To what extent can mastication functionality be restored following mandibular reconstruction surgery? A computer modeling approach.

STATEMENT OF PROBLEM: Advanced cases of head and neck cancer involving the mandible often require surgical removal of diseased sections and subsequent replacement with donor bone. During the procedure, the surgeon must make decisions regarding which bones or tissues to resect. This requires balancing tradeoffs related to issues such as surgical access and post-operative function; however, the latter is often difficult to predict, especially given that long-term functionality also depends on the impact of post-operative rehabilitation programs. PURPOSE: To assist in surgical decision-making, we present an approach for estimating the effects of reconstruction on key aspects of post-operative mandible function. MATERIAL AND METHODS: We develop dynamic biomechanical models of the reconstructed mandible considering different defect types and validate them using literature data. We use these models to estimate the degree of functionality that might be achieved following post-operative rehabilitation. RESULTS: We find significant potential for restoring mandibular functionality, even in cases involving large defects. This entails an average trajectory error below 2 mm, bite force comparable to a healthy individual, improved condyle mobility, and a muscle activation change capped at a maximum of 20%. CONCLUSION: These results suggest significant potential for adaptability in the masticatory system and improved post-operative rehabilitation, leading to greater restoration of jaw function.

Estimating tongue deformation during laryngoscopy using a hybrid FEM-multibody model and intraoperative tracking - a cadaver study.

Throat tumour margin control remains difficult due to the tight, enclosed space of the oral and throat regions and the tissue deformation resulting from placement of retractors and scopes during surgery. Intraoperative imaging can help with better localization but is hindered by non-image-compatible surgical instruments, cost, and unavailability. We propose a novel method of using instrument tracking and FEM-multibody modelling to simulate soft tissue deformation in the intraoperative setting, without requiring intraoperative imaging, to improve surgical guidance accuracy. We report our first empirical study, based on four trials of a cadaveric head specimen with full neck anatomy, yields a mean TLE of 10.8 ± 5.5 mm, demonstrating methodological feasibility.

All Obstructive Sleep Apnea Events Are Not Created Equal: The Relationship Between Event-related Hypoxemia and Physiologic Response.

RATIONALE: Obstructive sleep apnea (OSA) severity is typically assessed by the apnea-hypopnea index (AHI), a frequency-based metric that allocates equal weight to all respiratory events. However, more severe events may have a greater physiologic impact. OBJECTIVE: The purpose of this study was to determine whether the degree of event-related hypoxemia would be associated with the post-event physiologic response. METHODS: OSA patients (AHI>5/h) from the multi-center Canadian Sleep and Circadian Network cohort were studied. Using mixed-effect linear regression, we examined associations between event-related hypoxic burden (HBev) assessed by the area under the event-related oxygen saturation recording with heart rate changes (ΔHRev), vasoconstriction (vasoconstriction burden (VCBev) assessed with photoplethysmography), and electroencephalographic responses (power ratio before and after events). RESULTS: Polysomnography recordings from 658 patients (Median [IQR] age= 55.00 [45.00, 64.00] years, AHI= 27.15 [14.90, 64.05] event/h, 42% female) were included in the analyses. HBev was associated with an increase in all physiologic responses after controlling for age, sex, body mass index (BMI), sleep stage, total sleep time, and study centers; for example, one standard deviation (SD) increase in HBev was associated with a 0.21 [95% CI: 0.2, 0.22], 0.08 [0.08, 0.09] and 0.22 [0.21, 0.23] SD increase in ΔHRev, VCBev and Beta power ratio respectively). CONCLUSION: Increased event-related hypoxic burden was associated with greater responses across a broad range of physiologic signals. Future metrics that incorporate information about the variability of these physiologic responses may have promise in providing a more nuanced assessment of OSA severity.

Computational models and their applications in biomechanical analysis of mandibular reconstruction surgery.

