Biology subjects

Sasai, S.

Publications and source records attributed to Sasai, S..

8 recordsLinked to original sources

bioRxiv2025

Seeing from the inside out: Intrinsic neural dynamics explain individual differences in movie watching

When viewing the same movie, individuals attend to different visual elements and construct distinct narratives. We hypothesized that these differences arise partly from intrinsic neural dynamics that specify upcoming visual content before eye movements occur, particularly when external visual saliency is low. Using fMRI and eye-tracking during a two-hour movie (n=12), we examined spontaneous gaze shifts during low versus high visual saliency periods. During low-saliency periods, when inter-individual variability was greatest, we found that visual features of upcoming gaze targets 2 seconds before eye movements could be predicted by right temporo-parieto-occipital junction (R-TPOJ) activity. Critically, the strength of connectivity between R-TPOJ and the right superior parietal lobule, which predicts gaze position, correlated with gaze-shift frequency during low-saliency periods. These findings suggest that R-TPOJ specifies the next scene content prior to foveation, and that eye movements serve to align this internally specified content with external stimuli, thereby shaping individual differences during movie-watching.

animal behavior and cognition↗

bioRxiv2025

Prenatal valproic acid-exposed autism marmosets increase mutual gaze and reduce inter-individual distance during dyadic social interactions

Autism spectrum disorder (ASD) is associated with differences in social interaction, including alterations in gaze behavior and interpersonal distance. Although most assessments of ASD remain individual-centric, emerging human studies indicate that social difficulties may arise from atypical dyadic coordination rather than from reduced social interest alone. We examined dyadic coordination in a non-human primate model of ASD, focusing on gaze and inter-individual distance during minimally constrained first encounters between prenatal valproic acid-exposed marmosets (VPA) and unexposed conspecifics (UE). Markerless pose estimation quantified frame-by-frame gaze direction and spacing, comparing VPA-UE with UE-UE dyads. VPA-UE pairs exhibited a higher proportion of mutual-gaze events than UE-UE pairs. VPA individuals showed more rapid looks in response to the partners gaze, yet spent less time looking at the partner when unobserved. VPA-UE dyads maintained shorter inter-individual distances than UE-UE dyads, and closer spacing was associated with decreased stability of mutual gaze. These findings reveal a dyadic coordination pattern in VPA-UE pairs, with rapid responsiveness coupled with distance-dependent fragility, paralleling features reported in human ASD. Our dyad-centric, markerless framework yields ecologically grounded readouts and potential intervention targets that leverage intact social responsiveness while accommodating altered spatial coordination.

neuroscience↗

bioRxiv2025

Direct Generation of Images from EEG using Schrödinger Bridge

Real-world data is often noisy, making it challenging to extract true signals Non-invasively recorded neural activities are among the most difficult data, yet its precise signal reconstruction is highly anticipated by communities developing non-invasive brain-machine interfaces. Several noise sources contribute to this challenge, including unrelated neuronal activity, non-brain bioelectricity, attenuation by the skull and scalp, and environmental noises. Additionally, the accumulation of noise varies significantly across subjects and recording sessions, resulting in widely diverging distributions of degraded observations. In this study, we propose modeling the noise accumulation process as a Schrodinger bridge and decoding the true signal by reversing this process. Compared to conventional guided Diffusion approach, our Schrodinger bridge approach effectively models diverse noise processes within a single framework, exhibiting greater robustness to inter-subject variability. Also, our approach doesnt require pre-aligning brain and image representations, which is an additional compute cost in the conventional approach.

neuroscience↗

bioRxiv2024

Delineating neural contributions to electroencephalogram-based speech decoding

Speech Brain-computer interfaces (BCIs) have emerged as a pivotal technology in facilitating communication for individuals with speech impairments. Utilizing electroencephalography (EEG) for noninvasive speech BCIs offers an accessible and affordable solution, potentially benefiting a broader audience. However, EEG-based speech decoding remains controversial especially for overt speech, due to difficulties in separating speech-related neural activities from myoelectric potential artifacts generated during articulation. Here we aim to delineate the extent of the neural contributions by employing Explainable AI techniques to a convolutional neural network predicting spoken words based on signals obtained by ultra-high-density (uhd)-EEG. We found that electrode-wise contributions to the decoding cannot be explained by their mutual information with electromyography (EMG). Furthermore, contributing periods of speech to EEG-based decoding are distinct from those to decoding solely relying on EMG. In contrast, there are significant overlaps in signal timings contributing to EEG-based decoding, regardless of vocal conditions such as overt or covert speech. Notably, the denoising process successfully enhanced the decoding contribution from electrodes within speech-related brain areas for all speech conditions. Altogether, our findings support the idea that, with appropriate preprocessing, EEG becomes a valuable tool for decoding spoken words based on underlying neural activities.

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↗

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

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↗

bioRxiv2019

A general spectral decomposition of causal influences applied to integrated information

Quantifying causal influences between elements of a system remains a central topic in many fields of research. In neuroscience, causal influences among neurons, quantified as integrated information, have been suggested to play a critical role in supporting subjective conscious experience. Recent empirical work has shown that the spectral decomposition of causal influences can reveal frequency-specific influences that are not observed in the time-domain. To date however, a spectral decomposition of integrated information has not been put forward. In this paper, we propose a spectral decomposition of integrated information in linear autoregressive processes. Our proposal is based on a general and flexible framework for deriving the spectral decompositions of causal influences in autoregressive processes. We show that the framework can retrieve the spectral decompositions of other well-known measures such as Granger causality. In simulation, we demonstrate a complex interplay between the spectral decomposition of integrated information and other measures that is not observed in the time-domain. We propose that the spectral decomposition of integrated information will be particularly useful when the underlying frequency-specific causal influences are masked in the time-domain. The proposed method opens the door for empirically investigating the relevance of integrated information to subjective conscious experience in a frequency-specific manner.\n\nAuthor summaryUnderstanding how different parts of the brain influence each other is fundamental to neuroscience. Integrated information measures overall causal influences in the brain and has been theorized to directly relate to subjective consciousness experience. For example, integrated information is predicted to be high during wakefulness and low during sleep or general anesthesia. At the same time, neural activity is characterized by well-known spectral signatures. For example, there is a prominent increase in low frequency power of neural activity during sleep and general anesthesia. Taking account of the spectral characteristics of neural activity, it is important to separately quantify integrated information at each frequency. In this paper, we propose a method for decomposing integrated information in the frequency domain. The proposed framework is general and can be used to derive the spectral decomposition of other well-known measures such as Granger causality. The spectral decomposition of integrated information we propose will allow empirically investigating the relationship between neural spectral signatures, integrated information and subjective consciousness experience.

neuroscience↗