Amyotrophic lateral sclerosis (ALS) a highly heterogeneous neurodegenerative condition. Accurate diagnostic, monitoring and prognostic biomarkers are urgently needed both for individualised patient care and clinical trials. A multimodal magnetic resonance imaging study is presented, where MRI measures of ALS-associated brain regions are utilised to predict 18-month survival. A total of 60 ALS patients and 69 healthy controls were included in this study. 20% of the patient sample was utilised as an independent validation sample. Surface-based morphometry and diffusion tensor white matter parameters were used to identify anatomical patterns of neurodegeneration in 80% of the patient sample compared to healthy controls. Binary logistic ridge regressions were carried out to predict 18-month survival based on clinical measures alone, MRI features, and a combination of clinical and MRI data. Clinical indices included age at symptoms onset, site of disease onset, diagnostic delay from first symptom to diagnosis, and physical disability (ALSFRS-r). MRI features included the average cortical thickness of the precentral and paracentral gyri, the average fractional anisotropy, radial-, medial-, and axial diffusivity of the superior and inferior corona radiata, internal capsule, cerebral peduncles and the genu, body and splenium of the corpus callosum. Clinical data alone had a survival prediction accuracy of 66.67%, with 62.50% sensitivity and 70.84% specificity. MRI data alone resulted in a prediction accuracy of 77.08%, with 79.16% sensitivity and 75% specificity. The combination of clinical and MRI measures led to a survival prediction accuracy of 79.17%, with 75% sensitivity and 83.34% specificity. Quantitative MRI measures of ALS-specific brain regions enhance survival prediction in ALS and should be incorporated in future clinical trial designs.

Post-polio syndrome (PPS) is a neurological condition that affects polio survivors decades after their initial infection. Despite its high prevalence, the etiology of PPS remains elusive, mechanisms of progression are poorly understood, and the condition is notoriously under-researched. While motor dysfunction is a hallmark feature of the condition, generalized fatigue, sleep disturbance, decreased endurance, neuropsychological deficits, sensory symptoms, and chronic pain are also often reported and have considerable quality of life implications in PPS. The non-motor aspects of PPS are particularly challenging to evaluate, quantify, and treat. Generalized fatigue is one of the most distressing symptoms of PPS and is likely to be multifactorial due to weight-gain, respiratory compromise, poor sleep, and polypharmacy. No validated diagnostic, monitoring, or prognostic markers have been developed in PPS to date and the mainstay of therapy centers on symptomatic relief and individualized rehabilitation strategies such as energy conservation and muscle strengthening exercise regimes. Despite a number of large clinical trials in PPS, no effective disease-modifying pharmacological treatments are currently available.

Amyotrophic lateral sclerosis (ALS) encompasses a heterogeneous group of phenotypes with different progression rates, varying degree of extra-motor involvement and divergent progression patterns. The natural history of ALS is increasingly evaluated by large, multi-time point longitudinal studies, many of which now incorporate presymptomatic and post-mortem assessments. These studies not only have the potential to characterize patterns of anatomical propagation, molecular mechanisms of disease spread, but also to identify pragmatic monitoring markers. Sensitive markers of progressive neurodegenerative change are indispensable for clinical trials and individualized patient care. Biofluid markers, neuroimaging indices, electrophysiological markers, rating scales, questionnaires, and other disease-specific instruments have divergent sensitivity profiles. The discussion of candidate monitoring markers in ALS has a dual academic and clinical relevance, and is particularly timely given the increasing number of pharmacological trials. The objective of this paper is to provide a comprehensive and critical review of longitudinal studies in ALS, focusing on the sensitivity profile of established and emerging monitoring markers.

Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disease primarily affecting motor function, with additional evidence of extensive nonmotor involvement. Despite increasing recognition of the disease as a multisystem network disorder characterised by impaired connectivity, the precise neuroelectric characteristics of impaired cortical communication remain to be fully elucidated. Here, we characterise changes in functional connectivity using beamformer source analysis on resting‐state electroencephalography recordings from 74 ALS patients and 47 age‐matched healthy controls. Spatiospectral characteristics of network changes in the ALS patient group were quantified by spectral power, amplitude envelope correlation (co‐modulation) and imaginary coherence (synchrony). We show patterns of decreased spectral power in the occipital and temporal (δ‐ to β‐band), lateral/orbitofrontal (δ‐ to θ‐band) and sensorimotor (β‐band) regions of the brain in patients with ALS. Furthermore, we show increased co‐modulation of neural oscillations in the central and posterior (δ‐, θ‐ and γ_l‐band) and frontal (δ‐ and γ_l‐band) regions, as well as decreased synchrony in the temporal and frontal (δ‐ to β‐band) and sensorimotor (β‐band) regions. Factorisation of these complex connectivity patterns reveals a distinct disruption of both motor and nonmotor networks. The observed changes in connectivity correlated with structural MRI changes, functional motor scores and cognitive scores. Characteristic patterned changes of cortical function in ALS signify widespread disease‐associated network disruption, pointing to extensive dysfunction of both motor and cognitive networks. These statistically robust findings, that correlate with clinical scores, provide a strong rationale for further development as biomarkers of network disruption for future clinical trials.