Skeletal muscle contracts either by shortening or lengthening (concentrically or eccentrically, respectively); however, the two contractions substantially differ from one another in terms of ...mechanisms of force generation, maximum force production and energy cost. It is generally known that eccentric actions generate greater force than isometric and concentric contractions and at a lower metabolic cost. Hence, by virtue of the greater mechanical loading involved in active lengthening, eccentric resistance training (ECC RT) is assumed to produce greater hypertrophy than concentric resistance training (CON RT). Nonetheless, prevalence of either ECC RT or CON RT in inducing gains in muscle mass is still an open issue, with some studies reporting greater hypertrophy with eccentric, some with concentric and some with similar hypertrophy within both training modes. Recent observations suggest that such hypertrophic responses to lengthening vs. shortening contractions are achieved by different adaptations in muscle architecture. Whilst the changes in muscle protein synthesis in response to acute and chronic concentric and eccentric exercise bouts seem very similar, the molecular mechanisms regulating the myogenic adaptations to the two distinct loading stimuli are still incompletely understood. Thus, the present review aims to, (a) critically discuss the literature on the contribution of eccentric vs. concentric loading to muscular hypertrophy and structural remodeling, and, (b) clarify the molecular mechanisms that may regulate such adaptations. We conclude that, when matched for either maximum load or work, similar increase in muscle size is found between ECC and CON RT. However, such hypertrophic changes appear to be achieved through distinct structural adaptations, which may be regulated by different myogenic and molecular responses observed between lengthening and shortening contractions.
Skeletal muscle has the remarkable ability to remodel and adapt, such as the increase in serial sarcomere number (SSN) or fascicle length (FL) observed after overstretching a muscle. This type of ...remodelling is termed longitudinal muscle fascicle growth, and its impact on biomechanical function has been of interest since the 1960s due to its clinical applications in muscle strain injury, muscle spasticity, and sarcopenia. Despite simplified hypotheses on how longitudinal muscle fascicle growth might influence mechanical function, existing literature presents conflicting results partly due to a breadth of methodologies. The purpose of this review is to outline what is currently known about the influence of longitudinal muscle fascicle growth on mechanical function and suggest future directions to address current knowledge gaps and methodological limitations. Various interventions indicate longitudinal muscle fascicle growth can increase the optimal muscle length for active force, but whether the whole force-length relationship widens has been less investigated. Future research should also explore the ability for longitudinal fascicle growth to broaden the torque-angle relationship's plateau region, and the relation to increased force during shortening. Without a concurrent increase in intramuscular collagen, longitudinal muscle fascicle growth also reduces passive tension at long muscle lengths; further research is required to understand whether this translates to increased joint range of motion. Lastly, some evidence suggests longitudinal fascicle growth can increase maximum shortening velocity and peak isotonic power, however, there has yet to be direct assessment of these measures in a neurologically intact model of longitudinal muscle fascicle growth.
This study aimed (i) to investigate the role of regional characteristics of the knee extensors muscles (vastus lateralis: VL, vastus intermedius: VI and rectus femoris: RF) in determining ...maximum-voluntary force (MVF); and (ii) to understand which regional parameter of muscle structure would best predict MVF. Muscle architecture (e.g., pennation angle and fascicle length), muscle volume (Vol), anatomical (ACSA) and physiological cross-sectional-area (PCSA) were measured in the proximal (0–33% of the muscle length), middle (33–66% of the muscle length) and distal (66–100% of the muscle length) portions of each muscle in fifteen healthy males using ultrasound and Magnetic Resonance Imaging (MRI). Knee extensors force was calculated in isometric condition at a single knee joint angle of 90 degrees. Regional ACSA, Vol and PCSA were correlated with MVF production. Regional muscle geometry showed no significant correlations with MVF. Among regions, the middle portion of each muscle was largely correlated with MVF compared to all the other regions (distal and proximal). To understand which regional structural parameter best predicted MVF, a stepwise multiple linear regression was performed. This model showed a significant explanatory power (P < 0.001, R
2
= 0.76, adjusted R
2
= 0.71), including muscle Vol collected in the mid portions of VL and RF. Even if no significant differences were reported between Vol, PCSA and ACSA in determining MVF, our results showed that the RF and VL volume collected in the middle portion of the muscle length are strong determinants of MVF produced by the knee extensors at 90 degrees joint angle.
