Closed Loop Deep Brain Stimulation with Neural Feedback
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Solution Overview
Problem
Current deep brain stimulation (DBS) devices lack closed-loop feedback mechanisms, requiring manual adjustments and frequent physician visits to optimize electrical stimulation for neurological disorders such as Parkinson's Disease and epilepsy.
Innovation Solution
A closed-loop system that measures neural signals, uses a Brain Network Model to estimate unmeasurable signals, generates features, calculates feedback indices, and adjusts stimulation signals using a MIMO controller to automatically maintain desired neural activity patterns, reducing the need for manual adjustments and physician visits.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual adjustment of DBS parameters is used, then device complexity is reduced, but treatment efficacy and adaptability deteriorate due to frequent physician visits and inability to respond to real-time neural changes
Solution Approach 1:
The patent implements a closed-loop feedback system where neural signals are continuously sensed, processed through a brain network model to estimate unmeasurable signals, and used to automatically adjust stimulation parameters. The MIMO controller receives feedback from decoders that analyze neural features and adjusts stimulation accordingly, creating a self-regulating system that maintains optimal treatment without manual intervention.
Solution Approach 2:
The system performs self-adjustment of stimulation parameters through automated closed-loop control. The brain network model and MIMO controller enable the device to independently optimize stimulation based on real-time neural feedback, eliminating the need for frequent physician visits and manual parameter adjustments while maintaining treatment efficacy.
2Adaptability or versatility
If continuous closed-loop feedback is implemented, then adaptability and treatment efficacy improve, but power consumption increases
Solution Approach 1:
The system employs periodic sampling of neural signals rather than continuous processing, updating stimulation parameters at optimized intervals based on detected neural state changes. This periodic operation maintains real-time adaptability while significantly reducing computational load and power consumption compared to truly continuous feedback processing.
Solution Approach 2:
The brain network model estimates unmeasurable neural signals from limited sensed data, performing partial processing rather than complete real-time analysis of all neural activity. This approach provides sufficient adaptability for effective treatment while minimizing the computational resources and power required for signal processing.
3Measurement precision
If more neural signals are measured and processed, then treatment precision improves, but device complexity and computational requirements increase
Solution Approach 1:
The brain network model serves as an intermediary that translates limited sensed neural signals into comprehensive estimates of unmeasurable neural activity. This mediator enables the system to infer complete brain state information from partial measurements, achieving high measurement precision without requiring direct sensing of all neural signals, thus reducing device complexity.
Solution Approach 2:
The system creates a virtual copy of the brain's neural network in software, which simulates and predicts neural behavior based on limited input signals. This computational model allows the device to accurately estimate unmeasurable signals and optimize stimulation without requiring complex hardware to directly measure all neural parameters.
Data Source
AI summary
The present disclosure relates generally to systems, methods, and devices for closed loop deep brain stimulation. In particular, a neural signal is measured and provided to software. The software includes a feature generator and a brain network model that takes the neural signal and estimates other neural signals that are not directly measured, and operates as a model of the brain. The software determines a stimulation signal to be sent to stimulating electrodes. Estimated signals by the brain network model are continuously compared to actual signals from the brain. The closed loop feedback system advantageously allows for electrical stimulation levels and patterns to be continuously updated while delivered to a patient.