Abstract: In some examples, a processor of a system evaluates a therapy program based on a score determined based on a volume of tissue expected to be activated (“VTA”) by therapy delivery according to the therapy program. The score may be determined using a three-dimensional (3D) grid comprising a plurality of voxels that are each assigned a value. The processor may register the VTA with the 3D grid and determine the score for the therapy program based on the values assigned to voxels with which the VTA overlaps. One or more therapy programs for electrical stimulation therapy (e.g., deep brain stimulation) may be selected based on the scores determined based on the 3D grid.

Abstract: In some examples, a processor of a system evaluates a therapy program based on a score determined based on a volume of tissue expected to be activated (“VTA”) by therapy delivery according to the therapy program. The score may be determined using a three-dimensional (3D) grid comprising a plurality of voxels that are each assigned a value. The processor may register the VTA with the 3D grid and determine the score for the therapy program based on the values assigned to voxels with which the VTA overlaps. One or more therapy programs for electrical stimulation therapy (e.g., deep brain stimulation) may be selected based on the scores determined based on the 3D grid.

Abstract: In some examples, a processor of a system evaluates a therapy program based on a score determined based on a volume of tissue expected to be activated (“VTA”) by therapy delivery according to the therapy program. The score may be determined using a three-dimensional (3D) grid comprising a plurality of voxels that are each assigned a value. The processor may register the VTA with the 3D grid and determine the score for the therapy program based on the values assigned to voxels with which the VTA overlaps. One or more therapy programs for electrical stimulation therapy (e.g., deep brain stimulation) may be selected based on the scores determined based on the 3D grid.

Abstract: In some examples, a processor of a system evaluates a therapy program based on a score determined based on a volume of tissue expected to be activated (“VTA”) by therapy delivery according to the therapy program. The score may be determined using a three-dimensional (3D) grid comprising a plurality of voxels that are each assigned a value. The processor may register the VTA with the 3D grid and determine the score for the therapy program based on the values assigned to voxels with which the VTA overlaps. One or more therapy programs for electrical stimulation therapy (e.g., deep brain stimulation) may be selected based on the scores determined based on the 3D grid.

Abstract: Various embodiments concern sensing bioelectrical signals using electrodes along a lead, the electrodes having a spatial configuration along the lead, generating signal data sets, one signal data set being generated for each bioelectrical signal, and graphically representing the electrodes and data representations of the signal data sets on a display. In various embodiments, each data representation indicates a parameter of a respective one of the data sets, the electrodes are graphically represented on the display in a spatial configuration representative of the spatial configuration of the electrodes along the lead, and each data representation is graphically represented on the display in spatial association with at least one electrode through which the bioelectrical signal on which the signal data set is based was sensed. The parameter can be indicative of the relative presence of a biomarker in the bioelectrical signals.

Abstract: A visualization of an area or volume of tissue activated during stimulation according to a set of stimulation parameters is generated. The area or volume of activation is modeled based on a non-uniform grid of model neurons. Select portions of the grid have the model neurons more closely spaced, resulting in finer resolution graphical representation, while less closely spaced model neurons in other portions of the grid may avoid additional computation time.

Abstract: A visualization of an area or volume of tissue activated during stimulation according to a set of stimulation parameters is generated. The area or volume of activation is modeled based on a non-uniform grid of model neurons. Select portions of the grid have the model neurons more closely spaced, resulting in finer resolution graphical representation, while less closely spaced model neurons in other portions of the grid may avoid additional computation time.

Abstract: In one example, the disclosure relates to a method comprising receiving at least one electrical stimulation parameter value defining electrical stimulation for delivery via one or more electrodes to a tissue site, and determining, via one or more processors, a volume of sub-activation threshold impact for tissue from the delivery of the electrical stimulation to the tissue site.

Abstract: A visualization of an area or volume of tissue activated during stimulation according to a set of stimulation parameters is generated. The area or volume of activation is modeled based on a non-uniform grid of model neurons. Select portions of the grid have the model neurons more closely spaced, resulting in finer resolution graphical representation, while less closely spaced model neurons in other portions of the grid may avoid additional computation time.

Abstract: A visualization of an area or volume of tissue activated during stimulation according to a set of stimulation parameters is generated. The area or volume of activation is modeled based on a non-uniform grid of model neurons. Select portions of the grid have the model neurons more closely spaced, resulting in finer resolution graphical representation, while less closely spaced model neurons in other portions of the grid may avoid additional computation time.

Abstract: In one example, the disclosure relates to a method comprising receiving at least one electrical stimulation parameter value defining electrical stimulation for delivery via one or more electrodes to a tissue site, and determining, via one or more processors, a volume of sub-activation threshold impact for tissue from the delivery of the electrical stimulation to the tissue site.

Abstract: Various embodiments concern sensing bioelectrical signals using electrodes along a lead, the electrodes having a spatial configuration along the lead, generating signal data sets, one signal data set being generated for each bioelectrical signal, and graphically representing the electrodes and data representations of the signal data sets on a display. In various embodiments, each data representation indicates a parameter of a respective one of the data sets, the electrodes are graphically represented on the display in a spatial configuration representative of the spatial configuration of the electrodes along the lead, and each data representation is graphically represented on the display in spatial association with at least one electrode through which the bioelectrical signal on which the signal data set is based was sensed. The parameter can be indicative of the relative presence of a biomarker in the bioelectrical signals.