Advanced head and neck cancers involving the mandible often require surgical removal of the diseased parts and replacement with donor bone or prosthesis to recreate the form and function of the premorbid mandible. The degree to which this reconstruction successfully replicates key geometric features of the original bone critically affects the cosmetic and functional outcomes of speaking, chewing, and breathing. With advancements in computational power, biomechanical modeling has emerged as a prevalent tool for predicting the functional outcomes of the masticatory system and evaluating the effectiveness of reconstruction procedures in patients undergoing mandibular reconstruction surgery. These models offer cost-effective and patient-specific treatment tailored to the needs of individuals. To underscore the significance of biomechanical modeling, we conducted a review of 66 studies that utilized computational models in the biomechanical analysis of mandibular reconstruction surgery. The majority of these studies employed finite element method (FEM) in their approach; therefore, a detailed investigation of FEM has also been provided. Additionally, we categorized these studies based on the main components analyzed, including bone flaps, plates/screws, and prostheses, as well as their design and material composition.

Motor control similarity between speakers saying "a souk" using inverse atlas tongue modeling.

Finite element models (FEM) of the tongue have facilitated speech studies through analysis of internal muscle forces indirectly derived from imaging data. In this work, we build a uniform hexahedral FEM of a tongue atlas constructed from magnetic resonance imaging data of a healthy population. The FEM is driven by inverse internal tongue tissue kinematics of speakers temporally aligned and deformed into the same atlas space, while performing the speech task "a souk" allowing muscle activation predictions. This work aims to investigate the commonalities in tongue motor strategies in the articulation of "a souk" predicted by the inverse tongue atlas model. Our findings report variability among five speakers for estimated muscle activations with a similarity index using a dynamic time warp function. Two speakers show similarity index > 0.9 and two others < 0.7 with respect to a reference speaker for most tongue muscles. The relative motion tracking error of the model is less than 2% which is promising for speech study applications.

Cortical control of posture in fine motor skills: evidence from inter-utterance rest position.

The vocal tract continuously employs tonic muscle activity in the maintenance of postural configurations. Gamma-band activity in the sensorimotor cortex underlies transient movements during speech production, yet little is known about the neural control of postural states in the vocal tract. Simultaneously, there is evidence that sensorimotor beta-band activations contribute to a system of inhibition and state maintenance that is integral to postural control in the body. Here we use electrocorticography to assess the contribution of sensorimotor beta-band activity during speech articulation and postural maintenance, and demonstrate that beta-band activity corresponds to the inhibition of discrete speech movements and the maintenance of tonic postural states in the vocal tract. Our findings identify consistencies between the neural control of posture in speech and what is previously reported in gross motor contexts, providing support for a unified theory of postural control across gross and fine motor skills.

Association of alternative polysomnographic features with patient outcomes in obstructive sleep apnea: a systematic review.

STUDY OBJECTIVES: Polysomnograms (PSGs) collect a plethora of physiologic signals across the night. However, few of these PSG data are incorporated into standard reports, and hence, ultimately, under-utilized in clinical decision making. Recently, there has been substantial interest regarding novel alternative PSG metrics that may help to predict obstructive sleep apnea (OSA)-related outcomes better than standard PSG metrics such as the apnea-hypopnea index. We systematically review the recent literature for studies that examined the use of alternative PSG metrics in the context of OSA and their association with health outcomes. METHODS: We systematically searched EMBASE, MEDLINE, and the Cochrane Database of Systematic Reviews for studies published between 2000 and 2022 for those that reported alternative metrics derived from PSG in adults and related them to OSA-related outcomes. RESULTS: Of the 186 initial studies identified by the original search, data from 31 studies were ultimately included in the final analysis. Numerous metrics were identified that were significantly related to a broad range of outcomes. We categorized the outcomes into 2 main subgroups: (1) cardiovascular/metabolic outcomes and mortality and (2) cognitive function- and vigilance-related outcomes. Four general categories of alternative metrics were identified based on signals analyzed: autonomic/hemodynamic metrics, electroencephalographic metrics, oximetric metrics, and respiratory event-related metrics. CONCLUSIONS: We have summarized the current landscape of literature for alternative PSG metrics relating to risk prediction in OSA. Although promising, further prospective observational studies are needed to verify findings from other cohorts, and to assess the clinical utility of these metrics. CITATION: Hajipour M, Baumann B, Azarbarzin A, et al. Association of alternative polysomnographic features with patient outcomes in obstructive sleep apnea: a systematic review. J Clin Sleep Med. 2023;19(2):225-242.

Speech Audio Synthesis from Tagged MRI and Non-Negative Matrix Factorization via Plastic Transformer.

