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Daniel Elbich, Giorgia Picci, Xiaoxiao Bai, Suzy Scherf; Functional Re-Organization in the Face-Processing Network Across Development. Journal of Vision 2016;16(12):774. doi: https://doi.org/10.1167/16.12.774.
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© ARVO (1962-2015); The Authors (2016-present)
It is increasingly evident that functional specialization in the brain emerges from interactions between regions in a distributed neural network. However, the understanding of how these functional networks develop is only just emerging. We evaluated how the topology of functional neural networks varies as a function of age while children (5-9 yrs), younger adolescents (YA: 10-14 yrs), older adolescents (OA: 15-18 yrs), and adults (19-35 yrs) observed dynamic movies of human faces, moving objects, buildings and navigational scenes while being scanned using fMRI. Core and extended regions in the face-processing network were defined at the group level and then subsequently fit to each participant's individual activation. To evaluate how functional connectivity is modulated by visual category, we employed a psychophysiological interaction approach by computing the interaction between the timing of the visual category presentation and fMRI timecourse and convolved this interaction with a canonical HRF from each ROI. To determine models of connectivity, these convolved timeseries were submitted to separate unified SEMs for each age group to estimate directional connections among all nodes. We used graph theory metrics to quantify global network topology as well as node (i.e., region) characteristics. Although, there were no group differences in the global network structure during face or object viewing, there were impressive age-related changes in the centrality of the nodes, which reflects the hub-like nature of the individual regions. Specifically, the pattern of directional connections into and out of the right FFA, right OFA, bilateral amygdala, and anterior temporal poles during face processing varied across each age group, resulting in different functional organization throughout the network. These findings reflect age-related functional re-organization of the neural network architecture underlying face processing in the transition from childhood through adolescence and adulthood.
Meeting abstract presented at VSS 2016
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