Deep Brain Stimulation Controller Modulating Hippocampal Biomarkers
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Solution Overview
Problem
Current deep brain stimulation techniques for memory disorders rely on open-loop control, which fails to capture dynamic neurological changes and can impair memory performance, while closed-loop systems are not accurate in modeling input-output dynamics between the posterior cingulate cortex and hippocampal theta and gamma power.
Innovation Solution
A system and method for deep brain stimulation that records electrical signals in the hippocampus using intracranial electrodes, processes them to extract biomarkers related to memory encoding, and uses a controller to modulate the input/output relationship between these biomarkers and electrical stimuli applied to the posterior cingulate cortex, achieving a desired level of theta and gamma power.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If open-loop control is used for deep brain stimulation, then the system is simple to operate with preset parameters, but it fails to capture dynamic neurological changes and can impair memory performance
Solution Approach 1:
The patent implements closed-loop control by recording hippocampal electrical signals, processing them to extract biomarkers, and using these biomarkers to dynamically adjust stimulation parameters. This feedback mechanism allows the system to respond to real-time neurological changes, resolving the contradiction between operational simplicity and memory performance reliability.
Solution Approach 2:
The system transitions from static preset parameters to dynamic parameter adjustment based on real-time biomarker analysis. The stimulation parameters are continuously adapted according to the detected neurological state, enabling the system to capture dynamic changes in brain activity while maintaining operational effectiveness.
2Reliability
If closed-loop stimulation systems are used to capture dynamic brain activity, then memory performance can be improved, but the systems are not accurate in modeling input-output dynamics between PCC stimulation and hippocampal theta and gamma power
Solution Approach 1:
The patent employs advanced signal processing techniques to extract multiple biomarkers from electrical signals, including theta and gamma power, phase information, and cross-frequency coupling metrics. By analyzing multiple parameters simultaneously and using machine learning algorithms to model their relationships, the system achieves accurate input-output dynamics modeling while maintaining effective memory modulation.
3Reliability
If closed-loop systems modulate theta and gamma power to desired target levels, then memory encoding can be enhanced, but the systems lack effectiveness in achieving and maintaining target power levels
Solution Approach 1:
The system continuously monitors hippocampal electrical signals to measure actual theta and gamma power levels, compares these measurements to target levels, and adjusts stimulation parameters accordingly. This real-time feedback control enables precise modulation of oscillatory power to achieve and maintain desired target levels for optimal memory encoding.
Solution Approach 2:
The patent replaces traditional mechanical or fixed-parameter control systems with data-driven machine learning models that analyze complex neural dynamics. These algorithms predict the relationship between stimulation parameters and resulting brain activity, enabling more accurate and adaptive control of theta and gamma power compared to conventional approaches.
Data Source
AI summary
A cystoscopy system with intracranial electrodes operable to be disposed in a hippocampus region of a brain and configured to record electrical signals that include biomarkers related to memory encoding, a neurostimulator configured to stimulate a posterior cingulate cortex (PCC) of the brain, a NARXNN plant model, and a controller configured to receive the electrical signals and modulate an input/output (I/O) relationship between the biomarkers and electrical stimuli applied to a posterior cingulate cortex (PCC) of the brain by controlling the neurostimulator to stimulate the PCC based on the I/O relationship to achieve a desired level of the biomarkers.