Microstate Dynamics in Working Memory: Exploring Spatial Information Coding of Stimulus and Behavioral Performance

EEG microstate analysis provides insights into the spatial and temporal dynamics of brain activity during cognitive tasks. The four canonical microstates (classes A, B, C, and D) have been widely reported and associated with various cognitive functions. However, the relationship between microstate parameters and behavioral responses in cognitive functions, such as working memory (WM), has not been sufficiently investigated. This study investigates how microstate dynamics relate to WM performance during a memory-guided saccade (MGS) task. Methods EEG and Eye-tracking data were recorded from participants performing an MGS task at two target eccentricities (near and far). Saccade error was used as a behavioral index of WM performance. Microstate parameters (occurrence, coverage, duration, and transition probability) were computed for the four canonical microstates during the trials. Results Our analysis revealed a significant reduction in the coverage of microstate C, often associated with the default mode network, during the memory maintenance interval compared to baseline. Moreover, a notable increase was observed in the duration of microstate D, considering polarity during the memory interval, which could be related to the frontoparietal control network (FPCN). Notably, the transition probability (TP) from D+ to D- during the memory duration correlated with saccade errors, indicating a behavioral predictive capacity. Furthermore, we identified distinct patterns of microstate D transitions to other microstates that differed significantly between the near and far target conditions, suggesting a functional role in spatial coding. Conclusion Microstate dynamics, particularly those of microstate D, play a dual role in spatial WM by supporting information coding and predicting behavioral accuracy. The polarity-specific transitions within microstate D provide a neural signature of WM performance, with implications for understanding network-level mechanisms underlying spatial memory and saccade control.