The tongue's intricate 3D structure, comprising localized functional units, plays a crucial role in the production of speech. When measured using tagged MRI, these functional units exhibit cohesive displacements and derived quantities that facilitate the complex process of speech production. Non-negative matrix factorization-based approaches have been shown to estimate the functional units through motion features, yielding a set of building blocks and a corresponding weighting map. Investigating the link between weighting maps and speech acoustics can offer significant insights into the intricate process of speech production. To this end, in this work, we utilize two-dimensional spectrograms as a proxy representation, and develop an end-to-end deep learning framework for translating weighting maps to their corresponding audio waveforms. Our proposed plastic light transformer (PLT) framework is based on directional product relative position bias and single-level spatial pyramid pooling, thus enabling flexible processing of weighting maps with variable size to fixed-size spectrograms, without input information loss or dimension expansion. Additionally, our PLT framework efficiently models the global correlation of wide matrix input. To improve the realism of our generated spectrograms with relatively limited training samples, we apply pair-wise utterance consistency with Maximum Mean Discrepancy constraint and adversarial training. Experimental results on a dataset of 29 subjects speaking two utterances demonstrated that our framework is able to synthesize speech audio waveforms from weighting maps, outperforming conventional convolution and transformer models.

Dysfunctional paraspinal muscles in adult spinal deformity patients lead to increased spinal loading.

PURPOSE: Decreased spinal extensor muscle strength in adult spinal deformity (ASD) patients is well-known but poorly understood; thus, this study aimed to investigate the biomechanical and histopathological properties of paraspinal muscles from ASD patients and predict the effect of altered biomechanical properties on spine loading. METHODS: 68 muscle biopsies were collected from nine ASD patients at L4-L5 (bilateral multifidus and longissimus sampled). The biopsies were tested for muscle fiber and fiber bundle biomechanical properties and histopathology. The small sample size (due to COVID-19) precluded formal statistical analysis, but the properties were compared to literature data. Changes in spinal loading due to the measured properties were predicted by a lumbar spine musculoskeletal model. RESULTS: Single fiber passive elastic moduli were similar to literature values, but in contrast, the fiber bundle moduli exhibited a wide range beyond literature values, with 22% of 171 fiber bundles exhibiting very high elastic moduli, up to 20 times greater. Active contractile specific force was consistently less than literature, with notably 24% of samples exhibiting no contractile ability. Histological analysis of 28 biopsies revealed frequent fibro-fatty replacement with a range of muscle fiber abnormalities. Biomechanical modelling predicted that high muscle stiffness could increase the compressive loads in the spine by over 500%, particularly in flexed postures. DISCUSSION: The histopathological observations suggest diverse mechanisms of potential functional impairment. The large variations observed in muscle biomechanical properties can have a dramatic influence on spinal forces. These early findings highlight the potential key role of the paraspinal muscle in ASD.

Biomechanical Properties of Paraspinal Muscles Influence Spinal Loading-A Musculoskeletal Simulation Study.

Paraspinal muscles are vital to the functioning of the spine. Changes in muscle physiological cross-sectional area significantly affect spinal loading, but the importance of other muscle biomechanical properties remains unclear. This study explored the changes in spinal loading due to variation in five muscle biomechanical properties: passive stiffness, slack sarcomere length (SSL), in situ sarcomere length, specific tension, and pennation angle. An enhanced version of a musculoskeletal simulation model of the thoracolumbar spine with 210 muscle fascicles was used for this study and its predictions were validated for several tasks and multiple postures. Ranges of physiologically realistic values were selected for all five muscle parameters and their influence on L4-L5 intradiscal pressure (IDP) was investigated in standing and 36° flexion. We observed large changes in IDP due to changes in passive stiffness, SSL, in situ sarcomere length, and specific tension, often with interesting interplays between the parameters. For example, for upright standing, a change in stiffness value from one tenth to 10 times the baseline value increased the IDP only by 91% for the baseline model but by 945% when SSL was 0.4 µm shorter. Shorter SSL values and higher stiffnesses led to the largest increases in IDP. More changes were evident in flexion, as sarcomere lengths were longer in that posture and thus the passive curve is more influential. Our results highlight the importance of the muscle force-length curve and the parameters associated with it and motivate further experimental studies on in vivo measurement of those properties.