Background
Quantitative ultrasound (QUS) techniques are new diagnostic tools able to identify changes in structural and material properties of the investigated tissue. For the first time, we ...evaluated the capability of QUS techniques in determining the in vivo transient changes in knee joint cartilage after a stressful task.
Methods
An ultrasound scanner collecting B‐mode and radiofrequency data simultaneously was used to collect data from the femoral cartilage of the right knee in 15 participants. Cartilage thickness (CTK), ultrasound roughness index (URI), average magnitude ratio (AMR), and Nakagami parameters (NA) were evaluated before, immediately after and every 5 min up to 45 min a stressful task (30 min of running on a treadmill with a negative slope of 5%).
Results
CTK was affected by time (main effect: p < 0.001). Post hoc test showed significant differences with CTK at rest, which were observed up to 30 min after the run. AMR and NA were affected by time (p < 0.01 for both variables), while URI was unaffected by it. For AMR, post hoc test showed significant differences with rest values in the first 35 min of recovery, while NA was increased compared to rest values in all time points.
Conclusion
Data suggest that a single running trial is not able to modify the integrity of the femoral cartilage, as reported by URI data. In vivo evaluation of QUS parameters of the femoral cartilage (NA, AMR, and URI) are able to characterize changes in cartilage properties over time.
High end ultrasonography devices lack in portability and are expensive. We investigated the agreement and reliability of a handheld and portable ultrasound system for human lower limb muscle ...architecture measurements. We captured ultrasound images of the rectus femoris (RF), vastus lateralis (VL) and gastrocnemius medialis (GM) in 36 active healthy participants (15 female, 21 male) at 50% of muscle length using the handheld Lumify (L12-4, linear-array 37 mm, Philips Healthcare, Amsterdam, Netherlands) and a high-end laboratory device (ACUSON Juniper, linear-array 54 mm, 12L3, SIEMENS Healthineers, Erlangen, Germany). We compared measurements of muscle fascicle length, pennation angle and thickness. To assess inter-session reliability of the Lumify system, participants were measured twice within 1 week. Comparing RF architecture measurements of both devices resulted in intra-class correlations (ICCs) ranging from 0.46–0.82 and standardized mean difference (SMDs) ranging from −0.45–0.05. For VL, ICCs ranged from 0.60–0.89 and SMDs ranged from −0.11–0.13. ICCs and SMDs for the GM ranged from 0.82–0.86 and −0.07–0.07. Calculating inter-session reliability for RF resulted in ICCs ranging from 0.44–0.76 and SMDs ranging from −0.38–0.15. For VL, ICCs and SMDs ranged from 0.57–0.75 and −0.13–0.02. ICCs for GM ranged from 0.75–0.92 and SMDs ranged from −0.15–0.16. Measurement of muscle thickness demonstrated the highest agreement (ICC ≥0.82) and reliability (ICC ≥0.75) across all muscles. The Lumify system was comparable to a high-end device and reliable for GM measurements. However, agreement and reliability were lower for the RF and VL. Of all evaluated architectural parameters, muscle thickness exhibited highest agreement and reliability.
Open-access scripts to perform muscle anatomical cross-sectional area (ACSA) evaluation in ultrasound images are currently unavailable. This study presents a novel semi-automatic ImageJ script (named ..."ACSAuto") for quantifying the ACSA of lower limb muscles. We compared manual ACSA measurements from 180 ultrasound scans of vastus lateralis (VL) and rectus femoris (RF) muscles to measurements assessed by the ACSAuto script. We investigated inter- and intra-investigator reliability of the script. Consecutive-pairwise intra-class correlations (ICC) and standard error of measurement (SEM) with 95% compatibility interval were calculated. Bland-Altman analyses were employed to test the agreement between measurements. Comparing manual and ACSAuto measurements, ICCs and SEMs ranged from 0.96 to 0.999 and 0.12 to 0.96 cm
(1.2-5.9%) and mean bias was smaller than 0.5 cm
(4.3%). Inter-investigator comparison revealed ICCs, SEMs and mean bias ranging from 0.85 to 0.999, 0.07 to 1.16 cm
(0.9-7.6%) and - 0.16 to 0.66 cm
(- 0.6 to 3.2%). Intra-investigator comparison revealed ICCs, SEMs and mean bias between 0.883-0.998, 0.07-0.93 cm
(1.1-7.6%) and - 0.80 to 0.15 cm
(- 3.4 to 1.8%). Image quality needs to be high for efficient and accurate ACSAuto analyses. Taken together, the ACSAuto script represents a reliable tool to measure RF and VL ACSA, is comparable to manual analysis and can reduce time needed to evaluate ultrasound images.
