Tools
Tools
Aponte, Eduardo A, Raman, Sudhir, Sengupta, Biswa, Penny, Will D. 
ORCID: https://orcid.org/0000-0001-9064-1191, Stephan, Klaas E and Heinzle, Jakob
(2016)
mpdcm:A toolbox for massively parallel dynamic causal modeling.
Journal of Neuroscience Methods, 257.
pp. 7-16.
ISSN 0165-0270
Abstract
BACKGROUND: Dynamic causal modeling (DCM) for fMRI is an established method for Bayesian system identification and inference on effective brain connectivity. DCM relies on a biophysical model that links hidden neuronal activity to measurable BOLD signals. Currently, biophysical simulations from DCM constitute a serious computational hindrance. Here, we present Massively Parallel Dynamic Causal Modeling (mpdcm), a toolbox designed to address this bottleneck. NEW METHOD: mpdcm delegates the generation of simulations from DCM's biophysical model to graphical processing units (GPUs). Simulations are generated in parallel by implementing a low storage explicit Runge-Kutta's scheme on a GPU architecture. mpdcm is publicly available under the GPLv3 license. RESULTS: We found that mpdcm efficiently generates large number of simulations without compromising their accuracy. As applications of mpdcm, we suggest two computationally expensive sampling algorithms: thermodynamic integration and parallel tempering. COMPARISON WITH EXISTING METHOD(S): mpdcm is up to two orders of magnitude more efficient than the standard implementation in the software package SPM. Parallel tempering increases the mixing properties of the traditional Metropolis-Hastings algorithm at low computational cost given efficient, parallel simulations of a model. CONCLUSIONS: Future applications of DCM will likely require increasingly large computational resources, for example, when the likelihood landscape of a model is multimodal, or when implementing sampling methods for multi-subject analysis. Due to the wide availability of GPUs, algorithmic advances can be readily available in the absence of access to large computer grids, or when there is a lack of expertise to implement algorithms in such grids.
| Item Type: | Article |
|---|---|
| Additional Information: | Copyright © 2015 Elsevier B.V. All rights reserved. |
| Uncontrolled Keywords: | access to information,algorithms,bayes theorem,brain,brain mapping,cerebrovascular circulation,computer graphics,computer simulation,magnetic resonance imaging,neurological models,statistical models,oxygen,computer-assisted signal processing,software,thermodynamics,comparative study,article |
| Faculty \ School: | Faculty of Social Sciences > School of Psychology |
| UEA Research Groups: | Faculty of Social Sciences > Research Centres > Centre for Behavioural and Experimental Social Sciences |
| Depositing User: | Pure Connector |
| Date Deposited: | 19 Aug 2017 05:06 |
| Last Modified: | 19 Apr 2023 22:33 |
| URI: | https://ueaeprints.uea.ac.uk/id/eprint/64578 |
| DOI: | 10.1016/j.jneumeth.2015.09.009 |
Actions (login required)
 |
View Item |