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… Click to show full 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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