A systematic investigation of the invariance of resting-state network patterns: is resting-state fMRI ready for pre-surgical planning?

Objectives: Measurements of resting-state networks (RSN) have been used to investigate a wide range of diseases, such as dementia or epilepsy. This raises the question whether this method could also serve as a pre-surgical planning tool. Generating reliable functional connectivity patterns is of crucial importance, particularly for pre-surgical planning, as these patterns may directly affect the outcome.Methods: This study investigated the reproducibility of four commonly used resting-state conditions: fixation on a black crosshair on a white screen; fixation on the center of a black screen; eyes closed and fixation on the words Entspann dich! (Engl., relax). Ten healthy, right-handed male subjects (mean age, 25 years; SD 2) participated in the experiment. The spatial overlap for different RSNs across the four conditions was calculated.Results: The spatial overlap across all four conditions was calculated for each seed region on a single subject and at the group level. Activation maps at the single-subject and group levels were highly stable, especially for the reading network. The lowest consistency measures were found for the visual network. At the single-subject level spatial overlap values ranged from 0.31 (visual network) to 0.45 (reading network).Conclusion: These findings suggest that the RSN pattern is a reliable tool with which to assess language-related networks in clinical settings. Generally, resting-state conditions showed comparable activation patterns, therefore no specific conditions appears to be preferable.