Adaptive Deep Brain Stimulation Using Evoked Potential Feedback
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Solution Overview
Problem
Existing deep brain stimulation (DBS) systems face challenges in optimizing electrode placement and stimulation parameters to effectively treat neurological disorders while minimizing side effects on non-target neural areas, particularly due to non-discriminative electrical field application and potential cognitive impairments.
Innovation Solution
An implantable pulse generator (IPG) system with integrated sensing and control circuitry adjusts stimulation based on evoked potentials (EPs) to maintain network activation within a predetermined range, using models like the Kuramoto model to optimize electrode configuration and minimize power usage, and incorporates sensors for side-effect monitoring.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If electrical stimulation is applied to treat neurological disorders, then therapeutic efficacy is improved, but side effects on non-target neural areas increase
Solution Approach 1:
The patent applies local quality by using multiple independently controllable electrode contacts that can be selectively activated. Each contact can be stimulated with different parameters (amplitude, pulse width, frequency) to create localized stimulation patterns. This allows the system to target specific neural pathways while avoiding adjacent non-target areas, thereby improving therapeutic efficacy while reducing side effects.
Solution Approach 2:
The patent segments the stimulation function by dividing the electrode array into multiple independently controllable contacts. The control system can activate specific segments (contacts) based on the recorded evoked potentials, allowing selective stimulation of target neural elements while leaving non-target areas unstimulated. This segmentation enables precise spatial control over the stimulation field.
2Reliability
If stimulation parameters are increased to improve treatment effectiveness, then therapeutic benefit is enhanced, but energy consumption increases
Solution Approach 1:
The patent implements feedback control by recording evoked potentials in response to stimulation and using these recordings to adjust subsequent stimulation parameters. The system monitors the neural response and automatically modulates the stimulation amplitude and other parameters to maintain optimal therapeutic effect while minimizing energy consumption. This closed-loop feedback prevents unnecessary high-amplitude stimulation when the therapeutic threshold is already met.
Solution Approach 2:
The patent applies dynamics by making stimulation parameters variable and adaptive rather than fixed. The system dynamically adjusts amplitude, pulse width, and frequency based on real-time evoked potential measurements. This dynamic adaptation allows the system to use minimal necessary energy while maintaining effective treatment, avoiding constant high-energy stimulation.
3Measurement precision
If evoked potentials are recorded to optimize stimulation, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent merges the stimulation and sensing functions into a single integrated system. The same electrode contacts used for delivering stimulation are also used for recording evoked potentials. This combination eliminates the need for separate sensing electrodes and reduces overall device complexity while maintaining precise measurement capability. The control circuitry handles both stimulation delivery and evoked potential recording through unified hardware.
Data Source
AI summary
Methods and systems for providing stimulation to a patient's brain using one or more electrode leads implanted in the patient's brain are described. The methods and systems use evoked potentials (EPs) and other indicators of therapeutic effectiveness/side effects to provide closed-loop control of the stimulation. Some embodiments involve recording EPs and using one or more features of the EPs to model how the stimulation activates networks within the patient's brain. A control algorithm can be used to maintain the network activation within a predetermined ranges.