A system for outputting a visual representation of a brain of a patient is configured to receive sensor data representing a behavior of a region of the brain of the patient. The system retrieves mapping data that maps a prediction value to the region. The prediction value is indicative of an effect on a behavior of the patient responsive to a treatment of the region, the mapping data being indexed to a patient identifier. The system receives, responsive to an application of a stimulation to the region, sensor data representing behavior of the region. The system executes a model that updates, based on the sensor data, the prediction value for the region. The system updates, responsive to executing the model, the mapping data by including the updated prediction value in the mapping data. The system outputs a visual representation of the updated mapping data comprising the updated prediction value.

Systems and methods for clinical neuronavigation in accordance with embodiments of the invention are illustrated. One embodiment includes a method for generating a brain stimulation target, including obtaining functional magnetic resonance imaging (fMRI) image data of a patient's brain, were brain imaging data describes neuronal activations within the patient's brain, determining a brain stimulation target by mapping at least one region of interest to the patient's brain, locating functional subregions within the at least one region of interest based on the fMRI image data, determining functional relationships between at least two brain regions of interest, generating parameters for each functional subregion, generating a target quality score for each functional subregion based on the parameters and selecting a brain stimulation target based on its target quality score and the patient's neurological condition.

A method for target identification for a deep brain stimulation procedure includes acquiring a set of magnetic resonance fingerprinting (MRF) data for a region of interest in a subject using a MRI system, comparing the set of MRF data to an MRF dictionary to determine at least one parameter for the MRF data for the region of interest, generating a quantitative map of the at least one parameter, segmenting a target area of the region of interest based on the MRF data, generating at least one trajectory for placement of at least one electrode in the target area of the region of interest based on the segmentation of the target area and displaying the quantitative map and the at least one trajectory on a display.

Systems and methods provide accelerated MR thermometry utilizing prior knowledge about the images to be reconstructed from incomplete k-space data, thereby facilitating accurate reconstruction. In various embodiments, missing data is computationally estimated using a machine learning algorithm such as a neural network, and an image is generated based on iteratively updated estimated missing information.

An imaging-based biomarker that indicates a neurological state of a subject is generated from magnetic resonance imaging data acquired from the subject while the subject was sleeping, or during both a sleep state and wake state. These magnetic resonance imaging data are acquired in such a way so that they simultaneously enable measurement of cerebrospinal fluid (“CSF”) flow and blood-oxygenation-level dependent (“BOLD”) signals. The imaging-based biomarker can be generated based on a correlation between CSF signals and BOLD signals extracted from these magnetic resonance imaging data. Using electroencephalography (“EEG”) data, CSF flow dynamics can also be estimated based on a physiological model in which coherent neural activity is modeled as entraining oscillations in blood volume and CSF.

The present disclosure provides methods and systems for high-resolution functional magnetic resonance imaging (fMRI), including real-time high-resolution functional MRI methods and systems.

Systems and methods obtain functional connectivity data in the whole brain to detect and predict brain disorders. This whole brain data is regionalized and then manipulated to derive functional connectivity data sets that can be used to show measured functional connectivity changes. This whole brain data may also be analyzed to determine changes in functional activity in both increased and decreased neural network connectivity. By identifying and then quantifying the functional connectivity differences between healthy and diseased subjects, a classification for individual subjects can be made.

The present invention relates generally to medical imaging and, more particularly, relates to systems and methods for obtaining magnetic resonance (MR) images of tissues and organs (particularly of the heart) or parts thereof.

Techniques are described for determining cognitive impairment, an example of which includes accessing a set of epochs of magnetoencephalography (MEG) data of responses of a brain of a test patient to a plurality of auditory stimulus events; processing the set of epochs to identify parameter values one or more of which is based on information from the individual epochs without averaging or otherwise collapsing the epoch data. The parameter values are input into a model that is trained based on the parameters to determine whether the test patient is cognitively impaired.

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.