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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.
Insights
This study developed a predictive age model for premature infants using electroencephalogram (EEG) functional connectivity. Brain network features accurately predict post-menstrual age, aiding in understanding infant neurodevelopment.
Area of Science:
- Neuroscience
- Developmental Biology
- Computational Biology
Background:
- Understanding early brain development in premature infants is crucial for timely interventions.
- Electroencephalogram (EEG) functional connectivity offers insights into developing neural networks.
- Existing predictive models for infant age have limitations.
Purpose of the Study:
- To investigate the development of EEG functional connectivity in premature infants.
- To create a predictive age model for premature infants based on EEG data.
- To characterize the maturation of brain network topology.
Main Methods:
- Assessed functional connectivity using coherency (ImCoh, MSC), phase locking value, and Hilbert-Schimdt dependence (HSD).
- Analyzed EEG data from 30 infants with post-menstrual ages ranging from 27 to 42 weeks.
- Employed graph-theory indices to investigate EEG coupling topology.
Main Results:
- Observed significant decreases in ImCoh (θ, α bands) and MSC (β band) with maturation.
- Found modest positive correlations between PMA and HSD, [Formula: see text], and MSC (γ band).
- Achieved a mean absolute error of 1.51 weeks for PMA prediction with an adjusted R² of 0.8.
Conclusions:
- Neonatal brain development is characterized by a segregation of cortex connectivity.
- Lagged-interaction network features effectively describe neonates' brain development.
- The developed model shows promise for accurate age prediction in premature infants.
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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