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.

Influence of intramuscular steroid receptor content and fiber capillarization on skeletal muscle hypertrophy.

Multiple intramuscular variables have been proposed to explain the high variability in resistance training induced muscle hypertrophy across humans. This study investigated if muscular androgen receptor (AR), estrogen receptor α (ERα) and ß (ERß) content and fiber capillarization are associated with fiber and whole-muscle hypertrophy after chronic resistance training. Male (n = 11) and female (n = 10) resistance training novices (22.1 ± 2.2 years) trained their knee extensors 3×/week for 10 weeks. Vastus lateralis biopsies were taken at baseline and post the training period to determine changes in fiber type specific cross-sectional area (CSA) and fiber capillarization by immunohistochemistry and, intramuscular AR, ERα and ERß content by Western blotting. Vastus lateralis volume was quantified by MRI-based 3D segmentation. Vastus lateralis muscle volume significantly increased over the training period (+7.22%; range: -1.82 to +18.8%, p < 0.0001) but no changes occurred in all fiber (+1.64%; range: -21 to +34%, p = 0.869), type I fiber (+1.33%; range: -24 to +41%, p = 0.952) and type II fiber CSA (+2.19%; range: -23 to +29%, p = 0.838). However, wide inter-individual ranges were found. Resistance training increased the protein expression of ERα but not ERß and AR, and the increase in ERα content was positively related to changes in fiber CSA. Only for the type II fibers, the baseline capillary-to-fiber-perimeter index was positively related to type II fiber hypertrophy but not to whole muscle responsiveness. In conclusion, an upregulation of ERα content and an adequate initial fiber capillarization may be contributing factors implicated in muscle fiber hypertrophy responsiveness after chronic resistance training.

Evidence for Simultaneous Muscle Atrophy and Hypertrophy in Response to Resistance Training in Humans.

PURPOSE: Human skeletal muscle has the profound ability to hypertrophy in response to resistance training (RT). Yet, this has a high energy and protein cost and is presumably mainly restricted to recruited muscles. It remains largely unknown what happens with non-recruited muscles during RT. This study investigated the volume changes of 17 recruited and 13 non-recruited muscles during a 10-week single-joint RT program targeting upper arm and upper leg musculature. METHODS: Muscle volume changes were measured by manual or automatic 3D segmentation in 21 RT novices. Subjects ate ad libitum during the study and energy and protein intake were assessed by self-reported diaries. RESULTS: Post-training, all recruited muscles increased in volume (range: +2.2% to +17.7%, p < 0.05) while the non-recruited adductor magnus (mean: -1.5 ± 3.1%, p = 0.038) and soleus (-2.4 ± 2.3%, p = 0.0004) decreased in volume. Net muscle growth (r = 0.453, p = 0.045) and changes in adductor magnus volume (r = 0.450, p = 0.047) were positively associated with protein intake. Changes in total non-recruited muscle volume (r = 0.469, p = 0.037), adductor magnus (r = 0.640, p = 0.002), adductor longus (r = 0.465, p = 0.039) and soleus muscle volume (r = 0.481, p = 0.032) were positively related to energy intake (p < 0.05). When subjects were divided into a HIGH or LOW energy intake group, overall non-recruited muscle volume (-1.7 ± 2.0%), adductor longus (-5.6 ± 3.7%), adductor magnus (-2.8 ± 2.4%) and soleus volume (-3.7 ± 1.8%) decreased significantly (p < 0.05) in the LOW but not the HIGH group. CONCLUSIONS: To our knowledge, this is the first study documenting that some non-recruited muscles significantly atrophy during a period of resistance training. Our data therefore suggest muscle mass reallocation, i.e., that hypertrophy in recruited muscles takes place at the expense of atrophy in non-recruited muscles, especially when energy and protein availability are limited.

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 3D-magnetic resonance imaging (MRI) volumetric scores (VS) was developed to provide comprehensive characterization of RC pathology. The purposes of this study were to: (1) Describe the correlation between GC and VS for supraspinatus changes in RC tears, (2) Characterize the chronicity of RC tears using the battery of 12 VS measurements, and (3) Compare GC and VS 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 VS included fat infiltration (FIS), muscle size (MSS) and relative volume contribution (RCS) for each RC muscle. Backwards linear regression was performed to compare GC stage with preoperative PROMIS PF/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 < 0.001); strong negative correlations were observed between MSS (r = -0.800, p < 0.001) and RCS (r = -0.745, p < 0.001) when compared to GC. A negligible linear correlation was observed between GC and preoperative PROMIS PF (r = -0.106, p = 0.343) and PI (r = -0.071, p = 0.528). On multivariate analysis, subscapularis MSS (beta > 0, p = 0.064) was a positive predictor, and subscapularis FIS (beta < 0, p = 0.137), teres minor MSS (beta < 0, p = 0.141) and FIS (beta < 0, p = 0.070) were negative predictors of preoperative PF (r = 0.343, p = 0.044); while supraspinatus MSS (beta > 0, p = 0.009) and FIS (beta > 0, p = 0.073), teres minor FIS (beta > 0, p = 0.072) and subscapularis FIS (beta > 0, p = 0.065) were positive predictors of preoperative PI (r = 0.410, p = 0.006). CONCLUSION: Although gold standard in evaluation of RC pathology, GC demonstrated negligible correlation with preoperative functional disability. Alternatively, a battery of 3D VS 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.

