Biology subjects

Wu, W.

Publications and source records attributed to Wu, W..

At least 109 records · Page 6Linked to original sources

bioRxiv2025

Infectome Analysis of Small Mammals in Southern China Reveals Ecological Associations and Emerging Threats from Diverse Pathogens

Small mammals harbor a diverse array of zoonotic pathogens. To date, however, metagenomic surveys of these species have primarily focused on viral diversity, with limited attention paid to bacteria and eukaryotic pathogens. Additionally, the ecological determinants of pathogen diversity within these mammals have not been systematically examined. Herein, we employed a metatranscriptomics approach to survey the pathogen infectome of 2,408 individual samples, representing lung, liver, and gut tissues from 858 animals collected throughout Guangdong province, China, with a study design that accounted for host species, tissue, season, and geographic location. We identified 76 pathogen species, including 29 RNA viruses, 12 DNA viruses, 5 bacteria, and 30 eukaryotic pathogens, 33 of which were newly discovered. Tissue distribution analysis revealed distinct organotropisms, suggesting varied transmission routes, while host distribution analysis showed that each animal carried an average of one pathogen, with 10 pathogens widely distributed among mammalian orders. Our characterization of the geographic and seasonal patterns revealed that pathogen richness was primarily influenced by region, host species, and season, while pathogen composition was largely shaped by host genetic distance. Collectively, these data provide the first comprehensive insight into the dynamics of the pathogen infectome in these key mammalian disease reservoirs, highlighting the major factors driving pathogen diversity and transmission.

microbiology↗

bioRxiv2025

Dissecting dynamic gene regulatory network using transformer-based temporal causality analysis

The AI Virtual Cell (AIVC) framework promises to revolutionize biological research through high-fidelity simulations of cellular behaviors and responses to perturbations. Central to realizing this vision is the ability to model cell differentiation dynamics, which requires accurate inference of gene regulatory network (GRN) that govern cell fate decisions. However, existing computational approaches rely on static GRN models that fail to capture the dynamic changes of regulatory relationships during differentiation, limiting their utility for simulating developmental processes and predicting perturbation outcomes. Here, we present CellProphet, an interpretable AI model that infers dynamic GRN by integrating temporal causality with transformer self-attention mechanism. CellProphet captures time-lagged dependencies between transcription factor (TF) expression and target gene activation while providing interpretable regulatory weights, enabling both accurate prediction and mechanistic insight. When benchmarked against nine state-of-the-art methods across seven differentiation datasets, CellProphet achieves superior performance in all evaluation metrics. Applied to mouse embryonic stem cell differentiation, CellProphet identifies both well-known and potentially novel TFs with substantially high sensitivity and successfully reconstructs dynamic regulatory relations validated through multi-modal epigenomic data. In mouse hematopoietic differentiation, CellProphet accurately predicts cell fate transitions and gene expression changes following in silico perturbation of key TFs Gata1 and Spi1, demonstrating its capability for virtual experimentation. These results establish CellProphet as a foundational tool for the AIVC framework, enabling researchers to decode the dynamic regulatory logic of differentiation, accelerate discovery of key regulatory factors, and design targeted cellular interventions for widespread applications.

bioinformatics↗

bioRxiv2025

Size switchable DNA origami structure enabled by dynamic crossovers

DNA nanostructures are formed by many DNA double helices connected via crossovers. If multiple DNA double helices are connected by crossovers at different base positions (different rotational angles), these DNA double helices are stacked together to obtain desired two- or three-dimensional DNA structures. Here we inserted i-motif or hairpin motifs in crossover regions of one layer DNA origami structure and employed dynamic DNA element to implement size-switchablity between expansion and contraction. By changing pH or adding complementary strands to regulate interspacings between DNA double helixes, multiple crossover replacements realize the dynamic size changes between 68 nm and 150 nm as characterized by atomic force microscopy. We further demonstrated dynamic sizes changes enabled or inhibited FRET (fluorescence resonance energy transfer) signals between Cy3 and Cy5 and the controlled distances between protein molecules. The results reveal the feasibility of fabricating size-switchable DNA origami nanostructures via crossover redesign, demonstrating their potential in nano-engineering, proximity enabled chemical reacitons, as well as for biomedical applications.

