Neuromodulation Planning Device Using Default Mode Network Data
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Solution Overview
Problem
Current neuromodulation therapies rely on anatomical and local electrophysiological data, which are insufficient for capturing global neuronal network aspects, limiting the effectiveness of treatment planning and execution.
Innovation Solution
A device that integrates brain default mode network data, using a database to normalize and compare patient data with template data, enabling the creation of deviation maps for pathology classification and optimizing neuromodulation therapy planning, including deep brain stimulation, by simulating implant positions and effects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If anatomical imaging and local electrophysiological exploration are used for target determination, then the device complexity remains low, but the measurement precision of global neuronal network aspects is insufficient
Solution Approach 1:
The system segments the complex task of neuromodulation planning into distinct functional modules: receiving means for acquiring multimodal data (anatomical imaging, electrophysiological data, network aspects), template means for storing reference data, normalizing means for data standardization, storage means for database management, and comparison means for analysis. This segmentation allows each module to specialize in specific data processing tasks, improving measurement precision while managing device complexity through modular architecture.
Solution Approach 2:
The device integrates multiple functions into a unified system that can process diverse data types (anatomical, electrophysiological, and network aspects) through a common framework. The normalizing means and comparison means serve universal purposes by handling different data formats and performing standardized analysis operations, thereby improving comprehensive measurement precision without proportionally increasing device complexity.
2Reliability
If anatomical patient images are used alone for determining optimum site, then the ease of operation is high, but the reliability of treatment planning is insufficient
Solution Approach 1:
The system merges multiple data sources including anatomical patient images, local electrophysiological information, and global network aspects into a unified analysis framework. The comparison means integrates these diverse inputs to generate comprehensive treatment planning recommendations, thereby improving reliability by considering multiple factors simultaneously while maintaining ease of operation through automated integration processes.
Solution Approach 2:
The normalizing means acts as an intermediary that standardizes and harmonizes data from different sources before they are processed by the comparison means. This intermediary processing ensures that anatomical images, electrophysiological data, and network aspects are all formatted and scaled consistently, improving reliability of integration while keeping the system easy to operate by handling data compatibility automatically.
3Productivity
If network aspects are integrated into treatment planning, then the productivity of treatment optimization is improved, but the device complexity increases
Solution Approach 1:
The template means stores pre-prepared reference data and standardized templates for common neuromodulation scenarios. The normalizing means applies pre-defined standardization protocols to patient data, and the comparison means uses pre-established analysis frameworks. These preliminary preparations enable rapid treatment optimization by avoiding redundant processing steps, thereby improving productivity while managing device complexity through reusable templates and protocols.
Data Source
AI summary
A device for planning a neuromodulation therapy, whereby the device comprises receiving means to receive brain default mode network data of a patient, template means comprising a template brain default mode network data, normalizing means being configured such that normalized patient brain default mode network data can be prepared on the basis on the received brain default mode network data and the template brain default mode network data, storage means comprising a brain default mode network database and comparison means configured such that the normalized patient brain default mode network data and the data contained in the brain default mode network database can be compared.