Model-Based Control for Parkinson's Disease
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Solution Overview
Problem
Current treatments for Parkinson's disease, including deep brain stimulation, lack predictive control mechanisms and rely on empirical feedback, failing to effectively manage the complex neural dynamics underlying the disease.
Innovation Solution
The integration of fundamental computational models of brain networks with nonlinear control filters, such as ensemble Kalman filters, to synchronize with and control the brain's neural circuits, allowing for predictive and energy-efficient modulation of symptoms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If empirical feedback methods are used to control Parkinson's disease symptoms, then treatment can be provided, but predictive control capability is lacking and treatment efficacy is limited
Solution Approach 1:
The computational model predicts future brain states before they occur, allowing the control system to prepare appropriate stimulation signals in advance. This predictive capability enables the system to anticipate pathological oscillations and apply corrective stimulation proactively rather than reactively, improving treatment efficacy.
Solution Approach 2:
The system continuously monitors brain activity through recorded signals, compares actual states with model predictions, and adjusts stimulation parameters accordingly. This closed-loop feedback mechanism ensures the treatment adapts to changing brain dynamics, maintaining optimal control efficacy.
2Reliability
If continuous deep brain stimulation is applied to manage Parkinson's disease, then symptoms can be controlled, but energy consumption is high
Solution Approach 1:
Instead of continuous stimulation, the system applies periodic pulsed stimulation synchronized with the predicted neural oscillation cycles. This periodic action maintains therapeutic effect while significantly reducing average power consumption compared to continuous stimulation.
Solution Approach 2:
The stimulation parameters (amplitude, frequency, pulse width) are dynamically adjusted based on real-time model predictions of brain state. This dynamic adaptation allows the system to provide optimal symptom control only when and where needed, minimizing unnecessary energy expenditure.
3Measurement precision
If complex computational models are integrated with control filters to achieve predictive control, then treatment precision is improved, but system complexity increases
Solution Approach 1:
The computational model acts as an intermediary layer between the recorded neural signals and the control filter. It transforms raw, complex neural data into simplified predicted state variables that the control filter can process efficiently, maintaining prediction precision while managing system complexity.
Solution Approach 2:
The integrated system performs multiple functions (signal recording, state prediction, control signal generation, and feedback adjustment) through a unified model-based framework. This multi-functionality reduces the need for separate specialized components, managing overall system complexity while enhancing prediction and control precision.
4Loss of energy
If model-based predictive control is implemented, then energy efficiency is improved, but real-time computational requirements increase
Solution Approach 1:
The computational model is pre-trained offline on patient-specific data to learn the unique neural dynamics. This preliminary action enables the online prediction phase to use simplified, patient-specific model parameters, reducing real-time computational requirements while maintaining prediction accuracy and energy efficiency.
Solution Approach 2:
The system transitions from complex, general neural network models to simplified, patient-specific parameterized models after initial training. This parameter change reduces the computational burden during real-time operation, enabling energy-efficient processing with acceptable prediction precision.
Data Source
AI summary
The present invention relates to a novel means to use a fundamental model of the parts of the brain that are dysfunctional in Parkinson's disease, and related dynamical diseases of the brain, as part of a feedback control system to modulate the signs and symptoms of disease. Fundamental computational models that embody our knowledge of the anatomy, neurons, and dynamics of the parts of the brain we wish to control, and use those models to reconstruct what is inaccessible to our measurements. Through emulation the controller synchronizes to the parts of the brain we wish to observe and track. By passing simultaneous control pulses to both the model controller, as well as the brain, we control both the model and the brain. The detailed framework to embed fundamental models of the brain within a control scheme to control symptoms of Parkinsons and related dynamical diseases of the brain are disclosed.