ArticleNetwork neuroscience (Cambridge, Mass.)2023
Temporal Mapper: Transition networks in simulated and real neural dynamics.
Article in Network neuroscience (Cambridge, Mass.), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 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
- Structural signatures of synergy and redundancy in human brain function.bioRxiv : the preprint server for biology · 2026Article
- Bridging subjective and neural state transitions in the rubber hand illusion: a neurophenomenological study.Neuroscience of consciousness · 2026Article
- A proposal for realizing cognitive functions using traveling waves, phase-locked and synchronized patterns, holography, neural mixing, and single sideband communications.Frontiers in computational neuroscience · 2026Article
- Article
- A topological data analysis method for revealing dynamic changes in psychotherapy microprocesses.Frontiers in psychology · 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
- 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
- Cross-attractor modeling of resting-state functional connectivity in psychiatric disorders.NeuroImage · 2023Article
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3 authors.
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Abstract
Characterizing large-scale dynamic organization of the brain relies on both data-driven and mechanistic modeling, which demands a low versus high level of prior knowledge and assumptions about how constituents of the brain interact. However, the conceptual translation between the two is not straightforward. The present work aims to provide a bridge between data-driven and mechanistic modeling. We conceptualize brain dynamics as a complex landscape that is continuously modulated by internal and external changes. The modulation can induce transitions between one stable brain state (attractor) to another. Here, we provide a novel method-Temporal Mapper-built upon established tools from the field of topological data analysis to retrieve the network of attractor transitions from time series data alone. For theoretical validation, we use a biophysical network model to induce transitions in a controlled manner, which provides simulated time series equipped with a ground-truth attractor transition network. Our approach reconstructs the ground-truth transition network from simulated time series data better than existing time-varying approaches. For empirical relevance, we apply our approach to fMRI data gathered during a continuous multitask experiment. We found that occupancy of the high-degree nodes and cycles of the transition network was significantly associated with subjects' behavioral performance. Taken together, we provide an important first step toward integrating data-driven and mechanistic modeling of brain dynamics.
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