Connectivity mapping based on resting-state fMRI is rapidly developing, and this methodology has great potential for clinical applications. However, before resting-state fMRI can be applied for diagnosis, prognosis, and monitoring treatment for an individual patient with neurologic or psychiatric diseases, it is essential to assess its long-term reproducibility and between-subject variations among healthy individuals. The purpose of the study was to quantify the long-term test-retest reproducibility of ICN measures derived from resting-state fMRI and to assess the between-subject variation of ICN measures across the whole brain. Longitudinal resting-state fMRI data of 6 healthy volunteers were acquired from 9 scan sessions during >1 year. The within-subject reproducibility and between-subject variation of ICN measures, across the whole brain and major nodes of the DMN, were quantified with the ICC and COV. Our data show that the long-term test-retest reproducibility of ICN measures is outstanding, with >70% of the connectivity networks showing an ICC > 0.60. The COV across 6 healthy volunteers in this sample was >0.2, suggesting significant between-subject variation. Our data indicate that resting-state ICN measures (eg, the correlation coefficients between fMRI signal-intensity profiles from 2 different brain regions) are potentially suitable as biomarkers for monitoring disease progression and treatment effects in clinical trials and individual patients. Because between-subject variation is significant, it may be difficult to use quantitative ICN measures in their current state as a diagnostic tool.
