ArticleNetwork neuroscience (Cambridge, Mass.)2022
Quantifying brain state transition cost via Schrödinger Bridge.
Article in Network neuroscience (Cambridge, Mass.), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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5 citing papers in PubMed.
- Multimodal Branched Transport Infers Anatomically Aligned Brain Reaction Maps.Neuroinformatics · 2026Article
- Quantifying State-Dependent Control Properties of Brain Dynamics from Perturbation Responses.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2026Article
- EEG microstate transition cost correlates with task demands.PLoS computational biology · 2024Article
- Contributions of network structure, chemoarchitecture and diagnostic categories to transitions between cognitive topographies.Nature biomedical engineering · 2024Article
- Optimal Control Costs of Brain State Transitions in Linear Stochastic Systems.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2023Article
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5 authors.
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Abstract
Quantifying brain state transition cost is a fundamental problem in systems neuroscience. Previous studies utilized network control theory to measure the cost by considering a neural system as a deterministic dynamical system. However, this approach does not capture the stochasticity of neural systems, which is important for accurately quantifying brain state transition cost. Here, we propose a novel framework based on optimal control in stochastic systems. In our framework, we quantify the transition cost as the Kullback-Leibler divergence from an uncontrolled transition path to the optimally controlled path, which is known as Schrödinger Bridge. To test its utility, we applied this framework to functional magnetic resonance imaging data from the Human Connectome Project and computed the brain state transition cost in cognitive tasks. We demonstrate correspondence between brain state transition cost and the difficulty of tasks. The results suggest that our framework provides a general theoretical tool for investigating cognitive functions from the viewpoint of transition cost.
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