A constrained NMF algorithm for bold detection in fMRI

In this paper the application of Nonnegative Matrix Factorization (NMF) to Functional Magnetic Resonance Images (fMRIs) is addressed. We attempt to blindly separate the sources of fMRI mixtures. However, our interest is to find only one particular source (task-related source), which indicates the active area in the brain. We utilize the prior knowledge about time course of the corresponding source to automatically extract it. By proposing a template based on this prior knowledge we set up a constrained local cost function which is to be minimized. In order to be able to achieve such an optimization, the Hierarchical Alternate Least Square (HALS) algorithm is adopted. The advantage of the proposed method is to simultaneously separate and distinguish the source of interest from other sources.