Human brain networks: a graph theoretical analysis of cortical connectivity normative database from EEG data in healthy elderly subjects

Maria Cotelli, Fabrizio Vecchio, Elda Judica, Francesca Alù

Risultato della ricerca: Contributo in rivistaArticolo in rivistapeer review

8 Citazioni (Scopus)

Abstract

Moving from the hypothesis that aging processes modulate brain connectivity networks, 170 healthy elderly volunteers were submitted to EEG recordings in order to define age-related normative limits. Graph theory functions were applied to exact low-resolution electromagnetic tomography on cortical sources in order to evaluate the small-world parameter as a representative model of network architecture. The analyses were carried out in the whole brain—as well as for the left and the right hemispheres separately—and in three specific resting state subnetworks defined as follows: attentional network (AN), frontal network (FN), and default mode network (DMN) in the EEG frequency bands (delta, theta, alpha 1, alpha 2, beta 1, beta 2, gamma). To evaluate the stability of the investigated parameters, a subgroup of 32 subjects underwent three separate EEG recording sessions in identical environmental conditions after a few days interval. Results showed that the whole right/left hemispheric evaluation did not present side differences, but when individual subnetworks were considered, AN and DMN presented in general higher SW in low (delta and/or theta) and high (gamma) frequency bands in the left hemisphere, while for FN, the alpha 1 band was lower in the left with respect to the right hemisphere. It was also evident the test-retest reliability and reproducibility of the present methodology when carried out in clinically stable subjects. Evidences from the present study suggest that graph theory represents a reliable method to address brain connectivity patterns from EEG data and is particularly suitable to study the physiological impact of aging on brain functional connectivity networks.
Lingua originaleEnglish
pagine (da-a)575-584
Numero di pagine10
RivistaGeroScience
Volume42
DOI
Stato di pubblicazionePubblicato - 2020

Keywords

  • EEG
  • eLORETA, biotechnical innovation
  • Small world
  • Graph theory
  • Functional connectivity

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