title: Time-dependence of graph theory metrics in functional connectivity analysis.
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 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.
authors.affiliation: Department of Statistics, Rice University, Houston, TX, USA. Electronic address: sc4712@rice.com.
authors.forename: Sharon
authors.initials: S
authors.lastname: Chiang
authors.affiliation: Department of Statistics, Rice University, Houston, TX, USA; Department of Biostatistics, University of Texas at MD Anderson Cancer Center, Houston, TX, USA; Department of Methodology and Statistics, Maastricht University, Maastricht, The Netherlands.
authors.forename: Alberto
authors.initials: A
authors.lastname: Cassese
authors.affiliation: Department of Statistics, Rice University, Houston, TX, USA; Department of Biostatistics, University of Texas at MD Anderson Cancer Center, Houston, TX, USA.
authors.forename: Michele
authors.initials: M
authors.lastname: Guindani
authors.affiliation: Department of Statistics, Rice University, Houston, TX, USA.
authors.forename: Marina
authors.initials: M
authors.lastname: Vannucci
authors.affiliation: Department of Neurology, University of California Los Angeles, Los Angeles, CA, USA.
authors.forename: Hsiang J
authors.initials: HJ
authors.lastname: Yeh
authors.affiliation: Department of Neurology, Baylor College of Medicine, Houston, TX, USA; Neurology Care Line, Michael E. DeBakey VA Medical Center, Houston, TX, USA.
authors.forename: Zulfi
authors.initials: Z
authors.lastname: Haneef
authors.affiliation: Department of Neurology, University of California Los Angeles, Los Angeles, CA, USA.
authors.forename: John M
authors.initials: JM
authors.lastname: Stern
mesh_terms: D000328:Adult; D000465:Algorithms; D001499:Bayes Theorem; D001921:Brain / Q000502:physiology*; D063132:Connectome / Q000379:methods*; D004833:Epilepsy, Temporal Lobe / Q000503:physiopathology*; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods; D008297:Male; D008390:Markov Chains; D008875:Middle Aged; D009434:Neural Pathways / Q000502:physiology*; D055815:Young Adult
keywords: Dynamic functional connectivity; Functional magnetic resonance imaging; Graph theory; Hidden Markov Model; Temporal lobe epilepsy