  Connectivity studies using resting-state functional magnetic resonance
imaging are increasingly pooling data acquired at multiple sites. While this
may allow investigators to speed up recruitment or increase sample size,
multisite studies also potentially introduce systematic biases in connectivity
measures across sites. In this work, we measure the inter-site effect in
connectivity and its impact on our ability to detect individual and group
differences. Our study was based on real, as opposed to simulated, multisite
fMRI datasets collected in N=345 young, healthy subjects across 8 scanning
sites with 3T scanners and heterogeneous scanning protocols, drawn from the
1000 functional connectome project. We first empirically show that typical
functional networks were reliably found at the group level in all sites, and
that the amplitude of the inter-site effects was small to moderate, with a
Cohen's effect size below 0.5 on average across brain connections. We then
implemented a series of Monte-Carlo simulations, based on real data, to
evaluate the impact of the multisite effects on detection power in statistical
tests comparing two groups (with and without the effect) using a general linear
model, as well as on the prediction of group labels with a support-vector
machine. As a reference, we also implemented the same simulations with fMRI
data collected at a single site using an identical sample size. Simulations
revealed that using data from heterogeneous sites only slightly decreased our
ability to detect changes compared to a monosite study with the GLM, and had a
greater impact on prediction accuracy. Taken together, our results support the
feasibility of multisite studies in rs-fMRI provided the sample size is large
enough.
