Predicting functional networks from region connectivity profiles in task-based versus resting-state fMRI data

Abstract

Intrinsic Connectivity Networks, patterns of correlated activity emerging from "resting-state" Blood Oxygenation Level Dependent time series, are increasingly being associated to cognitive, clinical, and behavioral aspects, and compared with the pattern of activity elicited by specific tasks. In this study we use a large cohort of publicly available data to test to which extent one can associate a brain region to one of these Intrinsic Connectivity Networks looking only at its connectivity pattern, and examine at how the correspondence between resting and task-based patterns can be mapped in this context. By trying a battery of different supervised classifiers relying only on task-based measurements, we show that the highest accuracy is reached with a simple neural network of one hidden layer. In addition, when testing the fitted model on resting state measurements, such architecture yields a performance close to 90% for areas connected to the task performed, which mainly involve the visual and sensorimotor cortex. This clearly confirms the correspondence of Intrinsic Connectivity Networks in both paradigms and opens a window for future clinical applications to subjects whose participation in a required task cannot be guaranteed.