Skeletal muscle structural assembly (and its remodeling in response to loading-unloading states) can be investigated macroscopically by assessing muscle architecture, described as fascicle geometric ...disposition within the muscle. Over recent decades, various medical imaging techniques have been developed to facilitate the in vivo assessment of muscle architecture. However, the main advantages and limitations of these methodologies have been fragmentally discussed. In the present article, the main techniques used for the evaluation of muscle architecture are presented: conventional B-mode ultrasonography, extended-field-of-view ultrasound, 3-D ultrasound and magnetic resonance imaging-based diffusion tensor imaging. By critically discussing potentials and shortcomings of each methodology, we aim to provide readers with an overview of both established and new techniques for the in vivo assessment of muscle architecture. This review may serve as decision guidance facilitating selection of the appropriate technique to be applied in biomedical research or clinical routine.
PURPOSEThis study aimed to compare biceps femoris long head (BFlh) fascicle length (Lf) obtained with different ultrasound-based approaches1) single ultrasound images and linear Lf extrapolation, 2) ...single ultrasound images and one of two different trigonometric equations (termed equations A and B), and 3) extended field of view (EFOV) ultrasound images.
METHODSThirty-seven elite alpine skiers (21.7 ± 2.8 yr) without a previous history of hamstring strain injury were tested. Single ultrasound images were collected with a 5-cm linear transducer from BFlh at 50% femur length and were compared with whole muscle scans acquired by EFOV ultrasound.
RESULTSThe intrasession reliability (intraclass correlation coefficient ICC3,k) of Lf measurements was very high for both single ultrasound images (i.e., Lf estimated by linear extrapolation; ICC3,k = 0.96–0.99, SEM = 0.18 cm) and EFOV scans (ICC3,k = 0.91–0.98, SEM = 0.19 cm). Although extrapolation methods showed cases of Lf overestimation and underestimation when compared with EFOV scans, mean Lf measured from EFOV scans (8.07 ± 1.36 cm) was significantly shorter than Lf estimated by trigonometric equations A (9.98 ± 2.12 cm, P < 0.01) and B (8.57 ± 1.59 cm, P = 0.03), but not significantly different from Lf estimated with manual linear extrapolation (8.40 ± 1.68 cm, P = 0.13). Bland–Altman analyses revealed mean differences in Lf obtained from EFOV scans and those estimated from equation A, equation B, and manual linear extrapolation of 1.91 ± 2.1, 0.50 ± 1.0, and 0.33 ± 1.0 cm, respectively.
CONCLUSIONSThe typical extrapolation methods used for estimating Lf from single ultrasound images are reliable within the same session, but not accurate for estimating BFlh Lf at rest with a 5-cm field of view. We recommend that EFOV scans are implemented to accurately determine intervention-related Lf changes in BFlh.
B-mode ultrasound can be used to image musculoskeletal tissues, but one major bottleneck is analyses of muscle architectural parameters (i.e., muscle thickness, pennation angle and fascicle length), ...which are most often performed manually.
In this study we trained two different neural networks (classic U-Net and U-Net with VGG16 pre-trained encoder) to detect muscle fascicles and aponeuroses using a set of labeled musculoskeletal ultrasound images. We determined the best-performing model based on intersection over union and loss metrics. We then compared neural network predictions on an unseen test set with those obtained via manual analysis and two existing semi/automated analysis approaches (simple muscle architecture analysis SMA and UltraTrack). DL_Track_US detects the locations of the superficial and deep aponeuroses, as well as multiple fascicle fragments per image.
For single images, DL_Track_US yielded results similar to those produced by a non-trainable automated method (SMA; mean difference in fascicle length: 5.1 mm) and human manual analysis (mean difference: -2.4 mm). Between-method differences in pennation angle were within 1.5°, and mean differences in muscle thickness were less than 1 mm. Similarly, for videos, there was overlap between the results produced with UltraTrack and DL_Track_US, with intraclass correlations ranging between 0.19 and 0.88.
DL_Track_US is fully automated and open source and can estimate fascicle length, pennation angle and muscle thickness from single images or videos, as well as from multiple superficial muscles. We also provide a user interface and all necessary code and training data for custom model development.
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