Amyotrophic Lateral Sclerosis (ALS) is one of the most severe neurodegenerative

Amyotrophic Lateral Sclerosis (ALS) is one of the most severe neurodegenerative diseases, which is known to affect upper and lower motor neurons. dissimilarity and MST leaf fraction in the beta band. Moreover, some MST parameters (leaf, hierarchy and kappa) significantly correlated with disability. These findings suggest that the topology of resting-state functional networks in ALS is affected by the disease in relation to disability. EEG network analysis may be of help in monitoring and evaluating the clinical status of ALS patients. Amyotrophic Lateral Sclerosis (ALS) is one of the most severe neurodegenerative diseases, affecting the upper and lower motor neurons. All motor functions are progressively invalidated, and with a median survival of about GS-9620 IC50 3 years from the onset of symptoms1. However, in contrast to the classical tenet that ALS represents the outcome of extensive and progressive impairment of a fixed set of motor connections, recent neuroimaging findings suggest that the disease spreads along vast non-motor connections. Indeed, advanced neuroimaging techniques, which allow for the non-invasive investigation of structural and functional brain organization, have so far introduced new opportunities for the study of ALS and are currently supporting the multi-systemic pathophysiology of this disease2,3. Recently, modern network science has aided in the understanding of the human brain as a complex system of interacting units4,5. Indeed, the organization of brain networks can GS-9620 IC50 be characterised by means of several metrics that allow to estimate functional integration and segregation, quantify centrality of brain regions, and test resilience to insult6. Moreover, changes in network topology have been described for a range of neurological and psychiatric disorders7,5. In this view, structural and functional network studies based on diffusion tensor imaging (DTI) and functional magnetic resonance (fMRI) have contributed in elucidating basic mechanisms related to ALS onset, spread and progression. For instance, Verstraete et al.8 observed structural motor network degeneration and suggested a spread of disease along functional connections of the motor network. Moreover, the same group has also reported9 an increasing loss of network structure in patients with ALS, with the network of impaired connectivity expanding over time. Schmidt et al.10, have recently shown that structural and functional connectivity degeneration in ALS are coupled and that the pathogenic process strongly affects both structural and functional network organization. Other resting-state fMRI studies11,12,13 have reported alterations in specific resting-state networks. Recently, Iyer and colleagues14 have investigated the use of resting-state electroencephalographic (EEG) as a potential biomarker for ALS, suggesting that a pathologic disruption of the network can be observed in early stages of the disease. However, it still remains relevant to address methodological issues that may affect both connectivity estimation and network reconstruction15. Although the results described above are promising, GS-9620 IC50 it is not yet clearly understood how whole-brain functional networks are perturbed in ALS patients, and how this relates to disability. Resting-state EEG analysis may represent a practical tool to evaluate and monitor the progression of the disease. Despite the wide use of EEG in the assessment of brain disorders5,16,17, it has not been used widely to evaluate functional network changes induced by ALS. To test our hypothesis, we reconstructed functional networks from resting-state EEG recordings in 21 ALS patients and 16 age-matched healthy controls using the phase lag index (PLI)18, a widely used and robust measure of phase synchronization that is relatively insensitive to the effects of volume conduction. The topologies of frequency specific minimum spanning trees (MSTs) were subsequently characterised and compared between groups as it has been shown19,20 that GS-9620 IC50 it avoids important methodological biases that would otherwise limit a meaningful comparison between the groups21. Moreover, a correlation analysis was performed between the MST parameters and disability. Results and Discussion Age-matching No significant group differences were observed in age (W?=?145.5, p?=?0.499). Functional Connectivity No significant group differences were observed for the global mean PLI in any frequency band (both with and without FDR correction for number of frequency bands). Descriptive results and statistics are summarized in Table 1. No significant correlation was observed between the patients global mean PLI and the disability score for any frequency band (see Table 2). Table 1 Group descriptive and statistics Rabbit polyclonal to Lymphotoxin alpha from Mann-Whitney U test for the global mean PLI. Table 2 Correlations between global mean PLI and disability score. MST dissimilarity A significant MST dissimilarity between.