bioengineering↗

bioRxiv2025

Tracking mobilization uncovers an evolutionarily conserved mechanism in suppressing transpositions during somatic development

Transposon mobilizations could cause gene mutations and even genomic instability, whose aberrant activation is tightly linked with cancer, neurodegenerative disorder and other pathologies. However, the precise suppression mechanisms of transposon mobilizations during somatic development throughout evolution remain largely obscure. Here, by spatiotemporally monitoring transpositions with single-cell resolution, we determined a highly conserved mechanism in suppressing transposon mobilizations. We identified that Cramp1 safeguards the genomic integrity in both Drosophila hindgut regeneration and mouse embryonic erythropoiesis through silencing transposon mobility. Cramp1 is characterized as a major hallmark that specifically binds to the DNA sequences of histone gene cluster to initiate transcription of linker histone H1, which subsequently promotes H3K36me2-established heterochromatin for transposon silencing. Our finding highlights that with the endless arms race between hosts and transposons during evolution, the core mechanisms will also evolve to suppress transposon mobilizations during somatic development. One-sentence abstractCramp1 plays an evolutionarily conserved role in specifically initiating linker histone H1 transcription and triggering H1-H3K36me2-established heterochromatin to suppress transposon mobilizations and maintain genome integrity during somatic development.

genetics↗

bioRxiv2025

GenomeOcean: An Efficient Genome Foundation Model Trained on Large-Scale Metagenomic Assemblies

Functional genomic sequences occupy an infinitesimal fraction of the astronomically large DNA sequence space, implying that evolution explores a low-dimensional manifold shaped by universal biochemical and evolutionary constraints. To investigate this underexplored Genomic Manifold Hypothesis using scalable, data-driven evidence, we developed GenomeOcean, a 4-billion-parameter generative model trained on 219 TB of diverse, co-assembled global environmental metagenomic data using an optimized transformer. The model captures both universal phylogenetic syntax and protein-coding fidelity. Furthermore, we validate the embeddings intrinsic low dimensionality. Crucially, comparisons with the independently trained Evo 2 model demonstrate a strong linear correspondence between their embedding spaces and convergent generative behaviors. These results suggest that the genomic manifold represents a fundamental biological principle, offering a unified framework for understanding evolutionary constraints and guiding synthetic biology.

bioinformatics↗

bioRxiv2025

Alpha-frequency stimulation strengthens coupling between temporal fluctuations in alpha oscillation power and default mode network connectivity

Alpha (8-12 Hz) oscillations and default mode network (DMN) activity dominate the brains intrinsic activity in the temporal and spatial domains, respectively. They are thought to play crucial roles in the spatiotemporal organization of the complex brain system. Relatedly, both have been implicated, often concurrently, in diverse neuropsychiatric disorders, with accruing electroencephalogram/magnetoencephalogram (EEG/MEG) and functional magnetic resonance imaging (fMRI) data linking these two neural activities both at rest and during key cognitive operations. Prominent theories and extant findings thus converge to suggest a mechanistic relationship between alpha oscillations and the DMN. Here, we leveraged simultaneous EEG-fMRI data acquired before and after alpha-frequency transcranial alternating current stimulation (-tACS) and observed that -tACS tightened the dynamic coupling between spontaneous fluctuations in alpha power and DMN connectivity (especially, in the posterior DMN, between the posterior cingulate cortex and the bilateral angular gyrus). In comparison, no significant changes were observed for temporal correlations between power in other oscillatory frequencies and connectivity in other major networks. These results thus suggest an inherent coupling between alpha and DMN activity in humans. Importantly, these findings highlight the efficacy of -tACS in regulating the DMN, a clinically significant network that is challenging to target directly with non-invasive methods. Significance StatementAlpha (8-12 Hz) oscillations and the default mode network (DMN) represent two major intrinsic activities of the brain. Prominent theories and extant findings converge to suggest a mechanistic relationship between alpha oscillations and the DMN. Combining simultaneous electroencephalogram-functional-magnetic-resonance imaging (EEG-fMRI) with alpha-frequency transcranial alternating current stimulation (-tACS), we demonstrated tightened coupling between alpha oscillations and DMN connectivity. These results lend credence to an inherent alpha-DMN link. Given DMN dysfunctions in multiple major neuropsychiatric conditions, the findings also highlight potential utility of -tACS in clinical interventions by regulating the DMN.

