Biology subjects

Kitazono, J.

Publications and source records attributed to Kitazono, J..

9 recordsLinked to original sources

bioRxiv2025

Designing optimal perturbation inputs for system identification in neuroscience

AO_SCPLOWBSTRACTC_SCPLOWInvestigating the dynamics of neural networks, which are governed by connectivity between neurons, is a fundamental challenge in neuroscience. Because passive (spontaneous) activity provides only limited information for estimating connectivity, perturbation-based approaches are widely applied in neuroscience, as they can evoke underlying hidden dynamics. However, the characteristics of such perturbations have typically been designed based on empirical or biological intuition. To enable more accurate estimation of connectivity, we propose a data-driven and theoretically grounded framework for optimally designing perturbation inputs, based on formulating the neural model as a control system. The core theoretical insight underlying our approach is that neural signals observed in the passive state lack sufficient latent information, which leads to failures in the system identification. Perturbations reveal these hidden dynamics and lead to improved estimation. Guided by these insights, we derive a theoretical basis for optimizing perturbation inputs that minimize estimation errors in neural system identification. Building upon this, we further explore the relationship of this theory with stimulation patterns commonly used in neuroscience, such as frequency, impulse, and step inputs. We demonstrate the effectiveness of this framework for neuroscience through simulations grounded in experimental paradigms such as neural state classification and optimal control of neural states. Our theoretical analysis, together with multiple simulations, consistently shows that perturbations designed according to our framework achieve substantially more accurate system identification compared to the conventional, intuition-based inputs. This study provides a theoretical foundation for designing perturbation inputs to achieve accurate estimation of neural dynamics. This, in turn, enables reliable discrimination of neural states such as levels of consciousness and pathological conditions, and facilitates precise control of their transitions toward recovery from abnormal states.

neuroscience↗

bioRxiv2025

Quantifying state-dependent control properties of brain dynamics from perturbation responses

The brain can be conceptualized as a control system facilitating transitions between states, such as from rest to motor activity. Applying network control theory to measurements of brain signals enables characterization of brain dynamics through control properties. However, most prior studies that have applied network control theory have evaluated brain dynamics under unperturbed conditions, neglecting the critical role of external perturbations in accurate system identification. In this study, we combine a perturbation input paradigm with a network control theory framework and propose a novel method for estimating the controllability Gramian matrix in a simple, theoretically grounded manner. This method provides insights into brain dynamics, including overall controllability (quantified by the Gramians eigenvalues) and specific controllable directions (represented by its eigenvectors). As a proof of concept, we applied our method to transcranial magnetic stimulationinduced electroencephalographic responses across four motor-related states and two resting states. We found that states such as open-eye rest, closed-eye rest, and motor-related states were more effectively differentiated using controllable directions than overall controllability. However, certain states, like motor execution and motor imagery, remained indistinguishable using these measures. These findings indicate that some brain states differ in their intrinsic control properties as dynamical systems, while others share similarities that make them less distinguishable. This study underscores the value of control theory-based analyses in quantitatively how intrinsic brain states shape the brains responses to stimulation, providing deeper insights into the dynamic properties of these states. This methodology holds promise for diverse applications, including characterizing individual response variability and identifying conditions for optimal stimulation efficacy. Significant statementThe brain can be viewed as a control system transitioning between states, such as from rest to motor activity. Previous studies using network control theory mostly evaluated brain dynamics without external perturbations, neglecting their role in accurate system identification. This study integrates perturbation inputs with network control theory to propose a method for estimating the controllability Gramian, thereby providing insights into brain dynamics. We applied this approach to TMS-induced EEG responses in motor-related and resting states. Our findings show that controllable directions (eigenvectors) allow better discrimination between states than overall controllability. Our method can quantitatively assess brain state differences, and has potential applications in characterizing individual response variability and optimizing stimulation efficacy.

neuroscience↗

bioRxiv2024

Unsupervised alignment reveals structural commonalities and differences in neural representations of natural scenes across individuals and brain areas

