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Schooling CN, Healey T, McDonough HE, French SJ, McDermott CJ, Shaw PJ, Kadirkamanathan V, Alix JJ. Tensor electrical impedance myography identifies bulbar disease progression in amyotrophic lateral sclerosis. Clin Neurophysiol 2022; 139:69-75. [DOI: 10.1016/j.clinph.2022.04.015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2022] [Revised: 04/08/2022] [Accepted: 04/15/2022] [Indexed: 11/17/2022]
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Luo X, Wang S, Sanchez B. Modeling and simulation of needle electrical impedance myography in nonhomogeneous isotropic skeletal muscle. IEEE JOURNAL OF ELECTROMAGNETICS, RF AND MICROWAVES IN MEDICINE AND BIOLOGY 2022; 6:103-110. [PMID: 35434441 PMCID: PMC9012450 DOI: 10.1109/jerm.2021.3091515] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Objective Needle electrical impedance myography (EIM) is a recently developed technique for neuromuscular evaluation. Despite its preliminary successful clinical application, further understanding is needed to aid interpreting EIM outcomes in nonhomogeneous skeletal muscle measurements. Methods The framework presented models needle EIM measurements in a bidomain isotropic model. Finite element method (FEM) simulations verify the validity of our model predictions studying two cases: a spherical volume surrounded by tissue and a two-layered tissue. Results Our models show that EIM is influenced by the vicinity of tissue with different electrical properties. The apparent resistance, reactance and phase relative errors between our theoretical predictions and FEM simulations in the spherical volume case study are ≤0.2%, ≤1.2% and ≤1.0%, respectively. For the two-layered tissue model case study, the relative errors are ≤2%. Conclusions We propose a bio-physics driven analytical framework describing needle EIM measurements in a nonhomogeneous bidomain tissue model. Clinical impact Our theoretical predictions may lead to new ways for interpreting needle EIM data in neuromuscular diseases that cause compositional changes in muscle content, e.g. connective tissue deposition within the muscle. These changes will manifest themselves by changing the electric properties of the conductor media and will impact impedance values.
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Affiliation(s)
- Xuesong Luo
- Department of Automation Science and Electric Engineering, Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, Beihang University, Beijing 100083, China,Sanchez Research Lab, Department of Electrical and Computer Engineering, Sorenson Molecular Biotechnology Building, 36 South Wasatch Drive, University of Utah, Salt Lake City, UT 84112, USA
| | - Shaoping Wang
- Department of Automation Science and Electric Engineering, Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, Beihang University, Beijing 100083, China
| | - Benjamin Sanchez
- Sanchez Research Lab, Department of Electrical and Computer Engineering, Sorenson Molecular Biotechnology Building, 36 South Wasatch Drive, University of Utah, Salt Lake City, UT 84112, USA
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Pandeya SR, Nagy JA, Riveros D, Semple C, Taylor RS, Mortreux M, Sanchez B, Kapur K, Rutkove SB. Estimating myofiber cross-sectional area and connective tissue deposition with electrical impedance myography: A study in D2-mdx mice. Muscle Nerve 2021; 63:941-950. [PMID: 33759456 PMCID: PMC8883327 DOI: 10.1002/mus.27240] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2020] [Revised: 03/19/2021] [Indexed: 12/14/2022]
Abstract
INTRODUCTION Surface electrical impedance myography (sEIM) has the potential for providing information on muscle composition and structure noninvasively. We sought to evaluate its use to predict myofiber size and connective tissue deposition in the D2-mdx model of Duchenne muscular dystrophy (DMD). METHODS We applied a prediction algorithm, the least absolute shrinkage and selection operator, to select specific EIM measurements obtained with surface and ex vivo EIM data from D2-mdx and wild-type (WT) mice (analyzed together or separately). We assessed myofiber cross-sectional area histologically and hydroxyproline (HP), a surrogate measure for connective tissue content, biochemically. RESULTS Using WT and D2-mdx impedance values together in the algorithm, sEIM gave average root-mean-square errors (RMSEs) of 26.6% for CSA and 45.8% for HP, which translate into mean errors of ±363 μm2 for a mean CSA of 1365 μm2 and of ±1.44 μg HP/mg muscle for a mean HP content of 3.15 μg HP/mg muscle. Stronger predictions were obtained by analyzing sEIM data from D2-mdx animals alone (RMSEs of 15.3% for CSA and 34.1% for HP content). Predictions made using ex vivo EIM data from D2-mdx animals alone were nearly equivalent to those obtained with sEIM data (RMSE of 16.59% for CSA), and slightly more accurate for HP (RMSE of 26.7%). DISCUSSION Surface EIM combined with a predictive algorithm can provide estimates of muscle pathology comparable to values obtained using ex vivo EIM, and can be used as a surrogate measure of disease severity and progression and response to therapy.
