Network Analysis in Amyotrophic Lateral Sclerosis during Voluntary Motor Task: Neurophysiological Biomarkers of Disrupted Cortical Connections - Bista, Saroj (Trinity College Dublin. School of Medicine. Discipline of Clinical Medicine, 2024)
Amyotrophic lateral sclerosis (ALS) is a fatal progressive neurodegenerative disease that causes degeneration of both upper and lower motor neurons primarily affecting the motor system, but individual people with ALS show heterogenous presentations of motor and non-motor symptoms and disease progression rate. Therefore, diagnosis of ALS, which is based on the clinical examination and exclusion of mimic conditions like spinal muscular atrophy (SMA), Kennedy's disease, Myasthenia gravis, multiple sclerosis (MS), is extremely challenging. Additionally, ALS progression is measured using clinical scales that are subject to variance (i.e., cannot effectively capture heterogeneity) and are a proxy for underlying disease pathobiology. Therefore, there is an urgent need for reliable and quantitative biomarkers that can be used for early diagnosis, tracking disease progression, and importantly, deep phenotyping and stratification for clinical trials. Neurophysiological studies in ALS have illustrated the potential of network connectivity measures to enable early detection of brain networks impairments before manifestation of clinical symptoms and before structural alterations become visible in structural imaging. ALS is a multi-network dysfunction causing deficits in motor and extra-motor brain networks. Understanding the changes in motor networks is key to unveiling disease pathology in ALS. Impairment of sensorimotor and extra-motor networks in ALS has been identified from resting-state paradigm. However, motor paradigms, that involve the pre-motor stage, motor planning, and motor execution and can directly access sensorimotor pathways, might be needed to unravel motor networks pathology in ALS for biomarker design. In this project, high-density electroencephalogram (EEG) and bipolar surface electromyogram (EMG) were recorded from people with ALS and healthy controls when they were performing an isometric motor task: pincer grip between thumb and index finger of the right hand at 10% of their maximal voluntary contraction. The neuroelectric signal analysis was done at both sensor and source levels to interrogate ALS-related motor network pathology. The spectral power and banded spectral coherence were obtained from EEG signals at sensor level to investigate the effect of neurodegeneration in functional motor networks during different stages of the task such as rest, pre-motor stage and motor execution. Similarly, banded corticomuscular coherence (CMC) at source level, which measures the synchrony between EEG and EMG signals, was used to investigate the dysfunctional involvement of corticospinal tracts in the cortico-peripheral networks in ALS. Furthermore, at source level, generalised partial directed coherence (gPDC) was used to investigate the effect of neurodegeneration on effective (directional or causal) cortical networks in ALS during pre-motor (motor planning) and motor execution. This work has established that `banded spectral coherence,¿ based on non-parametric methods such as 1-sample signed rank statistics and 2-D spatial median, was a simpler and improved alternative to classical `magnitude squared coherence¿ to investigate functional network disruption in motor neuron disease. This study revealed more widespread point-to-point network connectivity (using banded spectral coherence), reflecting hyperactivation of cortical regions in ALS during rest and motor task. Such cortical hyperactivation is potentially due to a loss of inhibitory interneurons. Similarly, this study revealed increased beta event related spectral perturbations over non-dominant-motor and parietal regions. Furthermore, it demonstrated abnormal motor-parietal functional network at beta-band during motor execution, which was also negatively correlated with clinical motoric impairments. These findings indicate compensatory mechanism in ALS. More importantly, this study revealed that pre-motor networks that were impaired in ALS were distinct and not an extension of impairment in the primary motor cortex (M1). Furthermore, this study found reduction of CMC in alpha, beta, and gamma frequency bands in brain regions within primary sensorimotor cortices (M1/S1), the supplementary motor area (SMA) and the superior parietal lobule implying broader network impairment in ALS beyond the sensorimotor networks, potentially reflecting dysfunction of other aspects of motor control such as motor planning, task attention, and visuomotor processes. Finally, the study identified several disruptions in directional networks within motor systems in ALS with higher order motor regions such as SMA. Specifically, the SMA-driven sensorimotor network was notably weaker in ALS, suggesting impaired motor planning. Also, the SMA potentially compensated for M1 degeneration during motor execution, as evidenced by stronger connections from ipsilateral SMA to contralateral M1 which could be attributed to interhemispheric disinhibition and heightened motor demands in ALS. The cortico-cortical and cortico-muscular network impairments underpinned by this study have the potential to be used for clinical diagnostic, prognostic and phenotyping applications or as primary/secondary outcome measure to track network changes in the setting of disease modifying clinical trials.
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Keywords: Amyotrophic Lateral Sclerosis; Network Connectivity; Corticomuscular Coherence; Generalised Partial Directed Coherence; Biomarkers.