Graph signal processing extends the notion of frequency from signals in the time domain to signals defined on graphs. Graph signals arise in many applications including brain signals defined on functional connectivity networks. Most of the current work on graph signal processing focuses on static graphs. However, functional connectivity networks are dynamic and the signals on these networks change with time. In this paper, we introduce a new transform for dynamic networks named as Dynamic Graph Fourier Transform (DGFT). The proposed approach extends the notion of graph Laplacian from the static case to the dynamic case through the network Laplacian tensor. The basis functions for the transform are obtained through the Tucker decomposition of this Laplacian tensor. The proposed method detects nonstationary activity in the network structure and allows us to obtain information about the regions in the brain that contribute to different frequency contents in a cognitive control experiment.