Abstract: This document discusses, among other things, brain stimulation models, systems, devices, and methods, such as for deep brain stimulation (DBS) or other electrical stimulation. In an example, volumetric imaging data representing an anatomical volume of a brain of a patient can be obtained and transformed to brain atlas data. A patient-specific brain atlas can be created using the inverse of the transformation to map the brain atlas data onto the volumetric imaging data and a volume of influence can be calculated using the patient-specific brain atlas. In certain examples, the volume of influence can include a predicted volume of tissue affected by an electrical stimulation delivered by an electrode at a corresponding at least one candidate electrode target location.

Abstract: This document discusses, among other things, brain stimulation models, systems, devices, and methods, such as for deep brain stimulation (DBS) or other electrical stimulation. In an example, volumetric imaging data representing an anatomical volume of a brain of a patient can be obtained and transformed to brain atlas data. A patient-specific brain atlas can be created using the inverse of the transformation to map the brain atlas data onto the volumetric imaging data and a volume of influence can be calculated using the patient-specific brain atlas. In certain examples, the volume of influence can include a predicted volume of tissue affected by an electrical stimulation delivered by an electrode at a corresponding at least one candidate electrode target location.

Abstract: This document discusses, among other things, brain stimulation models, systems, devices, and methods, such as for deep brain stimulation (DBS) or other electrical stimulation. In an example, volumetric imaging data representing an anatomical volume of a brain of a patient can be obtained and transformed to brain atlas data. A patient-specific brain atlas can be created using the inverse of the transformation to map the brain atlas data onto the volumetric imaging data and a volume of influence can be calculated using the patient-specific brain atlas. In certain examples, the volume of influence can include a predicted volume of tissue affected by an electrical stimulation delivered by an electrode at a corresponding at least one candidate electrode target location.

Abstract: This document discusses, among other things, brain stimulation models, systems, devices, and methods, such as for deep brain stimulation (DBS) or other electrical stimulation. In an example, volumetric imaging data representing an anatomical volume of a brain of a patient can be obtained and transformed to brain atlas data. A patient-specific brain atlas can be created using the inverse of the transformation to map the brain atlas data onto the volumetric imaging data and a volume of influence can be calculated using the patient-specific brain atlas. In certain examples, the volume of influence can include a predicted volume of tissue affected by an electrical stimulation delivered by an electrode at a corresponding at least one candidate electrode target location.

Abstract: A system and method may provide for conducting a stimulation of anatomic regions to treat a neuromotor, neurocognitive or neuromotor and neurocognitive disorder, according to which stimulation, motor regions are stimulated, while creep of current to non-motor regions is minimized. Stimulation parameters may be selected based on tests of motor function, tests of cognitive function, and tests of a combination of motor and cognitive functions.

Abstract: This document discusses, among other things, brain stimulation models, systems, devices, and methods, such as for deep brain stimulation (DBS) or other electrical stimulation. In an example, volumetric imaging data representing an anatomical volume of a brain of a patient can be obtained and transformed to brain atlas data. A patient-specific brain atlas can be created using the inverse of the transformation to map the brain atlas data onto the volumetric imaging data and a volume of influence can be calculated using the patient-specific brain atlas. In certain examples, the volume of influence can include a predicted volume of tissue affected by an electrical stimulation delivered by an electrode at a corresponding at least one candidate electrode target location.

Abstract: Example apparatus and methods concern a clinical decision support system for the selection of candidates. A clinical decision support system includes a candidate data logic that receives electronic data that identifies candidate data, including symptom and non-symptom data, for a candidate. The clinical decision support system also includes a scoring logic that generates a score for the candidate based, at least in part, on a set of rules being applied to the candidate data. The set of rules is based on patient data of a set of patients. The clinical decision support system further includes an identification logic that identifies a personalized treatment for the candidate based, at least in part, on the score.

Abstract: A cognitive and/or motor skill testing system and method may include a processor that records respective time information for each of multiple positions of a display device that are traced during administration of a test, that determines speed and/or velocity values based on the recorded time information, and that outputs test result information based on the determined values. The test result information may be based on a comparison between a graphed curve of such values to an ideal curve. The positions may correspond to a path between displayed targets whose size and/or distance therebetween depend on past test performance. The system and/or method may output a change expected with a change to therapy parameters for a patient, where the expected change is determined based on changes which occurred in other patients having similar test results and therapy parameters as those of the patient.

Abstract: A method includes storing in memory preoperative brain atlas data. Neurophysiological data is obtained intra-operatively for a plurality of known sites in a brain of a given patient to provide corresponding intra-operative neurophysiological data for at least a portion of the sites. A constrained optimization is performed to fit the pre-operative brain atlas data based at least in part on the intra-operative neurophysiological data.