Neuroscience research has extensively explored the commonality of neural representations of sensory stimuli across individuals to uncover universal neural mechanisms in the encoding of sensory information. To compare neural representations across different brains, Representational Similarity Analysis (RSA) has been used, which focuses on the similarity structures of neural representations for different stimuli. Despite the broad applicability and utility of RSA, one limitation is that its conventional framework assumes that neural representations of particular stimuli correspond directly to those of the same stimuli in different brains. This assumption excludes the possibility that neural representations correspond differently and limits the exploration of finer structural similarities. To overcome this limitation, we propose to use an unsupervised alignment framework based on Gromov-Wasserstein Optimal Transport (GWOT) to compare similarity structures without presupposing stimulus correspondences. This method allows for the identification of optimal correspondence between neural representations of stimuli based solely on internal neural representation relationships, and thereby provides a more detailed comparison of neural similarity structures across individuals. We applied this unsupervised alignment to investigate the commonality of representational similarity structures of natural scenes, using large datasets of Neuropixels recordings in mice and fMRI recordings in humans. We found that the similarity structure of neural representations in the same visual cortical areas can be well aligned across individuals in an unsupervised manner in both mice and humans. In contrast, we found that the degree of alignment across different brain areas cannot be fully explained by proximity in the visual processing hierarchy alone, but also found some reasonable alignment results, such that the similarity structures of higher-order visual areas can be well aligned with each other but not with lower-order visual areas. We expect that our unsupervised approach will be useful for revealing more detailed structural commonalities or differences that may not be captured by the conventional supervised approach.

neuroscience↗

bioRxiv2024

Association of bidirectional network cores in the brain with conscious perception 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. However, the link between such subnetworks and conscious perception remains unclear due to the complexity of brain networks. In this study, we propose a framework for extracting subnetworks with strong bidirectional interactions--termed the "cores" of a network--from brain activity. We applied this framework to resting-state and task-based human fMRI data from participants of both sexes to identify regions forming strongly bidirectional cores. We then explored the association of these cores with conscious perception and cognitive functions. We found that the extracted central cores predominantly included cerebral cortical regions rather than subcortical regions. Additionally, regarding their relation to conscious perception, we demonstrated that the cores tend to include regions previously reported to be affected by electrical stimulation that altered conscious perception, although the results are not statistically robust due to the small sample size. Furthermore, in relation to cognitive functions, based on a meta-analysis and comparison of the core structure with a cortical functional connectivity gradient, we found that the central cores were related to unimodal sensorimotor functions. The proposed framework provides novel insights into the roles of network cores with strong bidirectional interactions in conscious perception and unimodal sensorimotor functions. Significance StatementTo understand the brains network, we need to decipher its structural features linked to cognitive functions. Recent studies suggest the importance of subnetworks with bidirectional interactions for conscious perception, but their exact relationship remains unclear due to the brains complexity. Here we propose a framework for extracting subnetworks with strong bidirectional interactions, or network "cores." We applied it to fMRI data and explored the association of the cores with conscious perception and cognitive functions. The central cores predominantly included cortical regions rather than subcortical ones, and tended to comprise previously reported regions wherein electrical stimulation altered perception, suggesting the potential importance of bidirectional cores for conscious perception. Additionally, further analysis revealed the relationship of the cores to unimodal sensorimotor functions.

neuroscience↗

bioRxiv2023

Single-cell resolution functional networks during sleep aresegregated into spatially intermixed modules

The common neural mechanisms underlying the reduction of consciousness during sleep and anesthesia remain unclear. Previous studies have examined changes in network structure only using recordings with limited spatial resolution, which has hindered the investigation of the critical spatial scales from the micro (single neurons) to the meso (groups of neurons) for the reduction of consciousness. To address this issue, by leveraging fast, single-cell resolution, and wide-field two-photon microscopy, we recorded calcium signals from approximately 10,000 neurons across multiple cortical regions in awake, sleeping, and anesthetized mice. This single-cell resolution data enabled us to investigate the scales at which changes in network structure compared to an awake state are commonly observed during sleep and anesthesia. We found that at the single-cell scale, both sleep and anesthesia exhibited higher network modularity, indicating a segregated network, compared to an awake state. Despite this segregation, modules were spatially intermixed in all three states. In contrast, at the mesoscale, there were no consistent differences in modularity between states, and modules were spatially localized. Our multi-scale analysis provides novel insights into the cellular-scale organization of functional networks commonly associated with the reduction of consciousness and highlights a scale-dependent organization of network structures.

