Intrinsic and task-evoked network architectures of the human brain

Abstract

Many functional network properties of the human brain have been identified during rest and task states, yet it remains unclear how the two relate. We identified a whole-brain network architecture present across dozens of task states that was highly similar to the resting-state network architecture. The most frequent functional connectivity strengths across tasks closely matched the strengths observed at rest, suggesting this is an "intrinsic," standard architecture of functional brain organization. Furthermore, a set of small but consistent changes common across tasks suggests the existence of a task-general network architecture distinguishing task states from rest. These results indicate the brain's functional network architecture during task performance is shaped primarily by an intrinsic network architecture that is also present during rest, and secondarily by evoked task-general and task-specific network changes. This establishes a strong relationship between resting-state functional connectivity and task-evoked functional connectivity-areas of neuroscientific inquiry typically considered separately.

Figure 1. Testing multiple tasks per subject

A, The first fMRI dataset involved 64 distinct tasks, composed of unique combinations of task rules (Cole et al., 2010). Each subject (N=15) performed all 64 tasks. B, The second dataset involved 7 tasks chosen to elicit the involvement of all major cognitive domains and brain systems (Barch et al., 2013). Each subject (N=118) performed all 7 tasks.

Figure 2. Multislice community detection reveals a network architecture across tasks similar to an independently-identified resting-state network architecture

A, Multislice community detection identifies clusters of highly connected nodes, either separately (low coupling parameter) or jointly (high coupling parameter) across multiple states. Adapted from Mucha et al. (2010). B, The community partition identified by Power, et al. (2011) using independent resting-state data, color-coded by community assignment. C, Similarity of each task partition to the resting state partition reported in Power, et al. (2011). When the coupling parameter is low, changes in community structure across tasks are readily apparent, indicating evoked FC changes. In contrast, as the coupling parameter increases, a consensus partition is identified that is highly similar to an independently identified resting-state FC partition (Power et al., 2011), suggesting the presence of an intrinsic network architecture across tasks. Error bars indicate standard errors across subjects. D, Similar results in the 7-task dataset.

Figure 3. Multi-task architecture is highly similar to the resting-state architecture, reflecting the existence of an intrinsic network organization

A, Group-averaged multi-task and resting-state functional connectivity matrices, with brain regions ordered according to putative functional systems (coded by color bands along the matrix edges) previously identified from resting-state data (Power et al., 2011). Strong intra-module FC demonstrates community structure consistent with functional systems. Multi-task FC (left) reflects the central tendency of inter-regional correlations across tasks, while resting-state FC (right) reflects inter-regional correlations in spontaneous activity. The high similarity between these two matrices (Pearson correlation coefficient r=0.90, p<0.00001) suggests that multi-task and resting-state FC both reflect an intrinsic functional network architecture. B, A standard community detection approach (Blondel et al., 2008) was used to partition multi-task and resting-state FC into putative functional brain systems. The partitions were similar to the independently defined resting-state FC partition (Figure 2B): z=94 for resting-state, z=79 for multi-task.

Figure 4. Multi-task intrinsic FC is also highly similar to resting-state FC in the 7-task dataset

The multi-task and resting-state FC comparison analysis was repeated with the 7-task dataset, with identical conclusions as with the 64-task dataset. Note the high similarity not only between these two matrices, but also their similarity to the matrices from the 64-task dataset (Figure 3A).

Figure 5. Multi-task modal FC matrices

The modal FC values across tasks are visualized for both datasets. This consisted of identifying the most frequently occurring value across all task states for each functional connection.

Figure 6. Comparison of intrinsic FC with individual task FC

Each of the 7-task dataset task FC matrices is visualized along with the multi-task and resting-state FC matrices. Note that the Pearson correlation coefficients (r) for comparisons with the multi-task FC matrix were based on 6 tasks: the to-be-compared task was removed from the multi-task estimates to remove circularity. These results illustrate the presence of intrinsic FC (a similar FC pattern across all tasks), along with evoked FC changes across tasks.

Figure 7. A single principal component accounts for most inter-task network architecture variance

A, The first principal component across the 7 task FC matrices accounted for 85% of the inter-task FC matrix variance. B, The first principal component (from panel A) was highly similar to the resting-state FC matrix (r=0.90). C, Another principal component analysis additionally included the resting-state FC matrix (for a total of 8 FC matrices). The first principal component (again accounting for 85% of variance) loaded most heavily on the resting-state FC matrix, suggesting this component is most related to the network architecture at rest (though it was also related to all individual task FC architectures).

Figure 8. Task-evoked FC changes from rest reveal a task-general dynamic network architecture

A, Each task’s whole-brain FC matrix was compared to the resting-state FC matrix (from Figure 4). The task order is the same as in Figure 1B. B, All task FC changes from rest are plotted (across all seven tasks) versus their resting-state FC values. Significant changes from rest are black, while non-significant changes are grey. Most of the connections (61%) were non-significant. The correlation between task FC changes and rest FC was negative for all seven tasks (mean r=−0.49). C, The count of how many tasks involved significant changes from rest plotted for each connection. Many connections changed for all seven tasks (11% of changed connections). D, Differences between the multi-task FC matrix and resting-state FC matrix (left vs. right sides of Figure 4), summarizing general changes from rest that are common across tasks. E, The same analysis for the 64-task dataset, on the same scale as panel D. The matrices in D and E were relatively similar (despite major differences between datasets): r=0.31, p<0.00001.