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: Embodiments discussed herein facilitate implementation of one or more DBS pulsing strategies that maximize synaptic suppression with the minimum number of stimuli. One example embodiment comprises a non-transitory computer-readable medium storing computer-executable instructions that, when executed, cause a processor to perform operations, comprising: applying deep brain stimulation (DBS) electrical stimulation according to a first mode to cause steady-state excitatory post-synaptic current (EPSC) suppression in a set of synapses; and applying DBS electrical stimulation according to a second mode that is different than the first mode to maintain EPSC suppression in the set of synapses.

Abstract: A method for target identification for a deep brain stimulation procedure includes acquiring a set of magnetic resonance fingerprinting (MRF) data for a region of interest in a subject using a MRI system, comparing the set of MRF data to an MRF dictionary to determine at least one parameter for the MRF data for the region of interest, generating a quantitative map of the at least one parameter, segmenting a target area of the region of interest based on the MRF data, generating at least one trajectory for placement of at least one electrode in the target area of the region of interest based on the segmentation of the target area and displaying the quantitative map and the at least one trajectory on a display.

Abstract: Embodiments discussed herein facilitate identification of a target area within a region of a brain for stimulation via one or more BS (Brain Stimulation) electrodes. One example embodiment comprises generating, based on radiological imaging of a region of a brain of a patient and BS electrode lead(s), a patient-specific anatomical model of the region and lead(s); populating the patient-specific anatomical model with neuron models based on associated neuronal densities of at least one of the region or one or more sub-regions of the region; constructing a patient-specific local field potential (LFP) model of the region based on the patient-specific anatomical model and location(s)/orientation(s) of the one or more BS electrode leads; and identifying, via the patient-specific LFP model of the region, a target area within the region for at least one of monitoring or treatment of a medical condition via the one or more BS electrode leads.

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-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: Embodiments discussed herein facilitate implementation of one or more DBS pulsing strategies that maximize synaptic suppression with the minimum number of stimuli. One example embodiment comprises a non-transitory computer-readable medium storing computer-executable instructions that, when executed, cause a processor to perform operations, comprising: applying deep brain stimulation (DBS) electrical stimulation according to a first mode to cause steady-state excitatory post-synaptic current (EPSC) suppression in a set of synapses; and applying DBS electrical stimulation according to a second mode that is different than the first mode to maintain EPSC suppression in the set of synapses.

Abstract: In an example embodiment, this disclosure provides a non-transitive computer-readable medium on which are stored instructions executable by a processor, the instructions which, when executed by the processor, cause the processor to perform a method. The method includes computing, based on test performance data of a user, at least one of a performance variable characterizing cognitive functioning and a performance variable characterizing neuromotor functioning. For each of the at least one performance variable, a respective score can be computed based on the respective performance variable and based on a set of performance metrics. The method can also include outputting, via an output device, the at least one computed score.

Abstract: A method for target identification for a deep brain stimulation procedure includes acquiring a set of magnetic resonance fingerprinting (MRF) data for a region of interest in a subject using a MRI system, comparing the set of MRF data to an MRF dictionary to determine at least one parameter for the MRF data for the region of interest, generating a quantitative map of the at least one parameter, segmenting a target area of the region of interest based on the MRF data, generating at least one trajectory for placement of at least one electrode in the target area of the region of interest based on the segmentation of the target area and displaying the quantitative map and the at least one trajectory on a display.

Abstract: Embodiments discussed herein facilitate identification of a target area within a region of a brain for stimulation via one or more BS (Brain Stimulation) electrodes. One example embodiment comprises generating, based on radiological imaging of a region of a brain of a patient and BS electrode lead(s), a patient-specific anatomical model of the region and lead(s); populating the patient-specific anatomical model with neuron models based on associated neuronal densities of at least one of the region or one or more sub-regions of the region; constructing a patient-specific local field potential (LFP) model of the region based on the patient-specific anatomical model and location(s)/orientation(s) of the one or more BS electrode leads; and identifying, via the patient-specific LFP model of the region, a target area within the region for at least one of monitoring or treatment of a medical condition via the one or more BS electrode leads.

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-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: 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: Example systems and methods concern systems and methods for modeling conduction in a volume. In one embodiment, diffusion eigenvalues of a plurality of diffusion tensors are received. The diffusion tensors are associated with an anatomical structure having heterogeneous and anisotropic tissues. In one embodiment, the diffusion eigenvalues of the diffusion tensors are calculated from imaging data. Then one or more conductance ratios of a conductivity tensor are set based, at least in part, on one or more diffusion ratios of a corresponding diffusion tensor. The conductance eigenvalues of a conductivity tensor can then be calculated based, at least in part, on the one or more conductance ratios of the conductivity tensor. A volume-conductor model of the anatomical structure is generated based, at least in part, on the plurality of calculated conductivity tensors.

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: 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: 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: Example systems and methods concern systems and methods for modeling conduction in a volume. In one embodiment, diffusion eigenvalues of a plurality of diffusion tensors are received. The diffusion tensors are associated with an anatomical structure having heterogeneous and anisotropic tissues. In one embodiment, the diffusion eigenvalues of the diffusion tensors are calculated from imaging data. Then one or more conductance ratios of a conductivity tensor are set based, at least in part, on one or more diffusion ratios of a corresponding diffusion tensor. The conductance eigenvalues of a conductivity tensor can then be calculated based, at least in part, on the one or more conductance ratios of the conductivity tensor. A volume-conductor model of the anatomical structure is generated based, at least in part, on the plurality of calculated conductivity tensors.

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.