ReviewFrontiers in neuroscience2021
Network Analysis of Time Series: Novel Approaches to Network Neuroscience.
Review in Frontiers in neuroscience, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- From visibility graphs to cognition.Frontiers in network physiology · 2026Article
- Multilevel irreversibility reveals higher-order organization of nonequilibrium interactions in human brain dynamics.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Unraveling the Invisible: Topological Data Analysis as the New Frontier in Radiology's Diagnostic Arsenal.Tomography (Ann Arbor, Mich.) · 2025Article
- Resting-state BOLD temporal variability of the default mode network predicts spontaneous mind wandering, which is negatively associated with mindfulness skills.Frontiers in human neuroscience · 2025Article
- Identification of brain-like complex information architectures in embryonic tissue ofCommunicative & integrative biology · 2025Article
- LSD flattens the hierarchy of directed information flow in fast whole-brain dynamics.Imaging neuroscience (Cambridge, Mass.) · 2025Article
- Similarity Metrics for Subcellular Analysis of FRET Microscopy Videos.The journal of physical chemistry. B · 2024Article
- Network Representation of fMRI Data Using Visibility Graphs: The Impact of Motion and Test-Retest Reliability.Neuroinformatics · 2024Article
- Hemoglobin signal network mapping reveals novel indicators for precision medicine.Scientific reports · 2023Article
- Partial entropy decomposition reveals higher-order information structures in human brain activity.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
- Machine learning approach for early onset dementia neurobiomarker using EEG network topology features.Frontiers in human neuroscience · 2023Article
- Brain effective connectome based on fMRI and DTI data: Bayesian causal learning and assessment.PloS one · 2023Article
- Visibility graph analysis for brain: scoping review.Frontiers in neuroscience · 2023Article
- Mild cognitive impairment prediction and cognitive score regression in the elderly using EEG topological data analysis and machine learning with awareness assessed in affective reminiscent paradigm.Frontiers in aging neuroscience · 2023Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In the last two decades, there has been an explosion of interest in modeling the brain as a network, where nodes correspond variously to brain regions or neurons, and edges correspond to structural or statistical dependencies between them. This kind of network construction, which preserves spatial, or structural, information while collapsing across time, has become broadly known as "network neuroscience." In this work, we provide an alternative application of network science to neural data: network-based analysis of non-linear time series and review applications of these methods to neural data. Instead of preserving spatial information and collapsing across time, network analysis of time series does the reverse: it collapses spatial information, instead preserving temporally extended dynamics, typically corresponding to evolution through some kind of phase/state-space. This allows researchers to infer a, possibly low-dimensional, "intrinsic manifold" from empirical brain data. We will discuss three methods of constructing networks from nonlinear time series, and how to interpret them in the context of neural data: recurrence networks, visibility networks, and ordinal partition networks. By capturing typically continuous, non-linear dynamics in the form of discrete networks, we show how techniques from network science, non-linear dynamics, and information theory can extract meaningful information distinct from what is normally accessible in standard network neuroscience approaches.
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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.