Abstract: Methods and devices for vector-based targeting of the human central thalamus (CT) to guide deep brain stimulation (DBS) are disclosed. In some examples, electrode(s) each with a plurality contacts are provided. A three-dimensional orientation of a dominant axis of a central lateral nucleus dorsal tegmental tract medial component (CL/DTTm) fiber bundle of a human subject is determined. The contacts of the electrode(s) are positioned in the subject's CT fibers in substantial alignment with the three-dimensional orientation. An electrical stimulus is applied to the contacts to selectively activate the CT fibers. The positioning and the applying are carried out to maximize activation of a central lateral nucleus and medial dorsal tegmental tract fiber pathway in the subject and to minimize activation of a centromedian-parafascicularis fiber pathway in the subject. Methods and devices for surgical planning involving for vector-based targeting of the human CT to guide DBS are also disclosed.

Abstract: A method for selective activation of central thalamus fibers in a subject is disclosed. The method involves providing one or more electrodes each with one or more contacts. The one or more electrodes are positioned in the subject’s central thalamus fibers. An electrical stimulus is applied to the positioned one or more electrodes to selectively activate the central thalamus fibers of the subject. The positioning and applying are carried out to maximize central lateral nucleus and medial dorsal tegmental tract fiber pathway activation in the subject and to minimize central median parafascicularis fiber pathway activation in the subject. Methods, devices, and computer readable media for surgical planning involving selective activation of central thalamus fibers in a subject are also disclosed.

Abstract: A system and method for providing a volume of activation (VOA) of a stimulation electrode leadwire may include a processor that calculates a VOA for each of a plurality of sets of parameter settings of the leadwire, stores in a database each of the calculated VOAs in association with the respective set of parameter settings for which it was calculated, performs a curve fitting on threshold values determined for a plurality of waveforms to obtain an equation, obtains a set of parameter settings of the leadwire for a stimulation, and determines a VOA for the obtained set of parameter settings based on the stored VOAs, for example, using the equation.

Abstract: Methods of selective neuromodulation in a live mammalian subject, such as a human patient. The method comprises applying an electrical signal to a target site in the nervous system, such as the brain, where the electrical signal comprises a series of pulses. The pulses includes a waveform shape that is more energy-efficient as compared to a corresponding rectangular waveform. Non-limiting examples of such energy-efficient waveforms include linear increasing, linear decreasing, exponential increasing, exponential decreasing, and Gaussian waveforms. The parameters for the energy-efficient waveform are chosen to selectively activate neural tissue on the basis of axonal diameter.

Abstract: Methods of selective neuromodulation in a live mammalian subject, such as a human patient. The method comprises applying an electrical signal to a target site in the nervous system, such as the brain, where the electrical signal comprises a series of pulses. The pulses includes a waveform shape that is more energy-efficient as compared to a corresponding rectangular waveform. Non-limiting examples of such energy-efficient waveforms include linear increasing, linear decreasing, exponential increasing, exponential decreasing, and Gaussian waveforms. The parameters for the energy-efficient waveform are chosen to selectively activate neural tissue on the basis of axonal diameter.

Abstract: A system and method for providing a volume of activation (VOA) of a stimulation electrode leadwire may include a processor that calculates a VOA for each of a plurality of sets of parameter settings of the leadwire, stores in a database each of the calculated VOAs in association with the respective set of parameter settings for which it was calculated, performs a curve fitting on threshold values determined for a plurality of waveforms to obtain an equation, obtains a set of parameter settings of the leadwire for a stimulation, and determines a VOA for the obtained set of parameter settings based on the stored VOAs, for example, using the equation.

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. 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, 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: A computer-assisted method can include defining a target volume of tissue activation to achieve a desired therapeutic effect for an identified anatomic region. At least one parameter can be computed for an electrode design as a function of the defined target volume of tissue activation. The computed at least one parameter can be stored in memory for the electrode design, which parameter can be utilized to construct an electrode.

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.