A system and a method is provided for assessing motion of a biological tissue of a subject including one or more superficial biological layers and a targeted biological layer. An optical perturbation is introduced within the one or more superficial biological layers but not within the targeted biological layer. A set of optical signal data is acquired preceding, during, or following the optical perturbation and, using the set of optical signal data, a set of optical characteristics is determined that is representative of light transiting the biological layers. Using the set of optical characteristics and a model of the biological layers, a target optical signal consistent with a target biological layer is separated and a movement of the desired biological tissue is determined using the target optical signal.

A system and method for measuring a functional lateralization of a subject brain is provided. The method includes providing a set of functional magnetic resonance imaging (fMRI) data acquired during a resting state of a subject, and selecting a plurality of seed voxels associated with locations in hemispheres of a brain of the subject. The method also includes determining a degree of within-hemisphere connectivity for each seed voxel using the fMRI data, determining a degree of cross-hemisphere connectivity for each seed voxel using the fMRI data, and computing an autonomy index for each seed voxel using the degree of within-hemisphere connectivity and the degree of cross-hemisphere connectivity, wherein the autonomy index is indicative of a connectivity asymmetry between the hemispheres. The method further includes generating a report indicative of a specialization profile determined for a region of interest in the brain of the subject.

A system for computer-aided triage can include a router, a remote computing system, and a client application. A method for computer-aided triage can include determining a parameter associated with a data packet, determining a treatment option based on the parameter, and transmitting information to a device associated with a second point of care.

Multiscale brain electrodes can be used for spatiotemporal mapping, probing, and therapeutic modulation of the human brain. The applications for such functional mapping and electrical stimulation modulation span, for example, neurological and psychiatric diseases, and brain rehabilitation.

[Object] An object is to provide a brain activity analyzing method for realizing a biomarker using brain function imaging for neurological/mental disorders. [Solution] From data of resting-state functional connectivity MRI obtained by measuring groups of healthy subjects and patients, a correlation matrix of degrees of activities among prescribed brain regions is derived. By a sparse canonical correlation analysis (SCCA) of attributes of subjects and the correlation matrix, elements of correlation matrix that connect to canonical variables corresponding only to the diagnosis label are extracted. By sparse logistic regression (SLR) during Leave-One-Out Cross Validation of a first sum-set of elements of correlation matrix obtained as a result of feature extraction by a sparse regularized canonical correlation analysis, a second sum-set of elements of the correlation matrix is extracted. By discrimination analysis using sparse logistic regression on the second sum-set, a discriminator is generated.

An echo planar imaging technique in which a quadruple echo gradient and spin echo echo-planar imaging pulse sequence is utilized. The pulse train includes generation of two echo trains between an excitation pulse (90) and a refocusing pulse (180) to achieve two gradient echo images (also called T2*-weighted images); with one echo train directly after the 180 pulse, leading to asymmetric spin echo images (T2′-weighted images); and a last echo train afterward that generates spin echo images (T2-weighted). The technique has a number of advantages over existing techniques with regard to voxel size, mapping relative oxygen extraction, determining permeability, determining relative cerebral blood volume, vessel parameters (diameter, density, size, arterial/venous, etc.), stroke imaging, imaging perfusion, fMRI imaging, and additional benefits.

A brain volume (BV) calculation method includes obtaining a first set of medical images at a first time point and a second set of medical images at a second time point, each set including at least two medical images. First and second algorithms may be used to calculate, respectively, first and third BV values at the first time point based on two or more images from the first set of medical images, and second and fourth BV values at the second time point based on two or more images from the second set of medical images. A mathematical weight may be applied to at least one of the first to fourth BV values. The first and third BV values may be averaged, and the second and fourth BV values may be averaged to determine overall BV values at the first and second time points, respectively.

Apparatuses and methods are provided to predict or diagnose an ischemic event, such as a stroke or a transient ischemic attack (TIA). A machine-learning model such as a neural network is generated that allows for recognition of an ECG consistent with an ischemic event. A system is trained and used to process a recording of ECG data from a patient to generate a prediction indicating a likelihood that the patient will experience a stroke. In other examples, a system is trained and used to process a recording of ECG data from a patient and detect an ischemic event for the patient who did not appear to have such an ischemic event.

In some embodiments, the present invention provides an exemplary inventive system that includes: an apparatus to record: individual's brain electrical activity, a physiological parameter of the individual, and iii) an environmental parameter; a computer processor configured to perform: obtaining a recording of the electrical signal data; projecting the obtained recording of electrical signal data onto a pre-determined ordering of a denoised optimal set wavelet packet atoms to obtain a set of projections; normalizing the particular set of projections of the individual using a pre-determined set of normalization factors to form a set of normalized projections; determining a personalized mental state of the individual by assigning a brain state; determining a relationship between: the physiological parameter, the environmental parameter, and the personalized mental state; generating an output, including: a visual indication, representative of the personalized mental state, and) a feedback output configured to affect the personalized mental state of the individual.

System for performing fully automatic brain tumor and tumor-aware cortex reconstructions upon receiving multi-modal MRI data (T1, T1c, T2, T2-Flair). The system outputs imaging which delineates distinctions between tumors (including tumor edema, and tumor active core), from white matter and gray matter surfaces. In cases where existing MRI model data is insufficient then the model is trained on-the-fly for tumor segmentation and classification. A tumor-aware cortex segmentation that is adaptive to the presence of the tumor is performed using labels, from which the system reconstructs and visualizes both tumor and cortical surfaces for diagnostic and surgical guidance. The technology has been validated using a publicly-available challenge dataset.