neuroscience↗

bioRxiv2025

A comprehensive comparison on clustering methods for multi-slide spatially resolved transcriptomics data analysis

Spatial transcriptomics (ST) data, by providing spatial information, enables simultaneous analysis of gene expression distributions and their spatial patterns within tissue. Clustering or spatial domain detection represents an essential methodology for ST data, facilitating the exploration of spatial organizations with shared gene expression or histological characteristics. Traditionally, clustering algorithms for ST have focused on individual tissue sections. However, the emergence of numerous contiguous tissue sections derived from the same or similar tissue specimens within or across individuals has led to the development of multi-slide clustering methods. In this study, we assess seven single-slide and three multi-slide clustering methods on two simulated datasets and three real datasets. Additionally, we investigate the effectiveness of pre-processing techniques, including spatial coordinate alignment (for example, PASTE) and gene expression batch effect removal (for example, Harmony), on clustering performance. Our study provides a comprehensive comparison of clustering methods for multi-slide ST data, serving as a practical guide for method selection in various scenarios.

bioinformatics↗

bioRxiv2025

Food pairing independently associated with cardiometabolic traits beyond dietary patterns across western and eastern populations

Cardiometabolic health is closely linked to diet. Both specific food choices and dietary indices are commonly used to guide cardiometabolic protection strategies. However, the balance between foods may also significantly impact cardiometabolic health. Here, we explore the balance and imbalance in long-term dietary food intake to quantify food pairing patterns (FPs), providing insights beyond conventional dietary indices and single-food intake frequencies. Using data from our GGMP (n = 6,994) and the NHANES (n = 7,350) cohorts, we observed that long-term food pairing patterns are independent of single food intake frequency and dietary indices. We identified 1,759 and 306 cardiometabolic-related long-term food pairing patterns from two cohorts (FDR < 0.05), respectively. Notably, around 80% of these pairing foods not individually associated with cardiometabolic traits, and food pairing pattern associations with cardiometabolic traits at hyper food group level were consistent across Eastern and Western populations. Besides, mediation analysis revealed that 72.7% of long-term food pairing patterns affected cardiometabolic traits through 31 microbial genera, with Clostridiumsensustricto1 playing a predominant role. Moreover, multi-step mediation analysis found that microbes mediated the impact of long-term food pairing patterns on cardiometabolic traits primarily through their metabolic pathways, such as pyruvate fermentation to propanoate and ergothioneine biosynthesis pathways. Our data suggest that balance between dietary foods is broadly associated with cardiometabolic traits by modulating gut microbial functionalities, in contrast to the lower occurrence when considering individual foods in isolation. These results offer a novel perspective for designing personalized dietary strategies beyond present dietary indices to enhance cardiometabolic health.

microbiology↗

bioRxiv2024

Oral delivery of semaglutide and tirzepatide using milk-derived small extracellular vesicles

Therapeutic proteins and peptides have revolutionized modern biomedicine. However, the large size and complex structure of these macromolecules preclude their oral administration, greatly limiting their applications in patient care. Biochemical degradation in the gastrointestinal tract, mucus, and cellular barrier are the major obstacles to oral development. Small extracellular vesicles (sEVs) are natural nano lipid vesicles serving as essential vehicles for intercellular communication. sEVs are resistant to biochemical degradation, permeable to mucus barriers, and can penetrate cellular barriers. sEVs are thus considered the next-generation vehicles with the potential for oral peptide/protein drug delivery. Herein, we report the use of milk-derived sEVs as delivery vehicles to achieve successful and highly efficient oral delivery of two therapeutic GLP-1 receptor agonists, semaglutide and tirzepatide. We showed that semaglutide and tirzepatide can be efficiently loaded onto sEVs in vitro, and oral gavage of semaglutide-loaded sEVs or tirzepatide-loaded sEVs can effectively lower blood glucose levels in db/db mouse models. This study demonstrates that sEVs is a platform technology for oral peptide drug delivery and opens a new avenue for oral peptide/protein therapeutics delivery.

