Publications

Objective: We compared thoracic electrical impedance tomography (EIT) with slow vital capacity (SVC) to determine if EIT could monitor pulmonary function in ALS patients longitudinally. Methods: Of 32 ALS patients and 32 age- and sex-matched healthy controls (HCs) initially enrolled in the Pulmonary Function via Impedance Tomography (PuFIT) study, 22 ALS and 20 HCs returned for a follow-up visit ∼3.9 months later. All participants had thoracic EIT measurements performed simultaneously with standard SVC in upright and supine positions at both visits. EIT data from each measurement were summarized as a single parameter, the impedance-SVC (zSVC), representing an averaged impedance change across both lungs. We assessed alterations over time for both cohorts of participants. Results: Sufficient quality EIT and SVC data were available for 18 of the patients with ALS and 19 HCs. Over time, mean upright SVC significantly declined by 5% in the ALS group and did not change in the healthy group. Supine SVC showed no change in either group. Although mean trajectories of zSVC mirrored mean SVC trajectories in both participant cohorts, changes in zSVC in ALS patients did not reach significance, due to greater variability in the repeated measures. Conclusion: Despite strong cross-sectional correlations to SVC, EIT did not detect a decline in pulmonary function over approximately four months. Increased variability in EIT data explains the lack of sensitivity to change. Technological improvements and special care with electrode placement will be needed for EIT to reach its full potential in longitudinal assessment of pulmonary function in ALS.

Objective.To evaluate electrical impedance myography (EIM) in conjunction with machine learning (ML) to detect infantile spinal muscular atrophy (SMA) and disease progression.Approach. Twenty-six infants with SMA and twenty-seven healthy infants had been enrolled and assessed with EIM as part of the NeuroNEXT SMA biomarker study. We applied a variety of modern, supervised ML approaches to this data, first seeking to differentiate healthy from SMA muscle, and then, using the best method, to track SMA progression.Main Results.Several of the ML algorithms worked well, but linear discriminant analysis (LDA) achieved 88.6% accuracy on subject muscles studied. This contrasts with a maximum of 60% accuracy that could be achieved using the single or multifrequency assessment approaches available at the time. LDA scores were also able to track progression effectively, although a multifrequency reactance-based measure also performed very well in this context.Significance.EIM enhanced with ML promises to be effective for providing effective diagnosis and tracking children and adults with SMA treated with currently available therapies. The normative trends identified here may also inform future applications of the technology in very young children. The basic analyses applied here could also likely be applied to other neuromuscular disorders characterized by muscle atrophy.

Amyotrophic lateral sclerosis (ALS), a devastating neurodegenerative disease, is characterized by progressive motor neuron degeneration, leading to widespread weakness and respiratory failure. While a variety of mechanisms have been proposed as causes of this disease, a full understanding remains elusive. Electrophysiological alterations, including increased motor axon excitability, likely play an important role in disease progression. There remains a critical need for non-animal disease models that can integrate electrophysiological tools to better understand underlying mechanisms, track disease progression, and evaluate potential therapeutic interventions. This review explores the integration of electrophysiological technologies with ALS disease models. It covers cellular and clinical electrophysiological tools and their applications in ALS research. Additionally, we examine conventional animal models and highlight advancements in humanized models and 3D organoid technologies. By bridging the gap between these models, we aim to enhance our understanding of ALS pathogenesis and facilitate the development of new therapeutic strategies.

This chapter discusses comprehensive neurophysiological biomarkers utilised in motor neuron disease (MND) and, in particular, its commonest form, amyotrophic lateral sclerosis (ALS). These encompass the conventional techniques including nerve conduction studies (NCS), needle and high-density surface electromyography (EMG) and H-reflex studies as well as novel techniques. In the last two decades, new methods of assessing the loss of motor units in a muscle have been developed, that are more convenient than earlier methods of motor unit number estimation (MUNE),and may use either electrical stimulation (e.g. MScanFit MUNE) or voluntary activation (MUNIX). Electrical impedance myography (EIM) is another novel approach for the evaluation that relies upon the application and measurement of high-frequency, low-intensity electrical current. Nerve excitability techniques (NET) also provide insights into the function of an axon and reflect the changes in resting membrane potential, ion channel dysfunction and the structural integrity of the axon and myelin sheath. Furthermore, imaging ultrasound techniques as well as magnetic resonance imaging are capable of detecting the constituents of morphological changes in the nerve and muscle. The chapter provides a critical description of the ability of each technique to provide neurophysiological insight into the complex pathophysiology of MND/ALS. However, it is important to recognise the strengths and limitations of each approach in order to clarify utility. These neurophysiological biomarkers have demonstrated reliability, specificity and provide additional information to validate and assess lower motor neuron dysfunction. Their use has expanded the knowledge about MND/ALS and enhanced our understanding of the relationship between motor units, axons, reflexes and other neural circuits in relation to clinical features of patients with MND/ALS at different stages of the disease. Taken together, the ultimate goal is to aid early diagnosis, distinguish potential disease mimics, monitor and stage disease progression, quantify response to treatment and develop potential therapeutic interventions.

OBJECTIVE: The laryngeal adductor reflex (LAR) is vital for airway protection and can be electrophysiologically obtained under intravenous general anesthesia (IGA). This makes the electrophysiologic LAR (eLAR) an important tool for monitoring of the vagus nerves and relevant brainstem circuitry during high-risk surgeries. We investigated the intra-class variability of normal and expected abnormal eLAR.

METHODS: Repeated measures of contralateral R1 (cR1) were performed under IGA in 58 patients. Data on presence/absence of cR2 and potential confounders were also collected. Review of neuroimaging, pathology and clinical exam, allowed classification into normal and expected abnormal eLAR groups. Using univariate and multivariate analysis we studied the variability of cR1 parameters and their differences between the two groups.

