Time-dependence of graph theory metrics in functional connectivity analysis

NeuroImage
Sharon ChiangJohn M Stern

Abstract

Brain graphs provide a useful way to computationally model the network structure of the connectome, and this has led to increasing interest in the use of graph theory to quantitate and investigate the topological characteristics of the healthy brain and brain disorders on the network level. The majority of graph theory investigations of functional connectivity have relied on the assumption of temporal stationarity. However, recent evidence increasingly suggests that functional connectivity fluctuates over the length of the scan. In this study, we investigate the stationarity of brain network topology using a Bayesian hidden Markov model (HMM) approach that estimates the dynamic structure of graph theoretical measures of whole-brain functional connectivity. In addition to extracting the stationary distribution and transition probabilities of commonly employed graph theory measures, we propose two estimators of temporal stationarity: the S-index and N-index. These indexes can be used to quantify different aspects of the temporal stationarity of graph theory measures. We apply the method and proposed estimators to resting-state functional MRI data from healthy controls and patients with temporal lobe epilepsy. Our analysis shows t...Continue Reading

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Citations

Mar 26, 2016·Frontiers in Human Neuroscience·Yanlu WangTie-Qiang Li
Apr 20, 2016·IEEE Transactions on Bio-medical Engineering·Arash Golibagh MahyariSelin Aviyente
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Jun 26, 2021·International Journal of Neural Systems·Qin TaoPeng Xu

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