Systems and a computer implemented method for classifying a brain status of a subject, from a neural activity response of the subject to an induced TMS stimulation; the method comprising: constructing a machine learning classifier (MLC) configured to classify a subjects brain status; training the MLC using a training set, the training set comprising pairs of training output-classification vectors and their corresponding training input vectors, all extracted from a database of subjects with known brain status classifications; and applying the trained MLC on an input vector comprising features extracted from a tested-subjects brain neural activity response to the induced TMS stimulation, to obtain an output classification vector for the tested-subjects brain status.

Provided herein are, inter alia, methods for identifying subjects suffering from depression that will respond to treatment with an antidepressant.

Techniques are provided for determining a qualitative, quantitative and/or categorical assessment of one or more users and/or images with respect to one or more populations. The eye movement data of the user may be obtained with respect to each image of the one or more images displayed for a period of time. One or more memory performance measures and/or one or more salience performance measures may be determined using the eye movement data with respect to the one or more regions of the one or more images for one or more of predetermined time ranges of the period of time. The quantitative, qualitative and/or categorical assessment of the user and/or images presented may be determined with respect to one or more populations, using the one or more memory performance measures and/or one or more salience performance measures.

There is provided a medical information acquisition device including an information acquisition unit that acquires functional change information obtained on the basis of a reference image and a past image acquired by capturing images of the same subject at a reference time and a past time closer to the past than the reference time, respectively, using a trained model.

A system for treating a patient comprises a stimulator for stimulating brain tissue, a controller for setting stimulation parameters and a diagnostic tool for measuring patient parameters and producing diagnostic data. The stimulation parameters comprise test stimulation parameters and treatment stimulation parameters. The stimulator delivers test stimulation energy to the brain tissue based on at least one test stimulation parameter and delivers treatment stimulation energy to the brain tissue based on at least one treatment stimulation parameter. One or more treatment stimulator parameters are determined based on the diagnostic data produced by the diagnostic tool The system is constructed and arranged to treat a neurological disease or a neurological disorder. Methods of treating a neurological disease or neurological disorder are also provided.

A patient therapy method and program product are provided for communicating with a patient monitor to obtain data from a patient engaged in physical therapy. A library of musical features includes a plurality of coordinated musical tracks associated with respective functional therapeutic outcomes. The functional therapeutic outcomes include gait characteristics comprising at least at strike, cadence, stride length deviations. Based on a selected functional therapeutic outcome, musical tracks are selected and mixed together at a desired tempo. The tracks are adjusted based on changes in patient data.

In a computer-implemented method of assessing performance of a cognitive function test performed by a subject, the cognitive function test requires the subject to reproduce on a testing device a reference image that comprises a plurality of segments to produce a reproduced image. The testing device comprises a user interface on which the subject reproduces the reference image. The method comprises: obtaining data for the reproduced image, the data comprising a first time series of inputs to the user interface of the testing device by the subject in reproducing the reference image, wherein each input in the first time series of inputs is a spatial representation of the position of that input on the user interface of the testing device; associating each input in the first time series of with a respective segment in the reference image; based on the segment associated with each input, forming a second time series that indicates an order in which segments in the reproduced image were produced by the subject; analyzing the second time series to determine a number of transitions between segments in the second time series; determining at least one performance measure for the subject in performing the cognitive function test based on the determined number of transitions, wherein the at least one performance measure is based on the determined number of transitions and a predetermined minimum number of transitions required to produce the reproduced image; and outputting a signal representing the determined at least one performance measure.

Machine learning systems and methods for multiscale Alzheimer's dementia recognition through spontaneous speech are provided. The system retrieves one or more audio samples and processes the one or more audio samples to extract acoustic features from audio samples. The system further processes the one or more audio samples to extract linguistic features from the audio samples. Machine learning is performed on the extracted acoustic and linguistic features, and the system indicates a likelihood of Alzheimer's disease based on output of machine learning performed on the extracted acoustic and linguistic features.

The present invention provides a system for detecting whether a subject having a target suffers from an Alzheimer's disease. The system includes a multi-harmonic generation microscope and a processor. The multi-harmonic generation microscope images the target by a second harmonic generation (SHG) and a third harmonic generation (THG) to respectively obtain an SHG image and a THG image. The processor couples to the multi-harmonic generation microscope and configures to add a first color to the SHG image and a second color to the THG image to respectively obtain a color-added SHG image and a color-added THG image, and combine the color-added SHG image and the color-added THG image to obtain a combined image, wherein the combined image is used to determine whether the subject suffers from the Alzheimer's disease.

Disclosed is a dementia patient training system using virtual reality (VR).