fMRI Functional Network Mapping for Precise Neuromodulation Targeting
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Solution Overview
Problem
Existing neuromodulation techniques, such as TMS and TDCS, lack precision due to insensitivity to individual brain functional topography, relying on relative anatomical distances rather than personalized brain mapping.
Innovation Solution
Utilizing probabilistic functional mapping data and MRI-based methods to generate individual-specific and probabilistic functional network maps, allowing for precise targeting of neuromodulation therapies by identifying target locations and monitoring therapy efficacy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If relative anatomical distances are used for neuromodulation targeting, then the method is simple and widely applicable, but precision is reduced due to insensitivity to individual brain functional topography
Solution Approach 1:
The patent applies preliminary action by pre-computing probabilistic functional network maps and integration zone maps from group fMRI data before individual neuromodulation sessions. These pre-computed maps serve as templates that can be rapidly matched to individual anatomy, eliminating the need for time-consuming individual functional mapping while maintaining precision. The pre-processing of group data creates a library of functional patterns that guide subsequent targeting decisions.
Solution Approach 2:
The patent uses copying by creating simplified representations of complex functional brain data through probabilistic maps and integration zone maps. These maps copy the essential functional topology information from detailed fMRI data into a format that can be efficiently integrated with individual anatomical scans. The copying process transforms voluminous functional data into condensed spatial probability distributions that guide targeting without requiring full functional mapping of each subject.
2Measurement precision
If individual-specific functional mapping is performed, then precision is improved, but time consumption increases due to requirement of resting-state fMRI data acquisition and processing
Solution Approach 1:
The patent applies universality by creating group-level probabilistic maps that serve multiple functions: they provide population-level functional topology, serve as templates for individual matching, and guide targeting decisions without requiring individual functional scanning. These universal maps can be applied across different subjects and clinical contexts, eliminating the need for time-consuming individual functional mapping while preserving individual anatomical variations through registration.
Solution Approach 2:
The patent uses integration zone maps as intermediaries between group-level functional data and individual anatomical scans. These intermediate representations condense complex functional connectivity patterns into spatial probability distributions that can be efficiently matched to individual anatomy. The intermediary maps bridge the gap between population-level findings and individual application, enabling rapid targeting without direct individual functional scanning.
3Adaptability or versatility
If probabilistic functional network maps are used, then individual brain differences are accounted for, but data processing complexity increases
Solution Approach 1:
The patent applies parameter changes by transforming functional connectivity data into probabilistic spatial distributions. Instead of working with complex time-series connectivity matrices, the method converts functional relationships into spatial probability maps where each location has a probability value indicating its likelihood of belonging to a particular functional network. This parameter transformation simplifies subsequent matching and integration operations while preserving individual variation through the probabilistic framework.
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 precise delivery and monitoring of neuromodulation therapies, accounting for individual brain differences, and facilitating targeted brain stimulation even in the absence of resting-state data, with applications in both clinical and research settings.
Implementation Method 1
the functional magnetic resonance image data comprise a time-series of images whose voxels depict blood-oxygen-level-dependent (BOLD) signals
Data Source
AI summary
Functional networks are mapped for individuals and group populations based on magnetic resonance imaging, and the resulting functional mapping data (e.g., probabilistic maps of functional networks and/or integration zones where multiple functional networks overlap and/or interact) are used to guide or otherwise monitor the delivery of neuromodulation therapies. Individual-specific functional network maps can be generated based on an overlapping template matching that is capable of assigning multiple networks to a given grayordinate.