Neural Feedback DBS for Precise Lead Placement and Adaptive Stimulation
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Solution Overview
Problem
Existing deep brain stimulation (DBS) systems face challenges in optimizing electrode placement and stimulation parameters due to non-selective activation of neural elements, leading to potential cognitive impairments and fluctuating therapeutic efficacy due to disease progression and medication states, necessitating improved methods for lead placement and dynamic adjustment of stimulation parameters.
Innovation Solution
An implantable pulse generator with electrode nodes and control circuitry that records and classifies evoked neural responses, adjusting stimulation based on extracted features to optimize lead placement and parameter settings, using techniques such as evoked resonant neural activity (ERNA) detection and classification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If DBS stimulation is applied to treat motor disorders, then therapeutic efficacy is improved, but non-selective activation of neural elements causes cognitive impairments
Solution Approach 1:
The electrode array is segmented into multiple independently controllable contacts arranged along the lead. This allows selective stimulation of specific neural structures (e.g., subthalamic nucleus) while avoiding non-target areas, thereby maintaining motor therapy efficacy while reducing cognitive side effects through precise spatial discrimination.
Solution Approach 2:
Different regions along the electrode lead are assigned different stimulation parameters (amplitude, pulse width, frequency) to create localized effect zones. This enables tailored stimulation that targets motor pathways while sparing cognitive-related neural elements, addressing the selectivity problem.
2Reliability
If stimulation parameters are increased to maintain therapeutic effect, then treatment efficacy is improved, but energy consumption increases
Solution Approach 1:
The system dynamically adjusts stimulation parameters based on real-time neural feedback and patient response. This closed-loop approach allows the device to optimize the minimum effective dose at each moment, preventing energy waste from overly aggressive parameter settings while ensuring therapeutic efficacy is maintained.
Solution Approach 2:
The system varies stimulation parameters (amplitude, frequency, pulse width) based on detected neural responses and clinical goals. By adapting parameters rather than using fixed high settings, the system achieves effective treatment with reduced energy consumption.
3Reliability
If DBS parameters are adjusted to address disease progression, then therapeutic efficacy is maintained, but device complexity increases
Solution Approach 1:
The system incorporates neural feedback sensors that continuously monitor brain activity and provide data to the control algorithm. This automated feedback loop enables the device to adapt to disease progression and medication changes without requiring complex manual reprogramming, maintaining efficacy while managing complexity through intelligent automation.
Solution Approach 2:
The system performs self-adjustment based on predefined algorithms and neural feedback, reducing the need for frequent clinician interventions. This self-service capability simplifies the overall system operation despite the complexity of adapting to progressive disease states.
4Measurement precision
If multiple electrodes are used to improve stimulation precision, then lead placement precision is improved, but device complexity increases
Solution Approach 1:
The electrode lead is divided into multiple segmented contacts that can be independently controlled. This segmentation provides precise spatial control over the stimulation field, allowing accurate targeting of deep brain structures while managing complexity through modular, independent electrode units.
Data Source
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AI summary
Methods and systems for using sensed evoked neural responses for informing aspects of neurostimulation therapy are disclosed. Electrical signals may be recorded during the provision of electrical stimulation to a patient's neural tissue. The electrical signals may be processed and analyzed using one or more classification criteria to determine if the electrical signals contain a neural response of interest. Examples of such neural responses include evoked neural responses that are oscillatory and/or resonant in nature. If the electrical signals include such responses of interest, one or more features may be extracted from the signals and used as biomarkers for informing aspects of neurostimulation therapy, such as directing lead placement, optimizing stimulation parameters, closed-loop feedback control of stimulation, and the like. Various methods and systems described herein are particularly relevant in the context of multi-site stimulation paradigms, such as coordinated reset neuromodulation.