ArticleImaging neuroscience (Cambridge, Mass.)2025
Regularized partial correlation provides reliable functional connectivity estimates while correcting for widespread confounding.
Article in Imaging neuroscience (Cambridge, Mass.), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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20 citing papers in PubMed.
- Brain network dynamics reflect psychiatric illness status and transdiagnostic symptom profiles across health and disease.Nature communications · 2026Article
- From topography to connectome: Towards an integrated understanding of the resting brain.bioRxiv : the preprint server for biology · 2026Article
- Using connectome-based predictive models to reveal the systems standardized tests and clinical symptoms are reflecting.Nature communications · 2026Article
- Benchmarking resting state fMRI connectivity pipelines for classification: robust accuracy despite processing variability in cross-site eye state prediction.Brain informatics · 2026Article
- Hierarchical and non-hierarchical network flows generate complementary representational dynamics in human visual cortex.bioRxiv : the preprint server for biology · 2026Article
- Distinct Resting-State Connectomes for Face and Scene Perception Predict Individual Task Performance.Human brain mapping · 2026Article
- Distributed cortical network dynamics of binocular convergent eye movements in humans.Network neuroscience (Cambridge, Mass.) · 2026Article
- The neuroimaging correlates of depression established across six large-scale population datasets.Nature. Mental health · 2026Article
- The motion sensitivity and predictive utility of different estimates of interregional functional coupling in resting-state functional MRI.Network neuroscience (Cambridge, Mass.) · 2026Article
- Dynamically shifting from compositional to conjunctive brain representations supports cognitive task learning.Nature communications · 2025Article
- Unraveling the interconnectedness of trauma-related common mental disorders in Somali refugees: a network analysis.BMC psychiatry · 2025Article
- Benchmarking methods for mapping functional connectivity in the brain.Nature methods · 2025Article
- Functional dysconnectivity of visual and somatomotor networks yields a simple and robust biomarker for psychosis.Molecular psychiatry · 2025Article
- Partial correlation as a tool for mapping functional-structural correspondence in human brain connectivity.Network neuroscience (Cambridge, Mass.) · 2025Article
- Extracting interpretable signatures of whole-brain dynamics through systematic comparison.PLoS computational biology · 2024Article
- Distributed network flows generate localized category selectivity in human visual cortex.PLoS computational biology · 2024Article
- Functional dysconnectivity of visual and somatomotor networks yields a simple and robust biomarker for psychosis.medRxiv : the preprint server for health sciences · 2024Article
- Extracting interpretable signatures of whole-brain dynamics through systematic comparison.bioRxiv : the preprint server for biology · 2024Article
- Network-level enrichment provides a framework for biological interpretation of machine learning results.Network neuroscience (Cambridge, Mass.) · 2024Article
- Increased whole-brain functional heterogeneity in psychosis during rest and task.NeuroImage. Clinical · 2024Article
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Abstract
Functional connectivity (FC) has been invaluable for understanding the brain's communication network, with strong potential for enhanced FC approaches to yield additional insights. Unlike with the fMRI field-standard method of pairwise correlation, theory suggests that partial correlation can estimate FC without confounded and indirect connections. However, partial correlation FC can also display low repeat reliability, impairing the accuracy of individual estimates. We hypothesized that reliability would be increased by adding regularization, which can reduce overfitting to noise in regression-based approaches like partial correlation. We therefore tested several regularized alternatives-graphical lasso, graphical ridge, and principal component regression-against unregularized partial and pairwise correlation, applying them to empirical resting-state fMRI and simulated data. As hypothesized, regularization vastly improved reliability, quantified using between-session similarity and intraclass correlation. This enhanced reliability then granted substantially more accurate individual FC estimates when validated against structural connectivity (empirical data) and ground truth networks (simulations). Graphical lasso showed especially high accuracy among regularized approaches, seemingly by maintaining more valid underlying network structures. We additionally found graphical lasso to be robust to noise levels, data quantity, and subject motion-common fMRI error sources. Lastly, we demonstrated that resting-state graphical lasso FC can effectively predict fMRI task activations and individual differences in behavior, further establishing its reliability, external validity, and ability to characterize task-related functionality. We recommend graphical lasso or similar regularized methods for calculating FC, as they can yield more valid estimates of unconfounded connectivity than field-standard pairwise correlation, while overcoming the poor reliability of unregularized partial correlation.
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