Within-individual precision mapping of brain networks exclusively using task data
Jingnan Du, Vaibhav Tripathi, Maxwell L. Elliott, Joanna Ladopoulou, Wendy Sun, Mark C. Eldaief, Randy L. Buckner
Abstract
Precision mapping of brain networks within individuals prevailingly relies on functional connectivity analysis of resting-state data. Here, we explored whether networks can be estimated using only task data. Correlation matrices estimated from task data were similar to those derived from resting-state data. The largest factor affecting similarity was the amount of data. Precision networks estimated from task data showed strong spatial overlap with those derived from resting-state data and predicted the same triple functional dissociation in independent data. To illustrate novel possibilities enabled by the present methods, we mapped the detailed organization of thalamic association zones within individuals by pooling extensive resting-state and task data. We also demonstrated how task data can be used to estimate networks while simultaneously extracting task responses. Broadly, these findings suggest that there is an underlying, stable network architecture that is idiosyncratic to the individual and persists across task states.