Jun Kitazono

This paper proposes a data-driven framework for optimally designing perturbation inputs to improve neural system identification and control.

The paper investigates network structure changes during sleep and anesthesia at single-cell and mesoscales, finding increased modularity at the single-cell level and spatially intermixed modules across states.

This study introduces a method to estimate the controllability Gramian matrix using perturbation inputs and network control theory to analyze brain dynamics, showing that controllable directions better differentiate brain states than overall controllability.

This paper introduces an unsupervised alignment method using Gromov-Wasserstein Optimal Transport to compare neural representations of natural scenes across individuals and brain areas, finding alignment within visual cortical areas but not across different areas.

This study identifies brain network cores with strong bidirectional interactions using fMRI data and links them to conscious perception and sensorimotor functions.

The paper introduces a framework to quantify control costs in brain state transitions using a linear stochastic model, identifying key brain regions involved in controlling mean and covariance of neural activity.

The paper identifies brain subnetworks essential for consciousness by analyzing bidirectional connections in a mouse connectome.

The paper introduces a framework to quantify brain state transition cost using stochastic optimal control theory and applies it to fMRI data to show a link between transition cost and task difficulty.

This paper introduces an algorithm to efficiently identify essential sub-networks (complexes) in brain networks using an information-theoretic approach, demonstrating its application to electrocorticogram data.