Biomechanics and Evolution of the Primate Tongue.

The tongue plays a crucial role in feeding by positioning, manipulating, and transporting the bolus during chewing and swallowing. As a muscular hydrostat, its biomechanical function relies on regional deformations and coordinated movements with the jaw. Sensory feedback from oral afferents, particularly via the trigeminal nerve, is critical for modulating these movements and deformations. This study investigates how food texture and oral sensory perturbations influence tongue kinematics in an omnivorous carnivoran, the skunk (Mephitis mephitis). Using X-ray Reconstruction of Moving Morphology (XROMM) and controlled nerve blocks to the tongue and teeth, we analyzed tongue protraction-retraction, regional lengthening-shortening, and their timing relative to the gape cycle across three foods-banana, carrot, and kibble. Results indicate that food properties significantly impact tongue movements, with soft foods like banana eliciting greater anteroposterior motion and posterior tongue deformation. Despite these kinematic differences, the timing of tongue movements relative to jaw cycles remains consistent, but there are differences in the timing of regional lengthening and shortening between foods. Bilateral nerve blocks altered tongue kinematics and deformations, particularly regional deformations, but did not disrupt overall coordination with the chewing cycle. These findings suggest that oral afferents refine motor commands, optimizing tongue-bolus interactions while rhythmic jaw-tongue coordination patterns are maintained. This study enhances our understanding of sensorimotor integration in mammalian feeding and provides insights on tongue biomechanics as a muscular hydrostat.

A new framework is presented for comprehensive analysis of the three-dimensional shape and architecture of human skeletal muscles from magnetic resonance and diffusion tensor imaging data. The framework comprises three key features: 1) identification of points on the surface of and inside a muscle that have a correspondence to points on and inside another muscle, 2) reconstruction of average muscle shape and average muscle fiber orientations, and 3) utilization of data on between-muscle variation to visualize and make statistical inferences about changes or differences in muscle shape and architecture. The general use of the framework is demonstrated by its application to three case studies. Analysis of data obtained before and after 8 wk of strength training revealed there was little regional variation in hypertrophy of the vastus medialis and vastus lateralis and no systematic change in pennation angle. Analysis of passive muscle lengthening revealed heterogeneous changes in shape of the medial gastrocnemius and confirmed the ability of the methods to detect subtle changes in muscle fiber orientation. Analysis of the medial gastrocnemius of children with unilateral cerebral palsy showed that muscles in the more-affected limb were shorter, thinner, and less wide than muscles in the less-affected limb and had slightly more pennate muscle fibers in the central and proximal part of the muscle. Among other applications, the framework can be used to explore the mechanics of muscle contraction, investigate adaptations of muscle architecture, build anatomically realistic computational models of skeletal muscles, and compare muscle shape and architecture between species.NEW & NOTEWORTHY Muscle architecture is conventionally measured using simple scalar metrics such as muscle volume and average fascicle lengths. Here, a new framework is proposed for analysis of complex changes in three-dimensional architecture of whole human muscles from magnetic resonance and diffusion tensor imaging data. The general use of the framework is demonstrated through visualization, quantification, and statistical analysis of the effect of strength training, passive lengthening and cerebral palsy on three-dimensional muscle shape and architecture.

Tongue anatomy and function is widely described as consisting of four extrinsic muscles to control position and four intrinsic muscles to control shape. This myoarchitecture cannot, however, explain independent tongue body and blade movement nor accurately model the subtlety of observed lingual shapes. This study presents the case for a finer neuromuscular structure and functional description. Using the theoretical framework of the partitioning hypothesis, evidence for neuromuscular compartments of each of the lingual muscles was discerned by reviewing studies of lingual anatomy, hypoglossal nerve staining, hypoglossal motoneuron axon tracing, muscle fiber type distribution, and electromyography. Muscle fibers of the visible human female were manually traced to produce a three-dimensional atlas of muscular compartments. A kinematic study was undertaken to determine the degree of independent movement between different parts of the tongue. A simple biomechanical model was used to demonstrate how synergistic groups of compartments can control sectors of the tongue. Results indicated as many as 10 compartments of genioglossus, two each of superior and inferior longitudinal, eight of styloglossus, three of hyoglossus, and six each of transversus and verticalis, while palatoglossus may not have a significant role in tongue function. Kinematic analysis indicated independent control of five sectors of the tongue body, and biomechanical modeling demonstrated how this control may be achieved. Evidence is presented for a lingual structure based on neuromuscular compartments, which work together to position and shape sectors of the tongue and independently control tongue body and blade.

