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: This document discusses brain stimulation models, systems, devices, and methods, such as for deep brain stimulation (DBS) or other electrical stimulation. A model computes a volume of influence region for a simulated electrical stimulation using certain stimulation parameters, such as electrode contact selection or location. The model uses a non-uniform tissue conductivity. This accurately represents brain tissue, which has highly directionally conductive neuron pathways yielding a non-homogeneous and anisotropic tissue medium. In one example, a second difference of an electric potential distribution is used to define a volume of activation (VOA) or similar volume of influence. In another example, a neuron or axon model is used to calculate the volume of influence without computing the second difference of the electric potential distribution.

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. A model computes a volume of influence region for a simulated electrical stimulation using certain stimulation parameters, such as amplitude, pulsewidth, frequency, pulse morphology, electrode contact selection or location, return path electrode selection, pulse polarity, etc. The model uses a non-uniform tissue conductivity. This accurately represents brain tissue, which has highly directionally conductive neuron pathways yielding a non-homogeneous and anisotropic tissue medium. In one example, the non-uniform tissue conductivity is obtained from diffusion tensor imaging (DTI) data. In one example, a second difference of an electric potential distribution is used to define a volume of activation (VOA) or similar volume of influence.

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 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. A model computes a volume of influence region for a simulated electrical stimulation using certain stimulation parameters, such as amplitude, pulsewidth, frequency, pulse morphology, electrode contact selection or location, return path electrode selection, pulse polarity, etc. The model uses a non-uniform tissue conductivity. This accurately represents brain tissue, which has highly directionally conductive neuron pathways yielding a non-homogeneous and anisotropic tissue medium. In one example, the non-uniform tissue conductivity is obtained from diffusion tensor imaging (DTI) data. In one example, a second difference of an electric potential distribution is used to define a volume of activation (VOA) or similar volume of influence.

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.

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. A model computes a volume of influence region for a simulated electrical stimulation using certain stimulation parameters, such as amplitude, pulsewidth, frequency, pulse morphology, electrode contact selection or location, return path electrode selection, pulse polarity, etc. The model uses a non-uniform tissue conductivity. This accurately represents brain tissue, which has highly directionally conductive neuron pathways yielding a non-homogeneous and anisotropic tissue medium. In one example, the non-uniform tissue conductivity is obtained from diffusion tensor imaging (DTI) data. In one example, a second difference of an electric potential distribution is used to define a volume of activation (VOA) or similar volume of influence.

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.

Abstract: Asymmetric charge-balanced stimulation waveforms are defined and used for CNS stimulation with selective stimulation of either neuronal cell bodies or axon fibers of passage in favor of the other. A pre-pulse is followed by an opposite polarity stimulation pulse, with the pre-pulse being relatively low-amplitude and long-duration as compared to the stimulation pulse. The pre-pulse and stimulation pulse are charge-balanced to prevent tissue damage and electrode corrosion. The polarity of the pre-pulse and stimulation pulse control the selectivity of stimulation with respect to neuronal cell bodies versus axon fibers of passage. The waveform used in the stimulation method optionally includes a zero-amplitude phase having a duration of 0 to 500 microseconds.

Abstract: Asymmetric charge-balanced stimulation waveforms are defined and used for CNS stimulation with selective stimulation of either neuronal cell bodies or axon fibers of passage in favor of the other. A pre-pulse is followed by an opposite polarity stimulation pulse, with the pre-pulse being relatively low-amplitude and long-duration as compared to the stimulation pulse. The pre-pulse and stimulation pulse are charge-balanced to prevent tissue damage and electrode corrosion. The polarity of the pre-pulse and stimulation pulse control the selectivity of stimulation with respect to neuronal cell bodies versus axon fibers of passage. The waveform used in the stimulation method optionally includes a zero-amplitude phase having a duration of 0 to 500 microseconds.