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Quantifying brain state transition cost via Schrödinger Bridge.

Genji Kawakita1, Shunsuke Kamiya1, Shuntaro Sasai2,3

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Network Neuroscience (Cambridge, Mass.)
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We developed a new method to quantify brain state transition cost in stochastic systems, finding it correlates with cognitive task difficulty. This approach offers a novel tool for understanding brain function.

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Area of Science:

  • Systems Neuroscience
  • Computational Neuroscience
  • Cognitive Neuroscience

Background:

  • Quantifying brain state transition cost is crucial in systems neuroscience.
  • Previous methods used deterministic models, neglecting neural system stochasticity.
  • Accurate quantification requires accounting for inherent randomness in neural dynamics.

Purpose of the Study:

  • To propose a novel framework for quantifying brain state transition cost in stochastic systems.
  • To introduce the Schrödinger Bridge as a measure of transition cost.
  • To apply this framework to neuroimaging data and assess its utility in cognitive tasks.

Main Methods:

  • Developed a novel framework based on optimal control in stochastic systems.
  • Quantified transition cost using Kullback-Leibler divergence (Schrödinger Bridge).
  • Applied the framework to Human Connectome Project functional magnetic resonance imaging (fMRI) data.

Main Results:

  • Successfully computed brain state transition cost during cognitive tasks.
  • Demonstrated a correspondence between computed transition cost and task difficulty.
  • Validated the framework's utility in analyzing cognitive functions.

Conclusions:

  • The proposed stochastic optimal control framework offers a more accurate measure of brain state transition cost.
  • Brain state transition cost is linked to cognitive effort and task demands.
  • This framework provides a general theoretical tool for investigating cognitive functions through the lens of transition cost.