Developmental Science

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Volume 21 Issue 6 (November 2018), Pages

Extremely preterm children exhibit increased interhemispheric connectivity for language: findings from fMRI‐constrained MEGanalysis


Children born extremely preterm are at significant risk for cognitive impairment, including language deficits. The relationship between preterm birth and neurological changes that underlie cognitive deficits is poorly understood. We use a stories‐listening task in fMRI and MEG to characterize language network representation and connectivity in children born extremely preterm (n = 15, <28 weeks gestation, ages 4–6 years), and in a group of typically developing control participants (n = 15, term birth, 4–6 years). Participants completed a brief neuropsychological assessment. Conventional fMRI analyses revealed no significant differences in language network representation across groups (p > .05, corrected). The whole‐group fMRI activation map was parcellated to define the language network as a set of discrete nodes, and the timecourse of neuronal activity at each position was estimated using linearly constrained minimum variance beamformer in MEG. Virtual timecourses were subjected to connectivity and network‐based analyses. We observed significantly increased beta‐band functional connectivity in extremely preterm compared to controls (p < .05). Specifically, we observed an increase in connectivity between left and right perisylvian cortex. Subsequent effective connectivity analyses revealed that hyperconnectivity in preterms was due to significantly increased information flux originating from the right hemisphere (p < 0.05). The total strength and density of the language network were not related to language or nonverbal performance, suggesting that the observed hyperconnectivity is a “pure” effect of prematurity. Although our extremely preterm children exhibited typical language network architecture, we observed significantly altered network dynamics, indicating reliance on an alternative neural strategy for the language task.

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