ArticleNetwork neuroscience (Cambridge, Mass.)2022
Test-retest reliability of regression dynamic causal modeling.
Article in Network neuroscience (Cambridge, Mass.), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- Directional Brain Connectivity Changes Induced by Heart Rate Variability Biofeedback: A Spectral Dynamic Causal Modeling Study.Research square · 2026Article
- Predicting individual differences of fear and cognitive learning and extinction.Nature communications · 2026Article
- Physiologically Guided Modeling for EEG Multichannel Signals.Bioengineering (Basel, Switzerland) · 2026Article
- Differential connectivity between core hubs underlying demanding executive functions in schizophrenia compared to autism and control adults.Frontiers in psychiatry · 2026Article
- Article
- Walking Capacity in Parkinson's Disease: Test-Retest Reliability of the 6-min Walk Test on a Non-Linear Circuit.Healthcare (Basel, Switzerland) · 2025Article
- Lorcaserin modulation of semantic drug cue-elicited effective brain connectivity in persons with cocaine and opioid use disorders.Psychiatry research. Neuroimaging · 2025Article
- Altered effective connectivity of emotion perception and regulation networks during an emotional face perception task in adults with alcohol use disorder.Brain structure & function · 2025Article
- Comparison of whole-brain task-modulated functional connectivity methods for fMRI task connectomics.Communications biology · 2024Article
- Relationship between central autonomic effective connectivity and heart rate variability: A Resting-state fMRI dynamic causal modeling study.NeuroImage · 2024Article
- Comparison of whole-brain task-modulated functional connectivity methods for fMRI task connectomics.bioRxiv : the preprint server for biology · 2024Article
- Reliability of dynamic causal modelling of resting-state magnetoencephalography.Human brain mapping · 2024Article
- An information-theoretic analysis of resting-state versus task fMRI.Network neuroscience (Cambridge, Mass.) · 2023Article
- Executive control network resting state fMRI functional and effective connectivity and delay discounting in cocaine dependent subjects compared to healthy controls.Frontiers in psychiatry · 2023Article
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Authors and funding
2 authors.
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Abstract
Regression dynamic causal modeling (rDCM) is a novel and computationally highly efficient method for inferring effective connectivity at the whole-brain level. While face and construct validity of rDCM have already been demonstrated, here we assessed its test-retest reliability-a test-theoretical property of particular importance for clinical applications-together with group-level consistency of connection-specific estimates and consistency of whole-brain connectivity patterns over sessions. Using the Human Connectome Project dataset for eight different paradigms (tasks and rest) and two different parcellation schemes, we found that rDCM provided highly consistent connectivity estimates at the group level across sessions. Second, while test-retest reliability was limited when averaging over all connections (range of mean intraclass correlation coefficient 0.24-0.42 over tasks), reliability increased with connection strength, with stronger connections showing good to excellent test-retest reliability. Third, whole-brain connectivity patterns by rDCM allowed for identifying individual participants with high (and in some cases perfect) accuracy. Comparing the test-retest reliability of rDCM connectivity estimates with measures of functional connectivity, rDCM performed favorably-particularly when focusing on strong connections. Generally, for all methods and metrics, task-based connectivity estimates showed greater reliability than those from the resting state. Our results underscore the potential of rDCM for human connectomics and clinical applications.
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