Fasciculation analysis reveals a novel parameter that correlates with predicted survival in amyotrophic lateral sclerosis

Neurophysiological and imaging biomarkers of lower motor neuron dysfunction in motor neuron diseases/amyotrophic lateral sclerosis: IFCN handbook chapter

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.

Artificial intelligence and statistical methods for stratification and prediction of progression in amyotrophic lateral sclerosis: A systematic review

BACKGROUND

Amyotrophic Lateral Sclerosis (ALS) is a fatal neurodegenerative disorder characterised by the progressive loss of motor neurons in the brain and spinal cord. The fact that ALS's disease course is highly heterogeneous, and its determinants not fully known, combined with ALS's relatively low prevalence, renders the successful application of artificial intelligence (AI) techniques particularly arduous.

OBJECTIVE

This systematic review aims at identifying areas of agreement and unanswered questions regarding two notable applications of AI in ALS, namely the automatic, data-driven stratification of patients according to their phenotype, and the prediction of ALS progression. Differently from previous works, this review is focused on the methodological landscape of AI in ALS.

METHODS

We conducted a systematic search of the Scopus and PubMed databases, looking for studies on data-driven stratification methods based on unsupervised techniques resulting in (A) automatic group discovery or (B) a transformation of the feature space allowing patient subgroups to be identified; and for studies on internally or externally validated methods for the prediction of ALS progression. We described the selected studies according to the following characteristics, when applicable: variables used, methodology, splitting criteria and number of groups, prediction outcomes, validation schemes, and metrics.

RESULTS

Of the starting 1604 unique reports (2837 combined hits between Scopus and PubMed), 239 were selected for thorough screening, leading to the inclusion of 15 studies on patient stratification, 28 on prediction of ALS progression, and 6 on both stratification and prediction. In terms of variables used, most stratification and prediction studies included demographics and features derived from the ALSFRS or ALSFRS-R scores, which were also the main prediction targets. The most represented stratification methods were K-means, and hierarchical and expectation-maximisation clustering; while random forests, logistic regression, the Cox proportional hazard model, and various flavours of deep learning were the most widely used prediction methods. Predictive model validation was, albeit unexpectedly, quite rarely performed in absolute terms (leading to the exclusion of 78 eligible studies), with the overwhelming majority of included studies resorting to internal validation only.

CONCLUSION

This systematic review highlighted a general agreement in terms of input variable selection for both stratification and prediction of ALS progression, and in terms of prediction targets. A striking lack of validated models emerged, as well as a general difficulty in reproducing many published studies, mainly due to the absence of the corresponding parameter lists. While deep learning seems promising for prediction applications, its superiority with respect to traditional methods has not been established; there is, instead, ample room for its application in the subfield of patient stratification. Finally, an open question remains on the role of new environmental and behavioural variables collected via novel, real-time sensors.

A shortened surface electromyography recording is sufficient to facilitate home fasciculation assessment

INTRODUCTION/AIMS

Fasciculations are an early clinical hallmark of amyotrophic lateral sclerosis (ALS), amenable to detection by high-density surface electromyography (HDSEMG). In conjunction with the Surface Potential Quantification Engine (SPiQE), HDSEMG offers improved spatial resolution for the analysis of fasciculations. This study aims to establish an optimal recording duration to enable longitudinal remote monitoring in the home.

METHODS

Twenty patients with ALS and five patients with benign fasciculation syndrome (BFS) underwent serial 30 min HDSEMG recordings from biceps brachii and gastrocnemii. SPiQE was independently applied to abbreviated epochs within each 30-min recording (0-5, 0-10, 0-15, 0-20, and 0-25 min), outputting fasciculation frequency, amplitude median and amplitude interquartile range. Bland-Altman plots and intraclass correlation coefficients (ICC) were used to assess agreement with the validated 30-min recording.

RESULTS

In total, 506 full recordings were included. The 5 min recordings demonstrated diverse and relatively poor agreement with the 30 min baselines across all parameters, muscles and patient groups (ICC = 0.32-0.86). The 15-min recordings provided more acceptable and stable agreement (ICC = 0.78-0.98), which did not substantially improve in longer recordings.

DISCUSSION

For the detection and quantification of fasciculations in patients with ALS and BFS, HDSEMG recordings can be halved from 30 to 15 min without significantly compromising the primary outputs. Reliance on a shorter recording duration should lead to improved tolerability and repeatability among patients, facilitating longitudinal remote monitoring in patients' homes.

Neuronal Hyperexcitability and Free Radical Toxicity in Amyotrophic Lateral Sclerosis: Established and Future Targets

Amyotrophic lateral sclerosis (ALS) is a devastating disease with evidence of degeneration involving upper and lower motor neuron compartments of the nervous system. Presently, two drugs, riluzole and edaravone, have been established as being useful in slowing disease progression in ALS. Riluzole possesses anti-glutamatergic properties, while edaravone eliminates free radicals (FRs). Glutamate is the excitatory neurotransmitter in the brain and spinal cord and binds to several inotropic receptors. Excessive activation of these receptors generates FRs, inducing neurodegeneration via damage to intracellular organelles and upregulation of proinflammatory mediators. FRs bind to intracellular structures, leading to cellular impairment that contributes to neurodegeneration. As such, excitotoxicity and FR toxicities have been considered as key pathophysiological mechanisms that contribute to the cascade of degeneration that envelopes neurons in ALS. Recent advanced technologies, including neurophysiological, imaging, pathological and biochemical techniques, have concurrently identified evidence of increased excitability in ALS. This review focuses on the relationship between FRs and excitotoxicity in motor neuronal degeneration in ALS and introduces concepts linked to increased excitability across both compartments of the human nervous system. Within this cellular framework, future strategies to promote therapeutic development in ALS, from the perspective of neuronal excitability and function, will be critically appraised.

Demystifying the spontaneous phenomena of motor hyperexcitability

Possessing a discrete functional repertoire, the anterior horn cell can be in one of two electrophysiological states: on or off. Usually under tight regulatory control by the central nervous system, a hierarchical network of these specialist neurons ensures muscular strength is coordinated, gradated and adaptable. However, spontaneous activation of these cells and their axons can result in abnormal muscular twitching. The muscular twitch is the common building block of several distinct clinical patterns, namely fasciculation, myokymia and neuromyotonia. When attempting to distinguish these entities electromyographically, their unique temporal and morphological profiles must be appreciated. Detection and quantification of burst duration, firing frequency, multiplet patterns and amplitude are informative. A common feature is their persistence during sleep. In this review, we explain the accepted terminology used to describe the spontaneous phenomena of motor hyperexcitability, highlighting potential pitfalls amidst a bemusing and complex collection of overlapping terms. We outline the relevance of these findings within the context of disease, principally amyotrophic lateral sclerosis, Isaacs syndrome and Morvan syndrome. In addition, we highlight the use of high-density surface electromyography, suggesting that more widespread use of this non-invasive technique is likely to provide an enhanced understanding of these motor hyperexcitability syndromes.