pharmacology and toxicology↗

bioRxiv2024

Same-Slide Spatial Multi-Omics Integration Reveals Tumor Virus-Linked Spatial Reorganization of the Tumor Microenvironment

The advent of spatial transcriptomics and spatial proteomics have enabled profound insights into tissue organization to provide systems-level understanding of diseases. Both technologies currently remain largely independent, and emerging same slide spatial multi-omics approaches are generally limited in plex, spatial resolution, and analytical approaches. We introduce IN-situ DEtailed Phenotyping To High-resolution transcriptomics (IN-DEPTH), a streamlined and resource-effective approach compatible with various spatial platforms. This iterative approach first entails single-cell spatial proteomics and rapid analysis to guide subsequent spatial transcriptomics capture on the same slide without loss in RNA signal. To enable multi-modal insights not possible with current approaches, we introduce k-bandlimited Spectral Graph Cross-Correlation (SGCC) for integrative spatial multi-omics analysis. Application of IN-DEPTH and SGCC on lymphoid tissues demonstrated precise single-cell phenotyping and cell-type specific transcriptome capture, and accurately resolved the local and global transcriptome changes associated with the cellular organization of germinal centers. We then implemented IN-DEPTH and SGCC to dissect the tumor microenvironment (TME) of Epstein-Barr Virus (EBV)-positive and EBV-negative diffuse large B-cell lymphoma (DLBCL). Our results identified a key tumor-macrophage-CD4 T-cell immunomodulatory axis differently regulated between EBV-positive and EBV-negative DLBCL, and its central role in coordinating immune dysfunction and suppression. IN-DEPTH enables scalable, resource-efficient, and comprehensive spatial multi-omics dissection of tissues to advance clinically relevant discoveries.

systems biology↗

bioRxiv2024

Disruption of basal CXCR4 oligomers impairs oncogenic properties in lymphoid neoplasms

The chemokine receptor CXCR4 is overexpressed in many cancers and contributes to pathogenesis, disease progression, and resistance to therapies. CXCR4 is known to form oligomers, but the potential functional relevance in malignancies remain elusive. Using a newly established nanobody-based BRET method, we demonstrate that oligomerization of endogenous CXCR4 on lymphoid cancer cell lines correlates with enhanced expression levels. Specific disruption of CXCR4 oligomers reduced basal cell migration and pro-survival signaling via changes in the phosphoproteome, indicating the existence of basal CXCR4-oligomer-mediated signaling. Oligomer disruption also inhibited growth of primary CLL 3D spheroids and sensitized primary malignant cells to clinically used Bcl-2 inhibitor venetoclax. Given its limited efficacy in some patients and the ability to develop resistance, sensitizing malignant B-cells to venetoclax is of clinical relevance. Taken together, we established a new, non-canonical and critical role for CXCR4 oligomers in lymphoid neoplasms and demonstrated that selective targeting thereof has clinical potential. Significance statementClass A GPCRs, including the chemokine receptor CXCR4, can form oligomers, but their functional relevance remains poorly understood. This study provides evidence for the role of basal CXCR4 oligomers in lymphoid neoplasms, where they drive pro-survival signaling, migration, and tumor growth. We use a novel nanobody-based BRET method to demonstrate that endogenous CXCR4 constitutively oligomerizes in lymphoid cancer cells, correlating with receptor expression levels. Pharmacological disruption of these oligomers reduces tumor- associated signaling, impairs spheroid growth, and sensitizes patient-derived malignant cells to the apoptosis-inducing drug Venetoclax. Since CXCR4 is frequently overexpressed and potentially clustered in various malignancies, this work offers broader implications for enhancing treatment efficacy, overcoming drug resistance, and potentially reducing side effects across multiple cancer types.

biochemistry↗

bioRxiv2024

Measuring regulatory network inheritance in dividing yeast cells using ordinary differential equations

