ArticleJournal of the American Statistical Association2016
Understanding the Impact of Stroke on Brain Motor Function: A Hierarchical Bayesian Approach.
Article in Journal of the American Statistical Association, 2016. 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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Who cites it
14 citing papers in PubMed, 28 citations in OpenAlex.
- A Bayesian Spatiotemporal Model for Joint Estimation of Brain Activation and Connectivity in fMRI Studies.Statistics in medicine · 2026Article
- A wearable sensor-based kinematic dataset collected under standardized rehabilitation tasks from 120 post-stroke patients.Scientific data · 2026Article
- Standardizing Domains and Metrics of Stroke Recovery: A Systematic Review.Brain sciences · 2024Review
- Time-varying dynamic Bayesian network learning for an fMRI study of emotion processing.Statistics in medicine · 2024Article
- Assessing Stroke-Related Sarcopenia in Chronic Stroke: Identification of Clinical Assessment Tools-A Pilot Study.Biomedicines · 2023Article
- A latent state space model for estimating brain dynamics from electroencephalogram (EEG) data.Biometrics · 2023Article
- Smooth online parameter estimation for time varying VAR models with application to rat local field potential activity data.Statistics and its interface · 2023Article
- Exploring the efficacy of virtual reality-based rehabilitation in stroke: a narrative review of current evidence.Annals of medicine · 2023Review
- Inducible Prostaglandin E Synthase as a Pharmacological Target for Ischemic Stroke.Neurotherapeutics : the journal of the American Society for Experimental NeuroTherapeutics · 2022Article
- BVAR-Connect: A Variational Bayes Approach to Multi-Subject Vector Autoregressive Models for Inference on Brain Connectivity Networks.Neuroinformatics · 2021Article
- A BCI System Based on Motor Imagery for Assisting People with Motor Deficiencies in the Limbs.Brain sciences · 2020Article
- Time series analysis of fMRI data: Spatial modelling and Bayesian computation.Statistics in medicine · 2018Article
- A Bayesian Approach for Estimating Dynamic Functional Network Connectivity in fMRI Data.Journal of the American Statistical Association · 2018Article
- A Bayesian Double Fusion Model for Resting-State Brain Connectivity Using Joint Functional and Structural Data.Brain connectivity · 2017Article
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
Abstract
Stroke is a disturbance in blood supply to the brain resulting in the loss of brain functions, particularly motor function. A study was conducted by the UCI Neurorehabilitation Lab to investigate the impact of stroke on motor-related brain regions. Functional MRI (fMRI) data were collected from stroke patients and healthy controls while the subjects performed a simple motor task. In addition to affecting local neuronal activation strength, stroke might also alter communications (i.e., connectivity) between brain regions. We develop a hierarchical Bayesian modeling approach for the analysis of multi-subject fMRI data that allows us to explore brain changes due to stroke. Our approach simultaneously estimates activation and condition-specific connectivity at the group level, and provides estimates for region/subject-specific hemodynamic response functions. Moreover, our model uses spike and slab priors to allow for direct posterior inference on the connectivity network. Our results indicate that motor-control regions show greater activation in the unaffected hemisphere and the midline surface in stroke patients than those same regions in healthy controls during the simple motor task. We also note increased connectivity within secondary motor regions in stroke subjects. These findings provide insight into altered neural correlates of movement in subjects who suffered a stroke.
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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.