Understanding the Impact of Stroke on Brain Motor Function: A Hierarchical Bayesian Approach. Issue 514 (2nd April 2016)
- Record Type:
- Journal Article
- Title:
- Understanding the Impact of Stroke on Brain Motor Function: A Hierarchical Bayesian Approach. Issue 514 (2nd April 2016)
- Main Title:
- Understanding the Impact of Stroke on Brain Motor Function: A Hierarchical Bayesian Approach
- Authors:
- Yu, Zhe
Prado, Raquel
Quinlan, Erin Burke
Cramer, Steven C.
Ombao, Hernando - Abstract:
- 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. Supplementary materials for this article are available online.
- Is Part Of:
- Journal of the American Statistical Association. Volume 111:Issue 514(2016)
- Journal:
- Journal of the American Statistical Association
- Issue:
- Volume 111:Issue 514(2016)
- Issue Display:
- Volume 111, Issue 514 (2016)
- Year:
- 2016
- Volume:
- 111
- Issue:
- 514
- Issue Sort Value:
- 2016-0111-0514-0000
- Page Start:
- 549
- Page End:
- 563
- Publication Date:
- 2016-04-02
- Subjects:
- Activation -- Connectivity -- fMRI -- Multi-subject
Statistics -- Periodicals
Statistics -- Periodicals
Statistiques -- Périodiques
États-Unis -- Statistiques -- Périodiques
519.5 - Journal URLs:
- http://www.jstor.org/journals/01621459.html ↗
http://www.ingentaconnect.com/content/asa/jasa ↗
http://www.tandfonline.com/loi/uasa20 ↗
http://www.tandfonline.com/ ↗ - DOI:
- 10.1080/01621459.2015.1133425 ↗
- Languages:
- English
- ISSNs:
- 0162-1459
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 4694.000000
British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 2611.xml