Altered structural connectivity networks in a mouse model of complete and partial dysgenesis of the corpus callosum

Corpus callosum dysgenesis (CCD) describes a collection of brain malformations in which the main fiber tract connecting the two hemispheres is either absent (complete CCD, or ‘agenesis of the corpus callosum’) or reduced in size (partial CCD). Humans with these neurodevelopmental disorders have a wi...

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Veröffentlicht in:NeuroImage (Orlando, Fla.) Fla.), 2020-08, Vol.217, p.116868-116868, Article 116868
Hauptverfasser: Edwards, Timothy J., Fenlon, Laura R., Dean, Ryan J., Bunt, Jens, Sherr, Elliott H., Richards, Linda J.
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Sprache:eng
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Zusammenfassung:Corpus callosum dysgenesis (CCD) describes a collection of brain malformations in which the main fiber tract connecting the two hemispheres is either absent (complete CCD, or ‘agenesis of the corpus callosum’) or reduced in size (partial CCD). Humans with these neurodevelopmental disorders have a wide range of cognitive outcomes, including seemingly preserved features of interhemispheric communication in some cases. However, the structural substrates that could underlie this variability in outcome remain to be fully elucidated. Here, for the first time, we characterize the global brain connectivity of a mouse model of complete and partial CCD. We demonstrate features of structural brain connectivity that model those predicted in humans with CCD, including Probst bundles in complete CCD and heterotopic sigmoidal connections in partial CCD. Crucially, we also histologically validate the recently predicted ectopic sigmoid bundle present in humans with partial CCD, validating the utility of this mouse model for fine anatomical studies of this disorder. Taken together, this work describes a mouse model of altered structural connectivity in variable severity CCD and forms a foundation for future studies investigating the function and mechanisms of development of plastic tracts in developmental disorders of brain connectivity.
ISSN:1053-8119
1095-9572
DOI:10.1016/j.neuroimage.2020.116868