Abstract
Brain development carries with it a large number of structural changes at the local level which impact on the functional interactions of distributed neuronal networks for perceptual processing. Such changes enhance information processing capacity, which can be indexed by estimation of neural signal complexity. Here, we show that during development, EEG signal complexity increases from one month to 5 years of age in response to auditory and visual stimulation. However, the rates of change in complexity were not equivalent for the two responses. Infants’ signal complexity for the visual condition was greater than auditory signal complexity, whereas adults showed the same level of complexity to both types of stimuli. The differential rates of complexity change may reflect a combination of innate and experiential factors on the structure and function of the two sensory systems.
Highlights
Infancy is a time of enormous changes in brain structure and function
Developmental information was gathered from interviews and a developmental questionnaire completed by the parents
The ERPs show the expected developmental changes in morphology, amplitude, and latency previously found with typical means of filtering
Summary
Infancy is a time of enormous changes in brain structure and function. For example, glucose metabolism rises (Chugani, 1998) and brain volume shows a 4-fold increase during the first 4 years of life (Courchesne et al, 2000). The functional consequence of these changes should be reflected in the increasing processing capacities of the developing child. The changes in functional responses are thought to represent an increase in the integration within local neural ensembles together with more precise re-entrant interactions between distal ensembles (Thatcher et al, 2007; Fair et al, 2009). Measures of complexity may be a sensitive index of maturational changes in brain function. A recent study by McIntosh et al (2008) showed an increase in EEG signal complexity with maturation in pre-adolescent children. They showed that this increase in complexity was strongly correlated with more stable and accurate behavioural performance
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