Fiber-type traps: revisiting common misconceptions about skeletal muscle fiber types with application to motor control, biomechanics, physiology, and biology.

Skeletal muscle is a highly complex tissue that is studied by scientists from a wide spectrum of disciplines, including motor control, biomechanics, exercise science, physiology, cell biology, genetics, regenerative medicine, orthopedics, and engineering. Although this diversity in perspectives has led to many important discoveries, historically, there has been limited overlap in discussions across fields. This has led to misconceptions and oversimplifications about muscle biology that can create confusion and potentially slow scientific progress across fields. The purpose of this synthesis paper is to bring together research perspectives across multiple muscle fields to identify common assumptions related to muscle fiber type that are points of concern to clarify. These assumptions include 1) classification by myosin isoform and fiber oxidative capacity is equivalent, 2) fiber cross-sectional area (CSA) is a surrogate marker for myosin isoform or oxidative capacity, and 3) muscle force-generating capacity can be inferred from myosin isoform. We address these three fiber-type traps and provide some context for how these misunderstandings can and do impact experimental design, computational modeling, and interpretations of findings, from the perspective of a range of fields. We stress the dangers of generalizing findings about "muscle fiber types" among muscles or across species or sex, and we note the importance for precise use of common terminology across the muscle fields.

Validity of Inertial Measurement Units to Measure Lower-Limb Kinematics and Pelvic Orientation at Submaximal and Maximal Effort Running Speeds.

Inertial measurement units (IMUs) have been validated for measuring sagittal plane lower-limb kinematics during moderate-speed running, but their accuracy at maximal speeds remains less understood. This study aimed to assess IMU measurement accuracy during high-speed running and maximal effort sprinting on a curved non-motorized treadmill using discrete (Bland-Altman analysis) and continuous (root mean square error [RMSE], normalised RMSE, Pearson correlation, and statistical parametric mapping analysis [SPM]) metrics. The hip, knee, and ankle flexions and the pelvic orientation (tilt, obliquity, and rotation) were captured concurrently from both IMU and optical motion capture systems, as 20 participants ran steadily at 70%, 80%, 90%, and 100% of their maximal effort sprinting speed (5.36 ± 0.55, 6.02 ± 0.60, 6.66 ± 0.71, and 7.09 ± 0.73 m/s, respectively). Bland-Altman analysis indicated a systematic bias within ±1° for the peak pelvic tilt, rotation, and lower-limb kinematics and -3.3° to -4.1° for the pelvic obliquity. The SPM analysis demonstrated a good agreement in the hip and knee flexion angles for most phases of the stride cycle, albeit with significant differences noted around the ipsilateral toe-off. The RMSE ranged from 4.3° (pelvic obliquity at 70% speed) to 7.8° (hip flexion at 100% speed). Correlation coefficients ranged from 0.44 (pelvic tilt at 90%) to 0.99 (hip and knee flexions at all speeds). Running speed minimally but significantly affected the RMSE for the hip and ankle flexions. The present IMU system is effective for measuring lower-limb kinematics during sprinting, but the pelvic orientation estimation was less accurate.

Objective analysis of partial three-dimensional rotator cuff muscle volume and fat infiltration across ages and sex from clinical MRI scans.

Objective analysis of rotator cuff (RC) atrophy and fatty infiltration (FI) from clinical MRI is limited by qualitative measures and variation in scapular coverage. The goals of this study were to: develop/evaluate a method to quantify RC muscle size, atrophy, and FI from clinical MRIs (with typical lateral only coverage) and then quantify the effects of age and sex on RC muscle. To develop the method, 47 full scapula coverage CTs with matching clinical MRIs were used to: correct for variation in scan capture, and ensure impactful information of the RC is measured. Utilizing this methodology and automated artificial intelligence, 170 healthy clinical shoulder MRIs of varying age and sex were segmented, and each RC muscle's size, relative contribution, and FI as a function of scapula location were quantified. A two-way ANOVA was used to examine the effect of age and sex on RC musculature. The analysis revealed significant (p < 0.05): decreases in size of the supraspinatus, teres minor, and subscapularis with age; decreased supraspinatus and increased infraspinatus relative contribution with age; and increased FI in the infraspinatus with age and in females. This study demonstrated that clinically obtained MRIs can be utilized for automatic 3D analysis of the RC. This method is not susceptible to coverage variation or patient size. Application of methodology in a healthy population revealed differences in RC musculature across ages and FI level between sexes. This large database can be used to reference expected muscle characteristics as a function of scapula location and could eventually be used in conjunction with the proposed methodology for analysis in patient populations.

