A method of identifying chronic pain in a patient including using functional magnetic resonance imaging (fMRI), performing a functional brain scan in the NAc (nucleus accumbens) of a patient brain including extracting activity from the NAc. Database information, which includes fMRI data obtained from healthy patients, may be compared to the extracted activity to determine if patient is a chronic pain patient. In patients with chronic pain, the method may be repeated to evaluate the effects of the treatment. A resting state brain scan may be performed initially, and a Fourier transform may be performed to obtain frequency content. The frequency bands of the method may be broken down to extract information in a 0.01-0.027 Hz frequency band.

In one embodiment, a cognitive load driving assistant increases driving safety based on cognitive loads. In operation, the cognitive load driving assistant computes a current cognitive load of a driver based on sensor data. If the current cognitive load exceeds a threshold cognitive load, then the cognitive load driving assistant modifies the driving environment to reduce the cognitive load required to perform the primary driving task and/secondary task(s), such as texting via a cellular phone. The cognitive load driving assistant may modify the driving environment indirectly via sensory feedback to the driver or directly through reducing the complexity of the primary driving task and/or secondary tasks. In particular, if the driver is exhibiting elevated cognitive loads typically associated with distracted driving, then the cognitive load driving assistant modifies the driving environment to allow the driver to devote appropriate mental resources to the primary driving task, thereby increasing driving safety.

Methods of the disclosure may include 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 brain volume (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, second, third, or 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.

Methods, compositions and kits useful in the detection, assessment, diagnosis, prognosis and/or treatment of brain injuries, especially mild traumatic brain injury (mTBI) or concussion, are based upon detection of changes in levels of certain protein biomarkers in a subject undergoing testing, or upon detection of changes in levels of certain protein biomarkers in conjunction with neuroimaging analyses to detect changes in vascular or blood brain barrier (BBB) permeability in the brain, or to detect damage to fiber tracts in the brain, in which changes in biomarker levels correlate with detection of changes in BBB permeability or in brain fiber tract or white matter damage in a subject with brain injury such as mTBI or concussion.

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.

A system or method for measuring human ocular performance can be implemented using an eye sensor, a head orientation sensor, an electronic circuit and a display that presents one of virtual reality information, augmented reality information, or synthetic computer-generated 3-dimensional information. The device is configured for measuring saccades, pursuit tracking during visual pursuit, nystagmus, vergence, eyelid closure, or focused position of the eyes. The eye sensor comprises a video camera that senses vertical movement and horizontal movement of at least one eye. The head orientation sensor senses pitch and yaw in the range of frequencies between 0.01 Hertz and 15 Hertz. The system uses a Fourier transform to generate a vertical gain signal and a horizontal gain signal.

Systems and methods for detecting a traumatic brain injury (TBI). The system comprises a sensing arrangement and a control unit. The sensing arrangement collects eye movement data of a user. The control unit is in communication with the sensing arrangement and configured to compare the eye movement data to one or more baseline measurements of eye movement dynamics. The control unit is also configured to generate an alert indicating the presence or severity of the TBI for delivery to control unit administrator if the eye movement data diverges from one or more of the baseline measurements by a threshold amount.

A method for providing electrical stimulation to a user as a user performs a set of tasks during a time window, the method comprising: providing an electrical stimulation treatment, characterized by a stimulation parameter and a set of portions, to a brain region of the user in association with the time window; for each task of the set of tasks: receiving a signal stream characterizing a neurological state of the user; from the signal stream, identifying a neurological signature characterizing the neurological state associated with the task; and modulating the electrical stimulation treatment provided to the brain region of the user based upon the neurological signature, wherein modulating comprises delivering a portion of the set of portions of the electrical stimulation treatment to the brain region of the user, while maintaining an aggregate amount of the stimulation parameter of the treatment provided during the time window below a maximum limit.

Methods and apparatus for predicting performance of an individual on a task, the method comprises receiving brain imaging data for the individual, wherein the brain imaging data comprises structural brain data, determining values for at least one characteristic of the structural brain data within regions of interest defined for a population of individuals having different performance levels, and predicting based on the determined values, a performance potential of the individual.

An estimation apparatus is configured to be capable of accessing a model storage unit that stores a model built by machine learning, using, as training data, information on a predetermined sound and information relating to a signal source of a signal indicating a brain activity of a first subject presented with the predetermined sound, the model outputting information on a sound estimated to be recognized by the subject. The estimation apparatus acquires a brain wave of a second subject presented with the predetermined sound. The estimation apparatus estimates, based on a mode of the brain wave acquired, a signal source of the brain wave, from among a plurality of regions in a brain of the second subject. The estimation apparatus inputs the information relating to the signal source estimated to the model and acquires information on a sound estimated to be recognized by the second subject.