RESULTS: In both groups, cR1 latencies had coefficients of variation of <2%. In the abnormal group, cR1 had longer latencies, required higher activation currents and was more frequently desynchronized and unsustained; cR2 was more frequently absent.

CONCLUSIONS: cR1 latencies show high analytical precision for measurements. Delayed onset, difficult to elicit, desynchronized and unsustained cR1, and absence of cR2 signal an abnormal eLAR.

SIGNIFICANCE: Understanding the variability and behavior of normal and abnormal eLAR under IGA can aid in the interpretation of its changes during monitoring.

Objective: Although studies have shown that digital measures of speech detected ALS speech impairment and correlated with the ALSFRS-R speech item, no study has yet compared their performance in detecting speech changes. In this study, we compared the performances of the ALSFRS-R speech item and an algorithmic speech measure in detecting clinically important changes in speech. Importantly, the study was part of a FDA submission which received the breakthrough device designation for monitoring ALS; we provide this paper as a roadmap for validating other speech measures for monitoring disease progression. Methods: We obtained ALSFRS-R speech subscores and speech samples from participants with ALS. We computed the minimum detectable change (MDC) of both measures; using clinician-reported listener effort and a perceptual ratings of severity, we calculated the minimal clinically important difference (MCID) of each measure with respect to both sets of clinical ratings. Results: For articulatory precision, the MDC (.85) was lower than both MCID measures (2.74 and 2.28), and for the ALSFRS-R speech item, MDC (.86) was greater than both MCID measures (.82 and .72), indicating that while the articulatory precision measure detected minimal clinically important differences in speech, the ALSFRS-R speech item did not. Conclusion: The results demonstrate that the digital measure of articulatory precision effectively detects clinically important differences in speech ratings, outperforming the ALSFRS-R speech item. Taken together, the results herein suggest that this speech outcome is a clinically meaningful measure of speech change.

OBJECTIVE: We sought to determine whether thoracic electrical impedance tomography (EIT) could characterize pulmonary function in amyotrophic lateral sclerosis (ALS) patients, including those with facial weakness. Thoracic EIT is a noninvasive, technology in which a multi-electrode belt is placed across the chest, producing real-time impedance imaging of the chest during breathing.

METHODS: We enrolled 32 ALS patients and 32 age- and sex-matched healthy controls (HCs) without underlying lung disease. All participants had EIT measurements performed simultaneously with standard pulmonary function tests (PFTs), including slow and forced vital capacity (SVC and FVC) in upright and supine positions and maximal inspiratory and expiratory pressures (MIPs and MEPs, respectively). Intraclass correlation coefficients (ICCs) were calculated to assess the immediate reproducibility of EIT measurements and Pearson's correlations were used to explore the relationships between EIT and PFT values.

RESULTS: Data from 30 ALS patients and 27 HCs were analyzed. Immediate upright SVC reproducibility was very high (ICC 0.98). Correlations were generally strongest between EIT and spirometry measures, with R values ranging from 0.64 to 0.82 (p < 0.001) in the ALS cohort. There were less robust correlations between EIT values and both MIPs and MEPs in the ALS patients, with R values ranging from 0.33 to 0.44. There was no significant difference for patients with and without facial weakness. There were no reported adverse events.

CONCLUSION: EIT-based pulmonary measures hold the promise of providing an alternative approach for lung function assessment in ALS patients. Based on these early results, further development and study of this technology are warranted.

Objective.The objective of this study was to describe and evaluate a smart-phone based method to rapidly generate subject-specific finite element method (FEM) meshes. More accurate FEM meshes should lead to more accurate thoracic electrical impedance tomography (EIT) images.Approach.The method was evaluated on an iPhone®that utilized an app called Heges, to obtain 3D scans (colored, surface triangulations), a custom belt, and custom open-source software developed to produce the subject-specific meshes. The approach was quantitatively validated via mannequin and volunteer tests using an infrared tracker as the gold standard, and qualitatively assessed in a series of tidal-breathing EIT images recorded from 9 subjects.Main results.The subject-specific meshes can be generated in as little as 6.3 min, which requires on average 3.4 min of user interaction. The mannequin tests yielded high levels of precision and accuracy at 3.2 ± 0.4 mm and 4.0 ± 0.3 mm root mean square error (RMSE), respectively. Errors on volunteers were only slightly larger (5.2 ± 2.1 mm RMSE precision and 7.7 ± 2.9 mm RMSE accuracy), illustrating the practical RMSE of the method.Significance.Easy-to-generate, subject-specific meshes could be utilized in the thoracic EIT community, potentially reducing geometric-based artifacts and improving the clinical utility of EIT.

With the technological advances made to expand space exploration, astronauts will spend extended amounts of time in space before returning to Earth. This situation of unloading and reloading influences human physiology, and readaptation to full weight-bearing may significantly impact astronauts' health. On Earth, similar situations can be observed in patients who are bedridden or suffer from sport-related injuries. However, our knowledge of male physiology far exceeds our knowledge of female's, which creates an important gap that needs to be addressed to understand the sex-based differences regarding musculoskeletal adaptation to unloading and reloading, necessary to preserve health of both sexes. Using a ground-based model of total unloading for 14 days and reloading at full weight-bearing for 7 days rats, we aimed to compare the musculoskeletal adaptations between males and females. Our results reveal the existence of significant differences. Indeed, males experienced bone loss both during the unloading and the reloading period while females did not. During simulated microgravity, males and females showed comparable muscle deconditioning with a significant decline in rear paw grip strength. However, after 7 days of recovery, muscle strength improved. Additionally, sex-based differences in myofiber size existing at baseline are significantly reduced or eliminated following unloading and recovery.