The functional organization of the human brain adapts dynamically in response to a rapidly changing environment. However, the relation of these rapid changes in functional organization to cognitive functioning is not well understood. This study used a graph-based time-frame modularity analysis approach to identify temporally recurrent functional configuration patterns in neural responses to an n-back working memory task during fMRI. Working memory load was manipulated to investigate the functional relevance of the identified brain states. Four distinct brain states were defined by the predominant patterns of activation in the task-positive, default-mode, sensorimotor, and visual networks. Associated with escalating working memory load, the occurrence of the task-positive state and the probability of transitioning into this state increased. In contrast, the occurrence of the default-mode and sensorimotor states and the probability of these 2 states transitioning away from the task-positive state decreased. The task-positive state occurrence rate and the probability of transitioning from the default-mode state back to the task-positive state explained a significant and unique portion of the variance in task performance. The results demonstrate that dynamic brain activities support successful cognitive functioning and may have heuristic value for understanding abnormal cognitive functioning associated with multiple neuropsychiatric disorders.
Keywords: dynamic brain state; fMRI; time-frame modularity analysis; working memory.
Published by Oxford University Press 2020.