ArticleHuman brain mapping2014
Inferring functional interaction and transition patterns via dynamic Bayesian variable partition models.
Article in Human brain mapping, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- A Bayesian Spatiotemporal Model for Joint Estimation of Brain Activation and Connectivity in fMRI Studies.Statistics in medicine · 2026Article
- Joint Bayesian Estimation of Voxel Activation and Inter-regional Connectivity in fMRI Experiments.Psychometrika · 2020Article
- Recognizing Brain States Using Deep Sparse Recurrent Neural Network.IEEE transactions on medical imaging · 2019Article
- Large-Scale Circuitry Interactions Upon Earthquake Experiences Revealed by Recurrent Neural Networks.IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society · 2018Article
- Spatio-temporal modeling of connectome-scale brain network interactions via time-evolving graphs.NeuroImage · 2018Review
- Functional connectomics from neural dynamics: probabilistic graphical models for neuronal network ofPhilosophical transactions of the Royal Society of London. Series B, Biological sciences · 2018Article
- Identification of Temporal Transition of Functional States Using Recurrent Neural Networks from Functional MRI.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2018Article
- Cerebral microhemorrhages due to traumatic brain injury and their effects on the aging human brain.Neurobiology of aging · 2018Review
- Time-Resolved Resting-State Functional Magnetic Resonance Imaging Analysis: Current Status, Challenges, and New Directions.Brain connectivity · 2017Review
- Bayesian Inference for Functional Dynamics Exploring in fMRI Data.Computational and mathematical methods in medicine · 2016Review
- Structure and Topology Dynamics of Hyper-Frequency Networks during Rest and Auditory Oddball Performance.Frontiers in computational neuroscience · 2016Article
- Highly adaptive tests for group differences in brain functional connectivity.NeuroImage. Clinical · 2015Article
- Behavioral relevance of the dynamics of the functional brain connectome.Brain connectivity · 2014Article
- Atomic dynamic functional interaction patterns for characterization of ADHD.Human brain mapping · 2014Article
- Diffuse axonal injury after traumatic cerebral microbleeds: an evaluation of imaging techniques.Neural regeneration research · 2014Review
- Bayesian Models for fMRI Data Analysis.Wiley interdisciplinary reviews. Computational statisticsArticle
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15 authors.
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Abstract
Multivariate connectivity and functional dynamics have been of wide interest in the neuroimaging field, and a variety of methods have been developed to study functional interactions and dynamics. In contrast, the temporal dynamic transitions of multivariate functional interactions among brain networks, in particular, in resting state, have been much less explored. This article presents a novel dynamic Bayesian variable partition model (DBVPM) that simultaneously considers and models multivariate functional interactions and their dynamics via a unified Bayesian framework. The basic idea is to detect the temporal boundaries of piecewise quasi-stable functional interaction patterns, which are then modeled by representative signature patterns and whose temporal transitions are characterized by finite-state transition machines. Results on both simulated and experimental datasets demonstrated the effectiveness and accuracy of the DBVPM in dividing temporally transiting functional interaction patterns. The application of DBVPM on a post-traumatic stress disorder (PTSD) dataset revealed substantially different multivariate functional interaction signatures and temporal transitions in the default mode and emotion networks of PTSD patients, in comparison with those in healthy controls. This result demonstrated the utility of DBVPM in elucidating salient features that cannot be revealed by static pair-wise functional connectivity analysis.
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