Scientific article
Open access

Large-scale functional network dynamics in human callosal agenesis: Increased subcortical involvement and preserved laterality

Published inNeuroImage, vol. 243, 118471
Publication date2021-11
First online date2021-08-27

In the human brain, the corpus callosum is the major white-matter commissural tract enabling the transmission of sensory-motor, and higher level cognitive information between homotopic regions of the two cerebral hemispheres. Despite developmental absence (i.e., agenesis) of the corpus callosum (AgCC), functional connectivity is preserved, including interhemispheric connectivity. Subcortical structures have been hypothesised to provide alternative pathways to enable this preservation. To test this hypothesis, we used functional Magnetic Resonance Imaging (fMRI) recordings in children with AgCC and typically developing children, and a time-resolved approach to retrieve temporal characteristics of whole-brain functional networks. We observed an increased engagement of the cerebellum and amygdala/hippocampus networks in children with AgCC compared to typically developing children. There was little evidence that laterality of activation networks was affected in AgCC. Our findings support the hypothesis that subcortical structures play an essential role in the functional reconfiguration of the brain in the absence of a corpus callosum.

  • Brain plasticity
  • Callosal agenesis
  • Dynamic functional connectivity
  • Subcortical networks
  • Adolescent
  • Agenesis of Corpus Callosum / diagnostic imaging
  • Cerebellum / diagnostic imaging
  • Child
  • Connectome
  • Corpus Callosum / diagnostic imaging
  • Female
  • Functional Laterality / physiology
  • Humans
  • Magnetic Resonance Imaging
  • Male
  • Neuronal Plasticity
  • White Matter
Citation (ISO format)
SIFFREDI, Vanessa et al. Large-scale functional network dynamics in human callosal agenesis: Increased subcortical involvement and preserved laterality. In: NeuroImage, 2021, vol. 243, p. 118471. doi: 10.1016/j.neuroimage.2021.118471
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ISSN of the journal1053-8119

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