Share:
Peer-Reviewed Publication
J Neuromuscul Dis2026;13(3):264-273.May 1, 2026Journal Article

Electrical impedance myography captures features of muscle structure measured by MRI and transcriptomic analysis in facioscapulohumeral muscular dystrophy.

Leo H Wang1, Buket Sonbas Cobb2,3, Lara Riem4, Olivia DuCharme4, Dennis Ww Shaw5, Michaela Walker6, Katy Eichinger7, Leann Lewis7, Rabi Tawil7, Johanna I Hamel7, Karlien Mul8, Silvia S Blemker4, Stephen J Tapscott9, Seth D Friedman10, Seward B Rutkove3, Jeffrey M Statland6
1Department of Neurology, University of Washington, Seattle, Washington, USA.
2Department of Electrical and Electronic Engineering, Harran University, Sanliurfa, Turkey.
3Department of Neurology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA.
4Springbok Analytics, Charlottesville, VA, USA.
5Department of Radiology, University of Washington, Seattle, Washington, USA.
6Department of Neurology, University of Kansas Medical Center, Kansas City, KS, USA.
7Department of Neurology, University of Rochester, Rochester, New York, USA.
8Department of Neurology, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, The Netherlands.
9Human Biology Division, Fred Hutchinson Research Center, Seattle, Washington, USA.
10Seattle Children's Hospital, Seattle, Washington, USA.

Abstract

BACKGROUND: Electrical impedance myography (EIM) has been proposed as an efficient, non-invasive biomarker of muscle composition in facioscapulohumeral muscular dystrophy (FSHD). OBJECTIVE: We investigate whether EIM parameters are associated with muscle structure measured by magnetic resonance imaging (MRI), muscle histology, and transcriptomic analysis as well as strength at the individual leg…

Create a free account to keep reading

Free members get 10 full research views every month across publications, clinical trials, FDA clearances, adverse events, and NIH grants. No credit card required.

Want unlimited research access? See Pro plans

Data Accuracy Notice: Research intelligence on Health AI Central is aggregated from public sources (PubMed, ClinicalTrials.gov, FDA, NIH, CMS, and others) and refreshed nightly. Classifications and derived metrics are produced by automated methods described in our Methodology. We recommend verifying critical data points against the primary sources before making decisions.