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Affiliation(s)
- Sarbesh R. Pandeya
- Department of Neurology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts
| | - Janice A. Nagy
- Department of Neurology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts
| | - Daniela Riveros
- Department of Neurology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts
| | - Carson Semple
- Department of Neurology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts
| | - Rebecca S. Taylor
- Department of Neurology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts
| | - Marie Mortreux
- Department of Neurology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts
| | - Benjamin Sanchez
- Department of Electrical and Computer Engineering, University of Utah, Salt Lake City, Utah
| | - Kush Kapur
- Department of Neurology, Boston Childrenʼs Hospital, Harvard Medical School, Boston, Massachusetts
| | - Seward B. Rutkove
- Department of Neurology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts
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Andreasen N, Crandall H, Brimhall O, Miller B, Perez-Tamayo J, Martinsen OG, Kauwe SK, Sanchez B. Skin Electrical Resistance as a Diagnostic and Therapeutic Biomarker of Breast Cancer Measuring Lymphatic Regions. IEEE ACCESS : PRACTICAL INNOVATIONS, OPEN SOLUTIONS 2021; 9:152322-152332. [PMID: 34888126 PMCID: PMC8654262 DOI: 10.1109/access.2021.3123569] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
Skin changes associated with alterations in the interstitial matrix and lymph system might provide significant and measurable effects due to the presence of breast cancer. This study aimed to determine if skin electrical resistance changes could serve as a diagnostic and therapeutic biomarker associated with physiological changes in patients with malignant versus benign breast cancer lesions. Forty-eight women (24 with malignant cancer, 23 with benign lesions) were enrolled in this study. Repeated skin resistance measurements were performed within the same session and 1 week after the first measurement in the breast lymphatic region and non-breast lymphathic regions. Intraclass correlation coefficients were calculated to determine the technique's intrasession and intersession reproducibility. Data were then normalized as a mean of comparing cross-sectional differences between malignant and benign lesions of the breast. Six months longitudinal data from six patients that received therapy were analyzed to detect the effect of therapy. Standard descriptive statistics were used to compare ratiometric differences between groups. Skin resistance data were used to train a machine learning random forest classification algorithm to diagnose breast cancer lesions. Significant differences between malignant and benign breast lesions were obtained (p<0.01), also pre- and post-treatment (p<0.05). The diagnostic algorithm demonstrated the capability to classify breast cancer with an area under the curve of 0.68, sensitivity of 66.3%, specificity of 78.5%, positive predictive value 70.7% and negative predictive value 75.1%. Measurement of skin resistance in patients with breast cancer may serve as a convenient screening tool for breast cancer and evaluation of therapy. Further work is warranted to improve our approach and further investigate the biophysical mechanisms leading to the observed changes.
