Biology subjects

Chen, J.

Publications and source records attributed to Chen, J..

At least 217 records · Page 12Linked to original sources

bioRxiv2026

CosMxScope: Scalable Reconstruction and Digital Pathology Integration of Imaging-Based Spatial Transcriptomics Data

Spatial transcriptomics technologies have transformed the capacity to quantify gene expression from human tissues, simultaneously capturing both the cell functional state and spatial organization at cellular and subcellular resolution. The CosMx Spatial Molecular Imager (SMI) is one of the leading platforms at single-cell spatial multi-modal omics profiling, capable of measuring thousands of RNA or protein targets per cell across whole slide sections. Data exported includes field-of-view (FOV) image tiles, subcellular transcript coordinates, and cell segmentation polygons, outputs that are rich in spatial information but not directly compatible with widely used digital pathology tools. Here we present CosMxScope, a lightweight open-source Python framework that bridges CosMx spatial outputs with histopathology visualization environments. CosMxScope provides functions for stitching FOV image tiles into reconstructed whole-slide images, converting cell segmentation polygons and transcript coordinates into GeoJSON objects which enabled further assessments within QuPath, and generating spatial visualization plots of cell types, transcript locations, and gene expression patterns. The framework is designed for practical use in translational research settings, enabling interactive exploration of spatial transcriptomic data alongside cell morphology. CosMxScope has been applied in multiple ongoing research projects involving CosMx profiling of human and mouse tissues, supporting pathology-based spatial analysis workflows. This open-source software is available at https://github.com/AivaraX-AI/CosMxScope.

bioinformatics↗

bioRxiv2026

Cognition emerges from phase dynamics of intrinsic coordination

The brain generates diverse cognitive states while maintaining a stable functional architecture, a duality that remains difficult to reconcile. Prevailing views assume that flexible cognition necessitates correspondingly flexible architecture with different tasks demanding distinct reconfigurations, leaving the coexistence of stability and flexibility unexplained. Here we introduce the intrinsic network flow (INF) framework as a complementary view. This framework is built on temporally coordinated signal flows across brain networks that constitute a universal scaffold, stable across diverse cognitive states and common across individuals. We show that a wide range of task-evoked activation patterns can be reconstructed by modulating only the temporal phase alignment of these flows, whose fixed structure determines functional connectivity topology, gradients, and large-scale networks, thereby preserving these properties across task states and reconciling flexibility with stability. This situates resting-state and task-state dynamics within a unified framework and suggests a generative relationship from flow-like dynamics to the full landscape of resting-state and task-state phenomena. Crucially, phase information achieved 89.9% accuracy in classifying 23 cognitive tasks, outperforming amplitude- or activation-based markers. These findings reframe task-evoked activation and deactivation as interference among concurrent flows. Together, our work indicates that a key variable for cognition is when intrinsic dynamics align in time, not where or how much the brain activates.

neuroscience↗

bioRxiv2026

Systematic detection of abnormal samples reveals widespread mislabeling in metagenomic studies

The human microbiome plays a critical role in health and disease, and its dynamic nature has made longitudinal sampling a key strategy for elucidating microbiome-disease relationships. Although the gut microbiome generally stabilizes over time, a subset of samples frequently shows marked deviations from an individuals baseline profile. We refer to these as abnormal samples. To analyze these abnormal samples, we developed a three-stage workflow to identify and classify these abnormal samples to figure out the underlying causes of these abnormal samples. Moreover, we systematically investigated abnormal samples across 16 publicly available metagenomic datasets, comprising a total of 5,171 metagenomes. Our analysis revealed that abnormal samples are often the result of mislabeling during sample collection, processing, or sequencing. Of which, fecal samples from family are more likely mislabeled. We found evidence of mislabeling in 75% of longitudinal datasets, involving up to dozens of samples per study, and in 25% of randomly selected cross-sectional datasets. Additional factors such as disease status (e.g., inflammatory bowel disease), sampling intervals, and sampling density may also contribute to sample abnormalities owing to true biological variations. These findings highlight that mislabeling is a common yet underrecognized issue in microbiome research. Our work underscores the importance of identifying and correcting abnormal samples to ensure data integrity in microbiome studies and provides a practical solution for quality control in large-scale metagenomic datasets.

microbiology↗

bioRxiv2026

Improved Biosynthesis of Ethylene Glycol from Xylose in Engineered E. coli Utilizing Two-Stage Dynamic Control

