ArticleNeuroImage2018
Improved estimation of subject-level functional connectivity using full and partial correlation with empirical Bayes shrinkage.
Article in NeuroImage, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 1 of them a synthesis that pooled it.
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Who cites it
21 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A decade of test-retest reliability of functional connectivity: A systematic review and meta-analysis.NeuroImage · 2019Pooled it
- Scalable geometric learning with correlation-based functional brain networks.Scientific reports · 2025Article
- Exploring Graph Theory Mechanisms of Fluid Intelligence in the DLPFC: Insights From Resting-State fNIRS Across Various Time Windows.Brain and behavior · 2025Article
- Regularized partial correlation provides reliable functional connectivity estimates while correcting for widespread confounding.Imaging neuroscience (Cambridge, Mass.) · 2025Article
- Partial correlation as a tool for mapping functional-structural correspondence in human brain connectivity.Network neuroscience (Cambridge, Mass.) · 2025Article
- Separating group- and individual-level brain signatures in the newborn functional connectome: A deep learning approach.NeuroImage · 2024Article
- Population-level task-evoked functional connectivity via Fourier analysis.Journal of the Royal Statistical Society. Series C, Applied statistics · 2024Article
- Stress-induced brain responses are associated with BMI in women.Communications biology · 2023Article
- The confound of hemodynamic response function variability in human resting-state functional MRI studies.Frontiers in neuroscience · 2023Article
- Single-index models with functional connectivity network predictors.Biostatistics (Oxford, England) · 2022Article
- Accounting for motion in resting-state fMRI: What part of the spectrum are we characterizing in autism spectrum disorder?NeuroImage · 2022Article
- Covariance shrinkage can assess and improve functional connectomes.NeuroImage · 2022Article
- Test-retest reliability of modular-relevant analysis in brain functional network.Frontiers in neuroscience · 2022Article
- The effect of general anesthesia on the test-retest reliability of resting-state fMRI metrics and optimization of scan length.Frontiers in neuroscience · 2022Article
- Identification of community structure-based brain states and transitions using functional MRI.NeuroImage · 2021Article
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- Individual-Specific Areal-Level Parcellations Improve Functional Connectivity Prediction of Behavior.Cerebral cortex (New York, N.Y. : 1991) · 2021Article
- A guide to the measurement and interpretation of fMRI test-retest reliability.Current opinion in behavioral sciences · 2021Article
- Principal regression for high dimensional covariance matrices.Electronic journal of statistics · 2021Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
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 (ICC
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.