Association of bidirectional network cores in the brain with perceptual awareness and cognition.

The brain comprises a complex network of interacting regions. To understand the roles and mechanisms of this intricate network, it is crucial to elucidate its structural features related to cognitive functions. Recent empirical evidence suggests that both feedforward and feedback signals are necessary for conscious perception, emphasizing the importance of subnetworks with bidirectional interactions.

Optimal Control Costs of Brain State Transitions in Linear Stochastic Systems.

The brain is a system that performs numerous functions by controlling its states. Quantifying the cost of this control is essential as it reveals how the brain can be controlled based on the minimization of the control cost, and which brain regions are most important to the optimal control of transitions. Despite its great potential, the current control paradigm in neuroscience uses a deterministic framework and is therefore unable to consider stochasticity, severely limiting its application to neural data.

Bidirectionally connected cores in a mouse connectome: towards extracting the brain subnetworks essential for consciousness.

Where in the brain consciousness resides remains unclear. It has been suggested that the subnetworks supporting consciousness should be bidirectionally (recurrently) connected because both feed-forward and feedback processing are necessary for conscious experience. Accordingly, evaluating which subnetworks are bidirectionally connected and the strength of these connections would likely aid the identification of regions essential to consciousness.

Quantifying brain state transition cost via Schrödinger Bridge.

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.

Efficient search for informational cores in complex systems: Application to brain networks.

An important step in understanding the nature of the brain is to identify "cores" in the brain network, where brain areas strongly interact with each other. Cores can be considered as essential sub-networks for brain functions. In the last few decades, an information-theoretic approach to identifying cores has been developed.

Efficient Algorithms for Searching the Minimum Information Partition in Integrated Information Theory.

The ability to integrate information in the brain is considered to be an essential property for cognition and consciousness. Integrated Information Theory (IIT) hypothesizes that the amount of integrated information ( Φ ) in the brain is related to the level of consciousness. IIT proposes that, to quantify information integration in a system as a whole, integrated information should be measured across the partition of the system at which information loss caused by partitioning is minimized, called the Minimum Information Partition (MIP).

Novel method to classify hemodynamic response obtained using multi-channel fNIRS measurements into two groups: exploring the combinations of channels.

Near-infrared spectroscopy (NIRS) in psychiatric studies has widely demonstrated that cerebral hemodynamics differs among psychiatric patients. Recently we found that children with attention-deficit/hyperactivity disorder (ADHD) and children with autism spectrum disorders (ASD) showed different hemodynamic responses to their own mother's face. Based on this finding, we may be able to classify the hemodynamic data into two those groups and predict to which diagnostic group an unknown participant belongs.

Object representation in inferior temporal cortex is organized hierarchically in a mosaic-like structure.

There are two dominant models for the functional organization of brain regions underlying object recognition. One model postulates category-specific modules while the other proposes a distributed representation of objects with generic visual features. Functional imaging techniques relying on metabolic signals, such as fMRI and optical intrinsic signal imaging (OISI), have been used to support both models, but due to the indirect nature of the measurements in these techniques, the existing data for one model cannot be used to support the other model.