In this study, we employ a two-stage dynamic metabolic control strategy to enhance the NADPH dependent biosynthesis of ethylene glycol from xylose in engineered E. coli. We evaluated the use of metabolic valves to dynamically reduce the enzymes involved in competitive pathways which compete for substrates with ethylene glycol biosynthesis, as well as regulatory pathways aimed at increasing NADPH fluxes. The performance of our initial strains with limits in pathway expression levels was improved by the addition of competitive valves, but not by increases in NADPH flux. In contrast, improving pathway expression levels, led to strains improved significantly by our regulatory valves which improved NADPH flux, but not by the competitive valves. This is consistent with a central hypothesis that faster pathways in and of themselves can compete with other metabolic fluxes by being faster and are better aided by regulatory changes capable of change rates elsewhere in metabolism. In this case in NADPH flux. Lastly, upon scale up to fed-batch bioreactors, our optimized strain, featuring dynamic control of two regulatory valves produced 140 g/L of EG in 70 hours at 92% of the theoretical yield.

synthetic biology↗

bioRxiv2026

Standalone nanopore sequencing for foodborne pathogen surveillance: a large-scale evaluation and quality control framework

Whole-genome sequencing (WGS) is central to foodborne pathogen surveillance and cross-border outbreak detection. Long-read sequencing using Oxford Nanopore Technologies (ONT) promises rapid, complete, and cost-effective genome assemblies in a single workflow. However, the adoption of standalone ONT sequencing of native DNA has been slowed by concerns that DNA modifications can compromise per-base sequencing accuracy and downstream genotyping. In this study, we evaluated ONT-only sequencing performance across 294 genetically diverse isolates representing ten major foodborne pathogens. Using the SUP@v5.2 basecalling model at 50x coverage, 97.3% (286/294) of the ONT assemblies produced identical or near-identical cgMLST profiles ([≤]3 allelic differences) as Illumina-polished hybrid assemblies. Elevated error rates were observed in four Salmonella enterica serovar Kentucky and four Listeria monocytogenes isolates and were associated with the presence of specific DNA phosphorothioation or methylation systems. Re-basecalling the same dataset with the newly released HAC@v6.0 model revealed a different error profile: although 93.5% (275/294) of assemblies remained highly accurate, all 13 isolates carrying dnd (DNA phosphorothioation) or dpd (7-deazaguanine modification) systems, including isolates of S. enterica, Cronobacter sakazakii, and Vibrio parahaemolyticus, exhibited high error rates, suggesting that such atypical modifications were not adequately represented in the models training dataset. To enable rapid identification of unreliable assemblies, we developed alpaqa, a lightweight computational tool that detects systematic nanopore assembly errors without requiring supplemental short-read data or reference genomes. By identifying affected assemblies, alpaqa provides a quality safeguard for ONT-only workflows. Masking low-quality bases in assemblies flagged by alpaqa improved cgMLST accuracy, although this reduced the number of callable loci and therefore genotyping resolution. Our findings demonstrate that standalone ONT sequencing of native DNA is sufficiently accurate for routine foodborne pathogen surveillance when combined with appropriate quality control, supporting its use in harmonised genomic surveillance frameworks. Data summaryAll sequencing data generated in this study have been submitted to the NCBI Sequence Read Archive. Accession numbers for Illumina and ONT (SUP@v5.2) reads are listed in Supplementary Table S1. Raw pod5 files from error-prone isolates have been deposited in SquiDBase (SQB000021). Alpaqa is available at github.com/MBiggel/alpaqa/. An automated ONT assembly and quality control pipeline integrating alpaqa is available at github.com/MBiggel/boap/. Impact statementThis study demonstrates that standalone Oxford Nanopore sequencing of native DNA can achieve highly accurate genotyping for routine foodborne pathogen surveillance across diverse species. We show that the remaining inaccuracies are linked to specific DNA modification systems, including phosphorothioation and 7-deazaguanine modifications, which are identified here as previously unrecognised sources of systematic sequencing errors. To address this limitation, we introduce alpaqa, a reference-free method for detecting such error-prone assemblies, providing a practical quality-control framework for ONT-only workflows. Together, these results support the reliable use of nanopore sequencing in routine genomic surveillance.

genomics↗

bioRxiv2026

Single-cell atlas of pig-to-monkey kidney xenotransplantation reveals macrophage chimerism and an IFN-ε orchestrated graft protective immune niche

