A brain-age model for preterm infants based on functional connectivity. (26th April 2018)

• Record Type: Journal Article
• Authors: Lavanga, M; De Wel, O; Caicedo, A; Jansen, K; Dereymaeker, A; Naulaers, G; Van Huffel, S
• Is Part Of: Physiological measurement. Volume 39:Number 4(2018:Apr.)
• Publication Date: 2018-04-26
• Subjects: early brain development -- functional connectivity -- preterm neonatal EEG -- preterm infant; Physiology -- Measurement -- Periodicals; Patient monitoring -- Periodicals; 612
• DOI: 10.1088/1361-6579/aabac4
• Languages: English
• ISSNs: 0967-3334

Abstract

Abstract: 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 ( ) 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, and MSC in, θ, α 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.