An object of the present invention is to perform a more accurate and detailed diagnosis of a brain disease. A diagnosis server 10 of the brain disease diagnosis system 1 for diagnosing a brain disease of an examined person includes: an acquiring unit 11 for acquiring a brain image of the examined person so as to obtain an acquired image; a region setting unit 12 for setting a plurality of regions in the acquired image; an individual index value calculating unit 13 for calculating an individual index value based on a pixel value of the acquired image, in each of the plurality of regions; a whole index value calculating unit 14 for calculating a whole index value by weighting the individual index value of each of the plurality of regions; a diagnosis unit 15 for diagnosing the brain disease of the examined person based on the whole index value; and an output unit 16 for outputting information indicating a diagnosis outcome.

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.

Aspects of the disclosure relate to mobile-optimized software tools that may be deployed and used to measure spatial working memory across diverse clinical trial populations. For example, some aspects describe computational optimization and participatory design of a novel spatial working memory task for clinical trials relating to autism spectrum disorders (ASD) or other neurological conditions (e.g., Alzheimer's disease). Software tools as described herein may be used for measuring treatment effects on spatial working memory and/or provide treatments to improve a patient's spatial working memory. Digital biomarkers may be generated for each patient based on the patient's spatial working memory.

A method of visual cognitive training is performed at a device with a display. The method includes sequentially displaying a plurality of visual stimuli in a first region of a subject's field of view. While sequentially displaying the plurality of visual stimuli, the device moves the first region periodically along a predefined path within the subject's field of view. The device prompts the subject to respond to a task associated with the sequential display of the plurality of visual stimuli. The device receives a response to the task associated with the sequential display of the plurality of visual stimuli and records information corresponding to the subject's response to the task associated with the sequential display of the plurality of visual stimuli.

The present invention relates to a method of preventing or reducing one or more adverse post-anaesthetic effects in a patient, comprising treating the patient with low level laser therapy (LLLT) prior to anaesthesia and/or during anaesthesia and/or following anaesthesia, and to a method for assessing whether a patient is at risk of suffering from adverse post-anaesthetic effects, comprising determining whether the patient contains a polymorphism in a gene encoding one of the following proteins: (a) protein associated with assembly and disassembly of microtubules; (b)ion channel protein; (c) membrane stability protein; (d) Melanocortin systems proteins; (e) proteins associated with Alzheimer's and/or Parkinson's vulnerability; or (f) proteins associated with migraine.

An embodiment of the invention provides a method for displaying a text box on a display screen of an electronic device, including determining a state of a user with an input device having a camera, a keyboard, and/or a mouse. A text box setting on the electronic device is modified with a processor connected to the input device based on the state of the user, the modifying of the text box setting includes modifying an amount of visual information in the text box, modifying an amount of audible information played with the text box, and/or modifying an amount of time required to display the text box. The text box is displayed on the display screen of the electronic device when a pointer is within a threshold degree of proximity to an item on the display screen for the amount of time required to display the text box.

An apparatus and method for sensing, recording and analyzing data representative events of memory lapses and function uses a wearable device (e.g., wrist, armband, pendant) having sensors to detect user gestures and vital signs for transmission and analysis by a computation unit to predict the onset of cognitive impairment related diseases.

The invention relates to in vitro method for quantitating the antibodies specific for High mobility group box I (HMGB1) contained in a sample, in particular a serum sample or a cerebrospinal fluid sample obtained from a patient, and the use of this method in the prognostic and/or diagnosis of neurological disorders. These methods are in particular applicable to the monitoring of the human immunodeficiency virus (HIV) infection of a subject who is known to be infected with HIV and in the prognostic and/or diagnostic of the state of progression of Acquired immune deficiency syndrome (AIDS) or the state of progression toward AIDS, in particular the state of progression or the state of progression toward neurological disorders associated with AIDS. Finally, the invention is also about method to determine the immune deficiency or level of immune activation of a patient, in particular a HIV-infected patient.

In one embodiment, the presence of anomalous material within tissue is detected by scanning a patient using magnetic resonance imaging (MRI) to obtain MRI data, identifying individual voxels of the MRI data, identifying multiple parameters of each voxel, and determining as to each voxel based upon the identified parameters the likelihood of tissue represented by the voxel containing anomalous material.

A system and methodology combines virtual reality (VR) with a plurality of machine learning analyses, and uses majority voting to detect dementia and the diseases under dementia. The accuracy of the classification in the Medical Visuospatial Dementia test is very high.