Foreground Detection Analysis of Ultrasound Image Sequences Identifies Markers of Motor Neurone Disease across Diagnostically Relevant Skeletal Muscles

Bedeutung der Muskelsonographie in der Detektion von Faszikulationen bei der ALS

Bei der amyotrophen Lateralsklerose sind Faszikulationen häufig bereits in frühen Stadien in mehreren Körperregionen vorzufinden und haben daher Eingang in die entsprechenden Leitlinien und Diagnosekriterien gefunden. Während die invasive EMG-Diagnostik unverzichtbar zum Nachweis von akut- und chronisch-neurogenen Veränderungen des elektrischen Signalverhaltens motorischer Einheiten und zur Bestätigung von Faszikulationspotenzialen bleibt, bietet die Muskelsonographie ein hochsensitives Verfahren, um schnell und nicht-invasiv Faszikulationen in den verschiedenen Muskel-Etagen zu erfassen. In dieser Übersichtsarbeit stellen wir die bisherigen Daten zum Einsatz der Muskelsonographie zur Faszikulationsdetektion dar. Durch ihren Einsatz ermöglicht die Muskelsonographie im klinischen Alltag eine zielgerichtete und hierdurch aussagekräftigere EMG-Diagnostik. Aktuelle Forschungsstudien zielen darauf ab, Faszikulationen sonomorphologisch genauer zu charakterisieren, zu quantifizieren und als Verlaufsparameter zu untersuchen.

Fasciculation electromechanical latency is prolonged in amyotrophic lateral sclerosis

OBJECTIVE

In amyotrophic lateral sclerosis (ALS), motor neurons become hyperexcitable and spontaneously discharge electrical impulses causing fasciculations. These can be detected by two noninvasive methods: high-density surface electromyography (HDSEMG) and muscle ultrasonography (MUS). We combined these methods simultaneously to explore the electromechanical properties of fasciculations, seeking a novel biomarker of disease.

METHODS

Twelve ALS patients and thirteen healthy participants each provided up to 24 minutes of recordings from the right biceps brachii (BB) and gastrocnemius medialis (GM). Two automated algorithms (Surface Potential Quantification Engine and a Gaussian mixture model) were applied to HDSEMG and MUS data to identify correlated electromechanical fasciculation events.

RESULTS

We identified 4,197 correlated electromechanical fasciculation events. HDSEMG reliably detected electromechanical events up to 30 mm below the skin surface with an inverse correlation between amplitude and depth in ALS muscles. Compared to Healthy-GM muscles (mean = 79.8 ms), electromechanical latency was prolonged in ALS-GM (mean = 108.8 ms; p = 0.0458) and ALS-BB (mean = 112.0 ms; p = 0.0128) muscles. Electromechanical latency did not correlate with disease duration, symptom burden, sum muscle power score or fasciculation frequency.

CONCLUSIONS

Prolonged fasciculation electromechanical latency indicates impairment of the excitation-contraction coupling mechanism, warranting further exploration as a potential novel biomarker of disease in ALS.

SIGNIFICANCE

This study points to an electromechanical defect within the muscles of ALS patients.

Physical and electrophysiological motor unit characteristics are revealed with simultaneous high-density electromyography and ultrafast ultrasound imaging

Electromyography and ultrasonography provide complementary information about electrophysiological and physical (i.e. anatomical and mechanical) muscle properties. In this study, we propose a method to assess the electrical and physical properties of single motor units (MUs) by combining High-Density surface Electromyography (HDsEMG) and ultrafast ultrasonography (US). Individual MU firings extracted from HDsEMG were used to identify the corresponding region of muscle tissue displacement in US videos. The time evolution of the tissue velocity in the identified region was regarded as the MU tissue displacement velocity. The method was tested in simulated conditions and applied to experimental signals to study the local association between the amplitude distribution of single MU action potentials and the identified displacement area. We were able to identify the location of simulated MUs in the muscle cross-section within a 2 mm error and to reconstruct the simulated MU displacement velocity (cc > 0.85). Multiple regression analysis of 180 experimental MUs detected during isometric contractions of the biceps brachii revealed a significant association between the identified location of MU displacement areas and the centroid of the EMG amplitude distribution. The proposed approach has the potential to enable non-invasive assessment of the electrical, anatomical, and mechanical properties of single MUs in voluntary contractions.

Comparison between surface electrodes and ultrasound monitoring to measure TMS evoked muscle contraction

INTRODUCTION

Transcranial magnetic stimulation (TMS) is widely used to explore cortical physiology in health and disease. Surface electromyography (sEMG) is appropriate for superficial muscles, but cannot be applied easily to less accessible muscles. Muscle ultrasound (mUS) may provide an elegant solution to this problem, but fundamental questions remain. We explore the relationship between TMS evoked muscle potentials and TMS evoked muscle contractions measured with mUS.

METHODS

In 10 participants, we performed a TMS recruitment curve, simultaneously measuring motor evoked potentials (MEPs) and mUS in biceps (BI), first dorsal interosseous (FDI), tibialis anterior (TA), and the tongue (TO).

RESULTS

Resting motor threshold (RMT) measurements and recruitment curves were found to be consistent across sEMG and mUS.

DISCUSSION

This work supports the use of TMS-US to study less accessible muscles. The implications are broad but could include the study of a new range of muscles in disorders such as amyotrophic lateral sclerosis.

The evolving role of surface electromyography in amyotrophic lateral sclerosis: A systematic review

OBJECTIVE

Amyotrophic lateral sclerosis (ALS) is an adult-onset neurodegenerative disease that leads to inexorable motor decline and a median survival of three years from symptom onset. Surface EMG represents a major technological advance that has been harnessed in the development of novel neurophysiological biomarkers. We have systematically reviewed the current application of surface EMG techniques in ALS.

