Role of the Extracellular Matrix in Loss of Muscle Force With Age and Unloading Using Magnetic Resonance Imaging, Biochemical Analysis, and Computational Models

Sinha, Usha; Malis, Vadim; Chen, Jiun‐Shyan; Csapo, Robert; Kinugasa, Ryuta; Narici, Marco V.; Sinha, Shantanu

doi:10.3389/fphys.2020.00626

Cited by 16 publications

(10 citation statements)

References 107 publications

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“…Direct measurement of the ECM with MRI is technically challenging due to extremely short relaxation times of ECM macromolecules, which renders them ostensibly invisible (Sinha et al . 2020). One alternative is to utilize gadolinium‐based contrast agents that accumulate in the extracellular volume between ECM and muscle, and create T1‐weighted contrast in proportion to water volume (Carlier et al .…”

Section: Introductionmentioning

confidence: 99%

T1ρ imaging as a non‐invasive assessment of collagen remodelling and organization in human skeletal muscle after ligamentous injury

Noehren

Hardy

Andersen

et al. 2021

The Journal of Physiology

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Dysregulation and fibrosis of the extracellular matrix (ECM) in skeletal muscle is a consequence of injury. Current ECM assessment necessitates muscle biopsies to evaluate alterations to the muscle ECM, which is often not practical in humans. The goal of this study was to evaluate the potential of a magnetic resonance imaging sequence that quantifies T1ρ relaxation time to predict ECM collagen composition and organization. T1ρ imaging was performed and muscle biopsies obtained from the involved and non‐involved vastus lateralis muscle on 27 subjects who had an anterior cruciate ligament (ACL) tear. T1ρ times were quantified via monoexponential decay curve fitted to a series of T1ρ‐weighted images. Several ECM indices, including collagen content and organization, were obtained using immunohistochemistry and histochemistry in addition to hydroxyproline. Model selection with multiple linear regression was used to evaluate the relationships between T1ρ times and ECM composition. Additionally, the ACL‐deficient and healthy limb were compared to determine sensitivity of T1ρ to detect early adaptations in the muscle ECM following injury. We show that T1ρ relaxation time was strongly associated with collagen unfolding (t = 4.093, P = 0.0007) in the ACL‐deficient limb, and collagen 1 abundance in the healthy limb (t = 2.75, P = 0.014). In addition, we show that T1ρ relaxation time is significantly longer in the injured limb, coinciding with significant differences in several indices of collagen content and remodelling in the ACL‐deficient limb. These results support the use of T1ρ to evaluate ECM composition in skeletal muscle in a non‐invasive manner. imageKey points Dysregulation and fibrotic transformation of the skeletal muscle extracellular matrix (ECM) is a common pathology associated with injury and ageing. Studies of the muscle ECM in humans have necessitated the use of biopsies, which are impractical in many settings. Non‐invasive MRI T1ρ relaxation time was validated to predict ECM collagen composition and organization with aligned T1ρ imaging and biopsies of the vastus lateralis in the healthy limb and anterior cruciate ligament (ACL)‐deficient limb of 27 subjects. T1ρ relaxation time was strongly associated with collagen abundance and unfolding in the ACL‐deficient limb, and T1ρ relaxation time was strongly associated with total collagen abundance in the healthy limb. T1ρ relaxation time was significantly longer in the ACL‐deficient limb, coinciding with significant increases in several indices of muscle collagen content and remodelling supporting the use of T1ρ to non‐invasively evaluate ECM composition and pathology in skeletal muscle.

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Section: Introductionmentioning

confidence: 99%

T1ρ imaging as a non‐invasive assessment of collagen remodelling and organization in human skeletal muscle after ligamentous injury

Noehren

Hardy

Andersen

et al. 2021

The Journal of Physiology

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“…The time-accumulated peaks of and were determined and labelled as and , respectively. Scheme 2 has been previously used to extract the strain rate in the brain (Reese et al, 2002; Zhou et al, 2022) and other biological tissues (e.g., leg muscles (Malis et al, 2020; Mazzoli et al, 2018; Sinha et al, 2020; Sinha et al, 2018), myocardium (Dou et al, 2003; Robson and Constable, 1996; Wedeen, 1992)).…”

