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

M. Lavanga, O. De Wel, A. Caicedo, K. Jansen, A. Dereymaeker, G. Naulaers, S. Van Huffel

Research output: Contribution to journalArticlepeer-review

8 Scopus citations

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 μ, (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, μ 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.

Original languageEnglish (US)
Article number044006
JournalPhysiological Measurement
Volume39
Issue number4
DOIs
StatePublished - Apr 26 2018
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Biophysics
  • Physiology
  • Biomedical Engineering
  • Physiology (medical)

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