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The human tongue exhibits an orchestrated arrangement of internal muscles, working in sequential order to execute tongue movements. Understanding the muscle coordination patterns involved in tongue protrusive motion is crucial for advancing knowledge of tongue structure and function. To achieve this, this work focuses on five muscles known to contribute to protrusive motion. Tagged and diffusion MRI data are collected for analysis of muscle fiber geometry and motion patterns. Lagrangian strain measurements are derived, and Granger causal analysis is carried out to assess predictive information among the muscles. Experimental results suggest sequential muscle coordination of protrusive motion among distinct muscle groups. [ABSTRACT FROM AUTHOR]

Magnetic resonance (MR) imaging is becoming an established tool in capturing articulatory and physiological motion of the structures and muscles throughout the vocal tract and enabling visual and quantitative assessment of real-time speech activities. Although motion capture speed has been regularly improved by the continual developments in high-speed MR technology, quantitative analysis of multi-subject group data remains challenging due to variations in speaking rate and imaging time among different subjects. In this paper, a workflow of post-processing methods that matches different MR image datasets within a study group is proposed. Each subject's recorded audio waveform during speech is used to extract temporal domain information and generate temporal alignment mappings from their matching pattern. The corresponding image data are resampled by deformable registration and interpolation of the deformation fields, achieving inter-subject temporal alignment between image sequences. A four-dimensional dynamic MR speech atlas is constructed using aligned volumes from four human subjects. Similarity tests between subject and target domains using the squared error, cross correlation, and mutual information measures all show an overall score increase after spatiotemporal alignment. The amount of image variability in atlas construction is reduced, indicating a quality increase in the multi-subject data for groupwise quantitative analysis. [ABSTRACT FROM AUTHOR]

The ability to differentiate post-cancer from healthy tongue muscle coordination patterns is necessary for the advancement of speech motor control theories and for the development of therapeutic and rehabilitative strategies. A deep learning approach is presented to classify two groups using muscle coordination patterns from magnetic resonance imaging (MRI). The proposed method uses tagged-MRI to track the tongue's internal tissue points and atlas-driven non-negative matrix factorization to reduce the dimensionality of the deformation fields. A convolutional neural network is applied to the classification task yielding an accuracy of 96.90%, offering the potential to the development of therapeutic or rehabilitative strategies in speech-related disorders. [ABSTRACT FROM AUTHOR]

Amyotrophic Lateral Sclerosis (ALS) is a neurological disorder, which impairs tongue function for speech and swallowing. A widely used Diffusion Tensor Imaging (DTI) analysis pipeline is employed for quantifying differences in tongue fiber myoarchitecture between controls and ALS patients. This pipeline uses both high-resolution magnetic resonance imaging (hMRI) and DTI. hMRI is used to delineate tongue muscles, while DTI provides indices to reveal fiber connectivity within and between muscles. The preliminary results using five controls and two patients show quantitative differences between the groups. This work has the potential to provide insights into the detrimental effects of ALS on speech and swallowing. [ABSTRACT FROM AUTHOR]

Magnetic resonance imaging has been widely used in speech production research. Often only one image stack (sagittal, axial, or coronal) is used for vocal tract modeling. As a result, complementary information from other available stacks is not utilized. To overcome this, a recently developed super-resolution technique was applied to integrate three orthogonal low-resolution stacks into one isotropic volume. The results on vowels show that the super-resolution volume produces better vocal tract visualization than any of the low-resolution stacks. Its derived area functions generally produce formant predictions closer to the ground truth, particularly for those formants sensitive to area perturbations at constrictions. [ABSTRACT FROM AUTHOR]

Biomechanical models of the oropharynx facilitate the study of speech function by providing information that cannot be directly derived from imaging data, such as internal muscle forces and muscle activation patterns. Such models, when constructed and simulated based on anatomy and motion captured from individual speakers, enable the exploration of inter-subject variability of speech biomechanics. These models also allow one to answer questions, such as whether speakers produce similar sounds using essentially the same motor patterns with subtle differences, or vastly different motor equivalent patterns. Following this direction, this study uses speaker-specific modeling tools to investigate the muscle activation variability in two simple speech tasks that move the tongue forward (/ə-ɡis/) vs backward (/ə-suk/). Three dimensional tagged magnetic resonance imaging data were used to inversely drive the biomechanical models in four English speakers. Results show that the genioglossus is the workhorse muscle of the tongue, with activity levels of 10% in different subdivisions at different times. Jaw and hyoid positioners (inferior pterygoid and digastric) also show high activation during specific phonemes. Other muscles may be more involved in fine tuning the shapes. For example, slightly more activation of the anterior portion of the transverse is found during apical than laminal /s/, which would protrude the tongue tip to a greater extent for the apical /s/.