ArticleNetwork neuroscience (Cambridge, Mass.)2019
Generating dynamical neuroimaging spatiotemporal representations (DyNeuSR) using topological data analysis.
Article in Network neuroscience (Cambridge, Mass.), 2019. 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.
- Topological data analysis captures complex behavioral dynamics during naturalistic social interaction between domestic ferrets.bioRxiv : the preprint server for biology · 2026Article
- Topological data analysis reveals rigid brain-state dynamics during self-viewing in trait rumination.bioRxiv : the preprint server for biology · 2025Article
- The topology and geometry of neural representations.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Psilocybin enhances insightfulness in meditation: a perspective on the global topology of brain imaging during meditation.Scientific reports · 2024Article
- Deconstructing the Mapper algorithm to extract richer topological and temporal features from functional neuroimaging data.Network neuroscience (Cambridge, Mass.) · 2024Article
- Deconstructing the Mapper algorithm to extract richer topological and temporal features from functional neuroimaging data.bioRxiv : the preprint server for biology · 2023Article
- Brain mitochondrial diversity and network organization predict anxiety-like behavior in male mice.Nature communications · 2023Article
- Tree representations of brain structural connectivity via persistent homology.Frontiers in neuroscience · 2023Article
- Article
- Temporal Mapper: Transition networks in simulated and real neural dynamics.Network neuroscience (Cambridge, Mass.) · 2023Article
- Neural resources shift under Methylphenidate: A computational approach to examine anxiety-cognition interplay.NeuroImage · 2022Article
- High-order functional redundancy in ageing explained via alterations in the connectome in a whole-brain model.PLoS computational biology · 2022Article
- Precision dynamical mapping using topological data analysis reveals a hub-like transition state at rest.Nature communications · 2022Article
- NeuMapper: A scalable computational framework for multiscale exploration of the brain's dynamical organization.Network neuroscience (Cambridge, Mass.) · 2022Article
- Topological data analysis in biomedicine: A review.Journal of biomedical informatics · 2022Review
- From calcium imaging to graph topology.Network neuroscience (Cambridge, Mass.) · 2022Review
- Spontaneous and deliberate modes of creativity: Multitask eigen-connectivity analysis captures latent cognitive modes during creative thinking.NeuroImage · 2021Article
- Cholinergic neuromodulation of inhibitory interneurons facilitates functional integration in whole-brain models.PLoS computational biology · 2021Article
- Embedding Functional Brain Networks in Low Dimensional Spaces Using Manifold Learning Techniques.Frontiers in neuroinformatics · 2021Article
- Interpreting Deep Learning Features for Myoelectric Control: A Comparison With Handcrafted Features.Frontiers in bioengineering and biotechnology · 2020Article
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4 authors.
Funding
Abstract
In this article, we present an open source neuroinformatics platform for exploring, analyzing, and validating distilled graphical representations of high-dimensional neuroimaging data extracted using topological data analysis (TDA). TDA techniques like Mapper have been recently applied to examine the brain's dynamical organization during ongoing cognition without averaging data in space, in time, or across participants at the outset. Such TDA-based approaches mark an important deviation from standard neuroimaging analyses by distilling complex high-dimensional neuroimaging data into simple-yet neurophysiologically valid and behaviorally relevant-representations that can be interactively explored at the single-participant level. To facilitate wider use of such techniques within neuroimaging and general neuroscience communities, our work provides several tools for visualizing, interacting with, and grounding TDA-generated graphical representations in neurophysiology. Through Python-based Jupyter notebooks and open datasets, we provide a platform to assess and visualize different intermittent stages of Mapper and examine the influence of Mapper parameters on the generated representations. We hope this platform could enable researchers and clinicians alike to explore topological representations of neuroimaging data and generate biological insights underlying complex mental disorders.
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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.