Neural Tissue Activation Volume Estimation Using Parametric Neural Networks
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Solution Overview
Problem
The mechanisms and effects of neurostimulation, such as deep brain stimulation, remain difficult to predict at the neuronal level, making it challenging to model and simulate effectively for therapeutic applications.
Innovation Solution
An artificial neural network (ANN) is programmed to estimate the volume of tissue activation by using input parameters like electrode configuration and stimulation parameters, allowing for the calculation of the volume of activation without requiring retraining for different types of electrodes, and employing parametric equations to model the activated region.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional simulations or clinical studies are performed to determine tissue activation volumes, then accuracy of prediction is improved, but time consumption and resource requirements increase significantly
Solution Approach 1:
The patent pre-trains the artificial neural network with extensive simulation data and clinical study results during an offline preparation phase. This preliminary training allows the model to learn complex relationships between electrode parameters and tissue activation volumes, so that during actual clinical use, predictions can be made rapidly without requiring time-consuming new simulations or studies.
Solution Approach 2:
The patent creates a virtual model (artificial neural network) that copies and simulates the complex biological and physical processes of tissue activation. Instead of performing actual physical simulations or clinical studies for each new electrode configuration, the system uses the trained neural network model to generate predictions, effectively replacing time-consuming real-world experiments with rapid computational inference.
2Reliability
If extensive simulations or clinical studies are conducted for each electrode configuration, then reliability of results is improved, but device complexity and computational resources increase
Solution Approach 1:
The patent transforms the complex simulation problem into a parameter-based prediction model. The artificial neural network is trained to map electrode parameters (voltage, current, frequency, pulse width, electrode geometry) directly to tissue activation volume parameters. This parameter transformation approach maintains reliability by capturing the essential relationships while reducing computational complexity during actual use.
Solution Approach 2:
The patent replaces complex mechanical and biological simulation systems with an artificial neural network model. Instead of performing detailed finite element analyses or cellular-level simulations, the system uses the trained neural network to predict tissue activation volumes, substituting complex computational mechanics with a more efficient machine learning approach that maintains predictive reliability.
3Measurement precision
If the system is trained for each specific electrode type, then prediction accuracy for that electrode type is improved, but adaptability to new electrode types decreases
Solution Approach 1:
The patent designs the artificial neural network with a universal architecture that can handle multiple electrode types through a unified parameter space. The model accepts various electrode configurations (different geometries, materials, arrangements) as input parameters and predicts tissue activation volumes for all types using the same trained model, eliminating the need for separate training processes for each electrode type while maintaining prediction accuracy.
Data Source
AI summary
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.