The brain performs various cognitive and behavioral functions in on a regular basis life, flexibly transitioning to numerous states to perform these functions. Scientists view the brain as a system that performs these quite a few functions by controlling its states. To raised understand the properties of this control within the brain, scientists look for tactics to estimate the problem of control, or control cost, when the brain transitions from one state to a different. So a team of researchers undertook a study to quantify such control costs within the brain, and was successful in constructing a framework that evaluates these costs.

Controlling transitions to some states incurs greater “costs” than controlling transitions to others. With the event of a framework for quantifying transition costs, scientists may have a solution to evaluate the problem of the shifts between various brain states. Possibly, they may additionally have a quantifiable measure for explaining cognitive loads, sleep-wake differences, habituation of cognitive tasks and psychiatric disorders.

The work is published within the Journal of Neuroscience.

The team worked to construct a novel framework to quantify control cost that takes account of stochasticity, or the randomness, of neural activity. This stochasticity has been ignored in previous studies. The present control paradigm in neuroscience uses a deterministic framework that’s unable to think about stochasticity. But it surely is well-known that the neural dynamics are stochastic and the noise is ubiquitous throughout the entire brain. “On this work, we addressed the problem of stochasticity and first proposed a novel theoretical framework that quantifies the control cost taking account of the stochastic fluctuations of the neural dynamics,” said Shunsuke Kamiya, a doctoral student within the Graduate School of Arts and Sciences on the University of Tokyo.

Of their study, the researchers established the analytical expression of the stochastic control cost, which enabled them to compute the price in high-dimensional neural data. By the analytical expression, they found that the optimal control cost will be decomposed into the prices to manage the mean and covariance. “This decomposition enables us to analyze how various brain areas in another way contribute to controlling the transitions from one brain state to a different,” said Kamiya.

The researchers also identified the numerous brain regions for the optimal control in cognitive tasks in human whole-brain imaging data. They examined the numerous brain regions within the optimal control of transitions from the resting state to seven cognitive task states, using human whole-brain imaging data of 352 healthy adults. They found that, with these different transitions, the lower visual areas commonly played a big role in controlling the means, while the posterior cingulate cortex commonly played a big role in controlling the covariances. The posterior cingulate cortex is the upper a part of the limbic lobe, that region of the brain that plays a vital role in memory and emotional behaviors.

On this study, the team only considered the optimal control cost where brain state transitions are controlled in an optimal manner, with minimization of stochastic control cost. Nevertheless, in real neural systems, it shouldn’t be likely that state transitions are controlled in an optimal manner.

An intriguing future direction might be to match the optimally controlled dynamics and the actual dynamics using neural data during tasks.”

Masafumi Oizumi, Associate Professor, Graduate School of Arts and Sciences, University of Tokyo

Waiting for future research, Oizumi explains that the final word goal of his lab is to grasp the connection between brain dynamics and human behaviors, cognitions and consciousness. “For instance, we suspect that the decrease of controllability within the brain dynamics could also be related to mental fatigue or the lack of consciousness. We expect that control theoretical perspective will provide a recent insight to this goal,” said Oizumi.

Source:

Journal reference:

Kamiya, S., et al. (2023) Optimal Control Costs of Brain State Transitions in Linear Stochastic Systems,. Journal of Neuroscience. doi.org/10.1523/JNEUROSCI.1053-22.2022.