Biology subjects

Chiyohara, S.

Publications and source records attributed to Chiyohara, S..

4 recordsLinked to original sources

bioRxiv2026

Characterizing load-dependent changes in whole-brain activity patterns during an extended N-back task

Working memory (WM) is a core cognitive function that supports goal-directed behavior by temporarily maintaining and manipulating information. One of the most widely used paradigms for investigating WM function is the N-back task, and numerous neuroimaging studies have examined load-dependent neural responses using a variety of analytical approaches. However, most previous studies have focused on low-to-moderate load ranges (primarily 0-3-back), and it remains unclear how whole-brain activity patterns reconfigure across a broader range of WM demands, including conditions approaching capacity limits. In the present study, we investigated behavioral performance and whole-brain activity patterns across an extended N-back task ranging from 0-back to 7-back. Behavioral analyses revealed that discrimination sensitivity (d') decreased nonlinearly with increasing WM load, whereas reaction time (RT) exhibited an inverted-U pattern, peaking at intermediate load conditions. To characterize load-dependent whole-brain activity patterns, we computed relative activation maps by subtracting the participant-wise mean activation map across all conditions from each condition-specific activation map. Spatial similarity analyses with the Yeo 7-network templates revealed that low-load conditions showed relatively high similarity to default mode network (DMN)-related patterns. Similarity to the dorsal attention network (DAN) and frontoparietal network (FPN) was maximal at intermediate load levels, indicating load-dependent changes in network similarity profiles. High-load conditions were characterized by partial re-emergence of DMN-related patterns, accompanied by reduced DAN/FPN similarity. In addition, semantic similarity analysis using Neurosynth-derived semantic maps revealed relatively high similarity to default mode-related and self-referential representations under low-load conditions. Intermediate-load conditions showed strong correspondence with working memory- and executive control-related representations, whereas high-load conditions exhibited increased similarity to salience-, aversive/interoceptive-, and inhibitory-control-related representations. Together, these findings suggest that increasing WM load is associated not merely with stronger activation, but with changes in whole-brain activity patterns accompanied by nonlinear changes in network similarity profiles across levels of cognitive demand. Furthermore, the relative activation map-based whole-brain pattern analysis used in this study may provide a useful approach for evaluating changes in whole-brain state representations associated with cognitive load.

neuroscience↗

bioRxiv2026

Beyond the Forest and the Trees: Overlooking the Overlooked Terrain of Neural State Dynamics

State-transition approaches, including EEG microstate analysis and related fMRI methods such as hidden Markov models (HMMs) and co-activation pattern (CAP) analysis, provide widely used tools for coarse-graining neural dynamics into a small set of quasi-stable states. Its utility has been demonstrated across resting-state and task paradigms, with broad applications ranging from cognitive neuroscience to candidate biomarkers for psychiatric and neurological disorders. A fundamental limitation remains, however: nearly all downstream temporal measures are conditional on the template maps defined at the outset. In the conventional pipeline, templates are derived from polarity-invariant clustering of voltage maps at global field power (GFP) peaks, making the resulting state definitions sensitive to preprocessing, sampling, initialization, clustering algorithms, and the choice of cluster number. Consequently, the method captures coarse regularities in EEG dynamics, while only weakly constraining the larger geometric organization from which those states emerge. This template dependence poses a major challenge for reproducibility and for comparisons across studies and EEG caps. Here, we revisit this problem from a topological-geometric perspective. We treat templates not as cluster centroids extracted from GFP-peak maps, but as landmarks embedded in the global structure of a state space constructed from mutual similarities among scalp voltage maps. In this formulation, microstate templates are rediscovered as discrete representatives of dominant axes that organize continuous neural-state topography. This reformulation preserves polarity as a meaningful geometric relation instead of eliminating it at the outset as analytical redundancy. It also shifts attention from isolated state labels to the terrain of the state space itself: the broader relational structure within which local states become interpretable. Using this approach, we show that landmark-based state definitions outperform conventional templates in capturing state structure and improving analytical performance. These findings suggest that the central problem in EEG microstate analysis is broader than clustering optimization: it concerns how to define valid nodes for coarse-graining continuous dynamics without discarding the topology that organizes them. By shifting the conceptual basis of microstate analysis from templates to landmarks, the present approach provides a more principled and potentially more stable foundation for state definition, including in fMRI. This topolo-geometric reappraisal extends conventional microstate analysis and opens a path toward more unified comparisons across datasets, paradigms, and recording systems.

neuroscience↗

bioRxiv2026

Behavioural and neural signatures across diverse cognitive demands in a multimodal electroencephalography-functional magnetic resonance imaging design

Efficient multimodal designs that capture differences across cognitive domains and variations in cognitive demand remain limited. In this study, we tested a compact framework with 58 healthy participants who completed multimodal electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) sessions. The framework comprised two complementary batteries: the HCP-aligned multitask paradigm (HCP-mini), which integrates eight HCP-aligned cognitive tasks and rest within a single run, and an extended N-back task ranging from 0-back to 7-back. Designed to support broad cross-domain coverage and matched multimodal assessment, the two batteries captured the expected group-level behavioural structure across modalities. Behavioural performance exceeded chance levels or aligned with findings from previous studies in both EEG and fMRI. Descriptive intraclass correlation coefficient (ICC) analyses showed numerically higher within-modality run-to-run values than between-modality values. At the neural level, HCP-mini fMRI activation patterns closely recapitulated the canonical large-scale task organisation of the original HCP dataset, with corresponding task pairs showing the strongest spatial similarity. Together, these findings demonstrate a compact and efficient framework for multimodal characterisation of cognition across domains and graded cognitive demands.

neuroscience↗

bioRxiv2023

Spatio-temporal "global" neurodynamics in both continuous and discrete pictures for human brains

The neural manifold in state space represents the mass neural dynamics of a biological system. A challenging modern approach treats the brain as a whole in terms of the interaction between the agent and the world. Therefore, we need to develop a method for this global neural workspace. The current study aimed to visualize spontaneous neural trajectories regardless of their measuring modalities (electroencephalography [EEG], functional magnetic resonance imaging [fMRI], and magnetoencephalography [MEG]). First, we examined the possible visualization of EEG manifolds. These results suggest that a spherical surface can be clearly observed within the spatial similarity space where canonical microstates are on-manifold. Once valid (e.g., differentiable) and useful (e.g., low-dimensional) manifolds are obtained, the nature of the sphere, such as shape and size, becomes a possible target of interest. Because these should be practically useful, we suggest advantages of the EEG manifold (essentially continuous) or the state transition matrix (coarse-grained discrete). Finally, because our basic procedure is modality-independent, MEG and fMRI manifolds were also compared. These results strongly suggest the need to update our understanding of neural mass representations to include robust "global" dynamics.

neuroscience↗