Abstract: Example apparatus and methods concern a next generation clinical decision support system (ngCDSS) for the management of neurological conditions (e.g., advanced Parkinson's disease (PD)). Conventional coupled adjustment of pharmacologic therapy and stimulation parameter settings is a time-consuming process that sometimes yields sub-optimal outcomes. Example ngCDSS use a machine learning trained function that relates deep brain stimulation (DBS) parameters, medication dosages, and patient-specific pre and post operative clinical data with actual treatment outcomes for a population of previously treated patients. Example ngCDSS incorporate image-based patient-specific computer models of the estimated stimulation volume of tissue stimulated by DBS in a multi-linear regression analysis to produce a predictor function that is highly correlated with actual outcomes.

Abstract: This disclosure relates to a system and method to evaluate movement disorders. A movement data aggregator can combine data from a plurality of sensors into an aggregate movement data describing multi-dimensional movement of a handheld device. A calculator to compute an indication of a movement disorder based on the movement vector data and user input data, the user input data being generated in response to physical interaction between the handheld device and a human machine interface of a computing device/machine that is separate from the handheld device. In some examples, the handheld device can communicate with computing device via a wireless interface.

Abstract: Example apparatus and methods plan and control neuro-modulation of a distributed multi-region network in a brain. A location for a deep brain stimulation (DBS) electrode that participates in activating a combination of white matter pathways associated with the network is selected. The location is selected based on a pre-implantation image of the brain and a probabilistic activation model of the network. An initial stimulation parameter for DBS to be applied through the DBS electrode is selected based on a post-implantation image of the brain and the probabilistic activation model of the network. A modified stimulation parameter for DBS being applied through the DBS electrode is selected based on the initial stimulation parameter, a local field potential measured in the distributed multi-region network in response to DBS applied using the initial stimulation parameter, the probabilistic activation model of the distributed multi-region network, and the post-implantation image of the brain.

Abstract: This disclosure relates to a system and method to evaluate movement disorders. A movement data aggregator can combine data from a plurality of sensors into an aggregate movement data describing multi-dimensional movement of a handheld device. A calculator to compute an indication of a movement disorder based on the movement vector data and user input data, the user input data being generated in response to physical interaction between the handheld device and a human machine interface of a computing device/machine that is separate from the handheld device. In some examples, the handheld device can communicate with computing device via a wireless interface.

Abstract: A computer-implemented method for determining the volume of activation of neural tissue. In one embodiment, the method uses one or more parametric equations that define a volume of activation, wherein the parameters for the one or more parametric equations are given as a function of an input vector that includes stimulation parameters. After receiving input data that includes values for the stimulation parameters and defining the input vector using the input data, the input vector is applied to the function to obtain the parameters for the one or more parametric equations. The parametric equation is solved to obtain a calculated volume of activation.

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: 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.

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: The present invention provides an electrical lead configured for stimulation of the thalamus. The electrical lead provides for preferential stimulation of the medial or lateral thalamus as well as specific nuclei within the thalamus, such as the intralaminar nuclei.

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.