[PREPRINT❗️❗️] Very excited to share our latest work on precision mapping of brain networks exclusively using task data!🧠 With @maxwellelliott.bsky.social Joanna Ladopoulou, Mark Eldaief, and Randy Buckner www.biorxiv.org/content/10.1... (1/6) - ThreadSky

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jingnandu.bsky.social • 5 days ago

[PREPRINT❗️❗️] Very excited to share our latest work on precision mapping of brain networks exclusively using task data!🧠
With @maxwellelliott.bsky.social Joanna Ladopoulou, Mark Eldaief, and Randy Buckner
https://www.biorxiv.org/content/10.1101/2025.02.25.640090v1
(1/6)

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jingnandu.bsky.social•5 days ago

Precision mapping of brain networks has become a widely used tool that prevailingly relies on functional connectivity analysis of resting-state data. Here we explored whether networks could be precisely estimated solely using data acquired during active task paradigms. (2/6)

jingnandu.bsky.social•5 days ago

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)

jingnandu.bsky.social•5 days ago

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)

jingnandu.bsky.social•5 days ago

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)

jingnandu.bsky.social•5 days ago

Our results suggest that there is an underlying, stable network architecture that is idiosyncratic to the individual and persists across task states. For further discussion on the implications for task design and analysis, read the full preprint! (6/6)

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