Using fingerprinting as a testbed for strategies to improve reproducibility of functional connectivity

Abstract

Reproducible functional connectivity-based biomarkers have remained elusive despite the promise of deeply phenotyped consortia. An important component of reproducibility is reliability over repeated measures, often measured by the intraclass correlation coefficient (ICC). Here, we test the use of functional connectome fingerprinting as a way to select pre- and post-processing parameters. We hypothesized that whichever parameters yielded the best fingerprint accuracies would also improve the ICC across scans. Using five datasets from the Consortium for Replicability and Reproducibility, we found that higher identification accuracies were achieved when using: (I) global signal regression; (II) finer brain parcellations; (III) cortical regions compared to subcortical and cerebellar structures; (IV) medial frontal and frontoparietal networks relative to the whole-brain; (V) discriminative edges; (VI) longer scan duration; and (VII) lower sample size. We observed that the ICC was consistently "poor" across the five datasets even with the application of two optimal fingerprint-informed pipelines. The fingerprint-informed pipelines may enable comparison, benchmarking, and adjudication of functional connectivity-based analysis pipelines or novel analytic approaches, as a means to enhance their reproducibility in heterogeneous populations. Key PointsO_LIConnectome-based fingerprinting can provide a useful testbed for reproducible functional connectivity analysis pipelines. C_LIO_LIFingerprint-informed pipelines offer an intuitive and less resource intensive way to select data pre-/post-processing parameters for improving the reproducibility of the functional connectome. C_LIO_LIConnectome-based fingerprinting offers an alternative approach to test-retest reliability. C_LI