ArticleJournal of the American Statistical Association2018
A Bayesian Approach for Estimating Dynamic Functional Network Connectivity in fMRI Data.
Article in Journal of the American Statistical Association, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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Who cites it
25 citing papers in PubMed.
- Dynamic Bayesian networks for neural information flow: evaluation of continuous and discrete scoring metrics.Journal of computational neuroscience · 2026Article
- Causal functional mediation analysis with an application to functional magnetic resonance imaging data.Biostatistics (Oxford, England) · 2025Article
- Modeling Alzheimer's disease: Bayesian copula graphical model from demographic, cognitive, and neuroimaging data.Journal of Alzheimer's disease : JAD · 2025Article
- Population-level task-evoked functional connectivity via Fourier analysis.Journal of the Royal Statistical Society. Series C, Applied statistics · 2024Article
- Time-varying dynamic Bayesian network learning for an fMRI study of emotion processing.Statistics in medicine · 2024Article
- Identifying covariate-related subnetworks for whole-brain connectome analysis.Biostatistics (Oxford, England) · 2024Article
- A predictor-informed multi-subject bayesian approach for dynamic functional connectivity.PloS one · 2024Article
- Bayesian dynamic network modelling: an application to metabolic associations in cardiovascular diseases.Journal of applied statistics · 2024Article
- Functional connectivity across the human subcortical auditory system using an autoregressive matrix-Gaussian copula graphical model approach with partial correlations.Imaging neuroscience (Cambridge, Mass.) · 2024Article
- Time-varying functional connectivity as Wishart processes.Imaging neuroscience (Cambridge, Mass.) · 2024Article
- Automatic diagnosis of schizophrenia and attention deficit hyperactivity disorder in rs-fMRI modality using convolutional autoencoder model and interval type-2 fuzzy regression.Cognitive neurodynamics · 2023Article
- Detecting Changes in Correlation Networks with Application to Functional Connectivity of fMRI Data.Psychometrika · 2023Article
- Age-dependent changes in the dynamic functional organization of the brain at rest: a cross-cultural replication approach.Cerebral cortex (New York, N.Y. : 1991) · 2023Article
- Inferring Brain State Dynamics Underlying Naturalistic Stimuli Evoked Emotion Changes With dHA-HMM.Neuroinformatics · 2022Article
- Bayesian graphical models for modern biological applications.Statistical methods & applications · 2022Review
- Article
- Modelling state-transition dynamics in resting-state brain signals by the hidden Markov and Gaussian mixture models.The European journal of neuroscience · 2021Article
- A unified approach for characterizing static/dynamic connectivity frequency profiles using filter banks.Network neuroscience (Cambridge, Mass.) · 2021Article
- BVAR-Connect: A Variational Bayes Approach to Multi-Subject Vector Autoregressive Models for Inference on Brain Connectivity Networks.Neuroinformatics · 2021Article
- Joint Bayesian Estimation of Voxel Activation and Inter-regional Connectivity in fMRI Experiments.Psychometrika · 2020Article
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Authors and funding
6 authors.
Funding
Abstract
Dynamic functional connectivity, i.e., the study of how interactions among brain regions change dynamically over the course of an fMRI experiment, has recently received wide interest in the neuroimaging literature. Current approaches for studying dynamic connectivity often rely on ad-hoc approaches for inference, with the fMRI time courses segmented by a sequence of sliding windows. We propose a principled Bayesian approach to dynamic functional connectivity, which is based on the estimation of time varying networks. Our method utilizes a hidden Markov model for classification of latent cognitive states, achieving estimation of the networks in an integrated framework that borrows strength over the entire time course of the experiment. Furthermore, we assume that the graph structures, which define the connectivity states at each time point, are related within a super-graph, to encourage the selection of the same edges among related graphs. We apply our method to simulated task-based fMRI data, where we show how our approach allows the decoupling of the task-related activations and the functional connectivity states. We also analyze data from an fMRI sensorimotor task experiment on an individual healthy subject and obtain results that support the role of particular anatomical regions in modulating interaction between executive control and attention networks.
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Registered trials
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.