ArticleNetwork neuroscience (Cambridge, Mass.)2022
Latent functional connectivity underlying multiple brain states.
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 23 papers.
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Who cites it
23 citing papers in PubMed.
- Using connectome-based predictive models to reveal the systems standardized tests and clinical symptoms are reflecting.Nature communications · 2026Article
- Functional connectivity-based attractor dynamics of the human brain in rest, task, and disease.eLife · 2026Article
- Feature similarity, a sensitive method to capture the functional interaction of brain regions and networks to support flexible behavior.Communications biology · 2025Article
- Effects of measurement errors on relationships between resting-state functional connectivity and psychological phenotypes.Scientific reports · 2025Article
- Dynamic reconfiguration of brain coactivation states associated with active and lecture-based learning of university physics.NPJ science of learning · 2025Article
- Regularized partial correlation provides reliable functional connectivity estimates while correcting for widespread confounding.Imaging neuroscience (Cambridge, Mass.) · 2025Article
- How measurement noise limits the accuracy of brain-behaviour predictions.Nature communications · 2024Article
- Choosing explanation over performance: Insights from machine learning-based prediction of human intelligence from brain connectivity.PNAS nexus · 2024Article
- Network and state specificity in connectivity-based predictions of individual behavior.Human brain mapping · 2024Article
- The Prediction of Brain Activity from Connectivity: Advances and Applications.The Neuroscientist : a review journal bringing neurobiology, neurology and psychiatry · 2024Review
- The Brain's Topographical Organization Shapes Dynamic Interaction Patterns That Support Flexible Behavior Based on Rules and Long-Term Knowledge.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2024Article
- Network and State Specificity in Connectivity-Based Predictions of Individual Behavior.bioRxiv : the preprint server for biology · 2023Article
- Trait repetitive negative thinking in depression is associated with functional connectivity in negative thinking state rather than resting state.Journal of affective disorders · 2023Article
- Review
- Causally informed activity flow models provide mechanistic insight into network-generated cognitive activations.NeuroImage · 2023Article
- Trait repetitive negative thinking in depression is associated with functional connectivity in negative thinking state rather than resting state.bioRxiv : the preprint server for biology · 2023Article
- Connectome-based predictive modelling of cognitive reserve using task-based functional connectivity.The European journal of neuroscience · 2023Article
- An information-theoretic analysis of resting-state versus task fMRI.Network neuroscience (Cambridge, Mass.) · 2023Article
- Optimizing network neuroscience computation of individual differences in human spontaneous brain activity for test-retest reliability.Network neuroscience (Cambridge, Mass.) · 2023Article
- Improving power in functional magnetic resonance imaging by moving beyond cluster-level inference.Proceedings of the National Academy of Sciences of the United States of America · 2022Article
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5 authors.
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Abstract
Functional connectivity (FC) studies have predominantly focused on resting state, where ongoing dynamics are thought to reflect the brain's intrinsic network architecture, which is thought to be broadly relevant because it persists across brain states (i.e., is state-general). However, it is unknown whether resting state is the optimal state for measuring intrinsic FC. We propose that latent FC, reflecting shared connectivity patterns across many brain states, better captures state-general intrinsic FC relative to measures derived from resting state alone. We estimated latent FC independently for each connection using leave-one-task-out factor analysis in seven highly distinct task states (24 conditions) and resting state using fMRI data from the Human Connectome Project. Compared with resting-state connectivity, latent FC improves generalization to held-out brain states, better explaining patterns of connectivity and task-evoked activation. We also found that latent connectivity improved prediction of behavior outside the scanner, indexed by the general intelligence factor (
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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.