EEG Resting-State Brain Topological Reorganization as a Function of AgeReport as inadecuate

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Computational Intelligence and Neuroscience - Volume 2016 2016, Article ID 6243694, 10 pages -

Research Article

Department of Computer, Control, and Management Engineering -Antonio Ruberti- Sapienza University of Rome, Via Ariosto 25, 00185 Rome, Italy

Neuroelectrical Imaging and BCI Lab, Fondazione Santa Lucia, IRCCS, Via Ardeatina 306, I-00179 Rome, Italy

Department of Molecular Medicine, Sapienza University of Rome, Viale Regina Elena 291, 00161 Rome, Italy

Received 3 October 2015; Revised 17 January 2016; Accepted 19 January 2016

Academic Editor: J. A. Hernández

Copyright © 2016 Manuela Petti et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Resting state connectivity has been increasingly studied to investigate the effects of aging on the brain. A reduced organization in the communication between brain areas was demonstrated by combining a variety of different imaging technologies fMRI, EEG, and MEG and graph theory. In this paper, we propose a methodology to get new insights into resting state connectivity and its variations with age, by combining advanced techniques of effective connectivity estimation, graph theoretical approach, and classification by SVM method. We analyzed high density EEG signals recorded at rest from 71 healthy subjects age: 20–63 years. Weighted and directed connectivity was computed by means of Partial Directed Coherence based on a General Linear Kalman filter approach. To keep the information collected by the estimator, weighted and directed graph indices were extracted from the resulting networks. A relation between brain network properties and age of the subject was found, indicating a tendency of the network to randomly organize increasing with age. This result is also confirmed dividing the whole population into two subgroups according to the age young and middle-aged adults: significant differences exist in terms of network organization measures. Classification of the subjects by means of such indices returns an accuracy greater than 80%.

Author: Manuela Petti, Jlenia Toppi, Fabio Babiloni, Febo Cincotti, Donatella Mattia, and Laura Astolfi



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