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

Matteo Fraschini, Matteo Demuru, Arjan Hillebrand, Lorenza Cuccu, Silvia Porcu, Francesca Di Stefano, Monica Puligheddu, Gianluca Floris, Giuseppe Borghero, Francesco Marrosu

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

Amyotrophic 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 reorganization 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.

Original languageEnglish
Pages (from-to)38653
JournalScientific Reports
Volume6
DOIs
Publication statusPublished - 7 Dec 2016

Cite this

Fraschini, Matteo ; Demuru, Matteo ; Hillebrand, Arjan ; Cuccu, Lorenza ; Porcu, Silvia ; Di Stefano, Francesca ; Puligheddu, Monica ; Floris, Gianluca ; Borghero, Giuseppe ; Marrosu, Francesco. / EEG functional network topology is associated with disability in patients with amyotrophic lateral sclerosis. In: Scientific Reports. 2016 ; Vol. 6. pp. 38653.
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title = "EEG functional network topology is associated with disability in patients with amyotrophic lateral sclerosis",
abstract = "Amyotrophic 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 reorganization 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.",
author = "Matteo Fraschini and Matteo Demuru and Arjan Hillebrand and Lorenza Cuccu and Silvia Porcu and {Di Stefano}, Francesca and Monica Puligheddu and Gianluca Floris and Giuseppe Borghero and Francesco Marrosu",
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Fraschini, M, Demuru, M, Hillebrand, A, Cuccu, L, Porcu, S, Di Stefano, F, Puligheddu, M, Floris, G, Borghero, G & Marrosu, F 2016, 'EEG functional network topology is associated with disability in patients with amyotrophic lateral sclerosis' Scientific Reports, vol. 6, pp. 38653. https://doi.org/10.1038/srep38653

EEG functional network topology is associated with disability in patients with amyotrophic lateral sclerosis. / Fraschini, Matteo; Demuru, Matteo; Hillebrand, Arjan; Cuccu, Lorenza; Porcu, Silvia; Di Stefano, Francesca; Puligheddu, Monica; Floris, Gianluca; Borghero, Giuseppe; Marrosu, Francesco.

In: Scientific Reports, Vol. 6, 07.12.2016, p. 38653.

Research output: Contribution to journalArticleAcademicpeer-review

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AU - Fraschini, Matteo

AU - Demuru, Matteo

AU - Hillebrand, Arjan

AU - Cuccu, Lorenza

AU - Porcu, Silvia

AU - Di Stefano, Francesca

AU - Puligheddu, Monica

AU - Floris, Gianluca

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AU - Marrosu, Francesco

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AB - Amyotrophic 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 reorganization 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.

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