ArticleNature neuroscience2025
Connectome caricatures remove large-amplitude coactivation patterns in resting-state fMRI to emphasize individual differences.
Article in Nature neuroscience, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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9 citing papers in PubMed.
- Using connectome-based predictive models to reveal the systems standardized tests and clinical symptoms are reflecting.Nature communications · 2026Article
- Tract-explainable and underexplained synchrony play complementary roles in the functional organization of the brain.bioRxiv : the preprint server for biology · 2026Article
- Spatiotemporal asymmetries on brain energy landscape uncover system entrapment related to depression severity.Nature communications · 2026Article
- A low-variance subspace underlies individual differences in resting state fMRI.bioRxiv : the preprint server for biology · 2026Article
- HONeD-in on Brain Activity: Deconvolving Passive Diffusion on the Structural Network from Functional Brain Signals.bioRxiv : the preprint server for biology · 2026Article
- Variation in high-amplitude events across the human lifespan.Network neuroscience (Cambridge, Mass.) · 2026Article
- Generalizable and replicable brain-based predictions of cognitive functioning across common psychiatric illness.Science advances · 2024Article
- Connectome-based fingerprinting: reproducibility, precision, and behavioral prediction.Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology · 2024Review
- Mapping high-amplitude fMRI edge time series events across space and time.Imaging neuroscience (Cambridge, Mass.)Article
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Abstract
High-amplitude coactivation patterns are sparsely present during resting-state functional magnetic resonance imaging (fMRI), yet they drive functional connectivity and resemble task activation patterns. However, little research has characterized the remaining majority of the resting-state signal. Here, we introduce caricaturing, a method for projecting resting-state data onto a subspace orthogonal to a manifold of coactivation patterns estimated from task fMRI data. This removes linear combinations of these coactivation patterns from resting-state data to create caricatured connectomes. We used task data from two large-scale neuroimaging datasets to construct a manifold of task coactivation patterns and created caricatured connectomes. These connectomes exhibit lower between-individual similarity and higher identifiability and could be used to predict phenotypic measures, representing individual differences in behavior, often to a greater degree than standard connectomes. Our results show that there is a useful signal beyond the dominant coactivations that drive resting-state functional connectivity, which may better characterize the brain's intrinsic functional architecture.
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