Target Volume of Tissue Activation for Deep Brain Stimulation
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Solution Overview
Problem
Current deep brain stimulation technologies face challenges in predicting the mechanisms and effects of neurostimulation at the neuronal level, making it difficult to determine optimal target volumes for stimulation in the brain for effective therapeutic outcomes.
Innovation Solution
A system and method that utilize a computer-based approach to determine a target volume of tissue activation (VTA) by analyzing patient-specific data, incorporating anatomical, electric field, and neural activation models, and optimizing electrode design and stimulation parameters to achieve a desired therapeutic effect.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If electrical stimulation is applied to treat neurological disorders, then therapeutic effects are achieved, but the mechanisms and effects at the neuronal level remain difficult to predict
Solution Approach 1:
The system performs preliminary computational modeling and simulation of neural activation patterns before actual stimulation is applied. By predicting the volume of tissue activation (VTA) and neural response in advance, the system allows clinicians to optimize stimulation parameters and electrode design to achieve desired therapeutic effects while avoiding unwanted side effects.
Solution Approach 2:
The system incorporates feedback loops where actual stimulation outcomes are compared with predicted outcomes from computational models. This feedback is used to refine and update the models, improving prediction accuracy for future stimulation sessions and enabling adaptive optimization of treatment parameters.
2Measurement precision
If computational modeling and simulation are used to predict neural activation, then prediction accuracy is improved, but system complexity increases
Solution Approach 1:
The computational platform is designed as a universal system that can perform multiple functions: modeling electric field distribution, predicting neural activation patterns, optimizing electrode design, and evaluating different stimulation scenarios. This multi-functionality consolidates what would otherwise require multiple separate tools into a single integrated system.
Solution Approach 2:
The system introduces computational models and simulation algorithms as intermediaries between the physical stimulation device and the biological tissue. These computational intermediaries translate electrical stimulation parameters into predicted neural responses, enabling accurate prediction without directly measuring complex biological processes.
3Reliability
If electrode design and stimulation parameters are optimized, then therapeutic outcomes are improved, but the process becomes more complex
Solution Approach 1:
The optimization process is designed as a dynamic, iterative procedure where electrode design parameters and stimulation settings are continuously adjusted based on computational predictions. The system allows clinicians to explore multiple design options and parameter combinations, with the computational model providing real-time feedback on expected outcomes for each configuration.
Data Source
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AI summary
One embodiment provides a computer- implemented method that includes storing a volume of tissue activation (VTA) data structure that is derived from analysis of a plurality of patients. Patient data is received for a given patient, the patient data representing an assessment of a patient condition. The VTA data structure is evaluated relative to the patient data to determine a target VTA for achieving a desired therapeutic effect for the given patient.