Thank you, Rocco!

Looking forward, future data collections might consider acquiring only task data and using that data to both estimate networks and also to quantify the evoked task response from regions within those networks. (13/13)

Overall, our findings suggest that there is an underlying, stable network architecture that is unique to the individual and persists across task states. For existing datasets with both task and resting-state data, power can be increased by combining all available data. (12/13)

A single MRI session of ∼1 h in length can thus be used both to estimate precision brain networks and quantify meaningful task responses. This strategy is particularly useful in translational studies seeking to minimize patient burden. (11/13)

To demonstrate this, we obtained both networks and network-level task response in a new participant during revision of this work, using only NBACK task data from a single ∼1 h session. There was a strong preferential response in FPN-A as compared with other networks. (10/13)

This finding suggests a novel and efficient strategy of using only task-based data to estimate networks within an individual’s own anatomy as well as estimate the task response within those networks. (9/13)

We further showed that between-individual differences in task responses can be obtained from network estimates derived from only task data, without acquiring separate resting-state data. (8/13)

Closely positioned seed regions in the thalamus recapitulate spatially distinct cortical networks. For instance, seeds in the small FPN-A thalamic subregion produced a correlation map closely matching the cortical FPN-A network boundaries. (7/13)

By pooling extensive resting-state and task data, we were able to triple the amount of data available for analysis within each individual, enabling precise mapping of five higher-order association networks within the thalamus. (6/13)

We then quantitatively demonstrated that pooling resting-state data with motor task data stabilizes the similarity of correlation matrices between test and retest datasets. This suggests that we can pool all resting-state and task data to increase statistical power. (5/13)

Furthermore, networks estimated solely from task data predicted functional specializations across multiple higher-order cognitive domains in independent task datasets just as well as traditional resting-state network estimates did. (4/13)

Direct comparisons of network estimates from both datasets reveal a convergent functional architecture of the brain. While the fine-grained spatial details of these networks varied across individuals, they were largely preserved within each individual. (3/13)

Using only task data, we derived a 15-network multi-session hierarchical Bayesian model (MS-HBM) estimate, and the results were remarkably similar to those derived from traditional resting-state data. (2/13)

Precision mapping of brain networks within individuals typically relies on functional connectivity from resting-state data. In this study, we asked: can task data generate precision network maps that are practical equivalents to those generated from resting-state data? (1/13)