more sensors characterizing a brain of a patient; determining a network graph from the brain data, wherein: a node of the network graph corresponds to a parcellation in the brain of the patient, and a subset of nodes of the network graph corresponds to a particular functional area of the brain; generating, for each of a plurality of nodes in the network graph, a measure of centrality of the node; generating, for the particular functional area of the brain and using the generated measures of centrality, a health score representing a measure of health of the functional area of the brain; generating a mental health prediction for the patient using the health score.

There is provided a method of generating deep brain stimulation signals, the method comprising receiving a plurality of sensor signals from a corresponding plurality of sensors on or in a subject, and using the received sensor signals to generate a plurality of stimulation signals for application at a corresponding plurality of target sites in the brain of the subject. There is further provided a method of generating stimulation signals, the method comprising receiving a plurality of sensor signals from a corresponding plurality of sensors on or in a subject, and using the received sensor signals to generate a plurality of stimulation signals for application at a corresponding plurality of target sites on or in the subject using a model of the response of neurons in the subject to the stimulation signals that models neural tissue as a plurality of coupled populations of neurons.

A method and system for monitoring impairment indicators. The method comprises, during a first time window, measuring a first movement signal related to movement of a person with a movement sensor associated with the person, and measuring a first environmental signal with an environmental sensor. The method further comprises electronically storing at least one numerical descriptor derived from the first movement signal and the first environmental signal as reference data for the person. The method further includes, during a second time window, measuring a second movement signal related to movement of the person with the movement sensor and measuring a second environmental signal with the environmental sensor; and comparing at least one numerical descriptor derived from the second movement signal and the second environmental signal to the reference data to identify an impairment indicator.

A gravity perception function inspection apparatus based on virtual/augmented reality and multiple bio-signal sensors, includes a head-wearable display device worn on an examinee's head and reproducing an inspection screen in which a target object is displayed in a center of the inspection screen, the bio-signal measuring device including an eye tracker that detects a pupil size of an examinee and a posture measuring device that infers a posture value of the examinee by using a plurality of inertial measurement sensors dispersedly arranged on a body of the examinee and obtains and outputs a position control value corresponding to the posture value of the examinee, a human interface device that obtains and outputs a position control value manually input by the examinee, and a control device that generates and provides the inspection screen.

Systems, methods, and computer program products are provided for determining one or more biomarker and/or health condition of a target patient. In various embodiments, a method is provided where a plurality of health data of the target patient and/or a plurality of first order features determined from the plurality of health data of the target patient are received as input to a pre-trained artificial neural network. The plurality of health data is derived from a plurality of modalities. A plurality of latent variables based on the plurality of health data and plurality of first order features are received from an intermediate layer of the pre-trained artificial neural network. The plurality of latent variables are provided to a pre-trained learning system. The pre-trained learning system is trained to receive as input the plurality of latent variables and output one or more biomarker and/or health condition of the target patient.

Disclosed are systems and methods for a computerized framework that detects medical conditions within patients and dynamically effectuates a treatment via a recursively trained machine learning (ML) algorithm. The disclosed framework is configured for controlling computerized equipment by analyzing data associated with a patient, determining underlying conditions of the patient, then automatically causing such equipment to output electronic stimuli that can address the medical condition(s) detected. The framework can determine a correlation between a patient's attributes, electronic data of a condition of a patient and a medical disorder, and cause a device (e.g., a neuromodulation device) to treat and monitor improvements of the condition and disorder. The treatments can be dynamically adjusted, controlled and monitored so that electronic stimulus patterns respective to a patient's conditions can be rendered.

Methods for the diagnosis, prognosis, patient stratification and prediction of pharmacological response in patients afflicted by psychiatric disorders based on electroretinography (ERG) parameters are described.

In an aspect of the invention, a method of identifying sensory inputs affecting working memory load of an individual is provided. The method comprises monitoring (S101) working memory load of the individual using a sensor device, detecting (S102) an increase in the working memory load of the individual, and identifying (S103), in response to the detected increase, at least one sensory input affecting the working memory load of the individual.

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.