Neural Feedback for DBS Lead Placement and Stimulation Tuning
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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 effects, especially in conditions like Parkinson's disease, where brain dynamics and medication states impact treatment efficacy.
Innovation Solution
An implantable pulse generator (IPG) with control circuitry that records electrical signals, classifies them for evoked neural responses, extracts features, and adjusts stimulation based on these responses to optimize lead placement and parameters, using methods such as peak detection and frequency analysis to ensure targeted neural activation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If DBS is applied to treat neurological disorders, then therapeutic benefit is provided, but non-selective activation of neural elements causes cognitive impairments and side effects
Solution Approach 1:
The electrode array is divided into multiple independently controllable electrodes or electrode groups distributed at different locations. This segmentation allows selective activation of specific neural pathways while avoiding non-target areas, thereby providing therapeutic benefit without causing cognitive impairments and side effects.
Solution Approach 2:
Different electrodes or electrode groups are assigned different stimulation parameters (amplitude, frequency, pulse width) tailored to their specific locations and target populations. This local differentiation enables precise control over which neural elements are activated, improving therapeutic efficacy while minimizing harmful effects on cognitive functions.
2Reliability
If stimulation parameters are increased to improve therapeutic effect, then treatment efficacy is enhanced, but energy consumption increases and non-target tissue stimulation occurs
Solution Approach 1:
By dividing the stimulation into multiple discrete electrode activations with optimized parameters for each segment, the system achieves effective treatment with lower overall energy consumption compared to single high-amplitude stimulation of the entire area.
Solution Approach 2:
The system applies stimulation to only the necessary portions of the target area using selective electrode activation, avoiding excessive stimulation of non-target tissues. This partial action approach reduces energy waste while maintaining sufficient therapeutic effect.
3Device complexity
If DBS is applied with fixed parameters, then device complexity is reduced, but adaptability to changing patient needs and brain dynamics is limited
Solution Approach 1:
The stimulation parameters are made dynamically adjustable based on real-time or near-real-time feedback from neural recordings. The system can adapt electrode activation patterns, amplitude, frequency, and pulse width in response to changing brain states, patient response, and disease progression, thereby improving adaptability without requiring overly complex manual reconfiguration.
Solution Approach 2:
Neural signals recorded from the electrode array or separate recording electrodes are processed to provide feedback on stimulation effectiveness and neural state. This feedback loop enables automatic or semi-automatic adjustment of stimulation parameters, enhancing adaptability to changing patient needs while keeping the control system manageable through intelligent algorithms rather than complex hardware.
4Area of stationary object
If multiple electrodes are used to improve stimulation coverage, then treatment coverage is enhanced, but difficulty in detecting and measuring optimal parameters increases
Solution Approach 1:
The multi-electrode array is controlled as independent segments, each capable of being optimized separately. This segmentation allows systematic evaluation of each electrode's contribution to stimulation coverage and therapeutic effect, making parameter optimization more manageable despite the increased number of electrodes.
Solution Approach 2:
Neural feedback signals are used to identify which electrodes are most effective at activating target neural populations and which produce unwanted effects. This feedback enables automated or guided optimization of parameters for each electrode, reducing the manual measurement and tuning burden that would otherwise increase with more electrodes.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enhances the precision of DBS by minimizing non-target tissue stimulation, reducing side effects, and adapting to changing patient needs, thereby improving therapeutic outcomes and patient-specific treatment efficacy.
Implementation Method 1
record an electrical signal at a second one or more of the plurality of electrode nodes
Implementation Method 2
converting the recorded electrical signal from a time domain signal to a frequency domain signal
Implementation Method 3
electrical pulses can be delivered from the neurostimulator to the stimulation electrode(s) to stimulate or activate a volume of tissue
Data Source
Figure 1A~1B
Figure 2A~3
Figure 4~5
AI summary
The present invention discloses a system for facilitating the implantation of an electrode lead in the brain of a patient, wherein the electrode lead comprises a plurality of electrodes, the system comprising control circuitry configured to receive an indication that the lead is positioned at a first position in the patient's brain, use one or more of the electrodes to apply stimulation at one or more stimulation locations upon the lead, record electrical signals at one or more of the plurality of electrodes, classify the recorded electrical signals according to one or more classification criteria to determine if the recorded electrical signals contain an evoked neural response of interest, if the recorded electrical signals contain a neural response of interest, extract one or more features of the neural response of interest, and use the one or more features to determine one or more of (i) whether to move the lead to a new position or (ii) to adjust stimulation parameters based on the evoked responses.