Motor neuron disease (MND) or ALS is a nervous system disease. ALS leads to a loss of movement ability that eventually leads to death. At the moment, there is no known treatment for ALS. Early diagnosis in individuals improves clinical care and facilitates timely entry into clinical trials. However, current methods for diagnosis are primarily clinical, and to date, no cost-effective biomarkers have been developed. Our objective is to identify a robust non-invasive neurophysiological-based system that can be used both as a biomarker of disease onset, and a measurement of progression using quantitative EEG and surface EMG (bipolar and high-density).

The investigators postulate that analysing the joint recordings of EEG and EMG (bipolar or high-density) can give measures that better distinguish healthy people and ALS patient subgroups and that the findings can be developed as biomarkers of early diagnosis and disease progression.

Amyotrophic Laterals Sclerosis (ALS) or Motor Neuron Disease (MND) is a terminal neurodegenerative disease, that leads to progressive loss of motor function. Treatment of ALS remains an unresolved challenge and despite intensive research, diagnosis and therapy are not yet adequately personalised . New therapeutics and the quality of care after diagnosis can be enhanced by early diagnosis at the individual patient level, enabling tailored care and individualised treatment.

To personalise the diagnosis, there is a need for reliable quantitative biomarkers, for early detection of disease onset and to distinguish the different sub-types of the disease. Specifically, several biomarkers have been investigated for use in ALS, including Motor Unit Number Estimation (MUNE), Motor Unit Number Index (MUNIX), Cortical Excitability in Transcranial Magnetic Stimulation (TMS), EMG Inter-muscular Coherence, Magnetic Resonance (MR) and other imaging techniques, and EEG signatures. However, the diagnostic utility of these techniques, especially the inexpensive non-invasive recordings of electrical muscle activity - bipolar or high-density surface electromyography (sEMG), and electrical brain activity -surface electroencephalography (sEEG)-, is limited: the biomarkers are not strongly linked to the neurophysiological mechanisms affected in ALS.

The human motor system encompasses 2 sub-systems: the α motor system directly innervates the motor neurons and spinal interneurons and the γ system that modulates the sensors of the muscles' feedback reflex loops to indirectly contribute to muscle activations. These 2 systems form a neuromuscular communication and control network, through which neural signals are communicated to and from muscles for coordinated movement. In ALS, there is a disruption of the function of both upper and lower motor neurons. In the lower motor neurons, the degeneration of the α-motor system starts prior to the γ-system, thus changing the relative contribution of the α and γ system which distorts the patterns of neuromuscular communication in movements. It is therefore of interest to distinguish and dissociate the electrophysiological signatures that reflect sensorimotor network communication patterns pertaining to each sub-system in function and dysfunction, which in turn can act as biomarkers. In specific subgroups of ALS, i.e. Primary Lateral Sclerosis (PLS) and Progressive Muscle Atrophy (PMA) - there is selective degeneration of the upper or lower motor neurons respectively. Therefore, more specific changes in network communication patterns are to be expected.

To analyse the cybernetic characteristics (communication, control, and information transfer), electrophysiological signals need to be analysed from several points of the neuromuscular system as an interconnected network. This can be achieved by joint recording and advanced analysis of co-variability of patterns in the EMG/EEG, e.g. (directional) cortico-muscular coherence and directional network influences, during functional motor tasks. It is hypothesised that neuromuscular communication measures based on both EEG and EMG (indicators of pathophysiological change, measured as a network) better reflect ALS onset and subtypes than measures based on either EEG or EMG in isolation (indicators of structural change, measured at nodes).

Successful discrimination of the electrophysiological signatures can be used to diagnose ALS which may be also useful in terms of better patient care and the development of novel neuro-motor rehabilitation.

Inclusion Criteria:

Healthy Volunteers:

• age and gender-matched to patient groups
• the intact physical ability to take part in the experiment.

Patients:

• Diagnosis of ALS, PLS, PMA, SMA, Polio or MS
• capable of providing informed consent.

Exclusion Criteria:

Healthy Controls:

• History of neuromuscular
• neurological or active psychiatric disease disease
• history of reaction or allergy to recording environments, equipment and the recording gels.

Patients:

• the presence of active psychiatric disease
• any medical condition associated with severe neuropathy (e.g. poorly controlled diabetes).
• History of reaction or allergy to recording environments, equipment and the recording gels.

MND (ALS, PLS, PMA, SMA, Polio or MS) patients will be recruited from the national MND/ALS register, an ongoing registry run in conjunction with Beaumont Hospital ALS clinic, run by Professor Orla Hardiman. Healthy controls will be recruited from the student population and age-matched controls from the general public, as well as a dataset of pre-consented healthy volunteers which is maintained at the Academic Unit of Neurology, Trinity College Dublin.