ArticleNeuroImage2016
Time-dependence of graph theory metrics in functional connectivity analysis.
Article in NeuroImage, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed.
- Characterizing the effects of age, puberty, and sex on variability in resting-state functional connectivity in late childhood and early adolescence.NeuroImage · 2025Article
- A predictor-informed multi-subject bayesian approach for dynamic functional connectivity.PloS one · 2024Article
- Article
- Attenuated effective connectivity of large-scale brain networks in children with autism spectrum disorders.Frontiers in neuroscience · 2022Article
- Identification of Pattern Completion Neurons in Neuronal Ensembles Using Probabilistic Graphical Models.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2021Article
- Dynamic functional connectivity in temporal lobe epilepsy: a graph theoretical and machine learning approach.Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology · 2021Article
- Dynamics of Segregation and Integration in Directional Brain Networks: Illustration in Soldiers With PTSD and Neurotrauma.Frontiers in neuroscience · 2019Article
- Changes in Centrality Frequency of the Default Mode Network in Individuals With Subjective Cognitive Decline.Frontiers in aging neuroscience · 2019Article
- Brain Connectivity and Information-Flow Breakdown Revealed by a Minimum Spanning Tree-Based Analysis of MRI Data in Behavioral Variant Frontotemporal Dementia.Frontiers in neuroscience · 2019Article
- Article
- Epilepsy as a dynamic disease: A Bayesian model for differentiating seizure risk from natural variability.Epilepsia open · 2018Article
- A Bayesian Approach for Estimating Dynamic Functional Network Connectivity in fMRI Data.Journal of the American Statistical Association · 2018Article
- Article
- Methods and Considerations for Dynamic Analysis of Functional MR Imaging Data.Neuroimaging clinics of North America · 2017Review
- Article
- Bayesian vector autoregressive model for multi-subject effective connectivity inference using multi-modal neuroimaging data.Human brain mapping · 2017Article
- From static to temporal network theory: Applications to functional brain connectivity.Network neuroscience (Cambridge, Mass.) · 2017Article
- A Hierarchical Bayesian Model for the Identification of PET Markers Associated to the Prediction of Surgical Outcome after Anterior Temporal Lobe Resection.Frontiers in neuroscience · 2017Article
- Organization and control of epileptic circuits in temporal lobe epilepsy.Progress in brain research · 2016Review
- Studying Sub-Dendrograms of Resting-State Functional Networks with Voxel-Wise Hierarchical Clustering.Frontiers in human neuroscience · 2016Article
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Authors and funding
7 authors.
Funding
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.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.