Task‐related effective connectivity reveals that the cortical rich club gates cortex‐wide communication

Abstract

Higher cognition may require the globally coordinated integration of specialized brain regions into functional networks. A collection of structural cortical hubs-referred to as the rich club-has been hypothesized to support task-specific functional integration. In the present paper, we use a whole-cortex model to estimate directed interactions between 68 cortical regions from functional magnetic resonance imaging activity for four different tasks (reflecting different cognitive domains) and resting state. We analyze the state-dependent input and output effective connectivity (EC) of the structural rich club and relate these to whole-cortex dynamics and network reconfigurations. We find that the cortical rich club exhibits an increase in outgoing EC during task performance as compared with rest while incoming connectivity remains constant. Increased outgoing connectivity targets a sparse set of peripheral regions with specific regions strongly overlapping between tasks. At the same time, community detection analyses reveal massive reorganizations of interactions among peripheral regions, including those serving as target of increased rich club output. This suggests that while peripheral regions may play a role in several tasks, their concrete interplay might nonetheless be task-specific. Furthermore, we observe that whole-cortex dynamics are faster during task as compared with rest. The decoupling effects usually accompanying faster dynamics appear to be counteracted by the increased rich club outgoing EC. Together our findings speak to a gating mechanism of the rich club that supports fast-paced information exchange among relevant peripheral regions in a task-specific and goal-directed fashion, while constantly listening to the whole network.

Network analysis of whole-brain fMRI dynamics: A new framework based on dynamic communicability

“…This relates to the propagating nature of fMRI signals, which is related to the asymmetry of estimated MOU-EC matrices [18] and is in line with previous results of the fMRI lag structure [63]. Compared to our previous analyses that focused on individual MOU-EC connections [36,64], communicability provides a description of the ROI role over time. ROIs usually classified as hubs according to their high degree in SC (in black in Fig.…”

“…For instance, we have 495 found that anatomical hub ROIs -precuneus, superior parietal and superior frontal cortex [7,1]-are listeners that globally integrate information from other ROIs in the resting state, but without broadcasting much. During visual and memory tasks, they can become selective broadcasters to specific target ROIs [64]. Previous studies have shown that the precuneus also acts as a selective integrator during memory and cognitive flexibility tasks [65,72].…”

Neural mechanisms of vibrotactile categorization

“…The model of EC applied here has been shown to reveal interesting properties of fMRI resting state dynamics that are not captured by temporal correlations of activity alone (Gilson et al, 2016; Glomb, Ponce-Alvarez, Gilson, Ritter, & Deco, 2017;Rolls, Cheng, Gilson, Qiu, & Psychiatry, 2018). Recently, it has also been applied to task-based fMRI Gravel et al, 2017;Senden et al, 2017). EC takes into account how the cortical network dynamics reflect task-related processing because EC depends both on changes in response to stimulus processing and the (task-dependent) network configuration .…”

Comparing Task-Relevant Information Across Different Methods of Extracting Functional Connectivity

“…Modelling of the oscillatory behaviour in brain regions has, for instance, shown that there are differences in this local parameter across task-dependent brain states (Senden, Reuter, van den Heuvel, Goebel, & Deco, 2017). On the other hand, models estimating directed connectivity based on the functional interactions between brain regions have also revealed differences in network parameters across task-dependent brain states (Pallares et al, 2018;Senden et al, 2018Senden et al, ). et al, 2013Preti, Bolton, & Van De Ville, 2017).…”

“…At the opposite end of the spectrum of timescales, FC can be obtained instantaneously with phase coherence (Cabral, Vidaurre, et al, 2017;Senden et al, 2017). Evidently, there is a multitude of studies using various FC metrics to investigate brain states during different tasks (Cabral, Vidaurre, et al, 2017;Senden et al, 2018Senden et al, , 2017. However, the interchangeable use of FC metrics rests on the assumption that the results are comparable across metrics.…”