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Interhemispheric connectivity estimated from EEG time-correlation analysis in preterm infants with normal follow-up
E J Meijer1, H J Niemarkt2,3, I P P C Raaijmakers1,4
1Clinical Physics, Máxima Medical Center, Veldhoven, the Netherlands.
Insights
Brain connectivity in preterm infants shows decreasing EEG signal similarity with age. This suggests increased functional differentiation in the developing preterm brain as neural networks mature.
Area of Science:
- Neuroscience
- Developmental Neuroscience
- Computational Neuroscience
Background:
- Interhemispheric brain connectivity is crucial for cognitive functions.
- Quantifying neuronal connectivity in preterm infants is essential for understanding early brain development.
- Electroencephalography (EEG) offers a non-invasive method to assess brain activity and connectivity.
Purpose of the Study:
- To quantitatively assess interhemispheric neuronal connectivity in healthy preterm infants.
- To investigate changes in EEG time-correlation patterns with advancing postmenstrual age (PMA).
- To explore the relationship between EEG signal similarity and brain maturation in early development.
Main Methods:
- Utilized automated quantitative EEG time-correlation analysis on 36 preterm infants (27-37 weeks PMA).
- Performed repeated EEG recordings (3-8 sessions) using a reduced 10-20 electrode system.
- Analyzed EEG time-correlation between homologous channels to measure interhemispheric signal similarity and lag times.
Main Results:
- A significant decrease (40-60%) in median EEG correlation values was observed across all channels with increasing PMA.
- Postnatal maturation showed a significant decreasing trend only in the central-temporal channel.
- Median lag times did not exhibit a uniform change with PMA, indicating age-related shifts in connectivity patterns.
Conclusions:
- Decreasing interhemispheric EEG signal similarity with advancing PMA suggests greater functional differentiation of cortical areas.
- This finding supports the development of more complex neural networks, incorporating both excitatory and inhibitory circuits.
- Quantitative EEG analysis provides valuable insights into the maturation of brain connectivity in preterm infants.
Abstract:
Brain connectivity is associated with axonal connections between brain structures. Our goal was to quantify the interhemispheric neuronal connectivity in healthy preterm infants by automated quantitative EEG time-correlation analysis. As with advancing postmenstrual age (PMA, gestational age + postnatal age) the neuronal connectivity between left and right hemisphere increases, we expect to observe changes in EEG time-correlation with age. Thirty-six appropriate-for-gestational age preterm infants (PMA between 27-37 weeks) and normal neurodevelopmental follow-up at 5 years of age were included. Of these, 22 infants underwent 3-8 repeated EEG recordings at weekly intervals. The reduced 10-20 EEG electrode system for newborns was used with five sets of bipolar channels: central-temporal, frontal polar-temporal, frontal polar-central, temporal-occipital and central-occipital. We performed EEG time-correlation analysis between homologous channels of the brain hemispheres to identify interhemispheric similarity in EEG signal shape. For each 8 s epoch of the EEG the time-correlation values and the corresponding lag times were calculated for homologous channels on both hemispheres. In all channels, the median correlation value decreased significantly (between -40% and -60% decrease) from 27 to 37 weeks PMA, for gestational maturation. For the postnatal maturation only the central-temporal channel showed a significantly decreasing trend. In contrast, the median lag time showed no uniform change with PMA. The decreasing median correlation values in all homologous channels indicate a decrease in similarity in signal shape with advancing PMA. This finding may reflect greater functional differentiation of cortical areas in the developing preterm brain and may be explained by the increase of complex neural networks with excitatory and inhibitory circuitries.