Measurements of muscle architecture are crucial for understanding muscle function but are often difficult to obtain in human muscles in vivo. This study aimed to create population-averaged atlases of human rotator cuff muscle shape and muscle fibre orientations from anatomical magnetic resonance images (MRI) and diffusion-weighted images (DWI), and to utilize these atlases to predict muscle fibre orientations from anatomical MRI data alone. An image registration framework was applied to co-register anatomical MRI and DWI data of 11 male and 9 female subjects into sex-specific common spaces, forming the basis for the atlases. The accuracy of registration was quantified using Dice coefficients, angular correlation coefficients (ACCs), and angular differences. The same metrics were used to assess the capability of the atlases to predict fibre orientations for subjects not included in the atlas construction, via leave-one-out cross-validation. The results showed that individual male and female image data were accurately registered into their respective atlas spaces, with high Dice coefficients (0.888 ± 0.002 for males, 0.856 ± 0.021 for females) and consistent angular alignment as evidenced by the ACCs and angular differences. Predicted fibre orientations for out-of-sample subjects closely matched those derived from DWI images, exhibiting improved smoothness and coverage (ACC: 0.909 ± 0.011 for males, 0.942 ± 0.011 for females; angular difference: 13.8 ± 1.3° for males, 11.2 ± 1.2° for females). These findings demonstrate that population-averaged atlases not only enhance muscle architecture reconstructions but also enable the accurate prediction of muscle fibre orientations using only anatomical MRI scans.

The tongue is a muscular hydrostat whose complex fibre architecture enables its diverse functions in swallowing, speech, and breathing. Current understanding of the tongue's structure is largely based on ex vivo dissections, which are not directly linked to function. This study aimed to develop an anatomical atlas of the living human tongue incorporating detailed muscle architecture. The pharynx of 20 healthy volunteers (10 females) were imaged with 3T MRI, collecting mDIXON and diffusion-weighted images (DWI). Multichannel registration was used to align scans from individual participants into a common spatial reference frame to create a population-averaged tongue atlas. The atlas was able to reliably predict tongue muscle architecture of tongues not used in atlas construction, although accuracy varied with the image types used. The best performance, assessed with a local angular correlation coefficient (LACC), was achieved when both anatomical (mDIXON) and diffusion-weighted images were used (LACC=0.66±0.04; p ≪ 0.001), but acceptable accuracy was achieved when only anatomical images were used (LACC=0.52±0.04; p ≪ 0.001). Principal component analysis of a tongue statistical shape model based on the atlas identified that the largest source of variation in tongue muscle architecture was related to the position of the hyoid relative to the mandibular plane. A lower hyoid was more common in males (M: 13mm, F: 9mm; p=0.009). This new atlas of in vivo tongue muscle architecture provides a new understanding of the relationships between the muscles and bony structures that may enable more accurate simulation of human living tongue function.

In mammals, the tongue plays a central role in many oral behaviors, but it is particularly essential for feeding and drinking. During feeding, the tongue is used for ingestion, manipulation and positioning of food within the oral cavity, and swallowing. When mammals drink, they do so by lapping, licking, or sucking, and the tongue is vital to uptake and transport of fluid into and through the oral cavity and oropharynx. All of these behaviors are performed with only a partial reliance on a bony support system; instead the tongue engages in complex movements as a muscular hydrostat – where selective contraction of intrinsic and extrinsic muscle fibers produce movements and deformations of the tongue. The tongue's anatomical complexity and its location inside the oral cavity make it particularly difficult to visualize and study the movements that contribute to these and other oral behaviors. The objective of this study is to test the ability of marker‐based XROMM (X‐ray reconstruction of moving morphology) to track shape changes throughout the body of the tongue. The striped skunk (Mephitis mephitis), a mammalian omnivore belonging to the order Carnivora, was used as a model for rhythmic lapping while drinking a barium liquid. Data was collected using XROMM with markers implanted in the bones of the skull as well as in the body of the tongue. Tongue markers were used to calculate length changes throughout the body of the tongue. Timing and movement parameters are also registered to the gape cycle derived from the rigid body motions derived from skeletal markers. Based on an analysis of 150 cycles, during rhythmic drinking, the middle and posterior tongue underwent the most variation in length change, while the anterior tongue underwent the least amount of variation in length. In contrast, the posterior tongue underwent the least amount of variation in tongue width, as compared to the anterior and middle tongue. Maximum overall tongue length occurred 2.74±29.3 msec before maximum gape, whereas minimal overall tongue length occurred 20.24±7.38 msec before minimum gape. Based on these preliminary results, this method shows promise for producing quantitative data for studying tongue deformations within the oral cavity. Future studies incorporating spatial parameters of the tongue to the rest of the feeding apparatus across mammals will further our understanding of how the tongue functions during feeding and drinking behaviors.Support or Funding InformationNational Science Foundation Grant DBI‐0922988, National Science Foundation Grant IOS‐1456810This abstract is from the Experimental Biology 2018 Meeting. There is no full text article associated with this abstract published in The FASEB Journal.

