LAUSR

Brain functional connectivity (FC), as measured by blood oxygenation level dependent (BOLD) signal, fluctuates at the scale of tens of seconds. It has recently been found that whole-brain dynamic FC (dFC) patterns contain sufficient information to permit identification of ongoing tasks. Here, we hypothesize that dFC patterns carry fine-grained information that allows for tracking short-term task engagement levels (i.e., tens of seconds long). To test this hypothesis, twenty-five subjects were scanned continuously for 25 mins while they performed and transitioned between four different tasks: working memory, visual attention, math, and rest. First, we estimated dFC patterns using a sliding window approach. Next, we extracted two engagement-specific FC patterns representing active engagement and passive engagement using k-means clustering. Then, we derived three metrics from whole-brain dFC patterns to track engagement level, i.e. dissimilarity between dFC patterns and engagement-specific FC patterns, and the level of brainwide integration level. Finally, those engagement markers were evaluated against windowed task performance using a linear mixed effects model. Significant relationships were observed between abovementioned metrics and windowed task performance for the working memory task only. These findings partially confirm our hypothesis and underscore the potential of whole-brain dFC to track short-term task engagement levels.