Quantifying the inheritance of protein regulation during asymmetric cell division remains a challenge due to the complexity of these systems and the lack of a formal mathematical definition. We introduce ODEinherit, a new statistical framework leveraging ordinary differential equations (ODEs) to measure how much a mother cells regulatory network is passed on to its daughters, addressing this gap. ODEin-herit first estimates cell-specific regulatory networks through ODE systems, incorporating novel adjustments for non-oscillatory trajectories. Then, inheritance is quantified by evaluating how well a mothers regulatory network explains its daughters trajectories. We demonstrate that precise quantification of this inheritance relies on pruning and adjustment for the network density. We benchmark ODEinherit on simulated data and apply it to live-cell, time-lapse microscopy data, where we track the expression dynamics of six proteins across 85 dividing S. cerevisiae cells over eight hours. Our results reveal substantial heterogeneity in inheritance rates among mother-daughter pairs, paving the way for applications in cellular stress response and cell-fate prediction studies across generations.

cell biology↗

bioRxiv2024

Sensitivity to Vγ9Vδ2TCR T cells is imprinted after single mutations during early oncogenesis

V{gamma}9V{delta}2T cells have the unique ability to recognize a broad range of malignant transformed cells. The tumor targeting event involving BTN2A1 and BTN3A1 dimers on the tumor cell surface is critical, leading to full activation of the TCR. Although the molecular mechanisms governing TCR engagement and T cell activation are well-characterized, the role of V{gamma}9V{delta}2 T cells in cancer immune surveillance remains to be fully elucidated, particularly the mechanisms that enable these cells to discriminate between healthy and malignant cells at an early stage of malignant transformation. We employed two independent, genetically engineered step-wise mutagenesis models of human colorectal and breast cancer that mimic the transformation steps leading to tumor formation. We demonstrate that various single oncogenic mutations introduced into healthy organoids or cells, are sufficient to upregulate surface expressed BTN2A1 and enable V{gamma}9V{delta}2 TCR binding to tumor cells. However, full activation of T cells through a V{gamma}9V{delta}2TCR required additional subsequent phosphorylation of juxtamembrane (JTM) amino acids of BTN3A1, leading to the activating heterodimerization of BTN2A1 and 3A1. Using a protein interactome mapping pipeline, we identified PHLDB2, SYNJ2 and CARMIL1 as key players in controlling these delicate dual surface dynamics of BTN2A1 and 3A1 during early transformation. This mode of action allowed V{gamma}9V{delta}2TCR T cells to control tumors in vitro and in vivo, emphasizing the crucial role of these molecules from early mutagenesis, to advanced cancer stages, and highlighting the therapeutic potential of a V{gamma}9V{delta}2TCR.

cancer biology↗

bioRxiv2024

The Virtual Lab: AI Agents Design New SARS-CoV-2 Nanobodies with Experimental Validation

Science frequently benefits from teams of interdisciplinary researchers. However, most scientists dont have access to experts from multiple fields. Fortunately, large language models (LLMs) have recently shown an impressive ability to aid researchers across diverse domains by answering scientific questions. Here, we expand the capabilities of LLMs for science by introducing the Virtual Lab, an AI-human research collaboration to perform sophisticated, interdisciplinary science research. The Virtual Lab consists of an LLM principal investigator agent guiding a team of LLM agents with different scientific backgrounds (e.g., a chemist agent, a computer scientist agent, a critic agent), with a human researcher providing high-level feedback. We design the Virtual Lab to conduct scientific research through a series of team meetings, where all the agents discuss a scientific agenda, and individual meetings, where an agent accomplishes a specific task. We demonstrate the power of the Virtual Lab by applying it to design nanobody binders to recent variants of SARS-CoV-2, which is a challenging, open-ended research problem that requires reasoning across diverse fields from biology to computer science. The Virtual Lab creates a novel computational nanobody design pipeline that incorporates ESM, AlphaFold-Multimer, and Rosetta and designs 92 new nanobodies. Experimental validation of those designs reveals a range of functional nanobodies with promising binding profiles across SARS-CoV-2 variants. In particular, two new nanobodies exhibit improved binding to the recent JN.1 or KP.3 variants of SARS-CoV-2 while maintaining strong binding to the ancestral viral spike protein, suggesting exciting candidates for further investigation. This demonstrates the ability of the Virtual Lab to rapidly make impactful, real-world scientific discovery.

