7. Physiological and pathological aging: A cortical connectivity analysis by graph theory model applied to brain network

Abstract

Cortical connectivity in physiological and pathological aging. Physiological aging was evaluated in 113 EEGs, divided in three groups: Young (18–45 years), Adult (50–70) and Elderly (>70). Pathological aging was evaluated in 378 EEGs, divided in: Alzheimer disease (AD), mild cognitive impaired (MCI) and normal elderly subjects. Graph theory parameters were applied to undirected and weighted networks obtained by lagged linear coherence evaluated by eLORETA in delta, theta, alpha1, alpha2, beta1, beta2 and gamma bands. In physiological aging, normalized Characteristic Path Length ( λ ) presented the pattern Young > Adult > Elderly in the higher alpha band, the pattern Elderly > Young in delta and theta bands. Normalized Clustering coefficient ( γ ) and Small-World ( σ ) showed no significant age-modulation. In pathological aging, λ presented differences between healthy and dementia in theta band while γ showed a significant increment for AD in delta, theta and alpha1 bands; finally, σ presented a significant rising in MCI respect to AD in theta band.Functional integration was prejudiced in both physiological and pathological aging, while functional segregation of brain networks is more evident only in the pathological aging. Graph theory can disclose brain network connectivity in physiological and pathological aging, allowing understanding functional integration and segregation processes.