Multi-task connectivity reveals flexible hubs for adaptive task control

Abstract

Extensive evidence suggests that the human ability to adaptively implement a wide variety of tasks is preferentially a result of the operation of a fronto-parietal brain network (FPN). We hypothesized that this network's adaptability is made possible by flexible hubs: brain regions that rapidly update their pattern of global functional connectivity according to task demands. Using recent advances in characterizing brain network organization and dynamics, we identified mechanisms consistent with the flexible hub theory. We found that the FPN's brain-wide functional connectivity pattern shifted more than those of other networks across a variety of task states and that these connectivity patterns could be used to identify the current task. Furthermore, these patterns were consistent across practiced and novel tasks, suggesting that reuse of flexible hub connectivity patterns facilitates adaptive (novel) task performance. Together, these findings support a central role for fronto-parietal flexible hubs in cognitive control and adaptive implementation of task demands.

Figure 1

Task-control flexible hubs are schematically illustrated as brain regions in the fronto-parietal control network (FPN) that exhibit a) global variable connectivity and b) compositional coding. These mechanisms may explain how the FPN contributes to a wide variety of tasks. Global variable connectivity is depicted by the shifting connectivity pattern (red lines connecting FPN to other brain networks) across multiple networks across the two example tasks. Compositional coding (enabling task skill transfer) is depicted by the reuse of a subset of the red connectivity pattern corresponding to the reuse of the ‘press left button’ task component. These mechanisms would likely allow the FPN to meaningfully contribute to a wide variety of task contexts by allowing rapid reconfiguration of information flow across multiple task-relevant networks via reuse of previously learned sets of connectivity patterns.

Figure 2

The permuted rule operations behavioral paradigm – in combination with recent advances in task-state connectivity methods – allows detection of flexible connectivity across a wide variety of task states. The paradigm was designed to efficiently visit a variety of task states (60 novel & 4 practiced previously per subject) while controlling for extraneous factors across those task states (e.g., input and output modalities, task timing, stimuli). Tasks were defined as unique combinations of rules, such that the same stimuli would elicit a distinct set of cognitive operations across distinct tasks. Twelve rules were included across three qualitatively distinct domains, allowing for a well-controlled sampling of a moderately sized space of possible task states spanning multiple cognitive (logical decision rules), sensory (sensory semantic rules), and motor (motor response rules) processes. Participants were over 90% accurate for both novel and practiced tasks.

Figure 3

Graph theoretical brain network partition and context-dependent functional connectivity estimation. (a) Network partition of 264 putative functional regions from Power et al.. The 10 major networks (node communities) are labeled on the right. (b) The linear regression model equation (called generalized psycho-physiological interaction analysis; gPPI) used to estimate context-dependent functional connectivity (between each pair of the 264 regions), while controlling for mean activation and context-independent functional connectivity. S is the ‘seed’ region’s time series and T is a given task’s timing (convolved with a hemodynamic response function). S×bin(T) is the seed time series multiplied by the binary version of a given task’s timing (all values above 0 set to 1), which results in the simple linear regression fitting of one region’s time series to another during each task context. Similar to the standard definition used for resting-state functional connectivity MRI, ‘functional connectivity’ is defined here as the linear association between two brain regions’ neural activity time series (likely reflecting direct or indirect communication), measured indirectly here using blood oxygen level dependent (BOLD) fMRI. See Online Methods for details.

Figure 4

Global variability coefficient (GVC) – a measure of global variable connectivity – is highest for the FPN. (a) We developed a measure to identify the highly global and flexible functional connectivity predicted by the flexible hub theory. A distribution of functional connection strengths across 64 task states was estimated for each region-to-region connection, and the variability (standard deviation) was then averaged across all of a given region’s connections. The distributions of an FPN region’s (LPFC region in Supplementary Fig. 1; Talairach coords: −45, 7, 24) connectivity with a visual network region (left) and a motor network region (right) for a single subject are shown as representative examples. The histograms summarize the spread of the 64 functional connectivity estimates in terms of connection strength (x-axis = 7 bins of connection strength, y-axis = count of task states with a given connection strength). (b) Each network’s GVC (mean of each network’s regions’ variable connectivity with all 264 regions). The error bars illustrate the inter-subject standard errors. The FPN had significantly (p<.05, FDR corrected) higher GVC than all other networks. Code for computing GVC is available at http://www.mwcole.net/cole-etal-2013/.

Figure 5

FPN’s variable connectivity is truly global. To rule out the possibility that FPN’s high GVC was driven by high variability of a small subset of connections: (a) Across-network variable connectivity (participation coefficient) was estimated for each network. Like GVC, FPN’s participation coefficient of variable connectivity was highest (p<.05, FDR corrected) across the 10 networks (the results for the top 2% variable connectivity are shown; see Supplementary Table 5 for results across all thresholds). This suggests FPN’s variable connectivity is truly global. The error bars illustrate the inter-subject standard errors. (b) Pairwise mean variable connectivity between networks was examined. Variable connections are highlighted that are significantly (p<.05, FDR corrected) greater for one of the networks included in a given link than the GVC of the other network included in that link. For example, the FPN-DMN link was highlighted because FPN’s mean variable connectivity with the DMN was significantly greater than the DMN’s mean variable connectivity with the entire brain (i.e., its GVC). Note that variable connectivity from the Pearson correlation analysis (see Supplementary Table 3) was used for illustration given that this method provides connectivity estimates that are identical in both directions (i.e., to/from seed and target regions). The three lines in the legend are the minimum, median, and maximum variable connectivity strengths. The FPN’s variable connectivity with each network was significantly greater than every network’s GVC (i.e., the mean over each network’s connections with all 264 regions), providing confirmation that this is truly a global effect.

Figure 6

The FPN’s connectivity varies systematically, suggesting the previous results were not simply due to network noise properties and that FPN connectivity likely represents task information. We assessed representational similarity between context-dependent connectivity patterns from FPN to the rest of the brain, revealing a relationship with the similarity between tasks.

Figure 7

Decoding of task identity from FPN’s context-dependent connectivity patterns. Using MVPA methods applied to functional connectivity, three separate 4-way classifiers (logic, sensory, and motor) were trained with FPN connectivity patterns during novel tasks and tested with FPN connectivity from practiced tasks. All three classifiers were above chance. These classifiers were then combined to decode each of the 64 tasks (e.g., SAME + GREEN + L.INDEX). Classification was again above chance (p<.05) – a notable result giving the tremendously challenging nature of the 64-way classification (chance accuracy is 1/64 = 1.56%). The results support the second mechanism of the flexible hub theory – compositional connectivity – by suggesting that FPN connectivity patterns are transferred across practiced and novel task contexts.