bioinformatics↗

bioRxiv2024

Single-Cell Analysis of Gene Regulatory Networks in the Mammary Glands of P4HA1-knockout mice

Prolyl hydroxylation, catalyzed by collagen prolyl-4 hydroxylase (P4H), is a crucial post-translational modification involved in collagen biosynthesis. P4HA1, an isoform of P4H, plays a prominent role in stabilizing hypoxia-inducible factor-1 (HIF-1). P4HA1 is frequently upregulated in highly aggressive triple-negative breast cancer and has been implicated in tumor progression, metastasis, and chemoresistance. In this study, we investigated the role of P4HA1 in mouse mammary glands by analyzing gene regulatory networks (GRNs) in basal epithelial cells across two mouse groups: control (5Ht) and P4HA1-knockout (6Ho) mice. Specifically, we employed a single-cell network inference approach, integrating single-cell RNA sequencing with the SCENIC pipeline, and incorporated multiple validation strategies to construct gene regulatory networks (GRNs) specific to basal epithelial cells from each mouse group. Despite the inherent challenges of single-cell data, our approach identified robust and reproducible GRN patterns across both mouse groups. Based on these patterns, we identified subclusters of basal epithelial cells with similar regulatory profiles across the two mouse groups and a unique subcluster in the control mice with a distinct regulatory pattern absent in the P4HA1-deficient 6Ho mice. This unique subcluster exhibited concurrent activation and potential crosstalks between stem cell development and inflammatory response pathways, underscoring the crucial role of P4HA1 in regulating these biological processes linked to cancer progression. We validated these findings through multiple approaches, including experimental validation. Given that the loss of P4HA1 disrupts the interplay between stem cells and inflammation, our results suggest that targeting P4HA1 may offer a promising therapeutic strategy for breast cancer treatment. Author SummaryIn this study, we aimed to understand how the P4HA1 gene affects the development and functions of mammary gland cells in mice using a single-cell network approach. P4HA1 plays an important role in collagen production, which is essential for maintaining the structure of normal tissues; however, it also helps stabilize the hypoxia-inducible factor-1 (HIF-1) protein that responds to low oxygen levels, a condition often found in various cancers. We generated and analyzed single-cell RNA sequencing data to understand the regulatory roles of P4HA1 in two groups of mice: one with a single functional allele of P4HA1 and the other with the P4HA1 gene knocked out. We constructed gene regulatory networks for basal epithelial cells in both groups using the SCENIC pipeline. Due to the inherent complexity and noise within single-cell data, we employed multiple validation strategies to ensure that the identified regulatory patterns in the networks across both mouse groups were robust and reproducible. In particular, we found that the P4HA1 gene plays a crucial role in pathways related to stem cell development and inflammation, which are essential for both tissue growth and repair, as well as cancer progression. One unique subcluster of basal epithelial cells found only in control mice with P4HA1 showed simultaneous activation of stem cell and inflammatory processes, suggesting that this gene plays a significant role in regulating the communication between these vital cellular processes. Our work provides new insights into how P4HA1 might serve as a therapeutic target to slow down or prevent the progression of aggressive cancers, such as triple-negative breast cancer, where treatment options are currently limited, leading to a high mortality rate.

bioinformatics↗

bioRxiv2024

Accelerated multi-shell diffusion MRI with Gaussian process estimated reconstruction of multi-band imaging

PurposeThis work aims to propose a robust reconstruction method exploiting shared information across shells to increase the acquisition speed of multi-shell diffusion MRI, enabling rapid tissue microstructure mapping. Theory and MethodsLocal q-space points share similar information. Gaussian Process can exploit the q-space smoothness in a data-driven way and provide q-space signal estimation based on the signals from a q-space neighborhood. The Diffusion Acceleration with Gaussian process Estimated Reconstruction (DAGER) method uses the signal estimation from Gaussian process as a prior in a joint k-q reconstruction and improves image quality under high acceleration factors compared to conventional (k-only) reconstruction. In this work, we extend the DAGER method by introducing a multi-shell covariance function and correcting for Rician noise distribution in magnitude data when fitting the Gaussian process model. The method was evaluated with both simulation and in vivo data. ResultsSimulated and in-vivo results demonstrate that the proposed method can significantly improve the image quality of reconstructed dMRI data with high acceleration both in-plane and slice-wise, achieving a total acceleration factor of 12. The improvement of image quality allows more robust diffusion model fitting compared to conventional reconstruction methods, enabling advanced multi-shell diffusion analysis within much shorter scan time. ConclusionThe proposed method enables highly accelerated dMRI which can shorten the scan time of multi-shell dMRI without sacrificing quality compared to conventional practice. This may facilitate a wider application of advanced dMRI models in basic and clinical neuroscience.

