The recent deployment of functional networks to analyze fMRI images has been very promising. In this method, the spatio-temporal fMRI data is converted to a graph-based representation, where the nodes are voxels and edges indicate the relationship between the nodes, such as the strength of correlation or causality. Graph-theoretic measures can then be used to compare different fMRI scans. However, there is a significant computational bottleneck, as the computation of functional networks with directed links takes several hours on conventional machines with single CPUs. The study in this paper shows that a GPU can be advantageously used to accelerate the computation, such that the network computation takes a few minutes. Though GPUs have been used for the purposes of displaying fMRI images, their use in computing functional networks is novel. We describe specific techniques such as load balancing, and the use of a large number of threads to achieve the desired speedup. Our experience in utilizing the GPU for functional network computations should prove useful to the scientific community investigating fMRI as GPUs are a low-cost platform for addressing the computational bottleneck. © 2011 Copyright Society of Photo-Optical Instrumentation Engineers (SPIE).