Quantifying brain state transition cost via Schrödinger Bridge Article Swipe

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Genji Kawakita , Shunsuke Kamiya , Shuntaro Sasai , Jun Kitazono , Masafumi Oizumi ·

YOU? · · 2021 · Open Access · · DOI: https://doi.org/10.1162/netn_a_00213

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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Computer Science

Artificial Intelligence

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Human Connectome Project Divergence (linguistics) Computer science Bridge (graph theory) Artificial neural network Transition (genetics) State (computer science) Kullback–Leibler divergence Statistical physics Artificial intelligence Algorithm Physics Neuroscience Functional connectivity Psychology Gene Medicine Philosophy Biochemistry Linguistics Chemistry Internal medicine

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Type: article
Language: en
Landing Page: https://doi.org/10.1162/netn_a_00213
OA Status: gold
Cited By: 8
References: 67
Related Works: 10
OpenAlex ID: https://openalex.org/W4213318341

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DOI: https://doi.org/10.1162/netn_a_00213

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Title: Quantifying brain state transition cost via Schrödinger Bridge

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Language: en

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Publication year: 2021

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Publication date: 2021-10-25

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Authors: Genji Kawakita, Shunsuke Kamiya, Shuntaro Sasai, Jun Kitazono, Masafumi Oizumi

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Landing page: https://doi.org/10.1162/netn_a_00213

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Concepts: Human Connectome Project, Divergence (linguistics), Computer science, Bridge (graph theory), Artificial neural network, Transition (genetics), State (computer science), Kullback–Leibler divergence, Statistical physics, Artificial intelligence, Algorithm, Physics, Neuroscience, Functional connectivity, Psychology, Gene, Medicine, Philosophy, Biochemistry, Linguistics, Chemistry, Internal medicine

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Cited by: 8

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Related works (count): 10

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