Stochastic-Noise DBS Waveform for Brain Network Dynamics Identification
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Solution Overview
Problem
Current closed-loop deep brain stimulation (DBS) systems for neurological disorders lack a model-based design, relying on simple on-off control strategies and lacking accurate estimation of input-output dynamics, which limits their efficacy and requires manual adjustment by experts, leading to suboptimal treatment outcomes.
Innovation Solution
A computational framework is developed to identify brain network input-output dynamics using linear state-space models and a novel DBS waveform modulated by binary noise (BN) or generalized binary noise (GBN), enabling real-time adjustment of DBS parameters to optimize treatment effects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If simple on-off control schemes are used in closed-loop DBS systems, then the system complexity is reduced, but the control precision and treatment effectiveness deteriorate
Solution Approach 1:
The patent implements dynamic control by continuously adjusting DBS parameters (amplitude, frequency, pulse width) based on real-time neural feedback signals, transitioning from static on-off control to adaptive parameter modulation that responds to changing brain states
Solution Approach 2:
The system changes multiple stimulation parameters simultaneously based on feedback thresholds, including amplitude modulation, frequency adjustment, and pulse width variation, allowing precise control of neural activity patterns without requiring complex system architecture
2Device complexity
If manual trial-and-error adjustment of DBS parameters is performed, then the system simplicity is maintained, but the treatment effectiveness and symptom control deteriorate
Solution Approach 1:
The patent implements closed-loop feedback control where neural signals (LFP or spiking) are continuously monitored, processed through detection algorithms with configurable thresholds, and used to automatically adjust stimulation parameters, creating a self-regulating system that adapts to symptom dynamics
Solution Approach 2:
The system performs self-adjustment of DBS parameters based on intrinsic neural feedback, eliminating the need for continuous expert intervention and enabling automatic optimization of treatment parameters according to real-time brain state changes
3Reliability
If DBS parameters are adjusted frequently to match symptom dynamics, then the treatment effectiveness improves, but the device complexity and control difficulty increase
Solution Approach 1:
The system employs periodic sampling of neural feedback signals at clinically appropriate intervals, adjusting stimulation parameters in discrete steps based on threshold crossings, thereby achieving frequent adaptation without requiring continuous complex processing
Solution Approach 2:
The control system dynamically adapts stimulation parameters in response to changing neural activity patterns, implementing time-varying control that matches the temporal dynamics of Parkinsonian symptoms while maintaining manageable system complexity through efficient feedback algorithms
Data Source
AI summary
Time-efficient identification of a brain network input-output (IO) dynamics model for brain stimulation includes generating an input stochastic-switched noise-modulated waveform characterized by at least one parameter modulated according to a stochastic-switched noise sequence, inputting the input stochastic-switched noise-modulated waveform to a clinical brain-response system, recording one or more time-correlated outputs of the clinical brain-response system responsive to the input stochastic-switched noise-modulated waveform, and identifying a brain network IO dynamics model that optimally correlates the input stochastic-switched noise-modulated waveform to the one or more time-delimited outputs of the clinical brain-response system. A desired brain response to an input electrical signal may be obtained using the model, such as by modulating the input electrical signal using a closed-loop control algorithm based on the brain network IO dynamics model.