Paper Info
Title
Differential Effects Of Simulated Cortical Network Lesions On Synchrony And Eeg Complexity
Abstract
Brain function has been proposed to arise as a result of the coordinated activity between distributed brain areas. An important issue in the study of brain activity is the characterization of the synchrony among these areas and the resulting complexity of the system. However, the variety of ways to define and, hence, measure brain synchrony and complexity has sometimes led to inconsistent results. Here, we study the relationship between synchrony and commonly used complexity estimators of electroencephalogram (EEG) activity and we explore how simulated lesions in anatomically based cortical networks would affect key functional measures of activity. We explored this question using different types of neural network lesions while the brain dynamics was modeled with a time-delayed set of 66 Kuramoto oscillators. Each oscillator modeled a region of the cortex (node), and the connectivity and spatial location between different areas informed the creation of a network structure (edges). Each type of lesion consisted on successive lesions of nodes or edges during the simulation of the neural dynamics. For each type of lesion, we measured the synchrony among oscillators and three complexity estimators (Higuchi's Fractal Dimension, Sample Entropy and Lempel-Ziv Complexity) of the simulated EEGs. We found a general negative correlation between EEG complexity metrics and synchrony but Sample Entropy and Lempel-Ziv showed a positive correlation with synchrony when the edges of the network were deleted. This suggests an intricate relationship between synchrony of the system and its estimated complexity. Hence, complexity seems to depend on the multiple states of interaction between the oscillators of the system. Our results can contribute to the interpretation of the functional meaning of EEG complexity.
Year
DOI
Venue
2019
10.1142/S0129065718500247
INTERNATIONAL JOURNAL OF NEURAL SYSTEMS
Keywords
Field
DocType
Kuramoto model, network lesions, synchrony, EEG complexity
Pattern recognition,Computer science,Brain activity and meditation,Kuramoto model,Artificial intelligence,Electroencephalography
Journal
Volume
Issue
ISSN
29
4
0129-0657
Citations 
PageRank 
References 
0
0.34
18
Authors
3
Name
Order
Citations
PageRank
Antonio Ibañez-Molina121.74
Sergio Iglesias-Parro221.74
Escudero Javier317427.45