Development of an ultrasound-based metric of muscle functional capacity for use in patients with neuromuscular disease.

INTRODUCTION/AIMS: Spinal muscular atrophy (SMA) and Duchenne muscular dystrophy (DMD) are progressive neuromuscular disorders characterized by severe muscle weakness and functional decline (Pillen et al., Muscle Nerve 2008; 37(6):679-693). With new therapeutics, objective methods with increased sensitivity are needed to assess muscle function. Ultrasound imaging is a promising approach for assessing muscle fat and fibrosis in neuromuscular disorders. This study builds on prior work by combining ultrasound-based measurements of muscle size, shape, and quality, relating these measures to muscle strength, and proposing a multivariable image-based estimate of muscle function. METHODS: Maximum voluntary elbow flexion torque of 36 participants (SMA, DMD, and healthy controls) was measured by hand-held dynamometry and elbow flexor muscles were imaged using ultrasound. Muscle size (cross-sectional area, maximum Feret diameter or width, and thickness), quality (echogenicity, texture anisotropy index), and cross-sectional shape (diameter ratio) were measured. Multivariable regression was used to select ultrasound measurements that predict elbow flexion torque. RESULTS: Significant differences were observed in muscle size (decreased), shape (thinned), and quality (decreased) with increased disease severity and compared to healthy participants. CSA (brachioradialis R2 = 0.51), maximum Feret diameter (biceps R2 = 0.49, brachioradialis R2 = 0.58) and echogenicity (brachioradialis R2 = 0.61) were most correlated with torque production. Multivariable regression models identified that muscle size (CSA, maximum Feret diameter) and quality (echogenicity) were both essential to predict elbow flexion torque (R2 = 0.65). DISCUSSION: A multivariable approach combining muscle size and quality improves strength predictions over single variable approaches. These methods present a promising avenue for the development of sensitive and functionally relevant biomarkers of neuromuscular disease.

AI driven analysis of MRI to measure health and disease progression in FSHD.

Facioscapulohumeral muscular dystrophy (FSHD) affects roughly 1 in 7500 individuals. While at the population level there is a general pattern of affected muscles, there is substantial heterogeneity in muscle expression across- and within-patients. There can also be substantial variation in the pattern of fat and water signal intensity within a single muscle. While quantifying individual muscles across their full length using magnetic resonance imaging (MRI) represents the optimal approach to follow disease progression and evaluate therapeutic response, the ability to automate this process has been limited. The goal of this work was to develop and optimize an artificial intelligence-based image segmentation approach to comprehensively measure muscle volume, fat fraction, fat fraction distribution, and elevated short-tau inversion recovery signal in the musculature of patients with FSHD. Intra-rater, inter-rater, and scan-rescan analyses demonstrated that the developed methods are robust and precise. Representative cases and derived metrics of volume, cross-sectional area, and 3D pixel-maps demonstrate unique intramuscular patterns of disease. Future work focuses on leveraging these AI methods to include upper body output and aggregating individual muscle data across studies to determine best-fit models for characterizing progression and monitoring therapeutic modulation of MRI biomarkers.

Agent-based model demonstrates the impact of nonlinear, complex interactions between cytokinces on muscle regeneration.

Muscle regeneration is a complex process due to dynamic and multiscale biochemical and cellular interactions, making it difficult to identify microenvironmental conditions that are beneficial to muscle recovery from injury using experimental approaches alone. To understand the degree to which individual cellular behaviors impact endogenous mechanisms of muscle recovery, we developed an agent-based model (ABM) using the Cellular-Potts framework to simulate the dynamic microenvironment of a cross-section of murine skeletal muscle tissue. We referenced more than 100 published studies to define over 100 parameters and rules that dictate the behavior of muscle fibers, satellite stem cells (SSCs), fibroblasts, neutrophils, macrophages, microvessels, and lymphatic vessels, as well as their interactions with each other and the microenvironment. We utilized parameter density estimation to calibrate the model to temporal biological datasets describing cross-sectional area (CSA) recovery, SSC, and fibroblast cell counts at multiple timepoints following injury. The calibrated model was validated by comparison of other model outputs (macrophage, neutrophil, and capillaries counts) to experimental observations. Predictions for eight model perturbations that varied cell or cytokine input conditions were compared to published experimental studies to validate model predictive capabilities. We used Latin hypercube sampling and partial rank correlation coefficient to identify in silico perturbations of cytokine diffusion coefficients and decay rates to enhance CSA recovery. This analysis suggests that combined alterations of specific cytokine decay and diffusion parameters result in greater fibroblast and SSC proliferation compared to individual perturbations with a 13% increase in CSA recovery compared to unaltered regeneration at 28 days. These results enable guided development of therapeutic strategies that similarly alter muscle physiology (i.e. converting extracellular matrix [ECM]-bound cytokines into freely diffusible forms as studied in cancer therapeutics or delivery of exogenous cytokines) during regeneration to enhance muscle recovery after injury.

