Uncovering Multi-Site Identifiability Based on Resting-State Functional Connectomes

Multi-site studies are becoming important to increase statistical power, enhance generalizability, and to improve the likelihood of pooling relevant subgroups together activities. Even with harmonized imaging sequences, site-dependent variability can mask the advantages of these multi-site studies. The aim of this study was to assess multi-site reproducibility in resting-state functional connectivity fingerprints, and to improve identifiability of functional connectomes. The individual fingerprinting of functional connectivity profiles is promising due to its potential as a robust neuroimaging biomarker. We evaluated, on two independent multi-site datasets, individual fingerprints in test-retest visit pairs within and across two sites and present a generalized framework based on principal component analysis to improve identifiability. Those components that maximized differential identifiability of a training dataset were used as an orthogonal connectivity basis to reconstruct the functional connectomes of training and validation sets. The optimally reconstructed functional connectomes showed a substantial improvement in individual fingerprinting within and across the two sites relative to the original data. A notable increase in ICC values for functional edges and resting-state networks was also observed. Improvements in identifiability were not found to be affected by global signal regression. Post-hoc analyses assessed the effect of the number of fMRI volumes on identifiability and showed that multi-site differential identifiability was for all cases maximized after optimal reconstruction. The generalizability of the optimal set of orthogonal basis of each dataset was evaluated through a leave-one-out procedure. Overall, results demonstrate that the framework presented in this study systematically improves identifiability in resting-state functional connectomes in multi-site studies.