PT  - JOURNAL ARTICLE
AU  - Genji Kawakita
AU  - Shunsuke Kamiya
AU  - Shuntaro Sasai
AU  - Jun Kitazono
AU  - Masafumi Oizumi
TI  - Quantifying brain state transition cost via Schrödinger’s bridge
AID  - 10.1101/2021.05.24.445394
DP  - 2021 Jan 01
TA  - bioRxiv
PG  - 2021.05.24.445394
4099  - http://biorxiv.org/content/early/2021/05/24/2021.05.24.445394.short
4100  - http://biorxiv.org/content/early/2021/05/24/2021.05.24.445394.full
AB  - 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’s 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.Competing Interest StatementThe authors have declared no competing interest.