Novel 3D MRI-based volumetric assessment of rotator cuff musculature demonstrates stronger correlation with preoperative functional status when compared to the Goutallier grading scheme.

BACKGROUND: The Goutallier classification (GC) is used to assess fatty atrophy in rotator cuff (RC) tears, yet limitations exist. A battery of 3-dimensional (3D) magnetic resonance imaging (MRI) volumetric scores (VSs) was developed to provide comprehensive characterization of RC pathology. The purposes of this study were to (1) describe the correlation between GC and VSs for supraspinatus changes in RC tears, (2) characterize the chronicity of RC tears using a battery of 12 VS measurements, and (3) compare GC and VSs to determine which method most closely corresponds with preoperative patient-reported outcome measures (PROMs). METHODS: Preoperative shoulder MRIs were reviewed after arthroscopic RC repair. Preoperative GC stage and Patient-Reported Outcomes Measurement Information System (PROMIS) physical function (PF) and pain interference (PI) scores were collected. The battery of VSs included fat infiltration (FIS), muscle size (MSS), and relative volume contribution (RCS) for each RC muscle. Backward linear regression was performed to compare GC stage with preoperative PROMIS PF and PI to determine which VS measurement most closely correlated with preoperative PROMs. RESULTS: Eighty-two patients underwent RC repair (mean age 55 ± 8.2 years, 63% male, 68% GC stage ≤1). In evaluation of the supraspinatus, there was a moderate positive correlation between GC and FIS (r = 0.459, P < .001); strong negative correlations were observed between MSS (r = -0.800, P < .001) and RCS (r = -0.745, P < .001) when compared to GC. A negligible linear correlation was observed between GC and preoperative PROMIS PF (r = -0.106, P = .343) and PI (r = -0.071, P = .528). On multivariate analysis, subscapularis MSS (ß >0, P = .064) was a positive predictor and subscapularis FIS (ß <0, P = .137), teres minor MSS (ß <0, P = .141), and FIS (ß <0, P = .070) were negative predictors of preoperative PF (r = 0.343, P = .044); in contrast, supraspinatus MSS (ß >0, P = .009) and FIS (ß >0, P = .073), teres minor FIS (ß >0, P = .072), and subscapularis FIS (ß >0, P = .065) were positive predictors of preoperative PI (r = 0.410, P = .006). CONCLUSION: Although the criterion standard in evaluation of RC pathology, GC demonstrated negligible correlation with preoperative functional disability. Alternatively, a battery of 3D VSs showed strong correlation with GC through a quantitative, comprehensive evaluation of the RC unit including several moderate predictors of preoperative functional disability.

The need for speed - Does the force-velocity property significantly alter strain distributions within skeletal muscle?

Skeletal muscles are complex structures with nonlinear constitutive properties. This complexity often requires finite element (FE) modeling to better understand muscle behavior and response to activation, especially the fiber strain distributions that can be difficult to measure in vivo. However, many FE muscle models designed to study fiber strain do not include force-velocity behavior. To investigate force-velocity property impact on strain distributions within skeletal muscle, we modified a muscle constitutive model with active and passive force-length properties to include force-velocity properties. We implemented the new constitutive model as a plugin for the FE software FEBio and applied it to four geometries: 1) a single element, 2) a multiple-element model representing a single fiber, 3) a model of tapering fibers, and 4) a model representing the bicep femoris long head (BFLH) morphology. Maximum fiber velocity and boundary conditions of the finite element models were varied to test their influence on fiber strain distribution. We found that force-velocity properties in the constitutive model behaved as expected for the single element and multi-element conditions. In the tapered fiber models, fiber strain distributions were impacted by changes in maximum fiber velocity; the range of strains increased with maximum fiber velocity, which was most noted in isometric contraction simulations. In the BFLH model, maximum fiber velocity had minimal impact on strain distributions, even in the context of sprinting. Taken together, the combination of muscle model geometry, activation, and displacement parameters play a critical part in determining the magnitude of impact of force-velocity on strain distribution.

It's more than the amount that counts: implications of collagen organization on passive muscle tissue properties revealed with micromechanical models and experiments.

Collagen accumulation is often used to characterize skeletal muscle fibrosis, but the role of collagen in passive muscle mechanics remains debated. Here we combined finite-element models and experiments to examine how collagen organization contributes to macroscopic muscle tissue properties. Tissue microstructure and mechanical properties were measured from in vitro biaxial experiments and imaging in dystrophin knockout (mdx) and wild-type (WT) diaphragm muscle. Micromechanical models of intramuscular and epimuscular extracellular matrix (ECM) regions were developed to account for complex microstructure and predict bulk properties, and directly calibrated and validated with the experiments. The models predicted that intramuscular collagen fibres align primarily in the cross-muscle fibre direction, with greater cross-muscle fibre alignment in mdx models compared with WT. Higher cross-muscle fibre stiffness was predicted in mdx models compared with WT models and differences between ECM and muscle properties were seen during cross-muscle fibre loading. Analysis of the models revealed that variation in collagen fibre distribution had a much more substantial impact on tissue stiffness than ECM area fraction. Taken together, we conclude that collagen organization explains anisotropic tissue properties observed in the diaphragm muscle and provides an explanation for the lack of correlation between collagen amount and tissue stiffness across experimental studies.

