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Updated: Jul 16, 2026
Infant Auditory Processing and Event-related Brain Oscillations
Published on: July 1, 2015
Characterization of aperiodic and theta activity in preterm infants using EEG: Insights into cerebral maturation and
Aline González Carpinteiro1, Hala Nasser2, Amandine Pedoux3
1Université Paris Cité, INSERM, NeuroDiderot, Paris F-75019, France; Université Paris Saclay, CEA, NeuroSpin, UNIACT, Gif-sur-Yvette F-91191, France.
Insights
Preterm infants show similar brain activity development as full-term infants by corrected age. However, clinical factors and brain structure influence preterm brain maturation, highlighting EEG spectral parameterization
Area of Science:
- Neuroscience
- Developmental Neuroscience
- Computational Neuroscience
Background:
- Preterm birth can impact brain network maturation, affecting crucial theta oscillations.
- Traditional EEG analysis methods blend oscillatory and non-oscillatory activity, limiting mechanistic insights.
Purpose of the Study:
- To assess brain maturation using spectral parameterization in very preterm and full-term infants.
- To investigate the development of aperiodic activity and periodic theta power.
- To examine inter-individual variability in EEG metrics among preterm infants.
Main Methods:
- High-density EEG data were collected from preterm and full-term infants at term-equivalent age and 2 months corrected age.
- Spectral parameterization was used to extract metrics for aperiodic activity (offset, exponent) and theta power.
- Diffusion MRI assessed brain microstructure in relation to EEG findings.
Main Results:
- Both preterm and full-term infants showed increases in aperiodic activity and theta power from term-equivalent age to 2 months corrected age, with no group differences.
- A shift in the spatial distribution of aperiodic activity was observed between term-equivalent age and 2 months corrected age.
- In preterm infants, clinical risk factors and brain microstructure variations explained inter-individual differences in EEG metrics, with higher theta power correlating with advanced cortical maturation.
Conclusions:
- EEG spectral parameterization offers a sensitive method for characterizing early brain maturation.
- This approach can identify brain vulnerabilities associated with prematurity.
- Findings suggest that despite initial disruptions, preterm brain networks show developmental trajectories comparable to full-term infants by corrected age, though individual variability exists.
Abstract:
Preterm birth may disrupt maturation of brain networks and functional activity, including theta oscillations that play a key role in early network development. Traditional EEG spectral analyses show marked development of theta power in early infancy, but these approaches mix oscillatory and non-oscillatory activity, limiting insights into the underlying mechanisms. Using spectral parameterization, we assessed the development of aperiodic activity and periodic theta power in very preterm (born before 32 weeks gestational age, GA) and full-term infants, and examined inter-individual variability among preterms. High-density EEG was acquired during active/REM sleep at term-equivalent age (TEA) and 2 months corrected age (2mCA) in preterm (n = 41) and full-term (n = 13) infants. Spectral parameterization allowed extracting metrics of aperiodic activity (offset, exponent) and periodic theta power, globally and across spatial clusters of electrodes. From TEA to 2mCA, offset, exponent, and theta power increased with no differences between preterms and full-terms. At TEA, aperiodic activity metrics were stronger in anterior compared with posterior areas, but this regional landscape shifted by 2mCA due to pronounced changes in posterior areas from TEA to 2mCA. Within preterms, inter-individual variability in EEG metrics at TEA was partly explained by clinical risk factors (male sex, lower GA, being small for GA, and invasive ventilation) and variations in brain microstructure, as assessed with diffusion MRI: higher theta power correlated with more advanced cortical maturation. These findings indicate that EEG spectral parameterization combined with spatial analysis provides a sensitive framework for characterizing early brain maturation and vulnerabilities associated with prematurity.
