Biology subjects

Yun, S. D.

Publications and source records attributed to Yun, S. D..

6 recordsLinked to original sources

bioRxiv2026

Limb-Selective Regions in the Lateral Temporal Lobe Shrink from Childhood to Adulthood

Perceiving hand gestures and inferring others actions and emotions from movements of hands and limbs play an important role in every-day interactions, especially in young children. The perception of categories such as limbs, bodies, or faces is supported by category-selective regions in the temporal lobe. Some category-selective regions, such as those reacting selectively to body parts or limbs, exist both on the ventral and on the lateral side of the temporal lobe, and are part of the ventral and lateral stream, respectively. While it was recently shown that limb-selective regions in the ventral stream shrink from childhood to adulthood, the developmental trajectory of limb-selective regions in the lateral stream remains unknown. To close this gap in knowledge, we acquired functional magnetic resonance imaging (fMRI) data in 21 children aged 10 - 12 years and 20 adults while they watched images of 10 visual categories including limbs and whole bodies. We first replicate the decrease of limb-selectivity from childhood to adulthood in the ventral temporal lobe. Across several analyses, our results further demonstrate that limb-selective regions in the lateral temporal lobe shrink as well, particularly in the left hemisphere. Underlining the specificity of our finding, we show that lateral body-selective regions show no significant development from childhood to adulthood. These findings advance our understanding of the developmental trajectories of limb- and body-selective regions and of the ventral and lateral visual streams more broadly.

neuroscience↗

bioRxiv2025

Ultra-High Resolution TR-external EPIK with Deep Learning Image Reconstruction for Enhanced Characterisation of Cortical Depth-dependent Neural Activity

The detection of neural signals using functional MRI at the laminar or columnar level enables non-invasive exploration of fundamental brain function processing and the interconnected pathways within intracortical tissues. The growing interest in this research area is driven by advancements in fMRI acquisition techniques that enhance spatial resolution for high-fidelity mapping. However, the submillimetre voxel sizes commonly employed in layer-specific fMRI studies raise concerns about relatively low signal-to-noise ratios and increased image artefacts in reconstructed images, ultimately limiting the precise delineation of cortical depth-dependent functional activities. This work aims to address this issue by incorporating a deep learning technique for enhanced image reconstruction of the submillimetre fMRI data, acquired with echo-planar-imaging with keyhole (EPIK) combined with the repetition-time-external (TR-external) EPI phase correction scheme. Our network was trained in a self-supervised, scan-specific manner using the sampling strategy from zero-shot self-supervised learning (ZS-SSL) method, which has gained attention for high-resolution MR image reconstruction. Healthy volunteers participated in this study, and the performance of the developed method was evaluated in direct comparison to the conventional reconstruction method using datasets acquired at 7T. The deep learning reconstruction produced reconstructed images with significantly higher SNR than the conventional method, which was further quantitatively validated through histogram analysis. This enhancement was consistent across all slice locations, demonstrating the reliability of the scan-specific deep learning technique.

neuroscience↗

bioRxiv2024

The Simultaneous Measurement of Reversed Phase-Encoding EPI in a Single fMRI Session at 7T: Quantitative and Qualitative Evaluation of Geometric Distortion Correction in Submillimetre fMRI

The high temporal resolution of echo-planar imaging (EPI) has driven its widespread use in functional MRI (fMRI), and recent advancements in EPI have enabled the mapping of cortical layer-specific functional activities. Notwithstanding these technical innovations, geometric distortion correction in EPI remains essential to ensure accurate functional mapping onto anatomical references. A common approach for distortion correction involves the acquisition of an additional scan, with the phase-encoding direction reversed. However, this extra scan necessitates redundant acquisitions of calibration scans, significantly increasing acquisition time and energy deposition. To address this concern, this work presents a novel EPI scheme that simultaneously acquires both the original and reversed phase-encoding data within a single fMRI session. Furthermore, despite the widespread use of distortion correction, methods for qualitative and quantitative evaluation of its impact on submillimetre fMRI analysis have remained largely unexplored. This study acquires submillimetre visual fMRI data (0.73 x 0.73 mm2) at 7T using the suggested EPI scheme and evaluates the impact of distortion correction through various metrics proposed here, including spatial resolution, co-registration accuracy, functional mapping fidelity, and distribution of functional voxels. Our fMRI acquisition scheme effectively reduced redundant acquisition time and total energy deposition to subjects. Distortion-corrected fMRI data demonstrated significant improvements in co-registration with anatomical scans, thereby enhancing functional mapping accuracy. The improvements were achieved without significant degradation of spatial resolution or alteration of the functional activation distribution. These findings were verified through qualitative and quantitative assessments, highlighting the effectiveness of distortion correction in submillimetre fMRI. HighlightsO_LIWe present a novel fMRI scheme that effectively acquires reversed-PE EPI data C_LIO_LIOur fMRI scheme substantially reduces extra acquisition time and energy deposition C_LIO_LIThe impact of EPI distortion correction is evaluated for submillimetre fMRI C_LIO_LIWe propose diverse qualitative and quantitative metrics, enabling thorough analysis C_LIO_LIDistortion correction demonstrates its effectiveness for high-fidelity fMRI mapping C_LI