A Unified Representation of Control Logic in Human-Ultrasound Machine Interaction.

Advances in human-computer interaction (HCI) technologies have granted sonographers and radiologists a much improved user experience when operating different ultrasound (US) machines. Continued HCI improvements in US would benefit from a systematic study of the HCI control logic used in this domain. Such a study has not been presented previously and is the subject of this paper. We surveyed sonographers to determine the most frequently used controls in US machines. We standardized the representation of the US machine HCI control logic by using the unified modelling language (UML). We used UML diagrams to analyze the HCI control logic of 10 different cart-based US machines from several major manufacturers, and we discovered that the control logic for the most frequently used functions are identical. While this control logic does not follow an established standard, it has been commonly adopted. Using the UML for the visualization and formulation of control logic, we can target logically optimal interactions (whose operation steps cannot be further reduced), e.g., adjustment of B-mode gain, frequency and depth, and can derive methods to simplify logically sub-optimal interactions, e.g., the pointing and selecting operation, as well as image measurements.Our study provides insights into existing HCI approaches used in US machines and establishes a rigorous UML-based framework for future US machine design to improve interoperability, efficiency and ease-of-use.

Automated Segmentation of Spinal Muscles From Upright Open MRI Using a Multiscale Pyramid 2D Convolutional Neural Network.

STUDY DESIGN: Randomized trial. OBJECTIVE: To implement an algorithm enabling the automated segmentation of spinal muscles from open magnetic resonance images in healthy volunteers and patients with adult spinal deformity (ASD). SUMMARY OF BACKGROUND DATA: Understanding spinal muscle anatomy is critical to diagnosing and treating spinal deformity.Muscle boundaries can be extrapolated from medical images using segmentation, which is usually done manually by clinical experts and remains complicated and time-consuming. METHODS: Three groups were examined: two healthy volunteer groups (N = 6 for each group) and one ASD group (N = 8 patients) were imaged at the lumbar and thoracic regions of the spine in an upright open magnetic resonance imaging scanner while maintaining different postures (various seated, standing, and supine). For each group and region, a selection of regions of interest (ROIs) was manually segmented. A multiscale pyramid two-dimensional convolutional neural network was implemented to automatically segment all defined ROIs. A five-fold crossvalidation method was applied and distinct models were trained for each resulting set and group and evaluated using Dice coefficients calculated between the model output and the manually segmented target. RESULTS: Good to excellent results were found across all ROIs for the ASD (Dice coefficient >0.76) and healthy (dice coefficient > 0.86) groups. CONCLUSION: This study represents a fundamental step toward the development of an automated spinal muscle properties extraction pipeline, which will ultimately allow clinicians to have easier access to patient-specific simulations, diagnosis, and treatment.

A Video Game for Brazilian T1D Children about Knowledge of Disease and Self-care: A Methodological Study.

BACKGROUND: Video games are interactive technologies able to support children in health promotion, behavior changes, and chronic disease self-management. The use of health behavior change determinants in video game design can increase its effectiveness. This study describes the process of designing a video game for Brazilian children with T1D clarifying the use of health behavior change determinants that may influence self-management behaviors. METHODS: This was a methodological study based on health behavior change theories and the user-centered design approach. The results of a qualitative study conducted with children aged 7 to 12 years identified learning needs about knowledge on diabetes and self-care tasks which contribute to inappropriate behaviors. A Behavioral Diagnosis presented health behavior change determinants, capable of influencing children's learning needs and behaviors, that were considered to design The Heroes of Diabetes-the power of knowledge. RESULTS: The results presented the process of designing 4 mini games with its description and theory foundation to reach children's lack of understanding about T1D, insulin's role, SMBG requirements, food groups and physical activity's role in glycemic control. Knowledge, goal settings, extrinsic and intrinsic motivation determinants were related with video games design features. CONCLUSIONS: The findings support the use of health behavior change determinants into video game design as a guide to achieve children learning needs and that might influence self-management behaviors.

The impact of artificial intelligence on learner-instructor interaction in online learning.

