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Multi-site studies face variability challenges. This study introduces a framework using principal component analysis to enhance the reproducibility and identifiability of functional connectome fingerprints across sites.

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Area of Science:

  • Neuroscience
  • Medical Imaging
  • Biostatistics

Background:

  • Multi-site studies are crucial for increasing statistical power and generalizability in neuroimaging research.
  • Site-dependent variability in imaging data can obscure the benefits of multi-site collaborations.
  • Individual functional connectivity fingerprints show promise as neuroimaging biomarkers for single-subject inferences.

Purpose of the Study:

  • To assess multi-site reproducibility of resting-state functional connectivity fingerprints.
  • To improve the identifiability of functional connectomes in multi-site settings.
  • To develop a generalized framework for enhancing individual connectome fingerprinting.

Main Methods:

  • Evaluation of individual fingerprints on two independent multi-site datasets using test-retest pairs within and across sites.
  • Development of a generalized framework utilizing principal component analysis (PCA) for improved identifiability.
  • Reconstruction of functional connectomes using PCA-derived orthogonal connectivity bases to maximize differential identifiability.

Main Results:

  • Optimally reconstructed functional connectomes demonstrated substantial improvements in individual fingerprinting across sites and visits.
  • Increased intraclass correlation coefficient (ICC) values were observed for functional edges and resting-state networks in reconstructed connectomes.
  • Improvements in identifiability were independent of global signal regression and were maximized after optimal reconstruction, even with varying fMRI volumes.

Conclusions:

  • The presented data-driven framework systematically enhances the identifiability of resting-state functional connectomes in multi-site studies.
  • The PCA-based approach offers a robust method to overcome site-dependent variability.
  • This framework has the potential to improve the reliability of neuroimaging biomarkers in large-scale collaborative research.