Quantifying differences between passive and task-evoked intrinsic functional connectivity in a large-scale brain simulation

Establishing a connection between intrinsic and task-evoked brain activity is critical because it would provide a way to map task-related brain regions in patients unable to comply with such tasks. A crucial question within this realm is to what extent the execution of a cognitive task affects the intrinsic activity of brain regions not involved in the task. Computational models can be useful to answer this question because they allow us to distinguish task from non-task neural elements while giving us the effects of task execution on non-task regions of interest at the neuroimaging level. The quantification of those effects in a computational model would represent a step towards elucidating the intrinsic versus task-evoked connection. Here we used computational modeling and graph theoretical metrics to quantify changes in intrinsic functional brain connectivity due to task execution. We used our Large-Scale Neural Modeling framework to embed a computational model of visual short-term memory into an empirically derived connectome. We simulated a neuroimaging study consisting of ten subjects performing passive fixation (PF), passive viewing (PV) and delay match-to-sample (DMS) tasks. We used the simulated BOLD fMRI time-series to calculate functional connectivity (FC) matrices and used those matrices to compute several graph theoretical measures. After determining that the simulated graph theoretical measures were largely consistent with experiments, we were able to quantify the differences between the graph metrics of the PF condition and those of the PV and DMS conditions. Thus, we show that we can use graph theoretical methods applied to simulated brain networks to aid in the quantification of changes in intrinsic brain functional connectivity during task execution. Our results represent a step towards establishing a connection between intrinsic and task-related brain activity. Author Summary Studies of resting-state conditions are popular in neuroimaging. Participants in resting-state studies are instructed to fixate on a neutral image or to close their eyes. This type of study has advantages over traditional task-based studies, including its ability to allow participation of those with difficulties performing tasks. Further, a resting-state neuroimaging study reveals intrinsic activity of participants’ brains. However, task-related brain activity may change this intrinsic activity, much as a stone thrown in a lake causes ripples on the water’s surface. Can we measure those activity changes? To answer that question, we merged a computational model of visual short-term memory (task regions) with an anatomical model incorporating major connections between brain regions (non-task regions). In a computational model, unlike real data, we know how different regions are connected and which regions are doing the task. First, we simulated neuronal and neuroimaging activity of both task and non-task regions during three conditions: passive fixation (baseline), passive viewing, and visual short-term memory. Then, applying graph theory to the simulated neuroimaging of non-task regions, we computed differences between the baseline and the other conditions. Our results show that we can measure changes in non-task regions due to brain activity changes in task-related regions.