Study of Functional Network Topology Alterations after Injury via Embedding Methods

Author(s):  
Shiva Salsabilian ◽  
Elena Bibineyshvili ◽  
David J. Margolis ◽  
Laleh Najafizadeh
2016 ◽  
Author(s):  
Matteo Fraschini ◽  
Matteo Demuru ◽  
Arjan Hillebrand ◽  
Lorenza Cuccu ◽  
Silvia Porcu ◽  
...  

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.


2012 ◽  
Vol 32 (29) ◽  
pp. 9960-9968 ◽  
Author(s):  
J. Heinzle ◽  
M. A. Wenzel ◽  
J.-D. Haynes

2020 ◽  
Vol 266 ◽  
pp. 473-481
Author(s):  
Shankar Tumati ◽  
Jan-Bernard C. Marsman ◽  
Peter Paul De Deyn ◽  
Sander Martens ◽  
André Aleman

2007 ◽  
Vol 19 (1) ◽  
pp. 231-257 ◽  
Author(s):  
Enrique Castillo ◽  
Noelia Sánchez-Maroño ◽  
Amparo Alonso-Betanzos ◽  
Carmen Castillo

A new methodology for learning the topology of a functional network from data, based on the ANOVA decomposition technique, is presented. The method determines sensitivity (importance) indices that allow a decision to be made as to which set of interactions among variables is relevant and which is irrelevant to the problem under study. This immediately suggests the network topology to be used in a given problem. Moreover, local sensitivities to small changes in the data can be easily calculated. In this way, the dual optimization problem gives the local sensitivities. The methods are illustrated by their application to artificial and real examples.


2016 ◽  
Vol 6 (1) ◽  
Author(s):  
Matteo Fraschini ◽  
Matteo Demuru ◽  
Arjan Hillebrand ◽  
Lorenza Cuccu ◽  
Silvia Porcu ◽  
...  

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