【Summary】
Shunsuke Kamiya, a graduate student at the University of Tokyo Graduate School of Arts and Sciences, Masafumi Oizumi, Associate Professor, and Shuntaro Sasai, CRO and Director of Research and Development at Araya Inc, proposed a novel mathematical method to quantify the control cost required to control the transition of brain activity states.
We are able to perform a variety of cognitive and behavioral tasks every day. Why is the brain able to achieve various cognitive and behavioral tasks? From the perspective of the brain’s “control,” the brain is able to properly control its own state and switch to various states. Quantifying the “cost” required to control the transition from one state to another and identifying brain regions that play an important role in controlling state transitions is considered important in understanding how the brain realizes cognitive and behavioral tasks.
Although there have been studies quantifying the cost of controlling brain state transitions, the problem was that they did not consider the fact that brain activity contains noise and behaves in a stochastic manner. In this study, we propose a new method that quantifies the control cost by considering the probabilistic behavior of brain activity. We applied this method to fMRI data collected while human subjects performed various cognitive tasks and identified brain regions that contribute to the control of brain states.
This study aims to quantify the “difficulty of controlling the transition from one brain state to another.” Using this research as a starting point, in the future, it may provide new understanding of seemingly unrelated phenomena such as the workload and mental fatigue of human cognitive tasks, or mental illness, from a unified perspective of control.Additionally, this research provides a theoretical foundation for studies that aim to transition the brain to a specific state by applying external input.
Journal: The Journal of Neuroscience (January 11)
Title of paper: Optimal Control Costs of Brain State Transitions in Linear Stochastic Systems
Authors: Shunsuke Kamiya, Genji Kawakita, Shuntaro Sasai, Jun Kitazono, and Masafumi Oizumi*.
DOI Number: 10.1523/JNEUROSCI.1053-22.2022