BrainKnowledge: a human brain function mapping knowledge-base system.

Area of Science:

• Neuroimaging
• Bioinformatics
• Computational Neuroscience

Background:

• Functional magnetic resonance imaging (fMRI) data analysis requires robust association with existing scientific literature for accurate interpretation.
• Integrating experimental fMRI findings with a comprehensive knowledge base is essential for advancing brain function mapping.

Purpose of the Study:

• To introduce BrainKnowledge, a knowledge-base system designed to associate fMRI data analysis with literature search capabilities.
• To provide tools for comparing experimental fMRI data against literature-derived information.

Main Methods:

• Development of a literature extraction and mining module to process vast amounts of fMRI literature.
• Generation of co-occurrence models (structure-to-function, function-to-structure, structure-structure) from over 15,000 Medline abstracts.
• Implementation of three core functions: searching brain activation models by function, querying functions by brain structure, and comparing fMRI data with literature data.

Main Results:

• BrainKnowledge successfully integrates fMRI experimental results with indexed Medline abstracts.
• The system provides three distinct co-occurrence models derived from extensive literature mining.
• Demonstrated capability through an application example focusing on the studies of affect.

Conclusions:

• BrainKnowledge serves as a valuable tool for neuroscientists by organizing and retrieving relevant information from over ten thousand abstracts.
• The system facilitates efficient comparison of experimental fMRI data with published findings, saving researchers time and resources.
• BrainKnowledge enhances the analysis and interpretation of fMRI data by bridging experimental results and the scientific literature.