Brain Stimulation Volume Prediction Using Machine Learning
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Solution Overview
Problem
Deep brain stimulation (DBS) therapy for conditions like Parkinson's disease and tremors is inefficient due to the time-consuming empirical process of determining optimal brain stimulation sites, which often requires multiple sessions and focuses on solitary points rather than volume tissue activation.
Innovation Solution
The use of machine learning techniques to predict a volume of brain tissue for stimulation by mapping high-density electrophysiology data to clinically-validated areas, training models on data from similar patients to accurately determine tissue activation volumes and adjust stimulation parameters for more effective treatment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If empirical examination and trial-and-error attempts are used to determine optimal stimulation sites, then therapeutic effectiveness can be achieved, but the process becomes time-consuming and requires multiple sessions spanning several months
Solution Approach 1:
The patent applies preliminary action by using machine learning models to predict optimal stimulation sites before the actual therapeutic procedure. The model is trained on preoperative data including anatomical scans and electrophysiological recordings, allowing the optimal stimulation volume to be identified in advance, thereby eliminating the need for time-consuming trial-and-error attempts during multiple clinical sessions.
Solution Approach 2:
The machine learning model serves as an intermediary between the available preoperative data and the final stimulation site determination. It processes and integrates complex data from anatomical imaging and electrophysiological recordings to produce predictions about optimal stimulation volumes, acting as a mediator that translates raw data into clinically actionable insights without requiring extensive manual analysis.
2Reliability
If stimulation is focused on solitary points, then the procedure remains simple, but therapeutic effectiveness is limited compared to volume tissue activation
Solution Approach 1:
The patent applies dimensionality change by transitioning from two-dimensional point stimulation to three-dimensional volume stimulation. The machine learning model predicts stimulation volumes rather than single points, and the system implements this by activating multiple electrodes along trajectories to create a three-dimensional stimulation zone, thereby treating neural populations more comprehensively while maintaining procedural feasibility through automated planning.
3Reliability
If multiple distinct neural populations are stimulated for improved therapeutic effect, then treatment effectiveness improves, but the complexity of determining optimal sites increases
Solution Approach 1:
The patent applies segmentation by dividing the brain into distinct neural populations and stimulation volumes based on electrophysiological characteristics. The machine learning model identifies and segments different neural regions along the electrode trajectories, allowing targeted stimulation of multiple distinct populations simultaneously. This segmentation is achieved through automated analysis of electrophysiological data patterns, reducing the manual complexity of determining optimal sites while maintaining the ability to treat multiple neural populations.
Data Source
AI summary
A neural targeting system and method for determining placement of a stimulation probe within the brain for stimulation treatment of an individual afflicted with an illness or disorder. Electrophysiological data attained within the brain of the individual is utilized with clinically-determined stimulation treatment of a plurality of similarly-afflicted individuals to predict a tissue activation volume within the brain of the individual for stimulation treatment.