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

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.