Accelerating Computation of DCM for ERP with GPU-Based Parallel Strategy

This paper presents the use of graphic processing unit (GPU) to accelerate a brain-activity analytical tool, the Dynamic Causal Modelling for Event Related Potential (DCM for ERP) in MATLAB. DCM for ERP is a recently developed advanced method for studying neuronal effective connectivity and making inference about the brain functions. DCM utilizes an iterative procedure, the expectation maximization (EM) algorithm, to find the optimal parameters given a set of observed events (data) and the underlying probability model, such that the likelihood function is maximized. As the EM algorithm is computationally demanding, time consuming and largely data dependent, we propose a parallel computing scheme using GPUs to achieve a fast estimation of neural effective connectivity in DCM. The computational loading of EM was partitioned and dynamically distributed to either the threads or blocks according to the DCM model complex (i.e. the number of parameters to be estimated). The performance of this dynamic loading arrangement in terms of execution time and accuracy loss were evaluated using synthetic data. The results show that our method can accelerate a computation task by about 30 times as fast as the MATLAB version.