Semantic Network for Neurological Data Annotation
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Solution Overview
Problem
The integration of new neurological data into existing systems is hindered by large data file sizes and the need for custom software tools, limiting the ability to order and search this information intuitively, which stifles research and increases costs in neurology and brain science.
Innovation Solution
A computer-implemented method using a flexible semantic network approach that dynamically types data elements, allowing for the incorporation of new data sources and automatic annotation of neural connectivity data with anatomical and functional information, enabling intuitive querying and analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional neurological data systems are used, then data storage is achieved, but data integration and querying become inefficient due to large file sizes and lack of standardization
Solution Approach 1:
The patent segments neurological data into discrete annotated elements that can be independently processed and queried. Each data element represents a specific neurological feature with defined attributes, allowing the system to handle large datasets through modular processing rather than treating data as monolithic blocks, thereby improving processing efficiency while managing complexity.
Solution Approach 2:
The patent creates a universal annotated data model that can represent multiple types of neurological data (structural, functional, connectivity) using a common framework. This multi-functional annotation system allows diverse data sources to be integrated through standardized procedures, eliminating the need for separate processing pipelines for each data type and improving overall system productivity.
2Adaptability or versatility
If custom software tools are developed for each data source, then data integration is achieved, but development costs and time increase
Solution Approach 1:
The patent implements a universal annotation framework that can accommodate multiple neurological data sources through standardized procedures. Rather than developing custom integration tools for each data source, the system uses a single versatile annotation model that can represent structural MRI, functional MRI, DTI, and other neurological data types, significantly reducing development time while maintaining adaptability to new data sources.
Solution Approach 2:
The annotation model is designed to be dynamic and extensible, allowing new data sources and annotation types to be added without restructuring the entire system. The framework supports incremental adaptation to new neurological data formats through defined extension mechanisms, enabling the system to evolve with emerging technologies without requiring complete re-development.
3Measurement precision
If detailed neurological data is stored, then research accuracy is improved, but data management and searching become more difficult
Solution Approach 1:
The patent segments detailed neurological data into annotated elements with specific attributes and metadata. Each annotation contains precise measurements and descriptors that enable accurate representation of neurological features while organizing data into searchable categories. This segmentation allows researchers to query specific aspects of neurological data without needing to process entire datasets, improving both accuracy and ease of operation.
Solution Approach 2:
The patent introduces an annotation layer as an intermediary between raw neurological data and research queries. These annotations serve as structured mediators that capture essential features and relationships in a standardized format, enabling efficient searching and analysis while preserving the precision of underlying detailed data. The annotation system acts as a bridge that translates complex neurological measurements into queryable information structures.
Data Source
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AI summary
The present invention is in the technical field of bioinformatics, and the implementation of bioinformatics. Advances in technology have led to a large increase in the rate at which data, in particular in the medical domain, can be generated (from patient sources, clinical trials, and research campaigns). The researcher is thus confronted with a large amount of information, and it is difficult to discover connections in the data, and thus to improve medical knowledge, even in spite of the amount of data available. The present application proposes to process and to structure medical data using a computer-implemented semantic network, enabling undiscovered connections between experiments and data sources to be made, and to continually add new data to the semantic network. In summary, it is proposed to provide a computer-implemented method and associated system which are able to automatically provide neurological knowledge model data by annotating neural connectivity data with further data sources.