scholarly journals EEG functional network topology is associated with disability in patients with amyotrophic lateral sclerosis

2016 ◽  
Vol 6 (1) ◽  
Author(s):  
Matteo Fraschini ◽  
Matteo Demuru ◽  
Arjan Hillebrand ◽  
Lorenza Cuccu ◽  
Silvia Porcu ◽  
...  
Keyword(s):  
Amyotrophic Lateral Sclerosis ◽  
Network Topology ◽  
Functional Network ◽  
Lateral Sclerosis

scholarly journals EEG functional network topology is associated with disability in patients with amyotrophic lateral sclerosis

2016 ◽  
Author(s):  
Matteo Fraschini ◽  
Matteo Demuru ◽  
Arjan Hillebrand ◽  
Lorenza Cuccu ◽  
Silvia Porcu ◽  
...  
Keyword(s):  
Amyotrophic Lateral Sclerosis ◽  
Network Topology ◽  
Resting State ◽  
Minimum Spanning Tree ◽  
Clinical Status ◽  
Functional Network ◽  
Phase Lag ◽  
Beta Band ◽  
Significant Difference ◽  
Lateral Sclerosis

ABSTRACTAmyotrophic Lateral Sclerosis (ALS) is one of the most severe neurodegenerative diseases, which is known to affect upper and lower motor neurons. 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. Here, we hypothesised that functional network topology is perturbed in ALS, and that this reorganisation is associated with disability. We tested this hypothesis in 21 patients affected by ALS at several stages of impairment using resting-state electroencephalography (EEG) and compared the results to 16 age-matched healthy controls. We estimated functional connectivity using the Phase Lag Index (PLI), and characterized the network topology using the minimum spanning tree (MST). We found a significant difference between groups in terms of MST 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.

scholarly journals Patterned functional network disruption in amyotrophic lateral sclerosis

2019 ◽  
Vol 40 (16) ◽  
pp. 4827-4842 ◽  
Author(s):  
Stefan Dukic ◽  
Roisin McMackin ◽  
Teresa Buxo ◽  
Antonio Fasano ◽  
Rangariroyashe Chipika ◽  
...  
Keyword(s):  
Amyotrophic Lateral Sclerosis ◽  
Functional Network ◽  
Network Disruption ◽  
Lateral Sclerosis

Automated Acoustic Analysis of Oral Diadochokinesis to Assess Bulbar Motor Involvement in Amyotrophic Lateral Sclerosis

2020 ◽  
Vol 63 (1) ◽  
pp. 59-73 ◽  
Author(s):  
Panying Rong
Keyword(s):  
Amyotrophic Lateral Sclerosis ◽  
Acoustic Analysis ◽  
Speaking Rate ◽  
Disease Stage ◽  
Healthy Controls ◽  
Tongue Movement ◽  
Major Barrier ◽  
Syllable Repetition ◽  
Oral Diadochokinesis ◽  
Lateral Sclerosis

Purpose The purpose of this article was to validate a novel acoustic analysis of oral diadochokinesis (DDK) in assessing bulbar motor involvement in amyotrophic lateral sclerosis (ALS). Method An automated acoustic DDK analysis was developed, which filtered out the voice features and extracted the envelope of the acoustic waveform reflecting the temporal pattern of syllable repetitions during an oral DDK task (i.e., repetitions of /tɑ/ at the maximum rate on 1 breath). Cycle-to-cycle temporal variability (cTV) of envelope fluctuations and syllable repetition rate (sylRate) were derived from the envelope and validated against 2 kinematic measures, which are tongue movement jitter (movJitter) and alternating tongue movement rate (AMR) during the DDK task, in 16 individuals with bulbar ALS and 18 healthy controls. After the validation, cTV, sylRate, movJitter, and AMR, along with an established clinical speech measure, that is, speaking rate (SR), were compared in their ability to (a) differentiate individuals with ALS from healthy controls and (b) detect early-stage bulbar declines in ALS. Results cTV and sylRate were significantly correlated with movJitter and AMR, respectively, across individuals with ALS and healthy controls, confirming the validity of the acoustic DDK analysis in extracting the temporal DDK pattern. Among all the acoustic and kinematic DDK measures, cTV showed the highest diagnostic accuracy (i.e., 0.87) with 80% sensitivity and 94% specificity in differentiating individuals with ALS from healthy controls, which outperformed the SR measure. Moreover, cTV showed a large increase during the early disease stage, which preceded the decline of SR. Conclusions This study provided preliminary validation of a novel automated acoustic DDK analysis in extracting a useful measure, namely, cTV, for early detection of bulbar ALS. This analysis overcame a major barrier in the existing acoustic DDK analysis, which is continuous voicing between syllables that interferes with syllable structures. This approach has potential clinical applications as a novel bulbar assessment.

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