Abstract: This document discusses, among other things, brain stimulation models, systems, devices, and methods, such as for deep brain stimulation (DBS) or other electrical stimulation. In an example, a target volume of activation (VOA) can be received, a test VOA can be simulated, and at least one of a target electrode location or parameter can be provide using a relationship between the target VOA and the test VOA.

Abstract: This document discusses, among other things, brain stimulation models, systems, devices, and methods, such as for deep brain stimulation (DBS) or other electrical stimulation. In an example, a target volume of activation (VOA) can be received, a test VOA can be simulated, and at least one of a target electrode location or parameter can be provide using a relationship between the target VOA and the test VOA.

Abstract: This document discusses, among other things, systems and methods for determining volume of activation for deep brain stimulation (“DBS”) using a finite element model (FEM) circuit to determine a FEM of an implanted electrode and a tissue medium in which the electrode is implanted, a Fourier FEM solver circuit to calculate a potential distribution in the tissue medium using information from the FEM circuit and a capacitive component of at least one of the implanted electrode and the tissue medium, and a volume of activation (VOA) circuit to predict a VOA using the potential distribution and a neuron model.

Abstract: An apparatus is disclosed for providing efficient stimulation. As an example, a variable compliance regulator can be connected to supply a compliance voltage to a power supply rail, which compliance voltage can vary dynamically based on a stimulus waveform. A pulse generator can be configured to provide an output waveform to one or more output based on the stimulus waveform for delivery of electrical therapy.

Abstract: This document discusses, among other things, brain stimulation models, systems, devices, and methods, such as for deep brain stimulation (DBS) or other electrical stimulation. In an example, volumetric imaging data representing an anatomical volume of a brain of a patient can be obtained and transformed to brain atlas data. A patient-specific brain atlas can be created using the inverse of the transformation to map the brain atlas data onto the volumetric imaging data and a volume of influence can be calculated using the patient-specific brain atlas. In certain examples, the volume of influence can include a predicted volume of tissue affected by an electrical stimulation delivered by an electrode at a corresponding at least one candidate electrode target location.

Abstract: This document discusses, among other things, brain stimulation models, systems, devices, and methods, such as for deep brain stimulation (DBS) or other electrical stimulation. In an example, volumetric imaging data representing an anatomical volume of a brain of a patient can be obtained and transformed to brain atlas data. A patient-specific brain atlas can be created using the inverse of the transformation to map the brain atlas data onto the volumetric imaging data and a volume of influence can be calculated using the patient-specific brain atlas. In certain examples, the volume of influence can include a predicted volume of tissue affected by an electrical stimulation delivered by an electrode at a corresponding at least one candidate electrode target location.

Abstract: This disclosure relates generally to systems and methods for estimating a volume of tissue directly influenced (VTDI) by optogenetic stimulation. The systems and methods can also enable a user to quantitatively predict the spread of stimulation (e.g., action potential) resulting from optical stimulation in a patient's tissue.

Abstract: One embodiment provides a computer-implemented method that includes storing a volume of tissue activation (VTA) data structure that is derived from analysis of a plurality of patients. Patient data is received for a given patient, the patient data representing an assessment of a patient condition. The VTA data structure is evaluated relative to the patient data to determine a target VTA for achieving a desired therapeutic effect for the given patient.

Abstract: A method for identifying and activating specific axonal pathways for achieving therapeutic benefits during a neural stimulation, such as deep brain stimulation. Clinical data, diffusion tensor tractography, and computer models of patient-specific neurostimulation may be used to identify particular axonal pathways activated by deep brain stimulation and to determine their correlations with specific clinical outcomes.

Abstract: One embodiment provides a computer-implemented method that includes storing a volume of tissue activation (VTA) data structure that is derived from analysis of a plurality of patients. Patient data is received for a given patient, the patient data representing an assessment of a patient condition. The VTA data structure is evaluated relative to the patient data to determine a target VTA for achieving a desired therapeutic effect for the given patient.

Abstract: Systems and methods for blocking nerve impulses use an implanted electrode located on or around a nerve. A specific waveform is used that causes the nerve membrane to become incapable of transmitting an action potential. The membrane is only affected underneath the electrode, and the effect is immediately and completely reversible. The waveform has a low amplitude and can be charge balanced, with a high likelihood of being safe to the nerve for chronic conditions. It is possible to selectively block larger (motor) nerve fibers within a mixed nerve, while allowing sensory information to travel through unaffected nerve fibers.