mpdcm: A toolbox for massively parallel dynamic causal modeling

Eduardo A Aponte, Sudhir Raman, Biswa Sengupta, Will D. Penny, Klaas E Stephan, Jakob Heinzle

Research output: Contribution to journalArticlepeer-review

22 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)7-16
Number of pages10
JournalJournal of Neuroscience Methods
Volume257
Early online date16 Sep 2015
DOIs
Publication statusPublished - 15 Jan 2016

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

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