bioengineering↗

bioRxiv2024

Submillimeter diffusion MRI using an in-plane segmented 3D multi-slab acquisition and denoiser-regularized reconstruction

High-resolution diffusion MRI (dMRI) provides valuable insights into brain microstructure, particularly at submillimeter resolutions, where it enables more precise delineations of curved and crossing white matter pathways. However, achieving high-quality submillimeter dMRI in-vivo poses significant challenges due to the intrinsically low signal-to-noise ratio (SNR), along with the long echo spacing, readout time, and TE required for the large matrix size, leading to significant image distortion, T2* blurring, and T2 signal decay. In this study, we propose a novel acquisition and reconstruction framework to overcome these challenges. Based on numerical simulations, we introduce an in-plane segmented 3D multi-slab acquisition that leverages the optimal SNR efficiency of 3D multi-slab imaging while reducing echo spacing, readout times, and TE using in-plane segmentation. This approach minimizes distortion, improves image sharpness, and enhances SNR. Additionally, we develop a denoiser-regularized reconstruction to suppress noise while maintaining data fidelity, which reconstructs high-SNR images without introducing substantial blurring or bias. Comprehensive in-vivo experiments demonstrate that our method consistently produces high-quality dMRI data at 0.65 mm and 0.53 mm isotropic resolutions on a 3T scanner. The submillimeter dMRI datasets reveal richer microstructural details, reduce gyral bias, and improve U-fiber mapping compared to prospectively acquired 1.22 mm diffusion data. Our method demonstrates robustness at 7T and generates high-SNR 0.61 mm diffusion datasets, showing excellent agreement with previous post-mortem studies at the same scanner. Implemented using the open-source, scanner-agnostic framework Pulseq, our approach may facilitate broader adoption across different scanner platforms to benefit a wider range of applications. These results underscore the potential of our method to advance medical image analysis and neuroscientific research on human brain connectivity.

neuroscience↗

bioRxiv2024

A Wearable Mindfulness Brain-Computer Interface for Alleviating Car Sickness

Car sickness, an enormous vehicular travel challenge, affects a significant proportion of the population. Pharmacological interventions are limited by adverse side effects, and effective nonpharmacological alternatives remain to be identified. Here, we introduce a novel attention shifting method based on a closed-loop, artificial intelligence (AI)-driven, wearable mindfulness brain-computer interface (BCI) to alleviate car sickness. As the user performs an attentional task, i.e., focusing on breathing as in mindfulness, with a wearable headband, the BCI collects and analyses electroencephalography (EEG) data via a convolutional neural network to assess the users mindfulness state and provide real-time audiovisual feedback. This approach might sustainedly shift the users attention from physiological discomfort towards the BCI-based mindfulness practices, thereby mitigating car sickness symptoms. The efficacy of the proposed method was rigorously evaluated in two real-world experiments, namely, short and long car rides, with a large cohort of more than 100 participants susceptible to car sickness. Remarkably, over 83% of the participants rated the BCI-based attention shifting as effective, with significant reductions in car sickness severity, particularly in individuals with severe symptoms. Furthermore, EEG data analysis revealed a neurobiological signature of car sickness, which provided mechanistic insights into the efficacy of the BCI-based attention shifting for alleviating car sickness. This study proposed the first large-scale validated, nonpharmacological and wearable intervention method and system for car sickness, with the potential to transform the travel experiences of hundreds of millions of people suffering from car sickness, which also represents a new application of BCI technology.

bioengineering↗