In vivo imaging of skeletal muscle form and function: 50 years of insight.

Skeletal muscle form and function has fascinated scientists for centuries. Our understanding of muscle function has long been driven by advancements in imaging techniques. For example, the sliding filament theory of muscle, which is now widely leveraged in biomechanics research, stemmed from observations made possible by scanning electron microscopy. Over the last 50 years, advancing in medical imaging, combined with ingenuity and creativity of biomechanists, have provide a wealth of new and important insights into in vivo human muscle function. Incorporation of in vivo imaging has also advanced computational modeling and allowed our research to have an impact in many clinical populations. While this review does not provide a comprehensive or meta-analysis of the all the in vivo muscle imaging work over the last five decades, it provides a narrative about the past, present, and future of in vivo muscle imaging.

Athlete Muscular Phenotypes Identified and Compared with High-Dimensional Clustering of Lower Limb Muscle Volume Measurements.

INTRODUCTION: Athletes use their skeletal muscles to demonstrate performance. Muscle force generating capacity is correlated with volume, meaning that variations in sizes of different muscles may be indicative of how athletes meet different demands in their sports. Medical imaging enables in vivo quantification of muscle volumes; however, muscle volume distribution has not been compared across athletes of different sports. PURPOSE: The goal of this work was to define "muscular phenotypes" in athletes of different sports and compare these using hierarchical clustering. METHODS: Muscle volumes normalized by body mass of athletes (football, baseball, basketball, or track) were compared with control participants to quantify size differences using z -scores. z -Scores of 35 muscles described the pattern of volume deviation within each athlete's lower limb, characterizing their muscular phenotype. Data-driven high-dimensional clustering analysis was used to group athletes presenting similar phenotypes. Efficacy of clustering to identify similar phenotypes was demonstrated by grouping athletes' contralateral limbs before other athletes' limbs. RESULTS: Analyses revealed that athletes did not tend to cluster with others competing in the same sport. Basketball players with similar phenotypes grouped by clustering also demonstrated similarities in performance. Clustering also identified muscles with similar volume variation patterns across athletes, and principal component analysis revealed specific muscles that accounted for most of the variance (gluteus maximus, sartorius, semitendinosus, vastus medialis, vastus lateralis, and rectus femoris). CONCLUSIONS: Athletes exhibit heterogeneous lower limb muscle volumes that can be characterized and compared as individual muscular phenotypes. Clustering revealed that athletes with the most similar phenotypes do not always play the same sport such that patterns of muscular heterogeneity across a group of athletes reflect factors beyond their specific sports.

A Deep Learning Algorithm for Automatic 3D Segmentation of Rotator Cuff Muscle and Fat from Clinical MRI Scans.

The authors aimed to develop and validate an automated artificial intelligence (AI) algorithm for three-dimensional (3D) segmentation of all four rotator cuff (RC) muscles to quantify intramuscular fat infiltration (FI) and individual muscle volume. The dataset included retrospectively collected RC MRI scans in 232 patients (63 with normal RCs, 169 with RC tears). A two-stage AI model was developed to segment all RC muscles and their FI in each stage. For comparison, single-stage and Otsu filtering models were created. Using the two-stage model, segmentation performance demonstrated high Dice scores (mean, 0.92 ± 0.14 [SD]), low volume errors (mean, 5.72% ± 9.23), and low FI errors (mean, 1.54% ± 2.79) when validated in 30 scans. There was a significant correlation between the 3D FI in the RC tear scans with a Goutallier grade (ρ = 0.53, P < .001) and FI found from a single two-dimensional (2D) section (all muscles, ρ > 0.70; P < .001). However, Bland-Altman analysis of the 3D compared with the 2D analyses of FI demonstrated a proportional bias (all muscles, P < .001). Compared with Goutallier classification or single-image quantification, the AI method allowed for more variability in images and led to objective separate quantifications of muscle volume and FI in all RC muscles. Keywords: Rotator Cuff, Artificial Intelligence, Segmentation, Fat Infiltration, Muscle Volume, MRI, Shoulder Supplemental material is available for this article. © RSNA, 2023.

Can muscle typology explain the inter-individual variability in resistance training adaptations?

