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 γl‐band) and frontal (δ‐ and γl‐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.

Background: Amyotrophic lateral sclerosis (ALS) is a rapidly progressive neurodegenerative disease. Executive dysfunction is common in patients with ALS, with up to 50% of patients performing within an impaired range. There is evidence that social cognitive deficits associated with ALS are a function of deficits in executive function. The ‘Reading the Mind in the Eyes’ Test is a recognized test of social cognitive function, although the reliability of this instrument remains to be established. Methodology: Patients with ALS (n = 106), and age and IQ matched controls (n = 50) were recruited and asked to perform the Reading the Mind in the Eyes Test as part of an on-going population-based study of cognitive function. ALS patients were sub-stratified based on the presence, and/or extent of executive dysfunction. Results: Cronbach’s Alpha of .73 was observed, indicating good reliability on this measure. Split-half reliability analysis further confirms these findings (p = 0.826). The Reading the Mind in the Eyes test had excellent psychometric properties when discriminating between ALS patients who are cognitively intact, and those who have executive impairment, with an overall medium difficulty. There was a large magnitude significant difference between patients and controls (p< 0.001; η2 = .19). Post-hoc analysis revealed that controls performed significantly higher than patients with executive impairment (p< 0.001), and patients with single executive deficits (p = 0.002). Conclusion: Executive dysfunction impacts on social cognitive performance. This study contributes not only to the psychometric knowledge of this measure, but also to the usability, efficacy, and reliability of social cognitive assessment in ALS. Using population-specific normative data, we confirm the Reading the Mind in the Eyes Test is a reliable measure of social cognitive processes in ALS.

BackgroundAmyotrophic lateral sclerosis (ALS) is often associated with cognitive and/or behavioural impairment. Cognitive reserve (CR) may play a protective role in offsetting cognitive impairment. This study examined the relationship between CR and longitudinal change in cognition in an Irish ALS cohort.MethodsLongitudinal neuropsychological assessment was carried out on 189 patients over 16 months using the Edinburgh cognitive and behavioural ALS screen (ECAS) and an additional battery of neuropsychological tests. CR was measured by combining education, occupation and physical activity data. Joint longitudinal and time-to-event models were fitted to investigate the associations between CR, performance at baseline and decline over time while controlling for non-random drop-out.ResultsCR was a significant predictor of baseline neuropsychological performance, with high CR patients performing better than those with medium or low CR. Better cognitive performance in high CR individuals was maintained longitudinally for ECAS, social cognition, executive functioning and confrontational naming. Patients displayed little cognitive decline over the course of the study, despite controlling for non-random drop-out.ConclusionsThese findings suggest that CR plays a role in the presentation of cognitive impairment at diagnosis but is not protective against cognitive decline. However, further research is needed to examine the interaction between CR and other objective correlates of cognitive impairment in ALS.

Amyotrophic lateral sclerosis (ALS) is a devastating disease characterised primarily by motor system degeneration, with clinical evidence of cognitive and behavioural change in up to 50% of cases. ALS is both clinically and biologically heterogeneous. Subgrouping is currently undertaken using clinical parameters, such as site of symptom onset (bulbar or spinal), burden of disease (based on the modified El Escorial Research Criteria) and genomics in those with familial disease. However, with the exception of genomics, these subcategories do not take into account underlying disease pathobiology, and are not fully predictive of disease course or prognosis. Recently, we have shown that resting-state EEG can reliably and quantitatively capture abnormal patterns of motor and cognitive network disruption in ALS. These network disruptions have been identified across multiple frequency bands, and using measures of neural activity (spectral power) and connectivity (co-modulation of activity by amplitude envelope correlation and synchrony by imaginary coherence) on source-localised brain oscillations from high-density EEG. Using data-driven methods (similarity network fusion and spectral clustering), we have now undertaken a clustering analysis to identify disease subphenotypes and to determine whether different patterns of disruption are predictive of disease outcome. We show that ALS patients (N = 95) can be subgrouped into four phenotypes with distinct neurophysiological profiles. These clusters are characterised by varying degrees of disruption in the somatomotor (α-band synchrony), frontotemporal (β-band neural activity and γl-band synchrony) and frontoparietal (γl-band co-modulation) networks, which reliably correlate with distinct clinical profiles and different disease trajectories. Using an in-depth stability analysis, we show that these clusters are statistically reproducible and robust, remain stable after re-assessment using a follow-up EEG session, and continue to predict the clinical trajectory and disease outcome. Our data demonstrate that novel phenotyping using neuroelectric signal analysis can distinguish disease subtypes based exclusively on different patterns of network disturbances. These patterns may reflect underlying disease neurobiology. The identification of ALS subtypes based on profiles of differential impairment in neuronal networks has clear potential in future stratification for clinical trials. Advanced network profiling in ALS can also underpin new therapeutic strategies that are based on principles of neurobiology and designed to modulate network disruption.

The Beaumont Behavioural Inventory (BBI) is a behavioural proxy report for the assessment of behavioural changes in ALS. This tool has been validated against the FrSBe, a non-ALS-specific behavioural assessment, and further comparison of the BBI against a disease-specific tool was considered. This study cross-validates the BBI against the ALS-FTD-Q. Sixty ALS patients, 8% also meeting criteria for FTD, were recruited. All patients were evaluated using the BBI and the ALS-FTD-Q, completed by a carer. Correlational analysis was performed to assess construct validity. Precision, sensitivity, specificity, and overall accuracy of the BBI when compared to the ALS-FTD-Q, were obtained. The mean score of the whole sample on the BBI was 11.45 ± 13.06. ALS-FTD patients scored significantly higher than non-demented ALS patients (31.6 ± 14.64, 9.62 ± 11.38; p < 0.0001). A significant large positive correlation between the BBI and the ALS-FTD-Q was observed (r = 0.807, p < 0.0001), and no significant correlations between the BBI and other clinical/demographic characteristics indicate good convergent and discriminant validity, respectively. 72% of overall concordance was observed. Precision, sensitivity, and specificity for the classification of severely impaired patients were adequate. However, lower concordance in the classification of mild behavioural changes was observed, with higher sensitivity using the BBI, most likely secondary to BBI items which endorsed behavioural aspects not measured by the ALS-FTD-Q. Good construct validity has been further confirmed when the BBI is compared to an ALS-specific tool. Furthermore, the BBI is a more comprehensive behavioural assessment for ALS, as it measures the whole behavioural spectrum in this condition.