The impact of sampling density upon cortical network analysis: regions or points

The choice of representation has a fundamental influence on the network analysis results of an empirical data set. The answers to two basic questions — how to define a node and how to define an edge between a pair of nodes — are not obvious in the network analysis of brain imaging data. We considered the first question in the case of magnetic resonance imaging (MRI)-based cortical thickness networks. We selected network nodes to represent vertices of a cortical surface mesh or cortical brain regions. The first network represents the maximal level of detail available in the analysis of cortical thickness networks, while the latter network represents the typical level of detail in the current network analysis studies. We compared the network analysis results between these two representations. The basic network measures behaved approximately as expected when the level of detail increased. However, the overall connectivity of nodes was greater in the vertex level, degree of clustering was smaller in the vertex level, and the node centralities were different between the levels. Further, many parameters of vertex-level network were more robust to the selection of the correlation threshold used to define the edges of network. We conclude that albeit many qualitative network properties were consistent between the two resolution levels, the vertex-level resolution revealed details that were not visible at the regional-level networks, and this additional detail could be useful for some applications. Finally, a similar methodology as the one used here could be used to study effects of the sampling density in other brain-imaging-based networks, for example, in resting-state functional MRI.