Gestational Duration and Postnatal Age‐Related Changes in Aperiodic and Periodic Parameters in Neonatal and Toddler Electroencephalogram (<scp>EEG</scp>)

The brain develops most rapidly during pregnancy and early neonatal months. While prior electrophysiological studies have shown that aperiodic brain activity undergoes changes across infancy to adulthood, the role of gestational duration in aperiodic and periodic activity remains unknown. In this study, we aimed to bridge this gap by examining the associations between gestational duration and aperiodic and periodic activity in the EEG power spectrum in both neonates and toddlers. This cross-sectional study involved EEG data from 73 neonates (postnatal age 1-5 days, 40 females) and 56 toddlers (postnatal age of 2.9-3.2 years, 28 females) from the FinnBrain Birth Cohort Study. EEG power spectra were parameterized to aperiodic and periodic components using the SpecParam tool. We tested the associations between gestational duration as well as postnatal age and SpecParam parameters in neonates and toddlers while including birth weight and child sex as covariates. For neonates, multilevel models were employed, considering different data acquisitions (sleep and auditory paradigm + sleep), while in toddlers, regression models were used as only data from the auditory paradigm was available. We found that longer gestational duration was associated with a steeper power spectrum across EEG frequencies both in neonates and toddlers. Effect was especially strong in toddlers (β = 0.45, p = 0.004), while in neonates, it remained nearly statistically significant (p = 0.061). In neonates, a quadratic association between gestational duration and beta center frequency (12.5-30 Hz) was found. In toddlers, beta center frequencies were overall higher in females compared to males. Offset (calculated as the power of the aperiodic curve at 2.5 Hz) and theta center frequency had negative associations with postnatal age in neonates, but not in toddlers. Our results suggest that gestational duration may have significant and relatively long-lasting effects on brain physiology. The possible behavioral and cognitive consequences of these changes are enticing topics for future research.

Authors

Institutions

Publication Details

Journal
Human Brain Mapping
Published
2025-01-01
DOI
https://doi.org/10.1002/hbm.70130
Citations
4
Primary Topic
Neural dynamics and brain function
Type
article
Field-Weighted Citation Impact
5.72

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Gestational Duration and Postnatal Age‐Related Changes in Aperiodic and Periodic Parameters in Neonatal and Toddler Electroencephalogram (EEG)

Henry Railo, Maria Lavonius, Henriette Acosta, Jetro J. Tuulari et al.
4 citations
Human Brain Mapping
Neural dynamics and brain function
5.72
article

Gestational Duration and Postnatal Age‐Related Changes in Aperiodic and Periodic Parameters in Neonatal and Toddler Electroencephalogram (EEG)

Henry Railo, Maria Lavonius, Henriette Acosta, Jetro J. Tuulari, Linnéa Karlsson, Silja Luotonen, Hasse Karlsson, Minna Huotilainen
article en
4 citations

Abstract

The brain develops most rapidly during pregnancy and early neonatal months. While prior electrophysiological studies have shown that aperiodic brain activity undergoes changes across infancy to adulthood, the role of gestational duration in aperiodic and periodic activity remains unknown. In this study, we aimed to bridge this gap by examining the associations between gestational duration and aperiodic and periodic activity in the EEG power spectrum in both neonates and toddlers. This cross-sectional study involved EEG data from 73 neonates (postnatal age 1-5 days, 40 females) and 56 toddlers (postnatal age of 2.9-3.2 years, 28 females) from the FinnBrain Birth Cohort Study. EEG power spectra were parameterized to aperiodic and periodic components using the SpecParam tool. We tested the associations between gestational duration as well as postnatal age and SpecParam parameters in neonates and toddlers while including birth weight and child sex as covariates. For neonates, multilevel models were employed, considering different data acquisitions (sleep and auditory paradigm + sleep), while in toddlers, regression models were used as only data from the auditory paradigm was available. We found that longer gestational duration was associated with a steeper power spectrum across EEG frequencies both in neonates and toddlers. Effect was especially strong in toddlers (β = 0.45, p = 0.004), while in neonates, it remained nearly statistically significant (p = 0.061). In neonates, a quadratic association between gestational duration and beta center frequency (12.5-30 Hz) was found. In toddlers, beta center frequencies were overall higher in females compared to males. Offset (calculated as the power of the aperiodic curve at 2.5 Hz) and theta center frequency had negative associations with postnatal age in neonates, but not in toddlers. Our results suggest that gestational duration may have significant and relatively long-lasting effects on brain physiology. The possible behavioral and cognitive consequences of these changes are enticing topics for future research.

Human Brain MappingVol. 46(1)
University of Helsinki (FI), University of Turku (FI), Philipps University of Marburg (DE), Turku University Hospital (FI)
Academy of Finland, Suomalainen Tiedeakatemia, Suomen Kulttuurirahasto, Jane ja Aatos Erkon Säätiö, Signe ja Ane Gyllenbergin Säätiö, Emil Aaltosen Säätiö, Sigrid Juséliuksen Säätiö, Varsinais-Suomen Sairaanhoitopiiri
Good health and well-being
Openalex Percentile: Top 5%
Neural dynamics and brain function
5.72
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.