METHODS

We searched PubMed to identify 42 studies focusing on surface EMG and its associated analytical methods in the diagnosis, prognosis and monitoring of ALS patients.

RESULTS

A wide variety of analytical techniques were identified, involving motor unit decomposition from high-density grids, motor unit number estimation and measurements of neuronal hyperexcitability or neuromuscular architecture. Some studies have proposed specific diagnostic and prognostic criteria however clinical calibration in large ALS cohorts is currently lacking. The most validated method to monitor disease is the motor unit number index (MUNIX), which has been implemented as an outcome measure in two ALS clinical trials.

CONCLUSION

Surface EMG offers significant practical and analytical flexibility compared to invasive techniques. To capitalise on this fully, emphasis must be placed upon the multi-disciplinary collaboration of clinicians, bioengineers, mathematicians and biostatisticians.

SIGNIFICANCE

Surface EMG techniques can enrich effective biomarker development in ALS.

Ultrasound imaging to assess skeletal muscle architecture during movements: a systematic review of methods, reliability, and challenges

B-mode ultrasound is often used to quantify muscle architecture during movements. Our objectives were to 1) systematically review the reliability of fascicle length (FL) and pennation angles (PA) measured using ultrasound during movements involving voluntary contractions; 2) systematically review the methods used in studies reporting reliability, discuss associated challenges, and provide recommendations to improve the reliability and validity of dynamic ultrasound measurements; and 3) provide an overview of computational approaches for quantifying fascicle architecture, their validity, agreement with manual quantification of fascicle architecture, and advantages and drawbacks. Three databases were searched until June 2019. Studies among healthy human individuals aged 17-85 yr that investigated the reliability of FL or PA in lower-extremity muscles during isoinertial movements and that were written in English were included. Thirty studies (n = 340 participants) were included for reliability analyses. Between-session reliability as measured by coefficient of multiple correlations (CMC), and coefficient of variation (CV) was FL CMC: 0.89-0.96; CV: 8.3% and PA CMC: 0.87-0.90; CV: 4.5-9.6%. Within-session reliability was FL CMC: 0.82-0.99; CV: 0.0-6.7% and PA CMC: 0.91; CV: 0.0-15.0%. Manual analysis reliability was FL CMC: 0.89-0.96; CV: 0.0-15.9%; PA CMC: 0.84-0.90; and CV: 2.0-9.8%. Computational analysis FL CMC was 0.82-0.99, and PA CV was 14.0-15.0%. Eighteen computational approaches were identified, and these generally showed high agreement with manual analysis and high validity compared with phantoms or synthetic images. B-mode ultrasound is a reliable method to quantify fascicle architecture during movement. Additionally, computational approaches can provide a reliable and valid estimation of fascicle architecture.

Objective Analysis of Neck Muscle Boundaries for Cervical Dystonia Using Ultrasound Imaging and Deep Learning

OBJECTIVE

To provide objective visualization and pattern analysis of neck muscle boundaries to inform and monitor treatment of cervical dystonia.

METHODS

We recorded transverse cervical ultrasound (US) images and whole-body motion analysis of sixty-one standing participants (35 cervical dystonia, 26 age matched controls). We manually annotated 3,272 US images sampling posture and the functional range of pitch, yaw, and roll head movements. Using previously validated methods, we used 60-fold cross validation to train, validate and test a deep neural network (U-net) to classify pixels to 13 categories (five paired neck muscles, skin, ligamentum nuchae, vertebra). For all participants for their normal standing posture, we segmented US images and classified condition (Dystonia/Control), sex and age (higher/lower) from segment boundaries. We performed an explanatory, visualization analysis of dystonia muscle-boundaries.

RESULTS

For all segments, agreement with manual labels was Dice Coefficient (64 ± 21%) and Hausdorff Distance (5.7 ± 4 mm). For deep muscle layers, boundaries predicted central injection sites with average precision 94 ± 3%. Using leave-one-out cross-validation, a support-vector-machine classified condition, sex, and age from predicted muscle boundaries at accuracy 70.5%, 67.2%, 52.4% respectively, exceeding classification by manual labels. From muscle boundaries, Dystonia clustered optimally into three sub-groups. These sub-groups are visualized and explained by three eigen-patterns which correlate significantly with truncal and head posture.

CONCLUSION

Using US, neck muscle shape alone discriminates dystonia from healthy controls.

SIGNIFICANCE

Using deep learning, US imaging allows online, automated visualization, and diagnostic analysis of cervical dystonia and segmentation of individual muscles for targeted injection.

Ultrasound-derived changes in thickness of human ankle plantar flexor muscles during walking and running are not homogeneous along the muscle mid-belly region

Skeletal muscle thickness is a valuable indicator of several aspects of a muscle’s functional capabilities. We used computational analysis of ultrasound images, recorded from 10 humans walking and running at a range of speeds (0.7–5.0 m s⁻¹), to quantify interactions in thickness change between three ankle plantar flexor muscles (soleus, medial and lateral gastrocnemius) and quantify thickness changes at multiple muscle sites within each image. Statistical analysis of thickness change as a function of stride cycle (1d statistical parametric mapping) revealed significant differences between soleus and both gastrocnemii across the whole stride cycle as they bulged within the shared anatomical space. Within each muscle, changes in thickness differed between measurement sites but not locomotor condition. For some of the stride, thickness measures taken from the distal-mid image region represented the mean muscle thickness, which may therefore be a reliable region for these measures. Assumptions that muscle thickness is constant during a task, often made in musculoskeletal models, do not hold for the muscles and locomotor conditions studied here and researchers should not assume that a single thickness measure, from one point of the stride cycle or a static image, represents muscle thickness during dynamic movements.