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Affiliation(s)
| | - Henry Crandall
- Department of Electrical and Computer Engineering, University of Utah, Salt Lake City, UT 84112, USA
| | | | - Brittny Miller
- Ogden Regional Medical Center, Department of Women's Imaging, Ogden, UT 84405, USA
| | - Jose Perez-Tamayo
- Ogden Regional Medical Center, Department of Women's Imaging, Ogden, UT 84405, USA
| | - Orjan G Martinsen
- Department of Physics, University of Oslo, 0371 Oslo, Norway
- Department of Clinical and Biomedical Engineering, Oslo University Hospital, 0372 Oslo, Norway
| | - Steven K Kauwe
- Department of Materials Science and Engineering, University of Utah, Salt Lake City, UT 84112, USA
| | - Benjamin Sanchez
- Department of Electrical and Computer Engineering, University of Utah, Salt Lake City, UT 84112, USA
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Pandeya SR, Nagy JA, Riveros D, Semple C, Taylor RS, Mortreux M, Sanchez B, Kapur K, Rutkove SB. Predicting myofiber cross-sectional area and triglyceride content with electrical impedance myography: A study in db/db mice. Muscle Nerve 2021; 63:127-140. [PMID: 33063867 PMCID: PMC8891989 DOI: 10.1002/mus.27095] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2020] [Revised: 08/02/2020] [Accepted: 10/11/2020] [Indexed: 12/14/2022]
Abstract
BACKGROUND Electrical impedance myography (EIM) provides insight into muscle composition and structure. We sought to evaluate its use in a mouse obesity model characterized by myofiber atrophy. METHODS We applied a prediction algorithm, ie, the least absolute shrinkage and selection operator (LASSO), to surface, needle array, and ex vivo EIM data from db/db and wild-type mice and assessed myofiber cross-sectional area (CSA) histologically and triglyceride (TG) content biochemically. RESULTS EIM data from all three modalities provided acceptable predictions of myofiber CSA with average root mean square error (RMSE) of 15% in CSA (ie, ±209 μm2 for a mean CSA of 1439 μm2 ) and TG content with RMSE of 30% in TG content (ie, ±7.3 nmol TG/mg muscle for a mean TG content of 25.4 nmol TG/mg muscle). CONCLUSIONS EIM combined with a predictive algorithm provides reasonable estimates of myofiber CSA and TG content without the need for biopsy.
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Affiliation(s)
- Sarbesh R. Pandeya
- Department of Neurology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts
| | - Janice A. Nagy
- Department of Neurology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts
| | - Daniela Riveros
- Department of Neurology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts
| | - Carson Semple
- Department of Neurology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts
| | - Rebecca S. Taylor
- Department of Neurology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts
| | - Marie Mortreux
- Department of Neurology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts
| | - Benjamin Sanchez
- Department of Neurology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts
- Department of Electrical and Computer Engineering, University of Utah, Salt Lake City, Utah
| | - Kush Kapur
- Department of Neurology, Boston Children's Hospital, Harvard Medical School, Boston, Massachusetts
| | - Seward B. Rutkove
- Department of Neurology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts
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Alix JJP, McDonough HE, Sonbas B, French SJ, Rao DG, Kadirkamanathan V, McDermott CJ, Healey TJ, Shaw PJ. Multi-dimensional electrical impedance myography of the tongue as a potential biomarker for amyotrophic lateral sclerosis. Clin Neurophysiol 2020; 131:799-808. [PMID: 32066098 DOI: 10.1016/j.clinph.2019.12.418] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2019] [Revised: 12/09/2019] [Accepted: 12/15/2019] [Indexed: 12/11/2022]
Abstract
OBJECTIVE In amyotrophic lateral sclerosis (ALS) bulbar disease biomarkers are lacking. We evaluated a novel tongue electrical impedance myography (EIM) system, utilising both 2D and 3D electrode configurations for detection of tongue pathology. METHODS Longitudinal multi-frequency phase angle spectra were recorded from 41 patients with ALS (baseline, 3 and 6 months) and 30 healthy volunteers (baseline and 6 months). ALS functional rating scale-revised (ALSFRS-R) data and quantitative tongue strength measurements were collected. EIM data were analysed for reliability (intra-class correlation coefficient; ICC) and differences between patients and volunteers ascertained using both univariate (Mann-Whitney U test) and multivariate techniques (feature selection and L2 norm). RESULTS The device produced highly reliable data (pooled ICC: 0.836). Significant EIM differences were apparent between ALS patients and healthy volunteers (P < 0.001). EIM data demonstrated a significant relationship to tongue strength and bulbar ALSFRS-R scores (P < 0.015). The EIM recordings revealed a group level longitudinal change over 6 months and consistently identified patients in whom symptoms or tongue strength changed. CONCLUSIONS The novel EIM tongue system produces reliable data and can differentiate between healthy muscle and ALS-related disease. SIGNIFICANCE Tongue EIM utilising multiple frequencies and electrode configurations has potential as a bulbar disease biomarker in ALS.
