Deep Brain Stimulation Parameter Optimization 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 inefficient energy consumption, inadequate treatment, and undesirable side effects due to non-selective activation of neural elements.
Innovation Solution
The method involves using imaging data to determine the position of electrode leads relative to anatomical features in the brain, combined with accumulated data from previous patients to suggest optimal stimulation parameters, thereby optimizing the delivery of DBS therapy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If DBS stimulation is applied to treat motor disorders in the subthalamic nucleus, then motor symptoms are improved, but cognitive function deteriorates due to non-selective activation of surrounding neural elements
Solution Approach 1:
The patent applies local quality by differentiating stimulation parameters across different spatial locations within the subthalamic nucleus. By identifying distinct neural elements (motor vs. cognitive pathways) and applying tailored stimulation patterns to each region, the system achieves selective activation that improves motor function while sparing cognitive areas from harmful stimulation effects.
Solution Approach 2:
The patent segments the subthalamic nucleus into functionally distinct regions (motor territory vs. cognitive pathways) and applies independent stimulation control to each segment. This segmentation allows the system to deliver therapy to motor elements while avoiding activation of cognitive neural elements, thereby resolving the contradiction between therapeutic efficacy and cognitive side effects.
2Reliability
If stimulation amplitude is increased to improve therapeutic coverage, then treatment efficacy is improved, but energy consumption increases
Solution Approach 1:
The patent applies local quality by assigning different stimulation amplitudes to different electrode contacts based on their spatial relationship to target neural elements. Contacts closer to motor pathways receive higher amplitude stimulation for effective therapy, while contacts near cognitive areas receive lower or no stimulation, optimizing the balance between treatment efficacy and energy consumption.
Solution Approach 2:
The patent implements dynamic stimulation parameter adjustment by continuously monitoring patient response and automatically modifying stimulation amplitude and pattern. This dynamic control allows the system to deliver maximum effective stimulation when needed while reducing amplitude during periods of sufficient symptom control, thereby optimizing energy consumption while maintaining treatment efficacy.
3Reliability
If multiple electrode contacts are activated simultaneously to broaden stimulation coverage, then therapeutic benefit is improved, but selectivity of neural element activation deteriorates
Solution Approach 1:
The patent applies local quality by assigning unique stimulation patterns and parameters to each electrode contact based on its local anatomical position. This allows simultaneous activation of multiple contacts while maintaining distinct stimulation characteristics for each, thereby broadening overall therapeutic coverage while preserving selectivity for specific neural elements through location-specific parameter optimization.
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 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 for the subject patient.