Neuromodulation System with Patient-Specific Interaction Model
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Solution Overview
Problem
Current deep brain stimulation (DBS) systems require trial-and-error for optimal parameter selection, and existing visualization tools are limited in efficiently determining the best stimulation sites and parameters for individual patients, leading to suboptimal therapeutic outcomes.
Innovation Solution
A system with a leadwire apparatus connected to an implanted pulse generator, featuring multiple electrodes that can both stimulate and sense, generates a patient-specific interaction model by associating stimulation sites with their corresponding parameters and affected neuromodulation sites, using tractography analysis and sensor data to dynamically adjust stimulation settings for personalized therapy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If trial-and-error method is used for parameter selection, then system complexity is reduced, but therapeutic efficacy is suboptimal
Solution Approach 1:
The system performs preliminary tractography analysis and generates patient-specific interaction models before the actual stimulation therapy. This pre-computation of neural pathway relationships and stimulation effects allows the system to predict optimal parameters in advance, reducing the need for extensive trial-and-error during treatment while maintaining high therapeutic efficacy.
Solution Approach 2:
The system incorporates feedback mechanisms where sensor data from during-therapy testing is used to refine and update the patient-specific interaction model. This iterative feedback loop allows the system to learn from actual patient responses and adjust stimulation parameters more effectively, improving therapeutic outcomes without requiring complete redesign of the stimulation approach.
2Measurement precision
If multiple electrodes and sensing sites are used, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The system divides the complex stimulation task into discrete, manageable segments by using multiple independently controllable electrodes along the leadwire. Each electrode can be stimulated individually or in combination, allowing precise control of stimulation parameters at different locations. This segmentation enables the system to achieve high measurement precision through systematic evaluation of individual electrode responses while managing complexity through modular control.
Solution Approach 2:
The leadwire system is designed with multi-functionality, where the same electrodes serve both as stimulation targets and as sensing sites for detecting neural responses. This universal design allows the system to perform both stimulation and measurement functions using the same hardware infrastructure, improving parameter selection precision without proportionally increasing device complexity.
3Adaptability or versatility
If patient-specific interaction model is generated, then adaptability is improved, but loss of time increases
Solution Approach 1:
The patient-specific interaction model is generated in advance using preoperative imaging data and tractography analysis before the actual therapy begins. This preliminary model construction allows the system to be highly adaptable to individual patient anatomy and neural pathways without requiring time-consuming real-time adjustments during treatment, as the foundational model is already in place to guide parameter selection.
Solution Approach 2:
The interaction model is designed to be dynamic and updatable rather than static. The system can incorporate new sensor data and patient response information to refine the model over time, allowing it to adapt to individual patients progressively. This dynamic approach balances the need for high personalization with time constraints, as the model evolves to better fit the patient's specific response characteristics without requiring complete redesign.
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 enables precise and adaptive neuromodulation, improving therapeutic efficacy by predicting optimal stimulation sites and parameters, reducing neural activity in targeted regions, and enhancing treatment outcomes for conditions like Parkinson's disease and epilepsy.
Implementation Method 1
the electrodes, or at least a portion thereof, are controllable for emitting electrical pulses to stimulate an anatomical region
Implementation Method 2
the electrodes, or at least a portion thereof, are usable as sensors for sensing effects of the stimulation
Data Source
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AI summary
A computer-implemented method determines, responsive to a user input, electrode neuromodulation settings estimated by a computer processor to, when applied to an implanted electrode leadwire, produce a volume of tissue activation that at least partially encompasses at least one target anatomical stimulation candidate region which is mapped by the computer processor to, and is at a distance from, a selected neuromodulation effect region.