期刊
NEUROIMAGE
卷 172, 期 -, 页码 478-491出版社
ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.neuroimage.2018.01.029
关键词
Functional connectivity; Connectome; Partial correlation; Reliability; Bayesian statistics; Shrinkage; Measurement error; Resting-state fMRI
资金
- Human Connectome Project, WU-Minn Consortium [1U54MH091657]
- McDonnell Center for Systems Neuroscience at Washington University
- NIH from the National Institute of Biomedical Imaging and Bioengineering [R01 EB016061, R01 EB012547, P41 EB015909]
- NIH from National Institute of Mental Health [R01 MH095836, K01 MH109766]
- Craig H. Neilsen Foundation [338419]
- NATIONAL INSTITUTE OF BIOMEDICAL IMAGING AND BIOENGINEERING [R01EB016061, R01EB012547, P41EB015909] Funding Source: NIH RePORTER
- NATIONAL INSTITUTE OF MENTAL HEALTH [R01MH095836, U54MH091657, K01MH109766] Funding Source: NIH RePORTER
- NATIONAL INSTITUTE ON AGING [P50AG005146] Funding Source: NIH RePORTER
Reliability of subject-level resting-state functional connectivity (FC) is determined in part by the statistical techniques employed in its estimation. Methods that pool information across subjects to inform estimation of subject-level effects (e.g., Bayesian approaches) have been shown to enhance reliability of subject-level FC. However, fully Bayesian approaches are computationally demanding, while empirical Bayesian approaches typically rely on using repeated measures to estimate the variance components in the model. Here, we avoid the need for repeated measures by proposing a novel measurement error model for FC describing the different sources of variance and error, which we use to perform empirical Bayes shrinkage of subject-level FC towards the group average. In addition, since the traditional intra-class correlation coefficient (ICC) is inappropriate for biased estimates, we propose a new reliability measure denoted the mean squared error intra-class correlation coefficient (ICCMSE) to properly assess the reliability of the resulting (biased) estimates. We apply the proposed techniques to test-retest resting-state fMRI data on 461 subjects from the Human Connectome Project to estimate connectivity between 100 regions identified through independent components analysis (ICA). We consider both correlation and partial correlation as the measure of FC and assess the benefit of shrinkage for each measure, as well as the effects of scan duration. We find that shrinkage estimates of subject-level FC exhibit substantially greater reliability than traditional estimates across various scan durations, even for the most reliable connections and regardless of connectivity measure. Additionally, we find partial correlation reliability to be highly sensitive to the choice of penalty term, and to be generally worse than that of full correlations except for certain connections and a narrow range of penalty values. This suggests that the penalty needs to be chosen carefully when using partial correlations.
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