EEG Resting-State Brain Topological Reorganization as a Function of Age

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

Research output: Contribution to journalArticlepeer-review


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%.

Original languageEnglish
Article number6243694
JournalComputational Intelligence and Neuroscience
Publication statusPublished - 2016

ASJC Scopus subject areas

  • Computer Science(all)
  • Mathematics(all)
  • Neuroscience(all)


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