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Integrating high-density ERP and fMRI measures of face-elicited brain activity in 9-12-year-old children: An ERP source localization study. | LitMetric

Integrating high-density ERP and fMRI measures of face-elicited brain activity in 9-12-year-old children: An ERP source localization study.

Neuroimage

Department of Psychology, Child Study Center, The Pennsylvania State University, University Park, PA, 16802, USA. Electronic address:

Published: January 2019

AI Article Synopsis

Article Abstract

Social information processing is a critical mechanism underlying children's socio-emotional development. Central to this process are patterns of activation associated with one of our most salient socioemotional cues, the face. In this study, we obtained fMRI activation and high-density ERP source data evoked by parallel face dot-probe tasks from 9-to-12-year-old children. We then integrated the two modalities of data to explore the neural spatial-temporal dynamics of children's face processing. Our results showed that the tomography of the ERP sources broadly corresponded with the fMRI activation evoked by the same facial stimuli. Further, we combined complementary information from fMRI and ERP by defining fMRI activation as functional ROIs and applying them to the ERP source data. Indices of ERP source activity were extracted from these ROIs at three a priori ERP peak latencies critical for face processing. We found distinct temporal patterns among the three time points across ROIs. The observed spatial-temporal profiles converge with a dual-system neural network model for face processing: a core system (including the occipito-temporal and parietal ROIs) supports the early visual analysis of facial features, and an extended system (including the paracentral, limbic, and prefrontal ROIs) processes the socio-emotional meaning gleaned and relayed by the core system. Our results for the first time illustrate the spatial validity of high-density source localization of ERP dot-probe data in children. By directly combining the two modalities of data, our findings provide a novel approach to understanding the spatial-temporal dynamics of face processing. This approach can be applied in future research to investigate different research questions in various study populations.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6409155PMC
http://dx.doi.org/10.1016/j.neuroimage.2018.09.070DOI Listing

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