Regional and bilateral MRI and gene signatures in facioscapulohumeral dystrophy: implications for clinical trial design and mechanisms of disease progression.

Identifying the aberrant expression of DUX4 in skeletal muscle as the cause of facioscapulohumeral dystrophy (FSHD) has led to rational therapeutic development and clinical trials. Several studies support the use of MRI characteristics and the expression of DUX4-regulated genes in muscle biopsies as biomarkers of FSHD disease activity and progression. We performed lower-extremity MRI and muscle biopsies in the mid-portion of the tibialis anterior (TA) muscles bilaterally in FSHD subjects and validated our prior reports of the strong association between MRI characteristics and expression of genes regulated by DUX4 and other gene categories associated with FSHD disease activity. We further show that measurements of normalized fat content in the entire TA muscle strongly predict molecular signatures in the mid-portion of the TA, indicating that regional biopsies can accurately measure progression in the whole muscle and providing a strong basis for inclusion of MRI and molecular biomarkers in clinical trial design. An unanticipated finding was the strong correlations of molecular signatures in the bilateral comparisons, including markers of B-cells and other immune cell populations, suggesting that a systemic immune cell infiltration of skeletal muscle might have a role in disease progression.

Agent-based model demonstrates the impact of nonlinear, complex interactions between cytokines on muscle regeneration.

Muscle regeneration is a complex process due to dynamic and multiscale biochemical and cellular interactions, making it difficult to identify microenvironmental conditions that are beneficial to muscle recovery from injury using experimental approaches alone. To understand the degree to which individual cellular behaviors impact endogenous mechanisms of muscle recovery, we developed an agent-based model (ABM) using the Cellular Potts framework to simulate the dynamic microenvironment of a cross-section of murine skeletal muscle tissue. We referenced more than 100 published studies to define over 100 parameters and rules that dictate the behavior of muscle fibers, satellite stem cells (SSC), fibroblasts, neutrophils, macrophages, microvessels, and lymphatic vessels, as well as their interactions with each other and the microenvironment. We utilized parameter density estimation to calibrate the model to temporal biological datasets describing cross-sectional area (CSA) recovery, SSC, and fibroblast cell counts at multiple time points following injury. The calibrated model was validated by comparison of other model outputs (macrophage, neutrophil, and capillaries counts) to experimental observations. Predictions for eight model perturbations that varied cell or cytokine input conditions were compared to published experimental studies to validate model predictive capabilities. We used Latin hypercube sampling and partial rank correlation coefficient to identify in silico perturbations of cytokine diffusion coefficients and decay rates to enhance CSA recovery. This analysis suggests that combined alterations of specific cytokine decay and diffusion parameters result in greater fibroblast and SSC proliferation compared to individual perturbations with a 13% increase in CSA recovery compared to unaltered regeneration at 28 days. These results enable guided development of therapeutic strategies that similarly alter muscle physiology (i.e. converting ECM-bound cytokines into freely diffusible forms as studied in cancer therapeutics or delivery of exogenous cytokines) during regeneration to enhance muscle recovery after injury.

A new look at an old problem: 3D modeling of accommodation reveals how age-related biomechanical changes contribute to dysfunction in presbyopia.

Presbyopia is an age-related ocular disorder where accommodative ability declines so that an individual's focusing range is insufficient to provide visual clarity for near and distance vision tasks without corrective measures. With age, the eye exhibits changes in biomechanical properties of many components involved in accommodation, including the lens, sclera, and ciliary muscle. Changes occur at different rates, affecting accommodative biomechanics differently, but individual contributions to presbyopia are unknown. We used a finite element model (FEM) of the accommodative mechanism to simulate age-related changes in lens stiffness, scleral stiffness, and ciliary contraction to predict differences in accommodative function. The FEM predicts how ciliary muscle action leads to lens displacement by initializing a tensioned unaccommodated lens (Phase 0) then simulating ciliary muscle contraction in accommodation (Phase 1). Model inputs were calibrated to replicate experimentally measured lens and ciliary muscle in 30-year-old eyes. Predictions of accommodative lens deformation were verified with additional imaging studies. Model variations were created with altered lens component stiffnesses, scleral stiffness, or ciliary muscle section activations, representing fifteen-year incremental age-related changes. Model variations predict significant changes in accommodative function with age-related biomechanical property changes. Lens changes only significantly altered lens thickening with advanced age (46% decrease at 75 years old) while sclera changes produced progressive dysfunction with increasing age (23%, 36%, 49% decrease at 45, 60, and 75 years old). Ciliary muscle changes effected lens position modulation. Model predictions identified potential mechanisms of presbyopia that likely work in combination to reduce accommodative function and could indicate effectiveness of treatment strategies and their dependency on patient age or relative ocular mechanical properties.