A brain-age model for preterm infants based on functional connectivity (2018)
In this study, the development of EEG functional connectivity during early development has been investigated in order to provide a predictive age model for premature infants. Approach: The functional connectivity has been assessed via the coherency function (its imaginary part (ImCoh) and its mean squared magnitude (MSC)), the phase locking value () and the Hilbert–Schimdt dependence (HSD) in a dataset of 30 patients, partially described and employed in previous studies (Koolen et al 2016 Neuroscience 322 298–307; Lavanga et al 2017 Complexity 2017 1–13). Infants' post-menstrual age (PMA) ranges from 27 to 42 weeks. The topology of the EEG couplings has been investigated via graph-theory indices. Main results: Results show a sharp decrease in ImCoh indices in ?, (4–8) Hz and ?, (8–16) Hz bands and MSC in ?, (16–32) Hz band with maturation, while a more modest positive correlation with PMA is found for HSD, and MSC in , ?, ? bands. The best performances for the PMA prediction were mean absolute error equal to 1.51 weeks and adjusted coefficient of determination equal to 0.8. Significance: The reported findings suggest a segregation of the cortex connectivity, which favours a diffused tasks architecture on the brain scalp. In summary, the results indicate that the neonates' brain development can be described via lagged-interaction network features.