Biology subjects

Miyawaki, Y.

Publications and source records attributed to Miyawaki, Y..

8 recordsLinked to original sources

bioRxiv2025

Feedforward representation of face information in the fusiform face area revealed by VASO 7T layer fMRI

Recognizing faces is a critical cognitive process in social interaction. Face recognition has been extensively studied with functional magnetic resonance imaging (fMRI), ranging from large coverage studies comprehensively examining the entire face pathway to high-resolution laminar-specific (layer) fMRI studies probing feedforward and feedback signals in early visual and face-selective areas during particular face processing. However, it is still unclear which brain region structures face information in the face pathway. To further elucidate this mechanism, we investigated fusiform face area (FFA) during passive face image presentation using a whole-brain vascular space occupancy (VASO) 7T layer fMRI open dataset combined with multi-voxel pattern analysis (MVPA). We analysed lateral occipital cortex (LOC) as a control region and BOLD data in the dataset as a control contrast. We trained multiple binary classification decoders with various image categories (e.g., face vs. others, house vs. others) and three cortical layer groups independently to assess information representation across cortical depths. Our results revealed that decoding accuracies in VASO data peaked in the middle layer of FFA, suggesting a feedforward signature specifically during face processing. This effect was not observed in LOC or in decoders for other image categories. These findings indicate that face information is structured prior to processing in the FFA, consistent with previous reports suggesting that face-related features are partially extracted before reaching higher-level face areas. Overall, this study highlights the potential of combining high-specificity VASO layer-fMRI with high-sensitivity MVPA to dissect cortical information flow.

neuroscience↗

bioRxiv2025

Rapid decoding of neural information representation from ultra-fast functional magnetic resonance imaging signals

High spatio-temporal resolution is crucial for neuroimaging techniques to improve our understanding of human brain function. While the fMRI signal is slow and shows a spread in latencies over space, the precision of hemodynamic response latency for each voxel is preserved and has been shown to be able to detect oscillatory hemodynamic changes approaching 1 Hz, suggesting its potential to reveal rapid neural dynamics. To examine how fast neural information can be derived from fMRI signals, we performed experiments that acquire high-field (7T) fMRI signals at an ultra-fast sampling rate (TR = 125 ms) from the visual cortex while participants observed naturalistic object stimuli. We applied multivariate pattern decoders to extract presented object-category information from the acquired signals at each sampling time after stimulus onset. Results showed that decoding accuracy rose above statistical significance less than 2 s after signal onset, faster than the peak latency of the hemodynamic response. The peak latency of the decoding accuracy was independent of variations in the hemodynamic latency of voxels used for decoding. The application of sparse decoders further revealed that rapid and accurate decoding was possible by pruning vein-rich voxels off from the multivariate voxel input to the decoders. These results suggest that a combination of ultra-fast sampling and multivariate decoding allows fast and temporally precise analysis of neural activity using fMRI signals. Significance statementFunctional magnetic resonance imaging (fMRI) is the most successful method to evaluate human brain function at fine spatial scales but is thought to lack temporal resolution because of slow hemodynamics. We challenge this conventional notion by fast sampling of fMRI signals, combined with multivariate decoding to extract information content represented in the fMRI signals. Results showed that the information content can be extracted faster than the magnitude of hemodynamic response rises, independent of the large spatial variation of hemodynamic latencies across individual voxels, and even after removing venous voxels from decoders. Our method is thus effective at filtering out slow and variable hemodynamic components, leading to the extraction of rapid and temporally precise components reflecting neuronal activity in human fMRI signals.

neuroscience↗

bioRxiv2025

Functionally-structured Bayesian model for localizing neural activity and information in magnetoencephalography signals

