title: mpdcm: A toolbox for massively parallel dynamic causal modeling.
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.
  
  
  METHODS
  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.
  
  
  CONCLUSIONS
  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.
authors.affiliation: Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich and Swiss Federal Institute of Technology (ETH), 8032 Zurich, Switzerland. Electronic address: aponte@biomed.ee.ethz.ch.
authors.forename: Eduardo A
authors.initials: EA
authors.lastname: Aponte
authors.affiliation: Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich and Swiss Federal Institute of Technology (ETH), 8032 Zurich, Switzerland.
authors.forename: Sudhir
authors.initials: S
authors.lastname: Raman
authors.affiliation: Wellcome Trust Centre for Neuroimaging, University College London, 12 Queen Square, London WC1N 3BG, UK.
authors.forename: Biswa
authors.initials: B
authors.lastname: Sengupta
authors.affiliation: Wellcome Trust Centre for Neuroimaging, University College London, 12 Queen Square, London WC1N 3BG, UK.
authors.forename: Will D
authors.initials: WD
authors.lastname: Penny
authors.affiliation: Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich and Swiss Federal Institute of Technology (ETH), 8032 Zurich, Switzerland; Wellcome Trust Centre for Neuroimaging, University College London, 12 Queen Square, London WC1N 3BG, UK; Max Planck Institute for Metabolism Research, 50931 Cologne, Germany.
authors.forename: Klaas E
authors.initials: KE
authors.lastname: Stephan
authors.affiliation: Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich and Swiss Federal Institute of Technology (ETH), 8032 Zurich, Switzerland.
authors.forename: Jakob
authors.initials: J
authors.lastname: Heinzle
mesh_terms: D022126:Access to Information; D000465:Algorithms; D001499:Bayes Theorem; D001921:Brain / Q000502:physiology; D001931:Brain Mapping / Q000379:methods*; D002560:Cerebrovascular Circulation / Q000502:physiology; D003196:Computer Graphics; D003198:Computer Simulation; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008959:Models, Neurological; D015233:Models, Statistical; D010100:Oxygen / Q000097:blood; D012815:Signal Processing, Computer-Assisted; D012984:Software; D013816:Thermodynamics
chemical_list: D010100:Oxygen
keywords: Bayesian model comparison; Dynamic causal modeling; GPU; Markov chain Monte Carlo; Model evidence; Model inversion; Parallel tempering; Thermodynamic integration