RT Journal Article
SR Electronic
T1 From static to temporal network theory – applications to functional brain connectivity
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 096461
DO 10.1101/096461
A1 William Hedley Thompson
A1 Per Brantefors
A1 Peter Fransson
YR 2016
UL http://biorxiv.org/content/early/2016/12/23/096461.abstract
AB Network neuroscience has become an established paradigm to tackle questions related to the functional and structural connectome of the brain. Recently, there has been a growing interest to examine the temporal dynamics of the brain's network activity. While different approaches to capture fluctuations in brain connectivity have been proposed, there have been few attempts to quantify these fluctuations using temporal network theory. Temporal network theory is an extension of network theory that has been successfully applied to the modeling of dynamic processes in economics, social sciences and engineering. The objective of this paper is twofold: (i) to present a detailed description of the central tenets and outline measures from temporal network theory; (ii) apply these measures to a resting-state fMRI dataset to illustrate their utility. Further, we discuss the interpretation of temporal network theory in the context of the dynamic functional brain connectome. All the temporal network measures and plotting functions described in this paper are freely available as a python package Teneto.