Time-dependence of graph theory metrics in functional connectivity analysis

• Published in: NeuroImage (Orlando, Fla.) Fla.), 2016-01, Vol.125, p.601-615
• Main Authors: Chiang, Sharon, Cassese, Alberto, Guindani, Michele, Vannucci, Marina, Yeh, Hsiang J., Haneef, Zulfi, Stern, John M.
• Format: Article
• Language: English
• Subjects: Adult; Age; Algorithms; Bayes Theorem; Brain - physiology; Connectome - methods; Dependence; Dynamic functional connectivity; Efficiency; Epilepsy; Epilepsy, Temporal Lobe - physiopathology; Estimates; Female; Functional magnetic resonance imaging; Graph theory; Hidden Markov Model; Humans; Image Processing, Computer-Assisted - methods; Magnetic Resonance Imaging - methods; Male; Markov analysis; Markov Chains; Middle Aged; Neural Pathways - physiology; Probability; Studies; Temporal lobe epilepsy; Young Adult
• ISSN: 1053-8119; 1095-9572
• DOI: 10.1016/j.neuroimage.2015.10.070

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 that several graph theory measures, including small-world index, global integration measures, and betweenness centrality, may exhibit greater stationarity over time and therefore be more robust. Additionally, we demonstrate that accounting for subject-level differences in the level of temporal stationarity of network topology may increase discriminatory power in discriminating between disease states. Our results confirm and extend findings from other studies regarding the dynamic nature of functional connectivity, and suggest that using statistical models which explicitly account for the dynamic nature of functional connectivity in graph theory analyses may improve the sensitivity of investigations and consistency across investigations. •Temporal stationarity of graph theory measures of functional connectivity are examined.•A Bayesian hidden Markov model is proposed to estimate temporal transitions.•Two estimators of temporal stationarity are proposed to capture different levels of probabilistic uncertainty.•Small-world index, global integration measures, and betweenness centrality exhibit greater temporal stationarity.•Differences in temporal stationarity may aid in disease group discrimination.