Functional Mixed-Effects Modeling of Longitudinal Duchenne Muscular Dystrophy Electrical Impedance Myography Data Using State-Space Approach

Modeling and simulation of needle electrical impedance myography in nonhomogeneous isotropic skeletal muscle

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.

Estimating myofiber cross-sectional area and connective tissue deposition with electrical impedance myography: A study in D2-mdx mice

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.

Skin Electrical Resistance as a Diagnostic and Therapeutic Biomarker of Breast Cancer Measuring Lymphatic Regions

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.

Predicting myofiber cross-sectional area and triglyceride content with electrical impedance myography: A study in db/db mice

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.

Multi-dimensional electrical impedance myography of the tongue as a potential biomarker for amyotrophic lateral sclerosis

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.

Electrical impedance myography for reducing sample size in Duchenne muscular dystrophy trials

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^® mView^TM 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