The Library
Searching multiregression dynamic models of resting-state fMRI networks using integer programming
Tools
Tools
Tools
Costa, Lilia, Smith, J. Q., Nichols, Thomas E., Cussens, James, Duff, Eugene P. and Makin, Tamar R. (2015) Searching multiregression dynamic models of resting-state fMRI networks using integer programming. Bayesian Analysis, 10 (2). pp. 441-478. doi:10.1214/14-BA913 ISSN 1931-6690.
|
PDF
WRAP_Costa_BA913.pdf - Published Version - Requires a PDF viewer. Download (3325Kb) | Preview |
Official URL: http://dx.doi.org/10.1214/14-BA913
Abstract
A Multiregression Dynamic Model (MDM) is a class of multivariate time series that represents various dynamic causal processes in a graphical way. One of the advantages of this class is that, in contrast to many other Dynamic Bayesian Networks, the hypothesised relationships accommodate conditional conjugate inference. We demonstrate for the first time how straightforward it is to search over all possible connectivity networks with dynamically changing intensity of transmission to find the MAP model within this class. This search method is made feasible by using a novel application of an Integer Programming algorithm. The efficacy of applying this particular class of dynamic models to this domain is shown and more specifically the computational efficiency of a corresponding search of 11-node DAG model space. We proceed to show how diagnostic methods, analogous to those defined for static Bayesian Networks, can be used to suggest embellishment of the model class to extend the process of model selection. All methods are illustrated using simulated and real resting-state functional Magnetic Resonance Imaging (fMRI) data.
| Item Type: | Journal Article | ||||||
|---|---|---|---|---|---|---|---|
| Subjects: | Q Science > QA Mathematics | ||||||
| Divisions: | Faculty of Science, Engineering and Medicine > Science > Statistics | ||||||
| Library of Congress Subject Headings (LCSH): | Multivariate analysis -- Graphic methods, Magnetic resonance microscopy, Integer programming | ||||||
| Journal or Publication Title: | Bayesian Analysis | ||||||
| Publisher: | International Society for Bayesian Analysis | ||||||
| ISSN: | 1931-6690 | ||||||
| Official Date: | 2015 | ||||||
| Dates: |
|
||||||
| Volume: | 10 | ||||||
| Number: | 2 | ||||||
| Number of Pages: | 40 | ||||||
| Page Range: | pp. 441-478 | ||||||
| DOI: | 10.1214/14-BA913 | ||||||
| Status: | Peer Reviewed | ||||||
| Publication Status: | Published | ||||||
| Date of first compliant deposit: | 28 July 2016 | ||||||
| Date of first compliant Open Access: | 28 July 2016 | ||||||
| Related URLs: |
Request changes or add full text files to a record
Repository staff actions (login required)
 |
View Item |
Downloads
Downloads per month over past year
