Multivariate consistency of resting-state fMRI connectivity maps acquired on a single individual over 2.5 years, 13 sites and 3 vendors

Studies using resting-state functional magnetic resonance imaging (rsfMRI) are increasingly collecting data at multiple sites in order to speed up recruitment or increase sample size. Multisite studies potentially introduce systematic biases in connectivity measures across sites, which may negatively impact the detection of clinical effects. Long-term multisite biases (i.e. over several years) are still poorly understood. The main objective of this study was to assess the long-term consistency of rsfMRI multisite connectivity measures derived from the harmonized Canadian Dementia Imaging Protocol (CDIP, www.cdip-pcid.ca). Nine to ten minutes of functional BOLD images were acquired from an adult cognitively healthy volunteer scanned repeatedly at 13 Canadian sites on three scanner makes (General Electric, Philips and Siemens) over the course of 2.5 years. RsfMRI connectivity maps were extracted for each session in seven canonical functional networks. The reliability (spatial Pearson’s correlation) of maps was about 0.6, with moderate effects (up to 0.2) of scanner makes and sites. The time elapsed between scans had a negligible effect on the consistency of connectivity maps. To assess the utility of such measures in machine learning models, we pooled the long-term longitudinal data with a single-site, short-term (1 month) data sample acquired on 26 subjects (10 scans per subject), called HNU1. Using randomly selected pairs of scans from each subject, we quantified the ability of a data-driven unsupervised cluster analysis to match the two scans. In this “fingerprinting” experiment, we found that scans from the Canadian subject could be matched with high accuracy (>85% for some networks), and fell in the range of accuracies observed for the HNU1 subjects. Overall, these results support the feasibility of multivariate, machine learning analysis of rsfMRI measures in a multisite study that extends for several years, even with fairly short (approximately ten minutes) time series.