Microstates of Dynamic Directed Connectivity Networks Revealing Visual Color Influences on the Brain Information Processing During Learning

Abstract

Color has the exceptional ability to capture visual attention and is also capable of enhancing positive emotions, leading to a significant impact on human learning and memory. However, the influence of color on the spatiotemporal dynamics of brain connectivity networks during learning has remained unexplored. This study aimed to propose an analytical approach based on time-frequency decomposition and microstate analysis to capture temporal variations in dynamic directed connectivity networks using electroencephalography (EEG) signals for investigating the influence of visual color on network dynamics of the brain during a learning task. Wavelet transform and phase slope index were employed to estimate the dynamic directed connectivity networks of EEG signals. The estimated dynamic directed connectivity networks were then characterized using graph theoretical analysis. The recurring patterns of dynamic directed connectivity networks were classified using cluster analysis before the temporal dynamics of directed connectivity networks were quantified using microstate analysis. Forty-five healthy participants participated in the experiment, which included memorizing learning materials presented in three different colors (achromatic, cool, and warm). The results revealed that the dynamic directed connectivity networks could be grouped into several quasi-stable states and the presence of common and unique brain states repetitive across frequency bands under individual conditions. A joint analysis of all conditions revealed that the temporal dynamics (coverage, mean duration, and state transition probability) differed significantly between the achromatic and colored conditions. Few dynamic brain states were shared between conditions and tended to remain in particular brain states for a longer duration in specific frequency bands. Our observations provided the first evidence of temporal dynamics of frequency-specific directed connectivity networks in the brain during multimedia learning tasks, that is, increased coverage of top-down interactions in the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\theta $ </tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\alpha $ </tex-math></inline-formula> bands and switching between top-down and bottom-up interactions (information flow from anterior to posterior regions and vice versa) in the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\alpha $ </tex-math></inline-formula> band, in colored conditions compared to that of achromatic conditions. Therefore, these results suggest that several frequency-specific directed connectivity networks cooperate during knowledge acquisition and may change over time (from one state to another). The proposed framework captures the temporal dynamics of directed connectivity networks, and provides implications for monitoring and assessing emotional and cognitive processes in various contexts.