ArticleHuman brain mapping2021
Nonlinear manifold learning in functional magnetic resonance imaging uncovers a low-dimensional space of brain dynamics.
Article in Human brain mapping, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers, 1 of them a synthesis that pooled it.
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Who cites it
37 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Better together: A systematic review of studies combining magnetic resonance imaging with ecological momentary assessment.Social neuroscience · 2024Pooled it
- Human learning of noninvasive brain-computer interfaces via manifold geometry.Nature neuroscience · 2026Article
- Spectral organization of individualized connectome harmonics across brain structure, function and cognition.bioRxiv : the preprint server for biology · 2026Article
- Generative AI as a transformational logic for cognitive neuroscience.Communications biology · 2026Review
- Riemannian Geometry for Noise-Robust Covariance Network Analysis of Schizophrenia EEG: Geometric-Entropic Signatures of Dysconnectivity.Entropy (Basel, Switzerland) · 2026Article
- Flexible brain state engagement predicts cognitive control transdiagnostically.bioRxiv : the preprint server for biology · 2026Article
- Cognition emerges from phase dynamics of intrinsic coordination.bioRxiv : the preprint server for biology · 2026Article
- Connectivity Regression.Biostatistics (Oxford, England) · 2025Article
- Connectome caricatures remove large-amplitude coactivation patterns in resting-state fMRI to emphasize individual differences.Nature neuroscience · 2025Article
- Variation in moment-to-moment brain state engagement follows a consistent trajectory during development.Neuron · 2025Article
- Neurospectrum: A Geometric and Topological Deep Learning Framework for Uncovering Spatiotemporal Signatures in Neural Activity.bioRxiv : the preprint server for biology · 2025Article
- Understanding the Link Between Functional Profiles and Intelligence Through Dimensionality Reduction and Graph Analysis.Human brain mapping · 2025Article
- Neural Variability and Cognitive Control in Individuals With Opioid Use Disorder.JAMA network open · 2025Article
- Network topological reorganization mechanisms of primary visual cortex under multimodal stimulation.Frontiers in neuroscience · 2025Article
- Modelling low-dimensional interacting brain networks reveals organising principle in human cognition.Network neuroscience (Cambridge, Mass.) · 2025Article
- Deep learning models reveal the link between dynamic brain connectivity patterns and states of consciousness.Scientific reports · 2024Article
- Symmetry breaking organizes the brain's resting state manifold.Scientific reports · 2024Article
- Variation in moment-to-moment brain state engagement changes across development and contributes to individual differences in executive function.bioRxiv : the preprint server for biology · 2024Article
- Manifold Regularizer for High-Resolution fMRI Joint Reconstruction and Dynamic Quantification.IEEE transactions on medical imaging · 2024Article
- Individual characteristics outperform resting-state fMRI for the prediction of behavioral phenotypes.Communications biology · 2024Article
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3 authors.
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Abstract
Large-scale brain dynamics are believed to lie in a latent, low-dimensional space. Typically, the embeddings of brain scans are derived independently from different cognitive tasks or resting-state data, ignoring a potentially large-and shared-portion of this space. Here, we establish that a shared, robust, and interpretable low-dimensional space of brain dynamics can be recovered from a rich repertoire of task-based functional magnetic resonance imaging (fMRI) data. This occurs when relying on nonlinear approaches as opposed to traditional linear methods. The embedding maintains proper temporal progression of the tasks, revealing brain states and the dynamics of network integration. We demonstrate that resting-state data embeds fully onto the same task embedding, indicating similar brain states are present in both task and resting-state data. Our findings suggest analysis of fMRI data from multiple cognitive tasks in a low-dimensional space is possible and desirable.
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