Abstract
Brain functional connectivity (FC), as measured by blood oxygenation level-dependent (BOLD) signal, fluctuates at the scale of 10s 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., 10s of seconds long). To test this hypothesis, 25 subjects were scanned continuously for 25 min while they performed and transitioned between four different tasks: working memory, visual attention, math, and rest. First, we estimated dFC patterns by using a sliding window approach. Next, we extracted two engagement-specific FC patterns representing active engagement and passive engagement by using k-means clustering. Then, we derived three metrics from whole-brain dFC patterns to track engagement level, that is, 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 by 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.
Highlights
Functional connectivity (FC) analyses of resting-state functional magnetic resonance imaging data have consistently revealed sets of spatially distributed and temporally correlated brain regions, which correspond to canonical functions such as vision, audition, language, memory, and attention (Smith et al, 2009)
The average clustering accuracy describing the overall agreement between k-means partitions and ground truth task engagement across all 24 participants is 78.52%, suggesting in general the k-means algorithm could successfully group dynamic FC (dFC) patterns according to ongoing tasks despite the algorithm not being provided with any information about task timing
For subjects with good performance (Figure 2A), dFCs appear to be highly organized according to the ongoing task, so that dFCs associated with a given task cluster together, and separate from those associated with the other tasks
Summary
Functional connectivity (FC) analyses of resting-state functional magnetic resonance imaging (fMRI) data have consistently revealed sets of spatially distributed and temporally correlated brain regions, which correspond to canonical functions such as vision, audition, language, memory, and attention (Smith et al, 2009). Mounting evidence emphasizes the potential biological and cognitive significance of blood oxygenation level-dependent (BOLD) fMRI FC dynamics evaluated on the brain as a whole (e.g., considering all possible region-to-region connections). Along those lines, Allen et al (2014) proposed a pipeline to investigate whole-brain dynamic FC (dFC) during rest, called dynamic functional network connectivity (dFNC). The identified FC states were suggested to reflect shifts in ongoing cognition during rest This approach has recently been shown to be highly replicable (Abrol et al, 2017), predictive of mental illness (Rashid et al, 2016), and correlate with multimodal imaging modalities (Allen, Eichele, Wu, & Calhoun, 2013)
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