Detecting spatially highly resolved network modules: a multi subject approach

The connectivity analysis of spatially highly resolved data results in networks comprising an immense number of nodes and edges which makes it hard or even impossible to investigate the high-dimensional (HD) network as a whole. A solution to this problem is offered by a connectivity-based segmentation of the HD networks into subsets of functionally similar nodes (network modules) that exhibit pronounced interaction. However, an investigation of the results at group level is problematic as identified modules are not assigned to each other across different subjects. In this work, we propose a rearrangement of the subject-specific networks into an integrative tensor which is subsequently decomposed into additive factors. This reorganization provides subject-independent networks together with subject-specific loadings enabling a group-wide segmentation of the resulting networks at the large scale.