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Updated: Dec 28, 2025

Infant Auditory Processing and Event-related Brain Oscillations
Published on: July 1, 2015
Anatomo-functional correlates of auditory development in infancy
Parvaneh Adibpour1, Jessica Lebenberg2, Claire Kabdebon1
1Cognitive Neuroimaging Unit U992, NeuroSpin Center, Gif/Yvette, France.
Insights
Infant auditory brain development shows faster responses and structural changes with age. This study links brain activity (event-related potentials) to white matter maturation in infants.
Area of Science:
- Neuroscience
- Developmental Neuroscience
- Auditory Neuroscience
Background:
- Infant brain development involves complex, asynchronous maturation of neural networks.
- Previous research linked visual functional maturation (event-related potentials) to white matter changes in infants.
- This study explores similar relationships in the auditory modality.
Purpose of the Study:
- To investigate the relationship between auditory functional maturation and neural substrate development in infants.
- To examine age-related changes in auditory event-related potentials (ERPs) and white matter properties.
- To explore the early structural and functional basis of auditory lateralization.
Main Methods:
- Measured auditory ERPs (P2 component) in 1- to 6-month-old infants.
- Utilized diffusion tensor imaging (DTI) to assess white matter microstructure in auditory pathways and related regions.
- Correlated ERP variability with DTI-derived microstructural properties, accounting for age.
Main Results:
- Auditory P2 latency decreased with age, alongside reduced diffusivity in auditory tracts and perisylvian regions.
- Demonstrated early functional and structural lateralization: stronger, faster contralateral responses (especially left hemisphere) and hemispheric DTI asymmetries.
- Found correlations between P2 response variability and microstructural properties of callosal fibers and inferior frontal regions after controlling for age.
Conclusions:
- Infant auditory functional responses are intricately linked to the maturational properties of underlying neural networks.
- The study provides insights into the early development of auditory processing and lateralization in the infant brain.
- Combining EEG and DTI offers a powerful approach to understanding infant neurodevelopment.
Abstract:
Infant brain development incorporates several intermingled mechanisms leading to intense and asynchronous maturation across cerebral networks and functional modalities. Combining electroencephalography (EEG) and diffusion magnetic resonance imaging (MRI), previous studies in the visual modality showed that the functional maturation of the event-related potentials (ERP) during the first postnatal semester relates to structural changes in the corresponding white matter pathways. Here investigated similar issues in the auditory modality. We measured ERPs to syllables in 1- to 6-month-old infants and related them to the maturational properties of underlying neural substrates measured with diffusion tensor imaging (DTI). We first observed a decrease in the latency of the auditory P2, and in the diffusivities in the auditory tracts and perisylvian regions with age. Secondly, we highlighted some of the early functional and structural substrates of lateralization. Contralateral responses to monoaural syllables were stronger and faster than ipsilateral responses, particularly in the left hemisphere. Besides, the acoustic radiations, arcuate fasciculus, middle temporal and angular gyri showed DTI asymmetries with a more complex and advanced microstructure in the left hemisphere, whereas the reverse was observed for the inferior frontal and superior temporal gyri. Finally, after accounting for the age-related variance, we correlated the inter-individual variability in P2 responses and in the microstructural properties of callosal fibers and inferior frontal regions. This study combining dedicated EEG and MRI approaches in infants highlights the complex relation between the functional responses to auditory stimuli and the maturational properties of the corresponding neural network.
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