Then, three network search algorithms have been adapted for DCM, and we demonstrated how modifications to these methods, based on DCM posterior parameter estimates, can enhance search performance. First, we reimplemented the deterministic bilinear DCM algorithm in the ReDCM R package, increasing computational speed during model estimation. For test data a publicly available fMRI dataset of 60 subjects was used. Because of the enormous computational demand of DCM calculations, we separated the model estimation procedure from the search algorithm by providing a database containing the parameters of all models in a full model-space. We aimed to utilize commonly used graph theoretical search algorithms for DCM to create a framework for characterizing them, and to investigate relevance of such methods for single-subject and group-level studies. However, the development of such methods is cumbersome in the case of large model-spaces. Using heuristic model search algorithms enables us to find an optimal model without having to define a model set a priori. ![]() Finding the model explaining measured data is one of the most important outstanding problems in Bayesian modeling. 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.ĭynamic causal modeling (DCM) is a widely used tool to estimate the effective connectivity of specified models of a brain network. Parallel tempering increases the mixing properties of the traditional Metropolis-Hastings algorithm at low computational cost given efficient, parallel simulations of a model.įuture 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. COMPARISON WITH EXISTING METHOD(S): mpdcm is up to two orders of magnitude more efficient than the standard implementation in the software package SPM. As applications of mpdcm, we suggest two computationally expensive sampling algorithms: thermodynamic integration and parallel tempering. We found that mpdcm efficiently generates large number of simulations without compromising their accuracy. mpdcm is publicly available under the GPLv3 license. Simulations are generated in parallel by implementing a low storage explicit Runge-Kutta's scheme on a GPU architecture. NEW METHOD: mpdcm delegates the generation of simulations from DCM's biophysical model to graphical processing units (GPUs). ![]() Here, we present Massively Parallel Dynamic Causal Modeling (mpdcm), a toolbox designed to address this bottleneck. Currently, biophysical simulations from DCM constitute a serious computational hindrance. DCM relies on a biophysical model that links hidden neuronal activity to measurable BOLD signals. Dynamic causal modeling (DCM) for fMRI is an established method for Bayesian system identification and inference on effective brain connectivity.
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