Resting-state functional magnetic resonance imaging (rs-fMRI) has an inherently low signal-to-noise ratio largely due to thermal and physiological noise that attenuates the functional connectivity (FC) estimates. Such attenuation limits the reliability of FC and may bias its association with other traits. Low reliability also limits heritability estimates. Classical test theory can be used to obtain a true correlation estimate free of random measurement error from parallel tests, such as split-half sessions of a rs-fMRI scan. We applied a measurement model to split-half FC estimates from the resting-state fMRI data of 1003 participants from the Human Connectome Project (HCP) to examine the benefit of reliability modelling of FC in association with traits from various domains. We evaluated the efficiency of the measurement model on extracting a stable and reliable component of FC and its association with several traits for various sample sizes and scan durations. In addition, we aimed to replicate our previous findings of increased heritability estimates when using a measurement model in a longitudinal adolescent twin cohort. The split-half measurement model improved test-retest reliability of FC on average with +0.33 points (from +0.49 to +0.82), improved strength of associations between FC and various traits on average 1.2-fold (range 1.09–1.35), and increased heritability estimates on average with +20% points (from 39% to 59%) for the full HCP dataset. On average, about half of the variance in split-session FC estimates was attributed to the stable and reliable component of FC. Shorter scan durations showed greater benefit of reliability modelling (up to 1.6-fold improvement), with an additional gain for smaller sample sizes (up to 1.8-fold improvement). Reliability modelling of FC based on a split-half using a measurement model can benefit genetic and behavioral studies by extracting a stable and reliable component of FC that is free from random measurement error and improves genetic and behavioral associations.
Bibliographical noteFunding Information:
This work was supported by the Gravitation program of the Dutch Ministry of Education, culture, and Science and the Netherlands Organization for Scientific Research ( https://www.nwo.nl/en ); Consortium on Individual Development (CID; https://individualdevelopment.nl ); Biobanking and BioMolecular resources Research Infrastructure The Netherlands (BBMRI-NL2.0; https://www.bbmri.nl ); NWO grant number 024.001.003 subproject to H.H., and NWO 51.02.061 to H.H., NWO 51.02.062 to D.B., and NWO-NIHC Programs of excellence 433-09-220 to H.H., and NWO-MagW 480-04-004 to D.B., and NWO/SPI 56-464-14192 to D.B., and NWO 184.033.111 subproject to H.H.; the European Research Council ( https://erc.europa.eu ; ERC-230374 to D.B.); and Utrecht University ( https://www.uu.nl/en ; High Potential Grant to H.H.). Data were provided [in part] by the Human Connectome Project, WU-Minn Consortium (Principal Investigators: David Van Essen and Kamil Ugurbil; 1U54MH091657) funded by the 16 NIH Institutes and Centers that support the NIH Blueprint for Neuroscience Research; and by the McDonnell Centre for Systems Neuroscience at Washington University. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
© 2021 The Authors
Copyright 2021 Elsevier B.V., All rights reserved.
- Functional connectivity
- Human connectome project
- Measurement error
- Measurement model
- Reliability modelling
- Test-retest reliability