Artificial intelligence (AI) systems offer effective support for online learning and teaching, including personalizing learning for students, automating instructors' routine tasks, and powering adaptive assessments. However, while the opportunities for AI are promising, the impact of AI systems on the culture of, norms in, and expectations about interactions between students and instructors are still elusive. In online learning, learner-instructor interaction (inter alia, communication, support, and presence) has a profound impact on students' satisfaction and learning outcomes. Thus, identifying how students and instructors perceive the impact of AI systems on their interaction is important to identify any gaps, challenges, or barriers preventing AI systems from achieving their intended potential and risking the safety of these interactions. To address this need for forward-looking decisions, we used Speed Dating with storyboards to analyze the authentic voices of 12 students and 11 instructors on diverse use cases of possible AI systems in online learning. Findings show that participants envision adopting AI systems in online learning can enable personalized learner-instructor interaction at scale but at the risk of violating social boundaries. Although AI systems have been positively recognized for improving the quantity and quality of communication, for providing just-in-time, personalized support for large-scale settings, and for improving the feeling of connection, there were concerns about responsibility, agency, and surveillance issues. These findings have implications for the design of AI systems to ensure explainability, human-in-the-loop, and careful data collection and presentation. Overall, contributions of this study include the design of AI system storyboards which are technically feasible and positively support learner-instructor interaction, capturing students' and instructors' concerns of AI systems through Speed Dating, and suggesting practical implications for maximizing the positive impact of AI systems while minimizing the negative ones.

Estimation and assessment of sagittal spinal curvature and thoracic muscle morphometry in different postures.

Spine models are typically developed from supine clinical imaging data, and hence clearly do not fully reflect postures that replicate subjects' clinical symptoms. Our objectives were to develop a method to: (i) estimate the subject-specific sagittal curvature of the whole spine in different postures from limited imaging data, (ii) obtain muscle lines-of-action in different postures and analyze the effect of posture on muscle fascicle length, and (iii) correct for cosine between the magnetic resonance imaging (MRI) scan plane and dominant fiber line-of-action for muscle parameters (cross-sectional area (CSA) and position). The thoracic spines of six healthy volunteers were scanned in four postures (supine, standing, flexion, and sitting) in an upright MRI. Geometry of the sagittal spine was approximated with a circular spline. A pipeline was developed to estimate spine geometry in different postures and was validated. The lines-of-action for two muscles, erector spinae (ES) and transversospinalis (TS) were obtained for every posture and hence muscle fascicle lengths were computed. A correction factor based on published literature was then computed and applied to the muscle parameters. The maximum registration error between the estimated spine geometry and MRI data was small (average RMSE∼1.2%). The muscle fascicle length increased (up to 20%) in flexion when compared to erect postures. The correction factor reduced muscle parameters (∼5% for ES and ∼25% for TS) when compared to raw MRI data. The proposed pipeline is a preliminary step in subject-specific modeling. Direction cosines of muscles could be used while improving the inputs of spine models.

Quantal biomechanical effects in speech postures of the lips.

The unique biomechanical and functional constraints on human speech make it a promising area for research investigating modular control of movement. The present article illustrates how a modular control approach to speech can provide insights relevant to understanding both motor control and observed variation across languages. We specifically explore the robust typological finding that languages produce different degrees of labial constriction using distinct muscle groupings and concomitantly distinct lip postures. Research has suggested that these lip postures exploit biomechanical regions of nonlinearity between neural activation and movement, also known as quantal regions, to allow movement goals to be realized despite variable activation signals. We present two sets of computer simulations showing that these labial postures can be generated under the assumption of modular control and that the corresponding modules are biomechanically robust: first to variation in the activation levels of participating muscles, and second to interference from surrounding muscles. These results provide support for the hypothesis that biomechanical robustness is an important factor in selecting the muscle groupings used for speech movements and provide insight into the neurological control of speech movements and how biomechanical and functional constraints govern the emergence of speech motor modules. We anticipate that future experimental work guided by biomechanical simulation results will provide new insights into the neural organization of speech movements.NEW & NOTEWORTHY This article provides additional evidence that speech motor control is organized in a modular fashion and that biomechanics constrain the kinds of motor modules that may emerge. It also suggests that speech can be a fruitful domain for the study of modularity and that a better understanding of speech motor modules will be useful for speech research. Finally, it suggests that biomechanical modeling can serve as a useful complement to experimental work when studying modularity.

