DBS Probe Trajectory Selection Using Electrophysiology Scoring
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Solution Overview
Problem
Existing deep brain stimulation (DBS) therapies are limited by the reliance on anatomical structures like the subthalamic nucleus (STN) for trajectory selection, failing to capture all electrophysiological indicators of neurological conditions, necessitating a method for selecting effective trajectories independent of such structures.
Innovation Solution
A system utilizing microelectrodes to gather electrophysiology data along multiple brain trajectories, correlating it with clinically-determined tissue activation volumes from previous successful treatments, to generate a score for selecting the optimal trajectory for stimulation probe insertion based on neural activity patterns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If trajectory selection is constrained within anatomical structures like the subthalamic nucleus (STN), then the procedure follows traditional anatomical guidance, but it fails to capture all electrophysiological indicators of neurological conditions
Solution Approach 1:
The system changes the selection criteria from purely anatomical parameters (STN boundaries) to electrophysiological parameters (neural activity patterns, LFP signals). By measuring and analyzing electrical activity along multiple trajectories, the system identifies optimal paths based on physiological response rather than fixed anatomical constraints, thereby capturing all electrophysiological indicators while maintaining procedural guidance.
2Reliability
If multiple trajectories are evaluated using electrophysiology data and algorithms, then the optimal trajectory can be selected for improved treatment outcomes, but the system complexity increases
Solution Approach 1:
The system performs preliminary electrophysiological measurements and algorithmic scoring along multiple candidate trajectories before the actual treatment trajectory is selected. By extending microelectrodes along several paths and evaluating their electrophysiological characteristics in advance, the system identifies the optimal trajectory beforehand, ensuring treatment effectiveness while managing complexity through structured pre-planning.
Solution Approach 2:
The system introduces an intermediary computational layer that processes electrophysiological data and generates trajectory scores. The algorithm acts as a mediator between raw neural signals and clinical decision-making, translating complex electrophysiological measurements into actionable trajectory recommendations, thereby managing system complexity through structured data processing.
3Measurement precision
If electrophysiology data is collected at multiple increments along each trajectory, then accurate neural activity measurement is achieved, but the time and resources required increase
Solution Approach 1:
The system collects electrophysiological data at multiple increments along each trajectory, using more measurement points than the minimum single-point approach. This excessive sampling ensures accurate characterization of neural activity patterns along the entire trajectory path, capturing variations in electrical activity that would be missed with fewer measurements, thereby achieving high measurement precision.
Data Source
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AI summary
A system and method for selecting a trajectory within the brain for placement of a stimulation probe for deep brain stimulation (DBS) treatment of an individual afflicted with an illness, condition, or disorder. Electrophysiological data attained within the brain of the individual is utilized with clinically determined regions of stimulation (for example, volume of tissue activated (VTA)) of other similarly-afflicted individuals having a positive operative outcome to assign an objective score, independent of anatomical structure (for example, not constrained within the subthalamic nucleus (STN)) to facilitate selection among prospective trajectories for placement of the stimulation probe during DBS.