# The temporal dynamics of resting-state EEG microstates reflected the differences in socioeconomic status among college students

## TL;DR

This study shows that differences in socioeconomic status are reflected in the brain's dynamic resting-state activity, as measured by EEG microstates.

## Contribution

The study introduces EEG microstate analysis to reveal how socioeconomic status influences brain dynamics, particularly in auditory-language and default mode networks.

## Key findings

- Low-SES individuals showed increased duration and coverage in microstate A and decreased occurrence and coverage in microstate C.
- Low-SES participants exhibited distinct microstate transition patterns, suggesting different cognitive control mechanisms.
- SES disparities are robustly associated with spatiotemporal EEG microstate dynamics, indicating brain reconfiguration in response to socioeconomic environments.

## Abstract

Socioeconomic status (SES) is a distal ecological factor that predicts the trajectory of human development. Exposure to low SES may have lasting effects on brain structure and function. Although prior research has identified static neural correlates of SES disparities, it remains unclear how socioeconomic contexts shape dynamic brain states. Therefore, the present study employs electroencephalography (EEG) microstate analysis to investigate how SES influences the dynamics of resting-state brain activity.

Based on SES scores, participants in the top and bottom 27% were categorized as the high-SES group (n = 29), and the low-SES group (n = 29). Resting-state EEG signals were collected from all participants, and microstate analysis identified the temporal features of four canonical large-scale neural networks (microstates A, B, C, and D) to explore socioeconomic differences in brain dynamics across different SES groups.

(1) The correlation between SES and the temporal characteristics of both microstates A (ps < 0.05) and C (ps < 0.05) was significant, suggesting that SES may be associated with neural dynamics involved in auditory-language processing and the default mode network (DMN). (2) High- and low-SES groups exhibited divergent temporal characteristics in microstate dynamics. Compared with the high-SES group, participants in the low-SES group demonstrated larger duration (p = 0.025), occurrence (p = 0.002), and time coverage (p < 0.001) in microstate A, while exhibiting reduced occurrence (p < 0.001) and time coverage (p = 0.005) in microstate C. The results indicate that the low-SES individuals may have compensatory reinforcement of the auditory-language network and a weakened DMN activity. (3) High- and low-SES groups exhibiting different microstate transition patterns may reflect distinct cognitive control mechanisms. Compared with the high-SES group, the low-SES group demonstrated that the transition probabilities between microstates A and B (ps < 0.05), A and D (ps < 0.05) were significantly higher, whereas those between microstates B and C (ps < 0.05), C and D (ps < 0.05) were significantly lower.

These findings reveal a robust association between SES disparities and spatiotemporal EEG microstate dynamics. The reconfiguration of metastable brain states may represent the way the brain responds to challenging environments.