ArticleNature computational science2023
Multi-view manifold learning of human brain-state trajectories.
Article in Nature computational science, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
What it found
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Who cites it
22 citing papers in PubMed.
- Human learning of noninvasive brain-computer interfaces via manifold geometry.Nature neuroscience · 2026Article
- Advances in Human Brain Dynamics in a Multimodal, Multitask, Multisensory, and Multi-Timescale Framework.Brain sciences · 2026Article
- Developmental tuning of functional manifold dimensionality across the human brain.bioRxiv : the preprint server for biology · 2026Article
- A multi-modal foundation model for brain disease diagnosis and medical imaging.Patterns (New York, N.Y.) · 2026Article
- Multimodal Representation Learning for Parsing Biological Heterogeneity in Psychiatric Neuroimaging.Biological psychiatry · 2026Review
- Dynamic Compression Flows for Neuroscience Data.bioRxiv : the preprint server for biology · 2026Article
- Connectome caricatures remove large-amplitude coactivation patterns in resting-state fMRI to emphasize individual differences.Nature neuroscience · 2025Article
- A Study of Non-Linear Manifold Feature Extraction in Spike Sorting.Neuroinformatics · 2025Article
- Neurospectrum: A Geometric and Topological Deep Learning Framework for Uncovering Spatiotemporal Signatures in Neural Activity.bioRxiv : the preprint server for biology · 2025Article
- Magnetoencephalography Dimensionality Reduction Informed by Dynamic Brain States.The European journal of neuroscience · 2025Article
- Manifold Learning Uncovers Nonlinear Interactions Between the Adolescent Brain and Environment That Predict Emotional and Behavioral Problems.Biological psychiatry. Cognitive neuroscience and neuroimaging · 2025Article
- Deep multimodal representations and classification of first-episode psychosis via live face processing.Frontiers in psychiatry · 2025Article
- Complexity in speech and music listening via neural manifold flows.Network neuroscience (Cambridge, Mass.) · 2025Article
- The human claustrum tracks slow waves during sleep.Nature communications · 2024Article
- Centering cognitive neuroscience on task demands and generalization.Nature neuroscience · 2024Review
- Manifold learning uncovers nonlinear interactions between the adolescent brain and environment that predict emotional and behavioral problems.bioRxiv : the preprint server for biology · 2024Article
- Never-Ending Learning for Explainable Brain Computing.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Article
- Predicting multiple observations in complex systems through low-dimensional embeddings.Nature communications · 2024Article
- Computational modelling in disorders of consciousness: Closing the gap towards personalised models for restoring consciousness.NeuroImage · 2023Article
- Manifold Learning for fMRI time-varying FC.bioRxiv : the preprint server for biology · 2023Article
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
The complexity of the human brain gives the illusion that brain activity is intrinsically high-dimensional. Nonlinear dimensionality-reduction methods such as uniform manifold approximation and t-distributed stochastic neighbor embedding have been used for high-throughput biomedical data. However, they have not been used extensively for brain activity data such as those from functional magnetic resonance imaging (fMRI), primarily due to their inability to maintain dynamic structure. Here we introduce a nonlinear manifold learning method for time-series data-including those from fMRI-called temporal potential of heat-diffusion for affinity-based transition embedding (T-PHATE). In addition to recovering a low-dimensional intrinsic manifold geometry from time-series data, T-PHATE exploits the data's autocorrelative structure to faithfully denoise and unveil dynamic trajectories. We empirically validate T-PHATE on three fMRI datasets, showing that it greatly improves data visualization, classification, and segmentation of the data relative to several other state-of-the-art dimensionality-reduction benchmarks. These improvements suggest many potential applications of T-PHATE to other high-dimensional datasets of temporally diffuse processes.
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Registered trials
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.