Time-varying whole-brain functional network connectivity coupled to task engagement

• Published in: Network neuroscience (Cambridge, Mass.) Vol. 3; no. 1; pp. 49 - 66
• Main Authors: Xie, Hua, Gonzalez-Castillo, Javier, Handwerker, Daniel A., Bandettini, Peter A., Calhoun, Vince D., Chen, Gang, Damaraju, Eswar, Liu, Xiangyu, Mitra, Sunanda
• Format: Journal Article
• Language: English
• Published: One Rogers Street, Cambridge, MA 02142-1209, USA MIT Press 2019; MIT Press Journals, The; The MIT Press
• Subjects: Attention; Blood levels; Brain; Brainwide integration; Cluster analysis; Clustering; Cognitive dynamics; Cognitive marker; Functional magnetic resonance imaging; Hypotheses; Memory; Memory tasks; Mental task performance; Neural networks; Oxygenation; Short term memory; Task-evoked connectivity dynamics; Tracking; Vector quantization; Visual perception; Visual tasks; Whole-brain connectivity pattern; Brainwide integration; Whole-brain connectivity pattern; Cognitive dynamics; Cognitive marker; Task-evoked connectivity dynamics
• ISSN: 2472-1751; 2472-1751
• DOI: 10.1162/netn_a_00051

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 -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. In this study, we hypothesized that whole-brain dynamic functional connectivity (FC) patterns carry fine-grained information that allows for tracking short-term task engagement levels. We derived three task engagement markers from whole-brain dynamic FC pattern, that is, dissimilarity between dynamic FC patterns and high/low-engagement FC patterns, as well as brainwide integration level. We employed a linear mixed effects model to relate those task engagement markers with short-term task performance, and confirmed our hypothesis with the working memory task.