Biology subjects

Hung, S.-C.

Publications and source records attributed to Hung, S.-C..

4 recordsLinked to original sources

bioRxiv2026

Division-Specific Organization of a Shared Functional Scaffold in the Early-Life Human Brain

Early cognition and behavior emerge from coordinated maturation of functional systems spanning the brain, making cross-division integration essential for defining whole-brain architecture and understanding its role in neurodevelopment. Yet early human functional development is still largely investigated through cortical networks or through isolated, coarsely resolved noncortical structures, leaving unclear how this integrated architecture is organized across the cerebral cortex, subcortex and cerebellum. Moving beyond structure-isolated and adult-derived maps, we create a reproducible, fine-grained Early-Life Whole-Brain Functional Parcellation (UNC-ELF) spanning birth to six years. Using this framework, we identify a shared functional scaffold expressed through distinct division-specific modes: differentiated and dual-polarity cortical organization, spatially graded cerebellar organization with blended functional transition zones, and nucleus-constrained subcortical mosaics of multi-system affiliation. Across early childhood, the scaffold is selectively refined through heterogeneous, nonlinear connectivity trajectories, with major inflections concentrated in infancy. Age-, sex- and prospective cognition-related variation is differentially encoded across distinct connectomic components. Together, these findings reframe early functional brain development as coordinated refinement of a shared but nonuniform whole-brain scaffold and establish UNC-ELF as a unified reference for resolving typical and atypical functional organization across the developing brain.

neuroscience↗

bioRxiv2025

Surface Expansion Regionalization of the Hippocampus in Early Brain Development

The hippocampal formation is implicated in a myriad of crucial functions, particularly centered around memory and emotion, with distinct subdivisions fulfilling specific roles. However, there is no consensus on the spatial organization of these subdivisions, given that the functional connectivity and gene expression-based parcellation along its longitudinal axis differs from the histology-based parcellation along its medial-lateral axis. The dynamic nonuniform surface expansion of the hippocampus during early development reflects the underlying changes of microstructure and functional connectivity, providing important clues on hippocampal subdivisions. Moreover, the thin and convoluted properties bring out the hippocampal maturity largely in the form of expanding surface area. We thus unprecedentedly explore the development-based surface area regionalization and patterns of the hippocampus by leveraging 513 high-quality longitudinal MRI scans during the first two postnatal years. Our findings imply two discrete hippocampal developmental patterns, featuring one pattern of subdivisions along the anterior-posterior axis (head, regions 1 and 5; body, regions 2, 4, 6, and 7; tail, region 3) and the other one along the medial-lateral axis (subiculum, regions 4, 5, and 6; CA fields, regions 1, 2, and 7). Most of the resulting 7 subdivisions exhibit region-specific and nonlinear spatiotemporal surface area expansion patterns with an initial high growth, followed by a transition to low increase. Each subregion displays bilaterally symmetric pattern. The medial portion of the hippocampal head experiences the most rapid surface area expansion. These results provide important references for exploring the fine-grained organization and development of the hippocampus and its intricate cognitions.

neuroscience↗

bioRxiv2021

Feature-based attention enables robust, long-lasting location transfer in human perceptual learning

Visual perceptual learning (VPL) is typically specific to the trained location and feature. However, the degree of specificity depends upon particular training protocols. Manipulating covert spatial attention during training facilitates learning transfer to other locations. Here we investigated whether feature-based attention (FBA), which enhances the representation of particular features throughout the visual field, facilitates VPL transfer, and how long such an effect would last. To do so, we implemented a novel task in which observers discriminated a stimulus orientation relative to two reference angles presented simultaneously before each block. We found that training with FBA enabled remarkable location transfer, reminiscent of its global effect across the visual field, but preserved orientation specificity in VPL. Critically, both the perceptual improvement and location transfer persisted after one year. Our results reveal robust, long-lasting benefits induced by FBA in VPL, and have translational implications for improving generalization of training protocols in visual rehabilitation.

neuroscience↗

bioRxiv2020

Multicohort Analysis of Publicly-available Monocyte Expression Data Identifies Gene Signatures to Accurately Monitor Subset-specific Changes in Human Diseases

Monocytes and monocyte-derived cells play important roles in the regulation of inflammation, both as precursors as well as effector cells. Monocytes are heterogeneous and characterized by three distinct subsets in humans. Classical and non-classical monocytes represent the most abundant subsets, each carrying out distinct biological functions. Consequently, altered frequencies of different subsets have been associated with inflammatory conditions, such as infections and autoimmune disorders including lupus, rheumatoid arthritis, inflammatory bowel disease, and, more recently, COVID-19. Dissecting the contribution of different monocyte subsets to disease is currently limited by samples and cohorts, often resulting in underpowered studies and, consequently, poor reproducibility. Public transcriptomes provide an alternative source of data characterized by high statistical power and real world heterogeneity. However, most transcriptome datasets profile bulk blood or tissue samples, requiring the use of in silico approaches to quantify changes in the levels of specific cell types. Here, we integrated 853 publicly available microarray expression profiles of sorted human monocyte subsets from 45 independent studies to identify robust and parsimonious gene expression signatures, consisting of 10 genes specific to each subset. These signatures, although derived using only datasets profiling healthy individuals, maintain their accuracy independent of the disease state in an independent cohort profiled by RNA-sequencing (AUC = 1.0). Furthermore, we demonstrate that our signatures are specific to monocyte subsets compared to other immune cells such as B, T, dendritic cells (DCs) and natural killer (NK) cells (AUC = 0.87~0.88, p<2.2e-16). This increased specificity results in estimated monocyte subset levels that are strongly correlated with cytometry-based quantification of cellular subsets (r = 0.69, p = 6.7e-4). Consequently, we show that these monocyte subset-specific signatures can be used to quantify changes in monocyte subsets levels in expression profiles from patients in clinical trials. Finally, we show that proteins encoded by our signature genes can be used in cytometry-based assays to specifically sort monocyte subsets. Our results demonstrate the robustness, versatility, and utility of our computational approach and provide a framework for the discovery of new cellular markers.

systems biology↗