neuroscience↗

bioRxiv2023

Contribution of cortical layers to human dynamic functional connectivity

Transitory states of the brain, sometimes linked to particular mental disorders, can be identified by conducting a dynamic analysis of connectivity in functional magnetic resonance imaging (fMRI) data. Here, we investigated whether the connectivity between cortical laminae (in contrast to between cortical areas), can vary during the course of a resting-state scan and whether this could impact the identification of brain states in the healthy brain. We performed a sliding-window connectivity analysis of high-resolution fMRI data and found that the differential participation of the cortical layers in the overall cortical connectivity constitutes a more dynamic feature of the brain compared to region-to-region connectivity, with two main states (superficial vs. deep-laminae connectivity) fluctuating over time. Laminar connectivity restricted to the default mode network appeared relatively stable (superficial connectivity preferred), while the central executive and salience networks showed a fluctuating laminar preference. We anticipate that the dynamic analysis of connectivity focused on the different depths of the cerebral cortex could play an important role in characterizing healthy brain states as well as in identifying novel targets of psychiatric diseases.

neuroscience↗

bioRxiv2021

Mapping of Whole-Brain Resting-State Networks with Half-Millimetre Resolution

Resting-state fMRI has been used in numerous studies to map networks in the brain that employ spatially disparate regions. However, attempts to map networks with high spatial resolution have been hampered by conflicting technical demands and associated problems. Results from recent fMRI studies have shown that spatial resolution remains around 0.7 x 0.7 x 0.7 mm3, with only partial brain coverage. This work presents a novel fMRI method, TR-external EPI with keyhole (TR-external EPIK), which can provide a nominal spatial resolution of 0.51 x 0.51 x 1.00 mm3 (0.26 mm3 voxel) with whole-brain coverage. TR-external EPIK enabled the identification of various resting-state networks distributed throughout the brain from a single fMRI session, with mapping fidelity onto the grey matter at 7T. The high-resolution functional image further revealed mesoscale anatomical structures, such as small cerebral vessels and the internal granular layer of the cortex within the postcentral gyrus.

neuroscience↗

bioRxiv2020

Cortical depth-dependent human fMRI of resting-state networks using EPIK

Recent laminar-fMRI studies have substantially improved understanding of the evoked cortical responses in multiple sub-systems; in contrast, the laminar component of resting-state networks spread over the whole brain has been less studied due to technical limitations. Animal research strongly suggests that the supragranular layers of the cortex play a critical role in maintaining communication within the default mode network (DMN); however, whether this is true in this and other human cortical networks remains unclear. Here, we used EPIK, which offers unprecedented coverage at sub-millimeter resolution, to investigate cortical broad resting-state dynamics with depth specificity in healthy volunteers. Our results suggest that human DMN connectivity is primarily supported by intermediate and superficial layers of the cortex, and furthermore, the preferred cortical depth used for communication can vary from one network to another. This has major implications for the characterization of network-related diseases and should occupy future high-resolution studies. In addition, the laminar connectivity profile of some networks showed a tendency to change upon engagement in a motor task. In line with these connectivity changes, we observed that the amplitude of the low-frequency-fluctuations (ALFF), as well as the regional homogeneity (ReHo), exhibited a different laminar slope when subjects were either performing a task or were in a resting state (less variation among laminae, i.e., lower slope, during task performance compared to rest). The varied laminar profiles concerning network connectivity, ALFF, and ReHo, observed across two brain states (task vs. rest) suggest the potential diagnostic value of laminar fMRI in psychiatric diseases, e.g., to differentiate the cortical dynamics associated with disease stages linked, or not linked, to behavioral changes. The evaluation of laminar-fMRI across the brain encompasses computational challenges; nonetheless, it enables the investigation of a new dimension of the human neocortex, which may be key to understanding neurological disorders from a novel perspective.

neuroscience↗