A propositional representation model of anatomical and functional brain data

A great number of natural systems can be represented by complex networks. Recent developments in the domain of complex networks have been translated to the neurosciences. The associated graph theoretical analysis of data provides a powerful framework to describe human brain structural and functional networks. In this work we propose a graph-based framework which is composed of: i) the definition of a propositional representation model of the anatomical and functional brain data and ii) the treatment of the embedded graph data by neurocomputational tools as a way to explore and visualize the graphs with the objective to enlarge anatomo-functional knowledge. The XML-based language GraphML is used for the representation of the data. The development of neurocomputational tools is doing with the software Gephi and the software Matlab. We will apply these framework and tools to a database belonging to the Center of Cognitive Anatomy (CAC) which provides neuropsychological data for patients presenting sequels of focal brain damage