Lung elastography aims at measuring the lung parenchymal tissue elasticity for applications ranging from diagnostic purposes to biomechanically guided deformations. Characterizing the lung tissue elasticity requires four-dimensional (4D) lung motion as an input, which is currently estimated by deformably registering 4D computed tomography (4DCT) datasets. Since 4DCT imaging is widely used only in a radiotherapy treatment setup, there is a need to predict the elasticity distribution in the absence of 4D imaging for applications within and outside of radiotherapy domain. In this paper, we present a machine learning-based method that predicts the three-dimensional (3D) lung tissue elasticity distribution for a given end-expiration 3DCT. The method to predict the lung tissue elasticity from an end-expiration 3DCT employed a deep neural network that predicts the tissue elasticity for the given CT dataset. For training and validation purposes, we employed five-dimensional CT (5DCT) datasets and a finite element biomechanical lung model. The 5DCT model was first used to generate end-expiration lung geometry, which was taken as the source lung geometry for biomechanical modeling. The deformation vector field pointing from end expiration to end inhalation was computed from the 5DCT model and taken as input in order to solve for the lung tissue elasticity. An inverse elasticity estimation process was employed, where we iteratively solved for the lung elasticity distribution until the model reproduced the ground-truth deformation vector field. The machine learning process uses a specific type of learning process, namely a constrained generalized adversarial neural network (cGAN) that learned the lung tissue elasticity in a supervised manner. The biomechanically estimated tissue elasticity together with the end-exhalation CT was the input for the supervised learning. The trained cGAN generated the elasticity from a given breath-hold CT image. The elasticity estimated was validated in two approaches. In the first approach, a L2-norm-based direct comparison was employed between the estimated elasticity and the ground-truth elasticity. In the second approach, we generated a synthetic four-dimensional CT (4DCT0 using a lung biomechanical model and the estimated elasticity and compared the deformations with the ground-truth 4D deformations using three image similarity metrics: mutual Information (MI), structured similarity index (SSIM), and normalized cross correlation (NCC). The results show that a cGAN-based machine learning approach was effective in computing the lung tissue elasticity given the end-expiration CT datasets. For the training data set, we obtained a learning accuracy of 0.44±0.2KPa. For the validation dataset, consisting of 13 4D datasets, we were able to obtain an accuracy of 0.87±0.4KPa. These results show that the cGAN-generated elasticity correlates well with that of the underlying ground-truth elasticity. We then integrated the estimated elasticity with the biomechanical model and applied the same boundary conditions in order to generate the end inhalation CT. The cGAN-generated images were very similar to that of the original end inhalation CT. The average value of the MI is 1.77 indicating the high local symmetricity between the ground truth and the cGAN elasticity-generated end inhalation CT data. The average value of the structural similarity for the 13 patients was observed to be 0.89 indicating the high structural integrity of the cGAN elasticity-generated end inhalation CT. Finally, the average NCC value of 0.97 indicates that potential variations in the contrast and brightness of the cGAN elasticity-generated end inhalation CT and the ground-truth end inhalation CT. The cGAN-generated lung tissue elasticity given an end-expiration CT image can be computed in near real time. Using the lung tissue elasticity along with a biomechanical model, 4D lung deformations can be generated from a given end-expiration CT image within clinically acceptable numerical accuracy.

