Template-guided Functional Network Identification via Supervised Dictionary Learning

Functional network analysis based on matrix decomposition/factorization methods including ICA and dictionary learning models have become a popular approach in fMRI study. Yet it is still a challenging issue in interpreting the result networks because of the inter-subject variability and image noises, thus in many cases, manual inspection on the obtained networks is needed. Aiming to provide a fast and reliable functional network identification tool for both normal and diseased brain fMRI data analysis, in this work, we propose a novel supervised dictionary learning model based on rank-1 matrix decomposition algorithm (S-r1DL) with sparseness constraint. Application on the Autism Brain Imaging Data Exchange (ABIDE) database showed that S-r1DL can fast and accurately identify the functional networks based on the given templates, comparing to unsupervised learning method.