Experimentally modulating cognitive control processes to uncover internal mechanisms of network regulation.

<p>(<i>A</i>) To monitor and regulate the demands placed on neural systems, empirical evidence suggests that the brain employs <i>cognitive control</i> processes that gate information and select among competing representations and processes [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1006234#pcbi.1006234.ref014" target="_blank">14</a>]. Functional brain networks that flexibly coordinate interactions between different sets of brain regions over time may be a key substrate for cognitive control, and moreover be essential for maintaining homeostasis between internally-driven brain dynamics and externally-elicited behavioral goals [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1006234#pcbi.1006234.ref008" target="_blank">8</a>]. We present here a conceptualized diagram of the graph theoretical framework that helps us model the dynamics of cognitive control networks. Brain regions are represented as <i>nodes</i> and the strength of functional interactions between brain regions are represented as <i>weighted edges</i>. (<i>B</i>) Recent advances in network neuroscience [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1006234#pcbi.1006234.ref015" target="_blank">15</a>] and machine learning [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1006234#pcbi.1006234.ref016" target="_blank">16</a>] enable us to cluster functional brain networks into composite subgraphs—cohesive sets of graph edges (<i>left</i>) from the observed network (<i>A</i>) that tend to co-vary in strength over time. The putative role of a subgraph in cognitive control is inferred by its relative level of weighted expression in the observed network at a specific task block during cognitive processing (<i>right</i>). To experimentally modulate cognitive demand, we recruit 28 healthy adult human participants to perform a response inhibition, Stroop task (<i>C</i>) and a task-switching, local-global feature perception task based on Navon figures (<i>D</i>). The Stroop task entails (i) a fixation condition consisting of a black crosshair at the center of the screen, (ii) a low demand condition consisting of a matched word-color pair, and (iii) a high demand, interference condition consisting of a mismatched word-color pair. Subjects are required to report the color of the presented word. The Navon task entails (i) a fixation condition consisting of a black crosshair at the center of the screen, (ii) a low demand condition consisting of only white or green Navon figures—local shapes embedded in a non-matching global shape, and (iii) a high demand condition consisting of Navon figures randomly alternating between white or green color. Subjects are required to report the local shape if the presented figure is white or to report the global shape if the presented figure is green. Differences in task condition are thought to invoke different levels of recruitment of cognitive control mechanisms. Participant reaction time on correct trials is used to measure performance, and the difference in performance between high and low cognitive control conditions is thought to represent the costs of cognitive control.</p>