Biology subjects

Koiso, K.

Publications and source records attributed to Koiso, K..

8 recordsLinked to original sources

bioRxiv2026

Stimulus dependent modulation of perceptual filling-in is predicted by the properties of early visual cortex

Filling-in occurs during the perceptual disappearance of a blank figure presented on a textured background. Current models of perceptual filling-in are based on a two-stage model where the figure boundary weakens after a period of adaptation, followed by the spreading of the background representation into the region representing the figure. This suggests a competition between figure boundary and background representations whereby filling-in is facilitated by a weaker boundary representation and a stronger background representation. Here, we test this interpretation, by using the oblique effect and surround-modulation suppression, which are functional properties of early visual cortex that modulate the expected strengths of the responses to the background texture and to the figure boundary. In a sample of N=58 participants, we found more filling-in with background textures of cardinal compared to oblique orientations (earlier onset time, with more and longer episodes of filling-in per trial), in line with a known, stronger neuronal response for cardinal than for oblique orientation in early visual cortex. We found more filling-in when the main axis of the rectangular figure was iso-oriented rather than cross-oriented with the background texture (more and longer episodes of filling-in per trial, but no change in onset time), in line with a lower response to oriented stimuli when surrounded by iso-oriented flankers compared to cross-oriented flankers. Overall, our results support the two-stage model and suggest the involvement of early visual cortical areas characterized by the oblique effect and orientation-tuned surround-suppression.

neuroscience↗

bioRxiv2026

Feedback to deep layers in human V1 during perceptual filling-in

Visual surface perception is a fundamental aspect of vision, yet its neural implementation remains poorly understood. Troxlers perceptual filling-in paradigm provides a tractable illusion for studying surface perception, in which a peripheral figure becomes perceptually assimilated into the surrounding background after a period of sustained fixation. Although neural correlates of this phenomenon have been reported in early visual cortex, the underlying mechanisms, particularly the contribution of feedback signaling, remain unresolved. Here we use ultra-high-field (7T) layer-fMRI to investigate perceptual filling-in in the human visual cortex. While experimentally controlling perceptual filling-in, we measured GE-BOLD responses in ten participants. Analyses across cortical depth in the independently localized figure representation in primary visual cortex (V1) revealed neural correlates of filling-in in deep cortical layers, which are associated with feedback input. These findings provide evidence that perceptual filling-in and visual surface perception in general are supported by feedback signals to early visual cortex.

neuroscience↗

bioRxiv2025

Unveiling early visual cortical mechanisms in perceptual filling-in: a parametric study of eccentricity and movement dependence

Surface perception relies on interactions between boundary encoding and surface filling-in. In a perceptual filling-in paradigm, a blank figure becomes perceptually replaced by a textured background after prolonged fixation of a point away from the figure. Filling-in begins when neurons representing the figures boundary adapt, allowing background-related activity to spread into the figures retinotopic representation. Adaptation proceeds faster the better the boundary is stabilized in a neurons receptive field (RF). We hypothesized that moving the figure boundary beyond a neurons RF would reduce adaptation and hinder filling-in, with larger movements permitting progressively less filling-in. As RF size increases with eccentricity, we further hypothesized that greater eccentricities would require larger movements to interfere with filling-in. Our results confirmed both predictions. The reduction in filling-in duration with increasing motion range permitted estimating RF size at each eccentricity. The slope of a linear function relating RF size to eccentricity matched values reported in human fMRI studies of V1/V2, suggesting that boundary adaptation involves early visual areas. We also explored whether microsaccade amplitude affects filling-in, but found no supporting evidence. Thus, external figure motion and microsaccades may disrupt adaptation through different mechanisms. These findings provide new insights into neural adaptation processes preceding perceptual filling-in.

neuroscience↗

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

In vivo reconstruction of Duvernoy's postmortem vasculature images

Non-invasive measurement of the human brains angioarchitecture is critical for understanding the basis of functional neuroimaging signals, diagnosing cerebrovascular diseases, and tracking neurodegeneration. Ultra-high-field magnetic resonance imaging (MRI) has achieved mesoscopic (< 0.5 mm) imaging of angioarchitecture, revealing fine vascular details that were previously inaccessible in vivo. However, current mesoscopic MRI methods for imaging angioarchitecture face two major limitations. First, acquisition times are prohibitively long, often exceeding 40 minutes, making integration into everyday clinical practice and research projects impractical. Second, even with data successfully acquired, conventional data visualization methods, such as 2D slice browsing and 3D vessel segmentation renders, are rudimentary and have limited effectiveness for navigating and interpreting the complex vascular network. In this paper, we present a fast whole-brain MRI protocol that provides robust images of the brains venous network at 0.35 mm resolution in under seven minutes. Additionally, we introduce novel data processing and visualization techniques that enable identification of specific vessel types and more informative navigation of the complex vascular network. We demonstrate that, with these advancements, we can reproduce, in vivo and without intravenous contrast application, the seminal postmortem vasculature images of Duvernoy and Vannson (1999). Furthermore, leveraging the ability for MRI to cover the entire brain, we achieve, for the first time, whole-brain intracortical mesoscopic vein maps in humans. Our acquisition and post-processing methods lay the groundwork for detailed examination of vascular organization across individuals, brain regions, and cortical layers. More generally, these methods make mesoscopic imaging of angioarchitecture viable for broad neuroscientific and clinical applications.

neuroscience↗

bioRxiv2024

Fuzzy ripple artifact in high resolution fMRI: identification, cause, and mitigation

PurposeHigh resolution fMRI is a rapidly growing research field focused on capturing functional signal changes across cortical layers. However, the data acquisition is limited by low spatial frequency EPI artifacts; termed here as Fuzzy Ripples. These artifacts limit the practical applicability of acquisition protocols with higher spatial resolution, faster acquisition speed, and they challenge imaging in lower brain areas. MethodsWe characterize Fuzzy Ripple artifacts across commonly used sequences and distinguish them from conventional EPI Nyquist ghosts, off-resonance effects, and GRAPPA artifacts. To investigate their origin, we employ dual polarity readouts. ResultsOur findings indicate that Fuzzy Ripples are primarily caused by readout-specific imperfections in k-space trajectories, which can be exacerbated by inductive coupling between third-order shims and readout gradients. We also find that these artifacts can be mitigated through complex-valued averaging of dual polarity EPI or by disconnecting the third-order shim coils. ConclusionThe proposed mitigation strategies allow overcoming current limitations in layer-fMRI protocols: O_LIAchieving resolutions beyond 0.8mm is feasible, and even at 3T, we achieved 0.53mm voxel functional connectivity mapping. C_LIO_LISub-millimeter sampling acceleration can be increased to allow sub-second TRs and laminar whole brain protocols with up to GRAPPA 8. C_LIO_LISub-millimeter fMRI is achievable in lower brain areas, including the cerebellum. C_LI

neuroscience↗

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↗