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A brain-age model for preterm infants based on functional connectivity
M Lavanga1,2, O De Wel1,2, A Caicedo1,2
1Department of Electrical Engineering (ESAT), STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics, KU Leuven, Kasteelpark Arenberg 10, Box 2446, 3001, Leuven, Belgium.
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.
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