Predicting Deep Brain Stimulation Volume of Influence
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Solution Overview
Problem
Current deep brain stimulation (DBS) techniques face challenges in understanding the therapeutic mechanisms and optimizing stimulation parameters, electrode geometries, and locations due to the complexity of the three-dimensional tissue medium, leading to inefficient and costly trial-and-error processes with potential side effects.
Innovation Solution
The development of three-dimensional finite element models incorporating diffusion tensor imaging data to estimate tissue conductivity and predict the volume of tissue affected by stimulation, allowing for the adjustment of electrode location and stimulation parameters to achieve desired therapeutic effects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If trial-and-error method is used to select DBS parameters and electrode locations, then therapeutic effects can be achieved, but the process becomes time-consuming and costly with potential side effects
Solution Approach 1:
The patent applies preliminary action by performing computational modeling and simulation of the volume of influence before actual DBS implantation. The system calculates and displays the predicted volume of tissue affected by stimulation parameters in advance, allowing clinicians to optimize electrode location and stimulation settings virtually before surgery, thereby reducing trial-and-error procedures and surgical time.
Solution Approach 2:
The patent enables skipping the lengthy trial-and-error process by using preoperative computational models to predict therapeutic outcomes. The volume of influence calculation allows clinicians to jump directly to optimized parameter selection without extensive intraoperative testing, effectively rushing through the optimization phase that would otherwise require significant time.
2Reliability
If trial-and-error method is used to select DBS parameters and electrode locations, then therapeutic effects can be achieved, but costs increase due to extended procedure time and potential complications
Solution Approach 1:
The system performs preliminary computational analysis to predict the volume of influence and optimize parameters before surgery. This preoperative planning reduces the need for expensive intraoperative adjustments and minimizes complications that would increase overall procedure costs.
Solution Approach 2:
The patent replaces the mechanical trial-and-error process with computational modeling. Instead of physically testing different electrode locations and parameters during surgery, the system uses software-based volume of influence calculations to predict outcomes, substituting computational analysis for costly physical experimentation.
3Reliability
If high frequency deep brain stimulation is applied to treat neurological disorders, then therapeutic benefits are achieved, but understanding of therapeutic mechanisms remains elusive
Solution Approach 1:
The patent introduces computational modeling as an intermediary between DBS application and mechanism understanding. The volume of influence calculations provide quantitative data about which tissue regions are affected by stimulation, serving as a mediator that bridges the gap between clinical application and mechanistic understanding by revealing spatial patterns of neural activation.
Solution Approach 2:
The system uses visual display of the volume of influence to represent different levels of tissue activation. By color-coding or visually distinguishing regions of varying stimulation intensity, the system makes invisible electrical fields and their effects visible, providing information about therapeutic mechanisms that would otherwise be inaccessible.
4Ease of operation
If the three-dimensional tissue medium complexity is not accounted for, then DBS procedures are simpler to perform, but accuracy of predicting affected tissue volume decreases
Solution Approach 1:
The patent replaces complex manual calculations of tissue conductivity and field distribution with automated computational modeling. The system handles the three-dimensional tissue complexity through software algorithms that account for anisotropic and inhomogeneous tissue properties, maintaining procedural simplicity while achieving high prediction accuracy.
Solution Approach 2:
The system incorporates multiple tissue parameters including conductivity, anisotropy, and inhomogeneity into the volume of influence calculations. By changing from simple geometric models to parameter-rich computational models, the system accurately captures three-dimensional tissue complexity without increasing operational burden on clinicians.
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 provides quantitative predictions of neural activation and helps avoid side effects by accurately modeling the electric field and tissue response, enabling more effective and targeted DBS therapy.
Implementation Method 1
the finite element model is solved for an electric potential distribution generated by the stimulating electrode in the tissue medium
Data Source
AI summary
This document discusses, among other things, brain stimulation models, systems, devices, and methods, such as for deep brain stimulation (DBS) or other electrical stimulation. In an example, volumetric imaging data representing an anatomical volume of a brain of a patient can be obtained and transformed to brain atlas data. A patient-specific brain atlas can be created using the inverse of the transformation to map the brain atlas data onto the volumetric imaging data and a volume of influence can be calculated using the patient-specific brain atlas. In certain examples, the volume of influence can include a predicted volume of tissue affected by an electrical stimulation delivered by an electrode at a corresponding at least one candidate electrode target location.