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)

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)

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)

Our new paper is out now in Neuron! 🎉 With @vaibhavtripathi.bsky.social @maxwellelliott.bsky.social Joanna Ladopoulou, Wendy Sun, Mark Eldaief, and Randy Buckner

Paper link: www.sciencedirect.com/science/arti...

Additionally, network estimates from task-regressed data predict functional response properties in independent contrasts similar to parallel analyses using traditional resting-state fixation data. (5/6)

We demonstrated that networks can be estimated robustly within individuals using solely task-regressed data. The idiosyncratic spatial details varied between individuals but were largely preserved within each individual across datasets under independent acquisition conditions. (4/6)

Our findings indicate that functional correlation matrices derived from task data are highly similar to those derived from traditional resting-state acquisitions. The largest factor affecting similarity between correlation matrices was the amount of data. (3/6)

Additionally, network estimates from task-regressed data predict functional response properties in independent contrasts similar to parallel analyses using traditional resting-state fixation data. (5/6)

We demonstrated that networks can be estimated robustly within individuals using solely task-regressed data. The idiosyncratic spatial details varied between individuals but were largely preserved within each individual across datasets under independent acquisition conditions. (4/6)