mpdcm:A toolbox for massively parallel dynamic causal modeling

Aponte, Eduardo A, Raman, Sudhir, Sengupta, Biswa, Penny, Will D. ORCID:, 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

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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
DOI: 10.1016/j.jneumeth.2015.09.009

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