Overcoming the effects of false positives and threshold bias in graph theoretical analyses of neuroimaging data.

ABSTRACT: Graph theory (GT) is a powerful framework for quantifying topological features of neuroimaging-derived functional and structural networks. However, false positive (FP) connections arise frequently and influence the inferred topology of networks. Thresholding is often used to overcome this problem, but an appropriate threshold often relies on a priori assumptions, which will alter inferred network topologies. Four common network metrics (global efficiency, mean clustering coefficient, mean betweenness and smallworldness) were tested using a model tractography dataset. It was found that all four network metrics were significantly affected even by just one FP. Results also show that thresholding effectively dampens the impact of FPs, but at the expense of adding significant bias to network me

SUBMITTER: Drakesmith M 

PROVIDER: S-EPMC4558463 | biostudies-literature | 2015 Sep

REPOSITORIES: biostudies-literature