Neurocranial Electrostimulation Forward Model Optimization
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Solution Overview
Problem
Current transcranial electrostimulation devices are rudimentary and do not effectively target brain modulation, requiring costly and time-consuming computations to alter treatment methods, and often result in inefficient electrode configuration and stimulation parameter determination.
Innovation Solution
A method that involves obtaining a model image of target tissue, developing a forward model of the electric field induced by electrode configurations and tissue properties, and optimizing stimulation parameters using a least squares approach to achieve a desired stimulation outcome, incorporating finite element model computations and matrix calculations to determine optimal electrode configurations and stimulation parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current transcranial electrostimulation devices are used, then electrode configuration and stimulation parameters can be determined, but the process requires costly and time-consuming computations and trial and error procedures
Solution Approach 1:
The patent pre-computes forward models of electric field distribution for multiple electrode configurations and stimulation parameters before actual treatment. These pre-computed models are stored and can be quickly queried during treatment planning, eliminating the need for time-consuming real-time computations and trial-and-error procedures while maintaining precise control over tissue activation.
2Measurement precision
If current transcranial electrostimulation devices are used, then stimulation can be applied, but the devices are rudimentary and do not effectively target brain modulation
Solution Approach 1:
The patent segments the brain into multiple tissue types with different electrical conductivities (e.g., gray matter, white matter, cerebrospinal fluid, skull, scalp) and creates separate forward models for each tissue type. This segmentation allows precise modeling of electric field distribution through heterogeneous brain tissue, enabling accurate targeting of specific brain regions while accounting for the complex anatomical structure.
Solution Approach 2:
The patent assigns different electrical conductivity values to different local regions of brain tissue based on tissue type classification. Each tissue type (gray matter, white matter, CSF, skull, scalp) has its own characteristic conductivity properties, allowing the model to accurately represent local variations in electrical properties and predict electric field distribution with high spatial precision.
3Adaptability or versatility
If costly and time-consuming computations are used to alter treatment methods, then treatment optimization can be achieved, but the computational resources and time required are excessive
Solution Approach 1:
The patent performs computationally intensive forward model computations in advance, storing the results for multiple electrode configurations and stimulation parameters. When treatment methods need to be altered, the system simply queries the pre-computed models rather than performing new full computations, dramatically reducing computational resource requirements while maintaining the ability to adapt treatment methods.
Solution Approach 2:
The patent creates simplified representations (copies) of the complex forward model results in the form of pre-computed lookup tables or reduced-order models. These copies capture the essential electric field distribution patterns for different configurations, allowing rapid treatment optimization without requiring access to the full computational complexity of the original forward models.
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
This approach reduces the time and cost of optimizing electrode configurations and stimulation parameters, allows for precise control of tissue activation, and enhances the safety and efficacy of neurocranial stimulation by minimizing trial and error procedures and computational resources.
Implementation Method 1
developing a forward model of the electric field induced in the target tissue based on electrode configurations and tissue properties
Implementation Method 2
assigning to the image tissue electrical conductance values, computing, from the locations of electrodes and tissue electrical conductances, a forward model of the response of the tissue to applied currents
Data Source
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AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for developing transcranial electrical stimulation protocols are disclosed. In one aspect, a method includes the actions of accepting an image model of target tissue, obtaining a forward model having a first electrode configuration and first electrical stimulation parameters based on electrical stimulation of the target tissue, accepting electrode configuration changes or electrical stimulation parameter changes resulting in a second electrode configuration or second electrical stimulation parameters, determining an optimized tissue model using a least square methodology and based on the second electrode configuration or second electrical stimulation parameter changes, comparing the optimized tissue model with a desired outcome, and providing a confirmation of the optimized model with the desired outcome.