Closed-Loop tFUS Stimulation with Physiological Feedback
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Solution Overview
Problem
Existing trans-cranial focused ultrasound (tFUS) systems lack effective feedback mechanisms to optimize stimulation efficacy, leading to suboptimal therapeutic outcomes and increased costs.
Innovation Solution
Implementing a closed-loop tFUS system that utilizes physiological measurements such as EEG, EMG, EOG, and imaging techniques like fMRI and fNIRS to provide real-time feedback, allowing for adaptive adjustment of stimulation parameters to enhance therapeutic efficacy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If closed-loop feedback systems are implemented in tFUS to optimize stimulation efficacy, then therapeutic effectiveness is improved, but system complexity and cost increase
Solution Approach 1:
The patent implements closed-loop feedback by recording physiological signals (EEG, EMG, EOG) during tFUS stimulation and using these signals to adjust stimulation parameters in real-time. The system monitors brain activity patterns and modifies ultrasound delivery based on the recorded responses, creating a feedback loop that optimizes therapeutic effectiveness while maintaining manageable system complexity through modular architecture.
2Measurement precision
If multiple physiological measurement modalities are integrated for comprehensive feedback, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The system integrates multiple physiological measurement modalities (EEG for electrical brain activity, EMG for muscle responses, EOG for eye movements) into a single unified platform that can detect various types of neural and physiological responses to tFUS stimulation. This multi-functional approach allows comprehensive monitoring of different bodily systems simultaneously, improving measurement precision across multiple dimensions while sharing common hardware and processing infrastructure.
Solution Approach 2:
The measurement system is divided into separate modular components, each responsible for a specific physiological modality (EEG sensors, EMG electrodes, EOG sensors). Each module can be independently configured, calibrated, and processed, allowing the system to achieve high measurement precision for each modality while managing overall complexity through modular architecture and independent signal processing pipelines.
3Productivity
If real-time adaptive adjustment of stimulation parameters is implemented, then treatment efficacy is improved, but processing requirements and system complexity increase
Solution Approach 1:
The system implements automated real-time adjustment of stimulation parameters based on recorded physiological responses, reducing the need for continuous manual intervention. The control algorithm automatically analyzes the feedback signals (EEG, EMG, EOG) and modifies ultrasound delivery parameters accordingly, enabling the system to self-optimize treatment efficacy while maintaining manageable complexity through algorithmic automation rather than complex manual control interfaces.
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
The closed-loop system enables precise modulation of brain activity, improving treatment outcomes for conditions such as anxiety, depression, and Alzheimer's disease, while reducing side effects and treatment sessions.
Implementation Method 1
perform transcranial stimulation of a subject using focused ultrasound
Implementation Method 2
The acoustic radiation pressure from the focused ultrasound can be used to evoke a response in the target structure
Implementation Method 3
electroencephalography (EEG) sensors arranged on the subject's head to record responses from the brain
Implementation Method 4
magnetic resonance imaging (MRI) system to provide anatomical and functional information
Implementation Method 5
functional near-infra-red spectroscopy (fNIRS) system
Data Source
AI summary
A transcranial Focused Ultrasound (tFUS) system uses a neural network to correct skull aberrations and maximizes the transmission of ultrasound waves through the skull. A method using supervised learning generates aberration correction parameters to be used by the receiver and transmitter of the tFUS system. A method utilizing these aberration correction parameters operating on the tFUS system maximizes the coherence of ultrasound waves passing through the skull. The method maximizes the amount of power transmitted through the skull, given a fixed maximum pressure (for example, determined by regulatory requirements).