  In this study, we propose a neural network approach to capture the functional
connectivities among anatomic brain regions. The suggested approach estimates a
set of brain networks, each of which represents the connectivity patterns of a
cognitive process. We employ two different architectures of neural networks to
extract directed and undirected brain networks from functional Magnetic
Resonance Imaging (fMRI) data. Then, we use the edge weights of the estimated
brain networks to train a classifier, namely, Support Vector Machines(SVM) to
label the underlying cognitive process. We compare our brain network models
with popular models, which generate similar functional brain networks. We
observe that both undirected and directed brain networks surpass the
performances of the network models used in the fMRI literature. We also observe
that directed brain networks offer more discriminative features compared to the
undirected ones for recognizing the cognitive processes. The representation
power of the suggested brain networks are tested in a task-fMRI dataset of
Human Connectome Project and a Complex Problem Solving dataset.
