A brain-age model for preterm infants based on functional connectivity.

OBJECTIVE

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 ([Formula: see text]) 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 θ, (4-8) Hz and α, (8-16) Hz bands and MSC in β, (16-32) Hz band with maturation, while a more modest positive correlation with PMA is found for HSD, [Formula: see text] and MSC in [Formula: see text], θ, α bands. The best performances for the PMA prediction were mean absolute error equal to 1.51 weeks and adjusted coefficient of determination [Formula: see text] 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.