Header

UZH-Logo

Maintenance Infos

mpdcm: A toolbox for massively parallel dynamic causal modeling


Aponte, Eduardo A; Raman, Sudhir; Sengupta, Biswa; Penny, Will D; Stephan, Klaas E; Heinzle, Jakob (2016). mpdcm: A toolbox for massively parallel dynamic causal modeling. Journal of Neuroscience Methods, 257:7-16.

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.

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.

Statistics

Citations

8 citations in Web of Science®
8 citations in Scopus®
Google Scholar™

Altmetrics

Downloads

7 downloads since deposited on 19 Nov 2015
4 downloads since 12 months
Detailed statistics

Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:04 Faculty of Medicine > Institute of Biomedical Engineering
Dewey Decimal Classification:170 Ethics
610 Medicine & health
Uncontrolled Keywords:Bayesian model comparison; Dynamic causal modeling; GPU; Markov chain Monte Carlo; Model evidence; Model inversion; Parallel tempering; Thermodynamic integration
Language:English
Date:2016
Deposited On:19 Nov 2015 08:31
Last Modified:08 Dec 2017 14:48
Publisher:Elsevier
ISSN:0165-0270
Publisher DOI:https://doi.org/10.1016/j.jneumeth.2015.09.009
PubMed ID:26384541

Download