Socioeconomic status (SES) and cognitive outcomes are predicted by resting-state EEG in school-aged children.

Children's socioeconomic status (SES) is related to patterns of intrinsic resting-state brain function that subserve relevant cognitive processes over the course of development. Although infant research has demonstrated the association between children's environments, cognitive outcomes, and resting-state electroencephalography (rsEEG), it remains unknown how these aspects of their environment, tied to SES, impact neural and cognitive development throughout the school years. To address this gap, we applied a multivariate pattern analysis (MVPA) to rsEEG data to identify which neural frequencies at rest are differentially associated with unique aspects of socioeconomic status (SES; income and maternal education) and cognitive (vocabulary, working memory) outcomes among school-aged children (8-15 years). We find that the alpha frequency is associated with both income and maternal education, while lower gamma and theta fluctuations are tied to dissociable aspects of SES and cognitive outcomes. Specifically, changes in the gamma frequency are predictive of both maternal education and vocabulary outcome, while changes in the theta frequency are related to both income and working memory ability. The current findings extend our understanding of unique pathways by which SES influences cognitive and neural development in school-aged children.