Before revision:  Here, we deploy an unsupervised machine learning algorithm to define brainstates as time point level activity patterns from functional magnetic resonance imaging data acquired dur- ing passive visual fixation (rest) and an n-back working memory task.
After revision:  Here, we analyze the brain's trajectories through a high-dimensional activity space at the level of single time point activity patterns from functional magnetic resonance imaging data acquired dur- ing passive visual fixation (rest) and an n-back working memory task.