Section: Methodsmentioning

confidence: 99%

“…The time-accumulated peaks of , respectively. Scheme 2 has been previously used to extract the principal strain rate and/or the tract-oriented strain rates in the brain [23,25,26,[41][42][43][44] and other biological tissues (e.g., leg muscles [45][46][47][48], tongue [49,50], myocardium [51][52][53]).…”

Section: Psr( )mentioning

confidence: 99%

Brain strain rate response: addressing computational ambiguity and experimental data for model validation

Zhou

Liu

et al. 2022

Preprint

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Traumatic brain injury (TBI) is an alarming global public health issue with high morbidity and mortality rates. Although the causal link between external insults and consequent brain injury remains largely elusive, both strain and strain rate are generally recognized as crucial factors for brain injury development. With respect to the flourishment of strain-based exploration, concerning ambiguity and inconsistency are noted in the scheme for strain rate calculation within the TBI research community. Furthermore, there is no experimental data that can be used to validate the strain rate responses of finite element (FE) models of the human brain. Thus, the current work presented a theoretical clarification of two commonly used strain rate computational schemes: the strain rate was either calculated as the time derivative of strain or derived from the strain-rate tensor. To further substantiate this theoretical disparity, these two schemes were respectively implemented to estimate the strain rate responses from a previous-published cadaveric experiment and an FE brain model secondary to a concussive impact. The results clearly showed scheme-dependent responses, both in the experimentally determined principal strain rate and FE-derived principal and tract-oriented strain rates. The results suggest that cross-scheme comparison of strain rate responses is inappropriate, and the utilized strain rate computational scheme needs to be reported in future studies. The newly calculated experimental strain rate curves can be used for strain rate validation of FE head models.

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“…It has been shown that the material phases of this composite type of microstructure are essential to the continuum-scale force transmission of the muscle [2][3][4][5] and to an improved understanding of the aging effects on skeletal muscles. [6][7][8][9][10] Hence, it becomes imperative to examine the contribution of various factors (material properties, microstructure, and morphology of the tissue) that affect these passive materials, to the deformation and force output of the muscle.…”

Section: Introductionmentioning

confidence: 99%

Multiscale modeling of passive material influences on deformation and force output of skeletal muscles

Taneja

Chen

et al. 2022

Numer Methods Biomed Eng

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Passive materials in human skeletal muscle tissues play an important role in force output of skeletal muscles. This paper introduces a multiscale modeling framework to investigate how age-associated variations on microscale passive muscle components, including microstructural geometry (e.g., connective tissue thickness) and material properties (e.g., anisotropy), influence the force output and deformations of the continuum skeletal muscle. We first define a representative volume element (RVE) for the microstructure of muscle and determine the homogenized macroscale mechanical properties of the RVE from the separate mechanical properties of the individual components of the RVE, including muscle fibers and connective tissue with its associated collagen fibers. The homogenized properties of the RVE are then used to define the elements of the continuum muscle model to evaluate the force output and deformations of the whole muscle. Conversely, the regional deformations of the continuum model are fed back to the RVE model to determine the responses of the individual microscale components. Simulations of muscle isometric contractions at a range of muscle lengths are performed to investigate the effects of muscle architectural changes (e.g., pennation angles) due to aging on force output and muscle deformation. The correlations between the pennation angle, the shear deformation in the microscale connective tissue (an indicator for the lateral force transmission), the angle difference between the fiber direction and principal strain direction and the resulting shear deformation at the continuum scale, as well as the force output of the skeletal muscle are also discussed.

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Role of the Extracellular Matrix in Loss of Muscle Force With Age and Unloading Using Magnetic Resonance Imaging, Biochemical Analysis, and Computational Models

Cited by 16 publications

References 107 publications

T1ρ imaging as a non‐invasive assessment of collagen remodelling and organization in human skeletal muscle after ligamentous injury

T1ρ imaging as a non‐invasive assessment of collagen remodelling and organization in human skeletal muscle after ligamentous injury

Brain strain rate response: addressing computational ambiguity and experimental data for model validation

Multiscale modeling of passive material influences on deformation and force output of skeletal muscles

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