Magnetoencephalography (MEG) is a noninvasive method that can measure human brain activity with high temporal resolution. However, the spatial resolution of MEG is not sufficient to reveal neural mechanisms. Although MEG source estimation overcomes this problem to some extent, the combination of MEG source estimation and multivariate analysis results in "information spreading", where significant predictions are observed in brain areas outside the true signal source location. This paper describes a model that achieves high source estimation accuracy and suppresses information spreading simultaneously. The proposed approach is based on a Bayesian estimation model that utilizes functional structure of the human brain. We compare the performance of the proposed model with simulated data generated under various signal-to-noise ratio conditions. The results show that the functionally-structured Bayesian model achieves source estimation accuracy that is better than that of conventional source estimation models. Additionally, the comparison of information spreading among the models reveals that our model outperforms the conventional ones. These results suggest that information spreading in the MEG source estimation can be suppressed while maintaining high source estimation accuracy.

neuroscience↗

bioRxiv2025

Block-structured Bayesian source estimation model for magnetoencephalography signals

The human brain consists of functionally specialized areas that process specific types of information and interact with one another. Magnetoencephalography (MEG) is a neuroimaging technique that captures brain activity at high temporal resolution. However, its spatial resolution is insufficient to accurately localize neural activation, and thus cannot capture the interactive nature of human brain activity. To resolve this issue, various MEG source estimation models have been proposed. In particular, models incorporating prior information from functional magnetic resonance imaging (fMRI), which offers superior spatial resolution, have improved source estimation accuracy. However, these models typically ignore the similarity of activity across different brain areas, and still fall short in tracking dynamic brain activity pattern at a sufficient spatial scale. In this study, we developed a block-structured model that integrates information about functional areas and the inter-areal relationships of the human brain into MEG source estimation based on a hierarchical Bayesian framework. We evaluated our model performance using simulation data with sequential activation across multiple brain areas. Results showed that our model outperformed conventional approaches in source estimation accuracy, suggesting that incorporating the functional areal information and inter-areal relationships may enhance MEG source estimation, enabling human neuroimaging at high spatiotemporal resolution.

neuroscience↗

bioRxiv2022

Reconstitution of ribosome self-replication outside a living cell

Ribosome biogenesis, a recursive process of pre-existing ribosomes self-replicating nascent ones, is pivotal in the self-replication of life. In Escherichia coli, three ribosomal RNAs (rRNAs) are transcribed, and 54 ribosomal proteins (r-proteins) are synthesized by pre-existing ribosomes as structural components1, 2. They are cotranscriptionally assembled in a cooperative hierarchy under the support of [~]100 accessory factors1-3. The reconstitution of ribosome biogenesis outside a living cell is an essential goal to understand the self-replication of life. However, this goal could not have been achieved so far due to its complexity. Here, we report the successful in vitro reconstitution of the entire ribosome biogenesis process. We hypothesized that mimicking in vivo ribosome biogenesis1-6 could result in in vitro ribosome biogenesis. Specifically, we found that coactivating the transcription of an rRNA operon, as well as the transcription and translation of 54 r-protein genes encoding r-proteins, and the coordinated ribosomal assembly in a cytoplasm-mimicking reaction solution, resulted in highly efficient in vitro reconstitution of ribosome biogenesis. Our achievement represents a critical step toward revealing fundamental principles underlying the self-replication of life and creating self-replicating artificial cells7. We also succeeded in engineering rRNA and r-proteins by only adding mutant ribosomal genes in the reaction, enabling high-throughput and unconstrained creation of artificial ribosomes with altered or enhanced functionality8-12.

synthetic biology↗

bioRxiv2022

Acquisition and processing methods of whole-brain layer-fMRI VASO and BOLD: The Kenshu dataset.

