Abstract: A method for supporting deep brain stimulation (DBS) programming includes receiving pre-operative magnetic resonance imaging (MRI) data and post-operative computed tomography (CT) data of a patient having an implanted DBS lead and registers the MRI and CT data into a common spatial reference frame. Anatomical and device-related features, including DBS lead placement and one or more neural regions of interest, are extracted from the registered images. A patient-specific electrical conductivity model of brain tissue is generated based on the MRI data. Using the conductivity model and a representation of the DBS lead, electric field distributions corresponding to one or more DBS contact configurations are simulated. The simulated electric field is optimized by determining stimulation parameters that increase electric field exposure within the neural regions of interest while limiting exposure outside those regions. One or more DBS stimulation settings are output for clinical programming of the DBS lead.

Abstract: A method and system for quantifying movement patterns in a subject includes receiving motion data representing voluntary and involuntary movements of the subject from a sensor device worn or held by the subject. A signal indicative of involuntary movement deviations occurring in a plane diverging from a voluntary movement trajectory is extracted and associated to a current position of the voluntary movement trajectory. Peaks within the extracted signal are identified, wherein each peak corresponds to a local maximum of involuntary movement magnitude. A probability density function (PDF) of the identified peaks is estimated and a symptom severity score is computed based on one or more quantiles of the estimated PDF. Feedback is provided to a user via a user interface based on the symptom severity score to adjust a parameter of a therapeutic device.

Abstract: A method of managing a neurological condition includes displaying a test target. An image capture device detects movement of a patient eye with respect to the test target. Data relating to the movement of the patient eye is received as an input to a machine learning model. The data relating to the movement of the patient eye with respect to the test target is correlated to a neurological condition, and an objective output information is produced by the machine learning model based on the correlation of the data to the neurological condition indicating a level of progression of the neurological condition.