Characterization of Language Abilities and Semantic Networks in Very Preterm Children at School-age

Author:

Décaillet Marion1,Christensen Alexander P.2,Besuchet Laureline1,Huguenin-Virchaux Cléo1,Fumeaux Céline J. Fischer1,Denervaud Solange3,Schneider Juliane1

Affiliation:

1. Clinic of Neonatology, Department of Mother-Woman-Child, Lausanne University Hospital and University of Lausanne

2. Psychology and Human Development, Vanderbilt University, Nashville, TN

3. CIBM Center for Biomedical Imaging

Abstract

Abstract

It has been widely assessed that very preterm children (< 32 weeks gestational age) present language and memory impairments compared to full-term children. However, differences in their underlying semantic memory structure have not been studied yet. Nevertheless, the way concepts are learned and organized across development relates to children’s capacities in retrieving and using information later. Therefore, the semantic memory organization could underlie several cognitive deficits existing in very preterm children. Computational mathematical models offer the possibility to characterize semantic networks through three coefficients; average shortest path length (i.e., distance between concepts), clustering (i.e., local interconnectivity), and modularity (i.e., vocabulary enrichment). Here we assessed these coefficients in 38 very preterm schoolchildren (aged 8–10 years) compared to 38 full-term schoolchildren (aged 7–10 years) based on a verbal fluency task. Using semantic network analysis, very preterm children showed a lower interconnectivity at a local level than full-term children. However, we found no differences between very preterm and full-term children regarding their average shortest path length between concepts and their modularity at a global level. These findings provide preliminary evidence that very preterm children demonstrate subtle impairments in the organization of their semantic network, encouraging the adaptation of the support and education they receive.

Publisher

Research Square Platform LLC

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