Cortical depth-dependent functional magnetic resonance image (fMRI), also known as layer-fMRI, has the potential to capture directional neural information flow of brain computations within and across large-scale cortical brain networks. E.g., layer-fMRI can differentiate feedforward and feedback cortical input in hierarchically organized brain networks. Recent advancements in 3D-EPI sampling approaches and MR contrast generation strategies have allowed proof-of-principle studies showing that layer-fMRI can provide sufficient data quality for capturing laminar changes in functional connectivity. These studies have however not shown how reliable the signal is and how repeatable the respective results are. It is especially unclear whether whole-brain layer-fMRI functional connectivity protocols are widely applicable across common neuroscience-driven analysis approaches. Moreover, there are no established preprocessing fMRI methods that are optimized to work for whole-brain layer-fMRI datasets. In this work, we aimed to serve the field of layer-fMRI and build tools for future routine whole-brain layer-fMRI in application-based neuroscience research. We have developed publicly available sequences, acquisition protocols, and processing pipelines for whole-brain layer-fMRI. These protocols are validated across 60 hours of scanning in nine participants. Specifically, we identified and exploited methodological advancements for maximizing tSNR efficiency and test-retest reliability. We are sharing an extensive multi-modal whole-brain layer-fMRI dataset (20 scan hours of movie-watching in a single participant) for the purpose of benchmarking future method developments: The Kenshu dataset. With this dataset, we are also exemplifying the usefulness of whole brain layer-fMRI for commonly applied analysis approaches in modern cognitive neuroscience fMRI studies. This includes connectivity analyses, representational similarity matrix estimations, general linear model analyses, principal component analysis clustering, etc. We believe that this work paves the road for future routine measurements of directional functional connectivity across the entire brain. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=132 SRC="FIGDIR/small/504502v1_ufig1.gif" ALT="Figure 1"> View larger version (89K): org.highwire.dtl.DTLVardef@1c6579forg.highwire.dtl.DTLVardef@a2bbecorg.highwire.dtl.DTLVardef@1dfa41aorg.highwire.dtl.DTLVardef@18ee67f_HPS_FORMAT_FIGEXP M_FIG C_FIG

neuroscience↗

bioRxiv2022

Spatiotemporal bias of the human gaze toward hierarchical visual features during natural scene viewing

The human gaze is directed at various locations from moment to moment in acquiring information necessary to recognize the external environment at the fine resolution of foveal vision. Previous studies showed that the human gaze is biased to particular locations in the visual field at a particular timing, but it remains unclear what visual features produce such spatiotemporal bias. In this study, we used a deep convolutional neural network model to extract hierarchical visual features from natural scene images and evaluated how much the human gaze is attracted to the visual features in space and time. Eye movement measurement and visual feature analysis using the deep convolutional neural network model showed that the gaze was strongly attracted to spatial locations containing higher-order visual features than lower-order visual features and conventional saliency. Analysis of the gaze time course revealed that the bias to higher-order visual features was prominent within a short period after the beginning of observation of the natural scene images. These results demonstrate that higher-order visual features are a strong gaze attractor in both space and time, suggesting that the human visual system uses foveal vision resources to extract information from higher-order visual features with higher spatiotemporal priority.

neuroscience↗

bioRxiv2021

Bodily ownership of an independent supernumerary limb: An exploratory study

Can our brain perceive a sense of ownership towards an independent supernumerary limb; one that can be moved independently of any other limb and provides its own independent movement feedback? Following the rubberhand illusion experiment, a plethora of studies has shown that the human representation of self is very plastic. But previous studies have almost exclusively investigated ownership towards substitute artificial limbs, which are controlled by the movements of a real limb and/or limbs from which non-visual sensory feedback is provided on an existing limb. Here, to investigate whether the human brain can own an independent artificial limb, we first developed a novel independent robotic sixth finger. We allowed participants to train using the finger and examined whether it induced changes in the body representation using behavioral as well as cognitive measures. Our results suggest that unlike a substituted artificial limb (like in the rubber hand experiment), it is more difficult for humans to perceive a sense of ownership towards an independent limb. However, ownership does seem possible, as we observed clear tendencies of changes in the body representation that correlated with the cognitive reports of the sense of ownership. Our results provide the first evidence to show that an independent supernumerary limb can be embodied by the human brain.

neuroscience↗