  Muscle coordination patterns of lingual behaviors are synergies generated by
deforming local muscle groups in a variety of ways. Functional units are
functional muscle groups of local structural elements within the tongue that
compress, expand, and move in a cohesive and consistent manner. Identifying the
functional units using tagged-Magnetic Resonance Imaging (MRI) sheds light on
the mechanisms of normal and pathological muscle coordination patterns,
yielding improvement in surgical planning, treatment, or rehabilitation
procedures. Here, to mine this information, we propose a matrix factorization
and probabilistic graphical model framework to produce building blocks and
their associated weighting map using motion quantities extracted from
tagged-MRI. Our tagged-MRI imaging and accurate voxel-level tracking provide
previously unavailable internal tongue motion patterns, thus revealing the
inner workings of the tongue during speech or other lingual behaviors. We then
employ spectral clustering on the weighting map to identify the cohesive
regions defined by the tongue motion that may involve multiple or undocumented
regions. To evaluate our method, we perform a series of experiments. We first
use two-dimensional images and synthetic data to demonstrate the accuracy of
our method. We then use three-dimensional synthetic and \textit{in vivo} tongue
motion data using protrusion and simple speech tasks to identify
subject-specific and data-driven functional units of the tongue in localized
regions.
