Uncovering Multi-Site Identifiability Based on Resting-State Functional Connectomes
Abstract
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.
- Publication:
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arXiv e-prints
- Pub Date:
- September 2018
- DOI:
- 10.48550/arXiv.1809.08959
- arXiv:
- arXiv:1809.08959
- Bibcode:
- 2018arXiv180908959B
- Keywords:
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- Quantitative Biology - Neurons and Cognition
- E-Print:
- 28 pages, 11 figures in main text, 5 figures in supplementary