ArticleBrain connectivity2017
A Bayesian Double Fusion Model for Resting-State Brain Connectivity Using Joint Functional and Structural Data.
Article in Brain connectivity, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Studying time-resolved functional connectivity via communication theory: on the complementary nature of phase synchronization and sliding window Pearson correlation.bioRxiv : the preprint server for biology · 2025Article
- TOPOLOGICAL LEARNING FOR BRAIN NETWORKS.The annals of applied statistics · 2023Article
- Brain effective connectome based on fMRI and DTI data: Bayesian causal learning and assessment.PloS one · 2023Article
- Group-level comparison of brain connectivity networks.BMC medical research methodology · 2022Article
- Bayesian multisource data integration for explainable brain-behavior analysis.Frontiers in neuroscience · 2022Article
- A Multimodal Multilevel Neuroimaging Model for Investigating Brain Connectome Development.Journal of the American Statistical Association · 2022Article
- Disrupted Topological Organization in White Matter Networks in Unilateral Sudden Sensorineural Hearing Loss.Frontiers in neuroscience · 2021Article
- BVAR-Connect: A Variational Bayes Approach to Multi-Subject Vector Autoregressive Models for Inference on Brain Connectivity Networks.Neuroinformatics · 2021Article
- A bayesian approach to examining default mode network functional connectivity and cognitive performance in major depressive disorder.Psychiatry research. Neuroimaging · 2020Article
- Linking structural and effective brain connectivity: structurally informed Parametric Empirical Bayes (si-PEB).Brain structure & function · 2019Article
- Topological Learning and Its Application to Multimodal Brain Network Integration.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted InterventionArticle
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Authors and funding
5 authors.
Funding
Abstract
Current approaches separately analyze concurrently acquired diffusion tensor imaging (DTI) and functional magnetic resonance imaging (fMRI) data. The primary limitation of these approaches is that they do not take advantage of the information from DTI that could potentially enhance estimation of resting-state functional connectivity (FC) between brain regions. To overcome this limitation, we develop a Bayesian hierarchical spatiotemporal model that incorporates structural connectivity (SC) into estimating FC. In our proposed approach, SC based on DTI data is used to construct an informative prior for FC based on resting-state fMRI data through the Cholesky decomposition. Simulation studies showed that incorporating the two data produced significantly reduced mean squared errors compared to the standard approach of separately analyzing the two data from different modalities. We applied our model to analyze the resting state DTI and fMRI data collected to estimate FC between the brain regions that were hypothetically important in the origination and spread of temporal lobe epilepsy seizures. Our analysis concludes that the proposed model achieves smaller false positive rates and is much robust to data decimation compared to the conventional approach.
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