Evidence map›Paper›PMID 32625114›Full record

ReviewFrontiers in physiology2020

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

Usha Sinha, Vadim Malis, Jiun-Shyan Chen, Robert Csapo, Ryuta Kinugasa, Marco Vincenzo Narici, Shantanu Sinha

Open access · goldAbstract readReview
In one paragraph

Review in Frontiers in physiology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.

0numbers the graph read from it
0cells of the map it votes in
19citing papers in PubMed
2.5field-weighted citation impact, top 12% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

19 citing papers in PubMed, 25 citations in OpenAlex.

  1. Article
  2. Review
  3. Article
  4. Review
  5. Article
  6. Impact of Wrist and Finger Extension on Passive Joint Torques and Their Association with Forearm Flexor Muscle Structure in Hemiparetic Cerebral Palsy: Early Findings.Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference · 2025
    Article
  7. Review
  8. Article
  9. Article
  10. Magnetic Resonance Imaging Biomarkers of Muscle.Tomography (Ann Arbor, Mich.) · 2024
    Review
  11. Article
  12. Review
  13. Article
  14. Review
  15. Article
  16. Review
  17. Article
  18. Article
  19. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors at 5 institutions in 4 countries.

Usha SinhaDepartment of Physics, San Diego State University, San Diego, CA, United States.
Vadim MalisDepartment of Physics, University of California, San Diego, San Diego, CA, United States.
Jiun-Shyan ChenDepartment of Structural Engineering, University of California, San Diego, San Diego, CA, United States.
Robert CsapoResearch Unit for Orthopaediic Sports Medicine and Injury Prevention, ISAG, Private University for Health Sciences, Medical Informatics and Technology, Hall in Tirol, Austria.
Ryuta KinugasaDepartment of Human Sciences, Kanagawa University, Yokohama, Japan.
Marco Vincenzo NariciInstitute of Physiology, Department of Biomedical Sciences, University of Padua, Padua, Italy.
Shantanu SinhaDepartment of Radiology, University of California, San Diego, San Diego, CA, United States.
University of California, San Diego · USRIKEN Center for Computational Science · JPSan Diego State University · USUMIT - Private Universität für Gesundheitswissenschaften, Medizinische Informatik und Technik · ATUniversity of Padua · IT

Funding

Role of the Extracellular Matrix in Age-Associated Strength Loss: Combining Imaging and Biochemistry to create a Multi-Scale Mesh-free ModelR01AG056999 · NIA · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI SINHA, SHANTANU · 2018 to 2022
$3.1M
NIA NIH HHS R01 AG056999
6 · The paper itself

Abstract

The focus of this review is the application of advanced MRI to study the effect of aging and disuse related remodeling of the extracellular matrix (ECM) on force transmission in the human musculoskeletal system. Structural MRI includes (i) ultra-low echo times (UTE) maps to visualize and quantify the connective tissue, (ii) diffusion tensor imaging (DTI) modeling to estimate changes in muscle and ECM microstructure, and (iii) magnetization transfer contrast imaging to quantify the macromolecular fraction in muscle. Functional MRI includes dynamic acquisitions during contraction cycles enabling computation of the strain tensor to monitor muscle deformation. Further, shear strain extracted from the strain tensor may be a potential surrogate marker of lateral transmission of force. Biochemical and histological analysis of muscle biopsy samples can provide "gold-standard" validation of some of the MR findings. The review summarizes biochemical studies of ECM adaptations with age and with disuse. A brief summary of animal models is included as they provide experimental confirmation of longitudinal and lateral force transmission pathways. Computational muscle models enable exploration of force generation and force pathways and elucidate the link between structural adaptations and functional consequences. MR image findings integrated in a computational model can explain and predict subject specific functional changes to structural adaptations. Future work includes development and validation of MRI biomarkers using biochemical analysis of muscle tissue as a reference standard and potential translation of the imaging markers to the clinic to noninvasively monitor musculoskeletal disease conditions and changes consequent to rehabilitative interventions.

Indexed as

age and disuse related muscle force lossextracellular matrixlateral transmission of forcestrain imagingstructural muscle MRI

Identifiers

PMID32625114
PMCPMC7315044
OpenAlexW3036737334

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.