ArticleNetwork neuroscience (Cambridge, Mass.)2021
Simplicial and topological descriptions of human brain dynamics.
Article in Network neuroscience (Cambridge, Mass.), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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20 citing papers in PubMed, 4 citations in OpenAlex.
- Charting higher-order models of brain function beyond pairwise interactions.Nature communications · 2026Article
- Time-varying synergy/redundancy dominance in the human cerebral cortex.Journal of physics. Complexity · 2025Article
- Topology Assisted Clustering of Temporal fMRI Brain Networks With Use-Case in Mitigating Non-Neural Multi-Site Variability.IEEE access : practical innovations, open solutions · 2025Article
- Revealing brain network dynamics during the emotional state of suspense using TDA.Network neuroscience (Cambridge, Mass.) · 2025Article
- An EEG-network-metric based approach to real-time trust inference in human-autonomy teaming.Frontiers in neuroergonomics · 2025Article
- A topological deep learning framework for neural spike decoding.Biophysical journal · 2024Article
- Topological Embedding of Human Brain Networks with Applications to Dynamics of Temporal Lobe Epilepsy.ArXiv · 2024Article
- Topological state-space estimation of functional human brain networks.PLoS computational biology · 2024Article
- Neural Geometrodynamics, Complexity, and Plasticity: A Psychedelics Perspective.Entropy (Basel, Switzerland) · 2024Article
- Topological features of spike trains in recurrent spiking neural networks that are trained to generate spatiotemporal patterns.Frontiers in computational neuroscience · 2024Article
- Topological Data Analysis Captures Task-Driven fMRI Profiles in Individual Participants: A Classification Pipeline Based on Persistence.Neuroinformatics · 2024Article
- What Is in a Simplicial Complex? A Metaplex-Based Approach to Its Structure and Dynamics.Entropy (Basel, Switzerland) · 2023Article
- Multivariate information theory uncovers synergistic subsystems of the human cerebral cortex.Communications biology · 2023Article
- 𝓗Network neuroscience (Cambridge, Mass.) · 2023Article
- Temporal Mapper: Transition networks in simulated and real neural dynamics.Network neuroscience (Cambridge, Mass.) · 2023Article
- Article
- The expanding horizons of network neuroscience: From description to prediction and control.NeuroImage · 2022Review
- Use of connectotyping on task functional MRI data reveals dynamic network level cross talking during task performance.Frontiers in neuroscience · 2022Article
- A simplicial analysis of the fMRI data from human brain dynamics under functional cognitive tasks.Frontiers in network physiology · 2022Article
- The Comet Toolbox: Improving robustness in network neuroscience through multiverse analysis.Imaging neuroscience (Cambridge, Mass.)Article
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Authors and funding
5 authors at 3 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
While brain imaging tools like functional magnetic resonance imaging (fMRI) afford measurements of whole-brain activity, it remains unclear how best to interpret patterns found amid the data's apparent self-organization. To clarify how patterns of brain activity support brain function, one might identify metric spaces that optimally distinguish brain states across experimentally defined conditions. Therefore, the present study considers the relative capacities of several metric spaces to disambiguate experimentally defined brain states. One fundamental metric space interprets fMRI data topographically, that is, as the vector of amplitudes of a multivariate signal, changing with time. Another perspective compares the brain's functional connectivity, that is, the similarity matrix computed between signals from different brain regions. More recently, metric spaces that consider the data's topology have become available. Such methods treat data as a sample drawn from an abstract geometric object. To recover the structure of that object, topological data analysis detects features that are invariant under continuous deformations (such as coordinate rotation and nodal misalignment). Moreover, the methods explicitly consider features that persist across multiple geometric scales. While, certainly, there are strengths and weaknesses of each brain dynamics metric space, wefind that those that track topological features optimally distinguish experimentally defined brain states.
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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.