ArticlePloS one2019
Graph-theoretical analysis for energy landscape reveals the organization of state transitions in the resting-state human cerebral cortex.
Article in PloS one, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
- Automating region selection with genetic algorithms for energy landscape analyses of brain dynamics.Patterns (New York, N.Y.) · 2026Article
- Resting-state fMRI using hidden Markov models reveals abnormal dynamic brain functional states in asthma.Scientific reports · 2026Article
- Resting-state fMRI analysis of functional connectivity and temporal dynamics differences between cocaine users and healthy controls.Neuroimage. Reports · 2026Article
- From microstates to macroscales: A Critical Review of Maximum Entropy Modeling and Energy Landscape Analysis in functional MRI.Research square · 2025Article
- Development of brain metastable dynamics during the equivalent of the third gestational trimester.Developmental cognitive neuroscience · 2025Review
- Energy landscape analysis based on the Ising model: Tutorial review.PLOS complex systems · 2025Article
- FNIRS-Based Energy Landscape Analysis to Signify Brain Activity Dynamics of Individuals With Depression.CNS neuroscience & therapeutics · 2024Article
- Age Related Functional Connectivity Signature Extraction Using Energy-Based Machine Learning Techniques.Sensors (Basel, Switzerland) · 2023Article
- Triple-network analysis of Alzheimer's disease based on the energy landscape.Frontiers in neuroscience · 2023Article
- Alterations in white matter network dynamics in patients with schizophrenia and bipolar disorder.Human brain mapping · 2022Article
- The Structured Mind at Rest: Low-Frequency Oscillations Reflect Interactive Dynamics Between Spontaneous Brain Activity and a Common Architecture for Task Control.Frontiers in neuroscience · 2022Article
- Interindividual Signatures of fMRI Temporal Fluctuations.Cerebral cortex (New York, N.Y. : 1991) · 2021Article
- Bayesian estimation of maximum entropy model for individualized energy landscape analysis of brain state dynamics.Human brain mapping · 2021Article
- State-Dependent Effective Connectivity in Resting-State fMRI.Frontiers in neural circuits · 2021Article
- Situational Understanding in the Human and the Machine.Frontiers in systems neuroscience · 2021Article
- A Computational Framework for Controlling the Self-Restorative Brain Based on the Free Energy and Degeneracy Principles.Frontiers in computational neuroscience · 2021Article
- Energy landscape of resting magnetoencephalography reveals fronto-parietal network impairments in epilepsy.Network neuroscience (Cambridge, Mass.) · 2020Article
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3 authors.
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Abstract
The resting-state brain is often considered a nonlinear dynamic system transitioning among multiple coexisting stable states. Despite the increasing number of studies on the multistability of the brain system, the processes of state transitions have rarely been systematically explored. Thus, we investigated the state transition processes of the human cerebral cortex system at rest by introducing a graph-theoretical analysis of the state transition network. The energy landscape analysis of brain state occurrences, estimated using the pairwise maximum entropy model for resting-state fMRI data, identified multiple local minima, some of which mediate multi-step transitions toward the global minimum. The state transition among local minima is clustered into two groups according to state transition rates and most inter-group state transitions were mediated by a hub transition state. The distance to the hub transition state determined the path length of the inter-group transition. The cortical system appeared to have redundancy in inter-group transitions when the hub transition state was removed. Such a hub-like organization of transition processes disappeared when the connectivity of the cortical system was altered from the resting-state configuration. In the state transition, the default mode network acts as a transition hub, while coactivation of the prefrontal cortex and default mode network is captured as the global minimum. In summary, the resting-state cerebral cortex has a well-organized architecture of state transitions among stable states, when evaluated by a graph-theoretical analysis of the nonlinear state transition network of the brain.
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