ArticleCerebral cortex (New York, N.Y. : 1991)2021
Boost in Test-Retest Reliability in Resting State fMRI with Predictive Modeling.
Article in Cerebral cortex (New York, N.Y. : 1991), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 papers.
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Who cites it
40 citing papers in PubMed.
- Whole-brain mechanism of neurofeedback therapy: predictive modeling of neurofeedback outcomes on repetitive negative thinking in depression.Translational psychiatry · 2024Trial
- Optimizing functional connectivity scanning conditions for predicting autistic traits.medRxiv : the preprint server for health sciences · 2026Article
- Optimizing functional connectivity scanning conditions for predicting autistic traits.Nature. Mental health · 2026Article
- Neuroticism heterogeneity through item-level associations in resting-state functional connectivity.Communications biology · 2025Article
- Computational mechanisms of neuroimaging biomarkers uncovered by multicenter resting-state fMRI connectivity variation profile.Molecular psychiatry · 2025Article
- Limited generalizability of dynamic fMRI correlates of adolescent rumination.Nature. Mental health · 2025Article
- Limited generalizability of dynamic fMRI correlates of adolescent rumination.bioRxiv : the preprint server for biology · 2025Article
- Regional, but not brain-wide, graph theoretic measures are robustly and reproducibly linked to general cognitive ability.Cerebral cortex (New York, N.Y. : 1991) · 2025Article
- Regulation of craving and underlying resting-state neural circuitry predict hazard of smoking lapse.Translational psychiatry · 2025Article
- Brain functional connectivity and anatomical features as predictors of cognitive behavioral therapy outcome for anxiety in youths.Psychological medicine · 2025Article
- Enhancing task fMRI individual difference research with neural signatures.medRxiv : the preprint server for health sciences · 2025Article
- Tuning into flavor: predicting coffee sensory attributes from EEG with boosted-tree regression models.Frontiers in human neuroscience · 2025Article
- Connectome-Based Predictive Modeling of Trait Mindfulness.Human brain mapping · 2025Article
- Brain functional connectivity predicts depression and anxiety during childhood and adolescence: A connectome-based predictive modeling approach.Imaging neuroscience (Cambridge, Mass.) · 2025Article
- Predicting cognitive functioning in early psychosis: factors supporting and limiting generalizability of connectome-based models.NPP - digital psychiatry and neuroscience · 2025Article
- Opportunities for use of neuroimaging in de-risking drug development and improving clinical outcomes in psychiatry: an industry perspective.Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology · 2024Review
- Connectome-based fingerprinting: reproducibility, precision, and behavioral prediction.Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology · 2024Review
- Dynamic Functional Connectivity Correlates of Trait Mindfulness in Early Adolescence.Biological psychiatry global open science · 2024Article
- Connectome predictive modeling of trait mindfulness.bioRxiv : the preprint server for biology · 2024Article
- Dynamic functional connectivity correlates of trait mindfulness in early adolescence.bioRxiv : the preprint server for biology · 2024Article
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4 authors.
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Abstract
Recent studies found low test-retest reliability in functional magnetic resonance imaging (fMRI), raising serious concerns among researchers, but these studies mostly focused on the reliability of individual fMRI features (e.g., individual connections in resting state connectivity maps). Meanwhile, neuroimaging researchers increasingly employ multivariate predictive models that aggregate information across a large number of features to predict outcomes of interest, but the test-retest reliability of predicted outcomes of these models has not previously been systematically studied. Here we apply 10 predictive modeling methods to resting state connectivity maps from the Human Connectome Project dataset to predict 61 outcome variables. Compared with mean reliability of individual resting state connections, we find mean reliability of the predicted outcomes of predictive models is substantially higher for all 10 modeling methods assessed. Moreover, improvement was consistently observed across all scanning and processing choices (i.e., scan lengths, censoring thresholds, volume- vs. surface-based processing). For the most reliable methods, the reliability of predicted outcomes was mostly, though not exclusively, in the "good" range (above 0.60). Finally, we identified three mechanisms that help to explain why predicted outcomes of predictive models have higher reliability than individual imaging features. We conclude that researchers can potentially achieve higher test-retest reliability by making greater use of predictive models.
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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.