ArticleHuman brain mapping2017
Bayesian vector autoregressive model for multi-subject effective connectivity inference using multi-modal neuroimaging data.
Article in Human brain mapping, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
14 citing papers in PubMed.
- Introducing Structural Reliance: A New Method to Assess Structure-Function Coupling in the Brain.Human brain mapping · 2026Article
- Structurally informed resting-state effective connectivity recapitulates cortical hierarchy.bioRxiv : the preprint server for biology · 2025Article
- Structurally informed models of directed brain connectivity.Nature reviews. Neuroscience · 2025Review
- Bayesian varying-effects vector autoregressive models for inference of brain connectivity networks and covariate effects in pediatric traumatic brain injury.Human brain mapping · 2024Article
- Flexible Bayesian Product Mixture Models for Vector Autoregressions.Journal of machine learning research : JMLR · 2024Article
- High-dimensional multisubject time series transition matrix inference with application to brain connectivity analysis.Biometrics · 2024Article
- Brain effective connectome based on fMRI and DTI data: Bayesian causal learning and assessment.PloS one · 2023Article
- Through the looking glass: Deep interpretable dynamic directed connectivity in resting fMRI.NeuroImage · 2022Article
- Effective connectivity in the default mode network after paediatric traumatic brain injury.The European journal of neuroscience · 2022Article
- Bayesian graphical models for modern biological applications.Statistical methods & applications · 2022Review
- BVAR-Connect: A Variational Bayes Approach to Multi-Subject Vector Autoregressive Models for Inference on Brain Connectivity Networks.Neuroinformatics · 2021Article
- A Hierarchical Bayesian Model for Differential Connectivity in Multi-trial Brain Signals.Econometrics and statistics · 2020Article
- Bayesian modeling of dependence in brain connectivity data.Biostatistics (Oxford, England) · 2020Article
- An interview with Sharon Chiang, 2019Epilepsia open · 2019Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
In this article a multi-subject vector autoregressive (VAR) modeling approach was proposed for inference on effective connectivity based on resting-state functional MRI data. Their framework uses a Bayesian variable selection approach to allow for simultaneous inference on effective connectivity at both the subject- and group-level. Furthermore, it accounts for multi-modal data by integrating structural imaging information into the prior model, encouraging effective connectivity between structurally connected regions. They demonstrated through simulation studies that their approach resulted in improved inference on effective connectivity at both the subject- and group-level, compared with currently used methods. It was concluded by illustrating the method on temporal lobe epilepsy data, where resting-state functional MRI and structural MRI were used. Hum Brain Mapp 38:1311-1332, 2017. © 2016 Wiley Periodicals, Inc.
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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.