Classification of brain activity using Synolitic networks

The purpose of this work is to present and validate a methodology of representing functional magnetic resonance imaging (fMRI) data in the form of graphs that effectively convey valuable insights into the interconnectedness of brain region activity for subsequent classification purposes. This paper explores the application of synolitic networks in the analysis of brain activity. We propose a method for constructing a graph, the vertices of which reflect fMRI voxels’ values, and the edges and edge weights reflect the relationships between fMRI voxels. Based on the classification of fMRI data by graph properties, the effectiveness of the method in conveying important information for classification in the construction of graphs was shown.