neuroscience↗

bioRxiv2022

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. Here, to resolve this limitation, we propose a novel framework for the evaluation of control costs based on a linear stochastic model. Following our previous work, we quantified the optimal control cost as the minimal Kullback-Leibler divergence between the uncontrolled and controlled processes. In the linear model, we established an analytical expression for minimal cost and showed that we can decompose it into the cost for controlling the mean and covariance of brain activity. To evaluate the utility of our novel framework, we examined the significant brain regions in the optimal control of transitions from the resting state to seven cognitive task states in human whole-brain imaging data. We found that, in realizing the different transitions, the lower visual areas commonly played a significant role in controlling the means, while the posterior cingulate cortex commonly played a significant role in controlling the covariances. Significance StatementThe brain performs many cognitive functions by controlling its states. Quantifying the cost of this control is essential as it reveals how the brain can be optimally controlled in terms of the cost, and which brain regions are most important to the optimal control of transitions. Here, we built a novel framework to quantify control cost that takes account of stochasticity of neural activity, which is ignored in previous studies. We established the analytical expression of the stochastic control cost, which enables us to compute the cost in high-dimensional neural data. We identified the significant brain regions for the optimal control in cognitive tasks in human whole-brain imaging data.

neuroscience↗

bioRxiv2021

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. Here, we propose a method for hierarchically decomposing a network into cores with different strengths of bidirectional connection, as a means of revealing the structure of the complex brain network. We applied the method to a whole-brain mouse connectome. We found that cores with strong bidirectional connections consisted of regions presumably essential to consciousness (e.g., the isocortical and thalamic regions, and claustrum) and did not include regions presumably irrelevant to consciousness (e.g., cerebellum). Contrarily, we could not find such correspondence between cores and consciousness when we applied other simple methods which ignored bidirectionality. These findings suggest that our method provides a novel insight into the relation between bidirectional brain network structures and consciousness.

neuroscience↗

bioRxiv2021

Quantifying brain state transition cost via Schrödinger's 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. 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 Schrodinger 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. Author SummaryIn our daily lives, we perform numerous tasks with different kinds and levels of cognitive demand. To successfully perform these tasks, the brain needs to modulate its spontaneous activity to reach an appropriate state for each task. Previous studies utilized optimal control in deterministic systems to measure cost for brain state transition. However, there has not been a unified framework for quantifying brain state transition cost that takes account of stochasticity of neural activities. Here, we propose a novel framework for measuring brain state transition cost, utilizing the idea of optimal control in stochastic systems. We assessed the utility of our framework for quantifying the cost of transitioning between various cognitive tasks. Our framework can be applied to very diverse settings due to its generality.

neuroscience↗

bioRxiv2020

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

To understand the nature of the complex behavior of the brain, one important step is to identify "cores" in the brain network, where neurons or 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. In this approach, many-to-many nonlinear interactions between parts are measured by an information loss function, which quantifies how much information would be lost if interactions between parts were removed. Then, a core called a "complex" is defined as a subsystem wherein the amount of information loss is locally maximal. Although identifying complexes can be a novel and useful approach to revealing essential properties of the brain network, its practical application is hindered by the fact that computation time grows exponentially with system size. Here we propose a fast and exact algorithm for finding complexes, called Hierarchical Partitioning for Complex search (HPC). HPC finds complexes by hierarchically partitioning systems to narrow down candidates for complexes. The computation time of HPC is polynomial, which is dramatically smaller than exponential. We prove that HPC is exact when an information loss function satisfies a mathematical property, monotonicity. We show that mutual information is one such information loss function. We also show that a broad class of submodular functions can be considered as such information loss functions, indicating the expandability of our framework to the class. In simulations, we show that HPC can find complexes in large systems (up to several hundred) in a practical amount of time when mutual information is used as an information loss function. Finally, we demonstrate the use of HPC in electrocorticogram recordings from monkeys. HPC revealed temporally stable and characteristic complexes, indicating that it can be reliably utilized to characterize brain networks. Author summaryAn important step in understanding the nature of the brain is to identify "cores" in the brain network, which can be considered as essential areas for brain functions and cognition. In the last few decades, a novel definition of cores has been developed, which takes account of many-to-many interactions among elements of the network. Although considering many-to-many interactions can be important in understanding the complex brain network, identifying cores in large systems has been impossible because of the extremely large computational costs required. Here, we propose a fast and exact algorithm for finding cores. We show that the proposed algorithm enables us to find cores in large systems consisting of several hundred elements in a practical amount of time. We applied our algorithm to electrocorticogram recordings from a monkey that monitored electrical activity of the brain with electrodes placed directly on the brain surface, and demonstrated that there are stable and characteristic core structures in the brain network. This result indicates that our algorithm can be reliably applied to uncovering the essential network structures of the brain.

neuroscience↗