Considerable inter-individual heterogeneity exists in the muscular adaptations to resistance training. It has been proposed that fast-twitch fibres are more sensitive to hypertrophic stimuli and thus that variation in muscle fibre type composition is a contributing factor to the magnitude of training response. This study investigated if the inter-individual variability in resistance training adaptations is determined by muscle typology and if the most appropriate weekly training frequency depends on muscle typology. In strength-training novices, 11 slow (ST) and 10 fast typology (FT) individuals were selected by measuring muscle carnosine with proton magnetic resonance spectroscopy. Participants trained both upper arm and leg muscles to failure at 60% of one-repetition maximum (1RM) for 10 weeks, whereby one arm and leg trained 3×/week and the contralateral arm and leg 2×/week. Muscle volume (MRI-based 3D segmentation), maximal dynamic strength (1RM) and fibre type-specific cross-sectional area (vastus lateralis biopsies) were evaluated. The training response for total muscle volume (+3 to +14%), fibre size (-19 to +22%) and strength (+17 to +47%) showed considerable inter-individual variability, but these could not be attributed to differences in muscle typology. However, ST individuals performed a significantly higher training volume to gain these similar adaptations than FT individuals. The limb that trained 3×/week had generally more pronounced hypertrophy than the limb that trained 2×/week, and there was no interaction with muscle typology. In conclusion, muscle typology cannot explain the high variability in resistance training adaptations when training is performed to failure at 60% of 1RM. KEY POINTS: This study investigated the influence of muscle typology (muscle fibre type composition) on the variability in resistance training adaptations and on its role in the individualization of resistance training frequency. We demonstrate that an individual's muscle typology cannot explain the inter-individual variability in resistance training-induced increases in muscle volume, maximal dynamic strength and fibre cross-sectional area when repetitions are performed to failure. Importantly, slow typology individuals performed a significantly higher training volume to obtain similar adaptations compared to fast typology individuals. Muscle typology does not determine the most appropriate resistance training frequency. However, regardless of muscle typology, an additional weekly training (3×/week vs. 2×/week) increases muscle hypertrophy but not maximal dynamic strength. These findings expand on our understanding of the underlying mechanisms for the large inter-individual variability in resistance training adaptations.

Validation of the association between MRI and gene signatures in facioscapulohumeral dystrophy muscle: implications for clinical trial design.

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, but reproducibility across studies needs further validation. 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. Together with moderate-to-strong correlations of gene signatures and MRI characteristics between the TA muscles bilaterally, these results suggest a whole muscle model of disease progression and provide a strong basis for inclusion of MRI and molecular biomarkers in clinical trial design.

Upper Extremity Examination for Neuromuscular Diseases (U-EXTEND): Protocol for a Multimodal Feasibility Study.

BACKGROUND: Neuromuscular diseases, such as spinal muscular atrophy (SMA) and Duchenne muscular dystrophy (DMD), may result in the loss of motor movements, respiratory failure, and early mortality in young children and in adulthood. With novel treatments now available, new evaluation methods are needed to assess progress that is not currently captured in existing motor scale tests. OBJECTIVE: With our feasibility study, our interdisciplinary team of investigators aims to develop a novel, multimodal paradigm of measuring motor function in children with neuromuscular diseases that will revolutionize the way that clinical trial end points are measured, thereby accelerating the pipeline of new treatments for childhood neuromuscular diseases. Through the Upper Extremity Examination for Neuromuscular Diseases (U-EXTEND) study, we hypothesize that the novel objective measures of upper extremity muscle structure and function proposed herein will be able to capture small changes and differences in function that cannot be measured with current clinical metrics. METHODS: U-EXTEND introduces a novel paradigm in which concrete, quantitative measures are used to assess motor function in patients with SMA and DMD. Aim 1 will focus on the use of ultrasound techniques to study muscle size, quality, and function, specifically isolating the biceps and pronator muscles of the upper extremities for follow-ups over time. To achieve this, clinical investigators will extract a set of measurements related to muscle structure, quality, and function by using ultrasound imaging and handheld dynamometry. Aim 2 will focus on leveraging wearable wireless sensor technology to capture motion data as participants perform activities of daily living. Measurement data will be examined and compared to those from a healthy cohort, and a motor function score will be calculated. RESULTS: Data collection for both aims began in January 2021. As of July 2022, we have enrolled 44 participants (9 with SMA, 20 with DMD, and 15 healthy participants). We expect the initial results to be published in summer 2022. CONCLUSIONS: We hypothesize that by applying the described tools and techniques for measuring muscle structure and upper extremity function, we will have created a system for the precise quantification of changes in motor function among patients with neuromuscular diseases. Our study will allow us to track the minimal clinically important difference over time to assess progress in novel treatments. By comparing the muscle scores and functional scores over multiple visits, we will be able to detect small changes in both the ability of the participants to perform the functional tasks and their intrinsic muscle properties. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/40856.