Extended Network-Based Statistics for Measuring Altered Directed Connectivity Components in the Human Brain.

The human brain is thought to be an interconnected network. In network analysis of the human brain, one of the most concerned issue is how to measure the altered directed connectivity component. In this paper, extended network-based statistics (e-NBS) is proposed to measure the directed connectivity component alteration. The proposed method can identify weakly and strongly connected components of directed networks using t-test and non-parametric test under a range of threshold values. A directed brain network construction method using convergent cross-mapping (CCM) is also proposed, which estimates causal relationships from the nonlinear state space reconstruction of time series of neural signal. We validated the proposed methods using resting-state BOLD fMRI data of 23 SCI patients and 22 healthy controls. The proposed methods showed significant different connected components under several e-NBS parameter settings. A common stable connected component pattern was also identified. The proposed method provides a new way to investigate altered directed connectivity components in the human brain.