Almost twenty years have passed since the muscular hydrostat theory of tongue function was set forth ([7][1]). Yet few studies have explored one of its central predictions: that “extrinsic” tongue muscles (muscles with origin outside the tongue body) and “intrinsic” tongue muscles (muscles

ABSTRACTMeasurements of muscle architecture are crucial for understanding muscle function but are often difficult to obtain in human muscles in vivo. This study aimed to create population‐averaged atlases of human rotator cuff muscle shape and muscle fibre orientations from anatomical magnetic resonance images (MRI) and diffusion‐weighted images (DWI) and to utilise these atlases to predict muscle fibre orientations from anatomical MRI data alone. An image registration framework was applied to coregister anatomical MRI and DWI data of 11 male and 9 female subjects into sex‐specific common spaces, forming the basis for the atlases. The accuracy of registration was quantified using Dice coefficients, angular correlation coefficients (ACCs) and angular differences. The same metrics were used to assess the capability of the atlases to predict fibre orientations for subjects not included in the atlas construction, via leave‐one‐out cross‐validation. The results showed that individual male and female image data were accurately registered into their respective atlas spaces, with high Dice coefficients (0.888 ± 0.002 for males, 0.856 ± 0.021 for females) and consistent angular alignment as evidenced by the ACCs and angular differences. Predicted fibre orientations for out‐of‐sample subjects closely matched those derived from DWI images, exhibiting improved smoothness and coverage (ACC: 0.909 ± 0.011 for males, 0.942 ± 0.011 for females; angular difference: 13.8° ± 1.3° for males, 11.2° ± 1.2° for females). These findings demonstrate that population‐averaged atlases enhance muscle architecture reconstructions and enable the accurate prediction of muscle fibre orientations using only anatomical MRI scans in younger individuals without shoulder injuries.

[This corrects the article DOI: 10.1371/journal.pone.0205944.].

The goal of this study is to validate the muscle architecture derived from both ex vivo and in vivo diffusion-weighted magnetic resonance imaging (dMRI) of the human tongue with histology of an ex vivo tongue. dMRI was acquired with a 200-direction high angular resolution diffusion imaging (HARDI) diffusion scheme for both a postmortem head (imaged within 48 hr after death) and a healthy volunteer. After MRI, the postmortem head was fixed and the tongue excised for hematoxylin and eosin (H&E) staining and histology imaging. Structure tensor images were generated from the stained images to better demonstrate muscle fiber orientations. The tongue muscle fiber orientations, estimated from dMRI, were visualized using the tractogram, a novel representation of crossing fiber orientations, and compared against the histology images of the ex vivo tongue. Muscle fibers identified in the tractograms showed good correspondence with those appearing in the histology images. We further demonstrated tongue muscle architecture in in vivo tractograms for the entire tongue. The study demonstrates that dMRI can accurately reveal the complex muscle architecture of the human tongue and may potentially benefit planning and evaluation of oral surgery and research on speech and swallowing.

Cerebral palsy (CP) is associated with movement disorders and reduced muscle size. This latter phenomenon has been observed by computing muscle volumes from conventional MRI, with most studies reporting significantly reduced volumes in leg muscles. This indicates impaired muscle growth, but without knowing muscle fiber orientation, it is not clear whether muscle growth in CP is impaired in the along-fiber direction (indicating shortened muscles and limited range of motion) or the cross-fiber direction (indicating weak muscles and impaired strength). Using Diffusion Tensor Imaging (DTI) we can determine muscle fiber orientation and construct 3D muscle architectures which can be used to examine both along-fiber length and cross-sectional area. Such an approach has not been undertaken in CP. Here, we use advanced DTI sequences with fast imaging times to capture fiber orientations in the soleus muscle of children with CP and age-matched, able-bodied controls. Cross sectional areas perpendicular to the muscle fiber direction were reduced (37 ± 11%) in children with CP compared to controls, indicating impaired muscle strength. Along-fiber muscle lengths were not different between groups. This study is the first to demonstrate along-fiber and cross-fiber muscle architecture in CP using DTI and implicates impaired cross-sectional muscle growth in children with cerebral palsy.

