Determining functional units of tongue motion via graph-regularized sparse non-negative matrix factorization

Tongue motion during speech and swallowing involves synergies of locally deforming regions, or functional units. Motion clustering during tongue motion can be used to reveal the tongue's intrinsic functional organization. A novel matrix factorization and clustering method for tissues tracked using tagged magnetic resonance imaging (tMRI) is presented. Functional units are estimated using a graph-regularized sparse non-negative matrix factorization framework, learning latent building blocks and the corresponding weighting map from motion features derived from tissue displacements. Spectral clustering using the weighting map is then performed to determine the coherent regions-i.e., functional units- defined by the tongue motion. Two-dimensional image data is used to verify that the proposed algorithm clusters the different types of images accurately. Three-dimensional tMRI data from five subjects carrying out simple non-speech/speech tasks are analyzed to show how the proposed approach defines a subject/task-specific functional parcellation of the tongue in localized regions.