Characterizing Motor Control of Mastication With Soft Actor-Critic.

The human masticatory system is a complex functional unit characterized by a multitude of skeletal components, muscles, soft tissues, and teeth. Muscle activation dynamics cannot be directly measured on live human subjects due to ethical, safety, and accessibility limitations. Therefore, estimation of muscle activations and their resultant forces is a longstanding and active area of research. Reinforcement learning (RL) is an adaptive learning strategy which is inspired by the behavioral psychology and enables an agent to learn the dynamics of an unknown system via policy-driven explorations. The RL framework is a well-formulated closed-loop system where high capacity neural networks are trained with the feedback mechanism of rewards to learn relatively complex actuation patterns. In this work, we are building on a deep RL algorithm, known as the Soft Actor-Critic, to learn the inverse dynamics of a simulated masticatory system, i.e., learn the activation patterns that drive the jaw to its desired location. The outcome of the proposed training procedure is a parametric neural model which acts as the brain of the biomechanical system. We demonstrate the model's ability to navigate the feasible three-dimensional (3D) envelope of motion with sub-millimeter accuracies. We also introduce a performance analysis platform consisting of a set of quantitative metrics to assess the functionalities of a given simulated masticatory system. This platform assesses the range of motion, metabolic efficiency, the agility of motion, the symmetry of activations, and the accuracy of reaching the desired target positions. We demonstrate how the model learns more metabolically efficient policies by integrating a force regularization term in the RL reward. We also demonstrate the inverse correlation between the metabolic efficiency of the models and their agility and range of motion. The presented masticatory model and the proposed RL training mechanism are valuable tools for the analysis of mastication and other biomechanical systems. We see this framework's potential in facilitating the functional analyses aspects of surgical treatment planning and predicting the rehabilitation performance in post-operative subjects.

Intravelar and Extravelar Portions of Soft Palate Muscles in Velic Constrictions: A Three-Dimensional Modeling Study.

Purpose This study predicts and simulates the function and relative contributions of the intravelar and extravelar portions of the levator veli palatini (LVP) and palatoglossus (PG) muscles in velic constrictions. Method A finite element-based model of the 3-dimensional upper airway structures (palate, pharynx, tongue, jaw, maxilla) was implemented, with LVP and PG divided into intravelar and extravelar portions. Simulations were run to investigate the contributions of these muscles in velopharyngeal port (VPP) closure and constriction of the oropharyngeal isthmus (OPI). Results Simulations reveal that the extravelar portion of LVP, though crucial for lifting the palate, is not sufficient to effect VPP closure. Specifically, the characteristic "bulge" appearing in the posterior soft palate during VPP closure ( Pigott, 1969 ; Serrurier & Badin, 2008 ) is found to result from activation of the intravelar portion of LVP. Likewise, the intravelar portion of posterior PG is crucial in bending the "veil" or "traverse" ( Gick, Francis, Klenin, Mizrahi, & Tom, 2013 ) of the velum anteriorly to produce uvular constrictions of the OPI ( Gick et al., 2014 ). Conclusions Simulations support the view that intravelar LVP and PG play significant roles in VPP and OPI constrictions.

Conceptual framework for designing video games for children with type 1 diabetes.

OBJECTIVE: to present a theoretically based conceptual framework for designing video games for children with type 1 diabetes mellitus. METHODS: this was a methodological study that developed a conceptual framework with nine steps in view of health behavior change theories and the user-centered design approach as theoretical and methodological frameworks, respectively. Twenty-one children, aged 7 to 12 years, participated by expressing their needs and preferences related to diabetes and video games. Data were analysed following content analysis guidelines. Then, a choice of appropriate health behavioral change theories and their determinants that should be capable of influencing children's behaviors and preferences. RESULTS: the conceptual framework proposes a video game that consists of six phases, each addressing one stage of behavioral change and specific determinants, aligned with the needs and preferences identified by the participating children. This study shows the applicability of this framework in view of each proposed phase presenting examples and the children's ideas. CONCLUSION: the results of this study contribute to advance the discussion on how behavioral theories and their determinants should be related to the design of creative and funny video games considering the profile of the target population as well as its needs and preferences.