The tongue has a wide variety of motor functions, which are driven by tongue muscle contractions and associated with movements of the hyoid bone (HB) connected to the tongue root. HB movement has been observed in many situations, including swallowing, breathing, and speech. However, the relationships between HB movement and tongue kinematic function have received little attention, and have not been considered in most previous biomechanical tongue modeling research, except studies of swallowing. The current study aimed to clarify the effects of HB movement on tongue kinematics during tongue forward protrusion, which is an essential tongue motor function associated with speech disorder. HB displacement during tongue forward protrusion was quantified using ultrasound imaging in four healthy controls. Furthermore, computational mechanical simulations of tongue forward protrusion were conducted with observed HB movements and active contraction of the genioglossus (GG) muscle, which is conventionally considered to be the driving muscle in tongue forward protrusion. Ultrasound imaging revealed anterosuperior HB displacement in tongue forward protrusion, with a similar magnitude in each direction (anterior: 6.3 ± 2.8 mm, superior: 5.8 ± 1.6 mm). Computational simulation demonstrated that the HB movement described above caused not only anterosuperior displacement, but also forward rotation of the tongue body, which was caused by kinematic constraints of GG. The resulting anterior displacement of the tongue tip was 1.5 times greater compared with that without HB movement. These findings indicate that the HB and associated tongue body movements play non-negligible roles in the tongue kinematics of forward protrusion.

BackgroundMusculoskeletal alterations causing reduced range of motion of the ankle joint are common in children with cerebral palsy (CP). Objective measurements of passive joint resistance and three-dimensional skeletal muscle volume and muscle architecture can lead to a comprehensive understanding of which factors influence joint range of motion.Research questionTo investigate the relation between the passive dorsiflexion of the ankle joint, biomechanical contributing factors to the passive joint resistance, and muscular architectural properties of the triceps surae muscle in children with CP.MethodsIn this cross-sectional observational study, 14 children with spastic CP (bilateral: 5, unilateral: 9, Gross Motor Function Classification System (GMFCS) level I:11, II:3) naïve to intramuscular tone reducing treatment, and 14 TD children were included. The passive dorsiflexion of the ankle was measured with a goniometer. Passive joint resistance and related parameters were estimated based on a biomechanical model and measurements using a motorized device, the Neuroflexor. Three-dimensional muscle architecture was quantified with diffusion tensor magnetic resonance imaging (DT-MRI).ResultsIn the CP group, the median [min, max] passive dorsiflexion was decreased in the most affected leg (MAL) compared to the less affected leg (LAL) (2.5° [-25°, 20°] vs. 12.5° [5°, 30°], p = 0.001). The stiffness coefficient (Nm/rad) in the MAL was significantly higher in children with CP compared to TD children (7.10 [3.39, 62.00] vs. 2.82 [1.24, 10.46], p = 0.015). Muscle architecture properties did not differ between CP and TD, except for pennation angle in the medial gastrocnemius (MG) of the MAL (CP 17.64° (2.29) vs. TD 21.46° (3.20), p = 0.017). The stiffness coefficient, in the MAL, correlated negatively to passive dorsiflexion (rs=-0.638) and pennation angle in medial gastrocnemius (rs=-0.964), and the non-linear coefficient (Non-linear 1) correlated negatively to the fascicle length of the medial gastrocnemius (rs=-0.857).ConclusionThis study shows that stiffness of the plantarflexors is related to decreased passive dorsiflexion of the ankle and muscle structure of the MG in high-functioning children with spastic CP. Assessments of how dynamic components as well as microscopic muscle alterations contribute to joint stiffness in the plantarflexors in individuals with CP are warranted.Trial registrationRetrospectively registered in ClinicalTrials.gov, NCT05447299. Observational study. Study start: 2019-01-15, register date: 2022-07-01.

The aim of this study was to compare the knee extension moment of older individuals with the muscle moment estimated through a biomechanical model. This was accomplished by using (1) the specific muscle architecture data of individuals, and (2) the generic muscle architecture available in the literature. The muscle force estimate was determined using a model with the muscle architecture from cadavers and the individual vastus lateralis muscle architecture of sixteen older volunteers. For the muscle moment comparison, all of the volunteers performed maximal voluntary isometric contractions (MVIC) in five different knee extension position angles. The architectural data was acquired using both resonance and ultrasound imaging. Both estimated muscle moments (generic and individual) were higher than the experimental. The architecture of the other vastii may be necessary to make the model more accurate for the older population. Although other factors inherent to ageing, such as co-contractions, fiber type percentage, and passive forces are not considered in the model, they could be responsible for the differences between moments in older people.