DBS Stimulation Parameter Prediction Using Anatomical Positioning
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Solution Overview
Problem
Current deep brain stimulation (DBS) systems face challenges in determining optimal stimulation parameters, leading to either inadequate treatment or excessive energy consumption, and can result in undesirable side effects due to non-selective activation of neural elements.
Innovation Solution
A method and system that utilize imaging data and accumulated data from previous patients to determine the position of electrode leads relative to anatomical features of the brain, specifically the subthalamic nucleus (STN), and use this information to predict suitable stimulation parameters, minimizing side effects and maximizing therapeutic benefits.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If stimulation amplitude is increased to improve therapeutic effect, then treatment efficacy is improved, but energy consumption increases and side effects increase
Solution Approach 1:
The patent applies local quality by differentiating between target and non-target neural elements through spatially selective stimulation. The system identifies specific anatomical regions (subthalamic nucleus vs. surrounding structures) and directs stimulation locally to the target area, thereby achieving therapeutic effects while minimizing energy consumption and avoiding side effects from non-selective activation.
Solution Approach 2:
The patent replaces traditional trial-and-error mechanical adjustment of stimulation parameters with a computational modeling approach. By using finite element models and image processing to predict stimulation fields, the system substitutes iterative clinical testing with in silico optimization, enabling precise parameter determination without excessive energy consumption or side effects.
2Reliability
If stimulation parameters are optimized for therapeutic effect, then treatment efficacy is improved, but determination of optimal parameters becomes more complex
Solution Approach 1:
The patent applies preliminary action by pre-calculating stimulation field distributions and predicting therapeutic effects before actual stimulation is applied. The system uses image processing and finite element modeling to determine optimal parameters in advance, based on patient-specific anatomy and electrode positioning, thereby simplifying the clinical decision-making process while maintaining high treatment efficacy.
Solution Approach 2:
The patent introduces computational models as intermediaries between the stimulation device and patient anatomy. These models mediate the complexity by translating anatomical data into predicted stimulation fields and therapeutic outcomes, allowing clinicians to optimize parameters without directly managing the complex interactions between electrode geometry, tissue properties, and stimulation physics.
3Area of stationary object
If stimulation field is expanded to cover larger tissue volume, then therapeutic coverage is improved, but activation of non-target neural elements increases causing side effects
Solution Approach 1:
The patent applies local quality by creating spatially selective stimulation fields that concentrate energy within the subthalamic nucleus while sparing surrounding structures. The system uses electrode geometry optimization and stimulation parameter tuning to achieve localized activation, thereby covering the necessary therapeutic area without activating non-target neural elements that would cause side effects.
Solution Approach 2:
The patent applies segmentation by dividing the stimulation field into distinct spatial zones corresponding to different neural structures. The electrode array is configured and controlled to produce segmented stimulation zones, allowing independent optimization of each region's activation. This enables comprehensive coverage of the subthalamic nucleus while excluding adjacent non-target areas, thereby reducing side effects.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach allows for more precise determination of stimulation parameters, reducing the risk of side effects and improving the efficacy of DBS therapy by ensuring targeted stimulation with minimal energy consumption.
Implementation Method 1
receiving imaging data for the subject patient, using the imaging data to determine a position of at least one of the electrode leads with respect to at least one anatomical feature of the subject patient's brain
Implementation Method 2
using the accumulated data and the imaging data to determine stimulation parameters for the subject patient
Data Source
AI summary
Methods and systems for assisting the programming of stimulation parameters for deep brain stimulation (DBS) for a subject patient are described. An accumulated database comprises data from a plurality of historical fitting/programming sessions. The data can include various stimulation parameter sets that were tried for patients in the database; data relating to the position of the electrode lead with respect to anatomical features the patients' brains, for example, with respect to the alignment of their subthalamic nucleus (STN); scores indicating the therapeutic effectiveness of the stimulation parameters and/or stimulation parameters that lead to side effects in the patients; and data relating to stimulation field models (SFMs) for the various stimulation parameter sets. The database may be used to predict stimulation parameter sets that are likely to be therapeutically effective or to cause side effects for the subject patient.