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Affiliation(s)
- James J P Alix
- Sheffield Institute for Translational Neuroscience, University of Sheffield, UK; Department of Clinical Neurophysiology, Sheffield Teaching Hospitals NHS Foundation Trust, UK.
| | - Harry E McDonough
- Sheffield Institute for Translational Neuroscience, University of Sheffield, UK
| | - Buket Sonbas
- Department of Automatic Control and Systems Engineering, University of Sheffield
| | - Sophie J French
- Sheffield Institute for Translational Neuroscience, University of Sheffield, UK
| | - D Ganesh Rao
- Department of Clinical Neurophysiology, Sheffield Teaching Hospitals NHS Foundation Trust, UK
| | | | - Christopher J McDermott
- Sheffield Institute for Translational Neuroscience, University of Sheffield, UK; Department of Neurology, Sheffield Teaching Hospitals NHS Foundation Trust, UK
| | - T Jamie Healey
- Department of Clinical Engineering, Sheffield Teaching Hospitals NHS Foundation Trust, UK
| | - Pamela J Shaw
- Sheffield Institute for Translational Neuroscience, University of Sheffield, UK; Department of Neurology, Sheffield Teaching Hospitals NHS Foundation Trust, UK
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Leitner ML, Kapur K, Darras BT, Yang M, Wong B, Dalle Pazze L, Florence J, Buck M, Freedman L, Bohorquez J, Rutkove S, Zaidman C. Electrical impedance myography for reducing sample size in Duchenne muscular dystrophy trials. Ann Clin Transl Neurol 2019; 7:4-14. [PMID: 31876124 PMCID: PMC6952321 DOI: 10.1002/acn3.50958] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2019] [Revised: 10/30/2019] [Accepted: 10/31/2019] [Indexed: 01/16/2023] Open
Abstract
Objective To evaluate the sensitivity of electrical impedance myography (EIM) to disease progression in both ambulatory and non‐ambulatory boys with DMD. Methods and Participants A non‐blinded, longitudinal cohort study of 29 ambulatory and 15 non‐ambulatory boys with DMD and age‐similar healthy boys. Subjects were followed for up to 1 year and assessed using the Myolex® mViewTM EIM system as part of a multicenter study. Results In the ambulatory group, EIM 100 kHz resistance values showed significant change compared to the healthy boys. For example, in lower extremity muscles, the average change in EIM 100 kHz resistance values over 12 months led to an estimated effect size of 1.58. Based on these results, 26 DMD patients/arm would be needed for a 12‐month clinical trial assuming a 50% treatment effect. In non‐ambulatory boys, EIM changes were greater in upper limb muscles. For example, biceps at 100kHz resistance gave an estimated effect size of 1.92 at 12 months. Based on these results, 18 non‐ambulatory DMD patients/arm would be needed for a 12‐month clinical trial assuming a 50% treatment effect. Longitudinal changes in the 100 kHz resistance values for the ambulatory boys correlated with the longitudinal changes in the timed supine‐to‐stand test. EIM was well‐tolerated throughout the study. Interpretation This study supports that EIM 100 kHz resistance is sensitive to DMD progression in both ambulatory and non‐ambulatory boys. Given the technology’s ease of use and broad age range of utility it should be employed as an exploratory endpoint in future clinical therapeutic trials in DMD. Trial Registration: Clincialtrials.gov registration #NCT02340923
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Affiliation(s)
| | - Kush Kapur
- Department of Neurology, Boston Children's Hospital, Boston, Massachusetts
| | - Basil T Darras
- Department of Neurology, Boston Children's Hospital, Boston, Massachusetts
| | - Michele Yang
- Department of Neurology, Children's Hospital Colorado, Denver, Colorado
| | - Brenda Wong
- Department of Pediatrics, University of Massachusetts Medical School, Worcester, Massachusetts
| | | | - Julaine Florence
- Department of Neurology, Washington University in St. Louis, St. Louis, Missouri
| | | | | | | | | | - Craig Zaidman
- Department of Neurology, Washington University in St. Louis, St. Louis, Missouri
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