Patient-Specific tACS Configuration via Computational Field Simulation
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Solution Overview
Problem
Current methods for electrical stimulation of neuronal tissue, particularly in the treatment of Alzheimer's disease, face limitations in focusing electrical currents within the brain, leading to insufficient therapeutic effectiveness due to non-specific targeting and inefficiencies in current delivery.
Innovation Solution
A computer-implemented method for computing patient-specific configuration parameters for transcranial alternating current stimulation (tACS) devices, using pre-existing medical images to optimize electrode placement and stimulation protocols, allowing for focused electrical field distribution and localized treatment of specific brain regions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If conventional electrical stimulation devices are used for treating Alzheimer's disease, then the treatment can be applied broadly, but the electrical current cannot be focused on specific brain regions, resulting in insufficient therapeutic effectiveness
Solution Approach 1:
The system performs preliminary computational simulations and optimizations before actual stimulation to determine the optimal electrode configuration and parameters. This pre-computation phase calculates the electrical field distribution and adjusts parameters to achieve precise focusing on target brain regions, eliminating the need for complex manual configuration during actual use.
Solution Approach 2:
The system uses patient-specific anatomical data (from MRI or CT scans) to create a virtual 3D model or copy of the patient's head and brain structure. This digital twin allows for simulation and optimization of electrical field distribution without requiring physical trial-and-error adjustments, achieving precise focusing through computational modeling.
2Productivity
If manual trial-and-error configuration is used to optimize electrode placement, then the device can be简单地 configured, but the process is time-consuming and costly
Solution Approach 1:
The system replaces manual mechanical adjustment and trial-and-error configuration with automated computational algorithms. The computer system performs simulations and optimizations automatically, substituting human manual operations with automated digital processing, thereby dramatically increasing configuration speed and reducing time loss.
Solution Approach 2:
The system automatically varies and optimizes multiple parameters including electrode positions, stimulation intensities, frequencies, and pulse durations through computational algorithms. This automated parameter optimization eliminates the need for time-consuming manual adjustments while achieving optimal therapeutic configuration.
3Adaptability or versatility
If standard electrode configurations are used, then the device is easy to manufacture and use, but the treatment cannot be personalized to individual patient needs
Solution Approach 1:
The system tailors the electrical stimulation parameters and electrode configurations to the specific anatomical and pathological characteristics of each patient's target brain regions. By analyzing individual patient data and optimizing parameters locally for each patient's unique brain structure, the system achieves personalized treatment without requiring different physical devices for each patient.
Solution Approach 2:
The system provides dynamic, adaptable configuration capabilities through software that can adjust stimulation parameters based on individual patient responses and treatment progress. The computational model allows real-time or between-session adjustments to optimize treatment efficacy for each patient's evolving needs.
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
Enables more effective and targeted electrical stimulation, potentially improving treatment outcomes for neurodegenerative diseases by optimizing electrical field strength and distribution within the brain, allowing for autonomous and efficient therapy sessions.
Implementation Method 1
electrical stimulation of neuronal tissue, in particular of cerebral tissue
Implementation Method 2
electrical field distribution that is generated with the device when configured with a specific set of configuration parameters
Data Source
AI summary
For improving the quality of electrostimulation therapy, a computer-implemented method is proposed that allows patient-specific choice and/or configuration of an electrical stimulation device 1 that is designed and usable for tACS electrostimulation therapy of neuronal tissue, for example in the brain. The method benefits from pre-existing images, for example acquired using a medical imaging technique such as MRT or PET. Based on data extracted from such an image, a computer simulates possible electrical field distributions E (x,y,z), which would be achievable if the device is configured with a certain set of configuration parameters. By varying these parameters and repeating the simulations, the method allows optimization of the configuration parameters for a given device design. As a result, the method delivers a set of optimized configuration parameters, which, if applied to the device, allow a “best-case” electrostimulation with the device that is tailor-made for the patient's needs.