Closed-loop adaptive AC stimulation neural network regulation
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Solution Overview
Problem
Current transcranial alternating current stimulation (TACS) techniques face challenges in focusing the regulation of brain network rhythms due to poor targeting, leading to potential interference with non-target regions, and are limited in multi-target regulation, necessitating individualized electrode arrangements to effectively synchronize brain rhythms.
Innovation Solution
A closed-loop adaptive AC stimulation neural network regulation method and system that utilizes EEG data, head models, and functional connectivity networks to optimize electrode positions and current parameters, incorporating real-time impedance measurement and feedback loops for precise frequency and phase adjustment to achieve synchronized brain rhythm regulation across multiple targets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional TACS is used for brain network rhythm regulation, then the regulation can be applied broadly, but the focus on target regions is poor leading to interference with non-target regions
Solution Approach 1:
The patent divides the brain network into multiple discrete target regions (e.g., left and right dorsal attention networks) and applies independent electrode pairs for each target. This segmentation allows precise focal stimulation of each region while avoiding interference with non-target areas, resolving the contradiction between broad applicability and focus precision.
Solution Approach 2:
The patent configures different stimulation parameters (frequency, intensity, phase) for different electrode pairs targeting specific brain regions. Each electrode pair is optimized for its local target region, enabling precise spatial control of stimulation effects while maintaining broad therapeutic applicability across different network disorders.
2Manufacturing precision
If single-region TACS is used, then the stimulation is focused on one region, but multi-target regulation is required for optimal neural network effect
Solution Approach 1:
The patent combines multiple HD-TACS electrode pairs into a unified system that can simultaneously stimulate multiple brain network regions. Each electrode pair maintains its focused stimulation capability while the integrated system achieves multi-target regulation, enabling optimal neural network effects through coordinated stimulation of distributed regions.
Solution Approach 2:
The patent designs a multi-channel stimulation system where each electrode pair can be independently configured for different target regions and stimulation parameters. This universal system can adapt to regulate various brain networks (dorsal attention, ventral attention, default mode, etc.) using the same hardware platform, achieving both focus and versatility.
3Ease of manufacture
If fixed electrode arrangements are used, then the setup is simple, but individual differences in brain electric field distribution require customized arrangements
Solution Approach 1:
The patent implements dynamic, adaptive electrode configuration based on individual participant characteristics. Using MRI data and electric field simulations, the system customizes electrode positions and stimulation parameters for each participant's unique brain anatomy and network topology, achieving precise individualized regulation while maintaining systematic implementation through automated optimization algorithms.
4Ease of operation
If external stimuli are applied without real-time coupling to brain rhythm state, then the stimulation is simple to apply, but effective regulation requires coupling to real-time brain rhythm state
Solution Approach 1:
The patent implements closed-loop feedback control where EEG recordings continuously monitor the actual brain rhythm state during stimulation. The system uses this feedback to dynamically adjust stimulation timing, phase, and intensity to maintain optimal coupling with the evolving brain rhythm state, ensuring reliable and effective regulation while providing real-time adaptability.
Data Source
AI summary
This invention relates to a closed-loop adaptive AC stimulation neural network control method and system, involving the technical field of AC stimulation neural regulation. This system is composed of an individualized navigation module, AC stimulation module, EEG acquisition module and adaptive coupling module. After the magnetic resonance image and functional magnetic resonance image of the regulation object are input into the individualized navigation module, the functional connectivity network is generated to finalize regulatory targets. The position of stimulating electrodes and the magnitude of the stimulating current are determined according to the target to be regulated. The stimulating frequency is determined according to the EEG of the regulation object and then input into the AC stimulation module for regulation.