Posterior parietal influences on visual network specialization during development: An fMRI study of functional
Jonathan F O'Rawe1, Anna S Huang1, Daniel N Klein1
1Department of Psychology, Stony Brook University, USA.
Abstract:
Visual processing in the primate brain is highly organized along the ventral visual pathway, although it is still unclear how categorical selectivity emerges in this system. While many theories have attempted to explain the pattern of visual specialization within the ventral occipital and temporal areas, the biased connectivity hypothesis provides a framework which postulates extrinsic connectivity as a potential mechanism in shaping the development of category selectivity. As the posterior parietal cortex plays a central role in visual attention, we examined whether the pattern of parietal connectivity with the face and scene processing regions is closely linked with the functional properties of these two visually selective networks in a cohort of 60 children ages 9 to 12. Functionally localized face and scene selective regions were used in deriving each visual network's resting-state functional connectivity. The children's face and scene processing networks appeared to show a weak network segregation during resting state, which was confirmed when compared to that of a group of gender and handedness matched adults. Parietal regions of these children showed differential connectivity with the face and scene networks, and the extent of this differential parietal-visual connectivity predicted individual differences in the degree of segregation between the two visual networks, which in turn predicted individual differences in visual perception performance. Finally, the pattern of parietal connectivity with the face processing network also predicted the foci of face-related activation in the right fusiform gyrus across children. These findings provide evidence that extrinsic connectivity with regions such as the posterior parietal cortex may have important implications in the development of specialized visual processing networks.
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