Time-dependence of graph theory metrics in functional connectivity analysis

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dc.citation.firstpage601
dc.citation.journalTitleNeuroImage
dc.citation.lastpage615
dc.citation.volumeNumber125
dc.contributor.authorChiang, Sharon
dc.contributor.authorCassese, Alberto
dc.contributor.authorGuindani, Michele
dc.contributor.authorVannucci, Marina
dc.contributor.authorYeh, Hsiang J.
dc.contributor.authorHaneef, Zulfi
dc.contributor.authorStern, John M.
dc.date.accessioned2017-05-23T19:32:18Z
dc.date.available2017-05-23T19:32:18Z
dc.date.issued2016
dc.description.abstractBrain graphs provide a useful way to computationally model the network structure of the connectome, and this has led to increasing interest in the use ofﾠgraph theoryﾠto 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 Bayesianﾠhidden Markov modelﾠ(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: theﾠS-index andﾠN-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-stateﾠfunctional MRIﾠdata from healthy controls and patients withﾠtemporal 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.
dc.identifier.citationChiang, Sharon, Cassese, Alberto, Guindani, Michele, et al.. "Time-dependence of graph theory metrics in functional connectivity analysis." <i>NeuroImage,</i> 125, (2016) Elsevier: 601-615. https://doi.org/10.1016/j.neuroimage.2015.10.070.
dc.identifier.doihttps://doi.org/10.1016/j.neuroimage.2015.10.070
dc.identifier.urihttps://hdl.handle.net/1911/94361
dc.language.isoeng
dc.publisherElsevier
dc.rightsThis is an author's peer-reviewed final manuscript, as accepted by the publisher. The published article is copyrighted by Elsevier.
dc.subject.keyworddynamic functional connectivity
dc.subject.keywordfunctional magnetic resonance imaging
dc.subject.keywordgraph theory
dc.subject.keywordHidden Markov Model
dc.subject.keywordtemporal lobe epilepsy
dc.titleTime-dependence of graph theory metrics in functional connectivity analysis
dc.typeJournal article
dc.type.dcmiText
dc.type.publicationpost-print
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