The clinical translation of xenotransplantation is constrained by an incomplete understanding of the cellular circuitry governing graft rejection versus adaptation under current immunosuppressive regimens. Here, we present a high-resolution single-cell atlas of pig-to-monkey kidney xenotransplantation in a preclinical model, capturing the early immune dynamics preceding graft loss. Our analysis reveals a landscape dominated by innate immunity, characterized by species-specific distribution of macrophages. We identify recipient-derived macrophage subsets (ACKR1+, FN1+, MARCO+, IDO1+) enriched for multiple immune checkpoint molecules, alongside donor-resident subpopulations (APOE+, CCL2+, IL-1A+) exhibiting pro-inflammatory pathogenic signatures, and leverage their transcriptional profiles to computationally predict candidate therapies (belatacept, abatacept, pexidartinib). Further analysis demonstrates that despite divergent differentiation trajectories, both recipient-derived and donor-resident macrophages converge toward hybrid M1/M2 states at their terminal stages. Notably, we uncover a graft-protective circuit orchestrated by epithelial-derived interferon-epsilon (IFN-{varepsilon}), which specifically engages multiple subsets including IDO1+ macrophages to establish a localized immune-tolerant niche. This study establishes the xenograft epithelium as an active participant in immune modulation via the IFN-{varepsilon} axis and reveals macrophage functional chimerism as a key actionable feature of cross-species immune responses.

genomics↗

bioRxiv2026

Glycoengineering of extracellular vesicles enhances cellular uptake and cargo delivery to target cells

The glycocalyx is a major regulator of membrane recognition, yet its specific influence on extracellular vesicles (EVs) cellular uptake remains poorly defined. We established a genetic glycoengineering platform to systematically investigate how the major glycan classes on small EVs (sEVs) modulate cell interactions and functional cargo delivery. Using an isogenic panel of HEK293F lines lacking distinct glycan biosynthetic pathways, we find that removing glycosaminoglycans ({Delta}GAG-sEVs) yields a strong increase in cellular uptake and delivery of diverse cargos, including DNA oligonucleotides, siRNA, proteins, and plasmid DNA. Glycan-modified recipient cells show that sEV-cell communication and internalization is jointly governed by glycan features on both membranes. {Delta}GAG-sEVs strongly improve gene delivery and expression in recipient cells and in a physiologically relevant human airway epithelial model. These findings establish glycan structures as tunable regulators of sEV uptake and position {Delta}GAG-sEVs as potent vehicles for improved drug delivery and gene therapy.

bioengineering↗

bioRxiv2026

ProAR: Probabilistic Autoregressive Modeling for Molecular Dynamics

Understanding the structural dynamics of biomolecules is crucial for uncovering biological functions. As molecular dynamics (MD) simulation data becomes more available, deep generative models have been developed to synthesize realistic MD trajectories. However, existing methods produce fixed-length trajectories by jointly denoising high-dimensional spatiotemporal representations, which conflicts with MDs frame-by-frame integration process and fails to capture time-dependent conformational diversity. Inspired by MDs sequential nature, we introduce a new probabilistic autoregressive (ProAR) framework for trajectory generation. ProAR uses a dual-network system that models each frame as a multivariate Gaussian distribution and employs an anti-drifting sampling strategy to reduce cumulative errors. This approach captures conformational uncertainty and time-coupled structural changes while allowing flexible generation of trajectories of arbitrary length. Experiments on ATLAS, a large-scale protein MD dataset, demonstrate that for long trajectory generation, our model achieves a 7.5% reduction in reconstruction RMSE and an average 25.8% improvement in conformation change accuracy compared to previous state-of-the-art methods. For conformation sampling task, it performs comparably to specialized time-independent models, providing a flexible and dependable alternative to standard MD simulations.

molecular biology↗

bioRxiv2026

Duplex Reverse Transcription Loop-Mediated Isothermal Amplification on a Nanofluidic Digital Chip (Nano-dChip)

Over the past decades, the frequency of viral outbreaks has increased substantially worldwide, driven in part by global travel and resulting in millions of deaths each year. This trend underscores the urgent need for rapid, simple, and accessible diagnostic tools for infectious disease detection. Here, we present a nanofluidic digital chip (Nano-dChip) for point-of-care viral RNA detection that delivers results within 30 minutes at a cost of less than $0.50 per chip. The Nano-dChip employs reverse transcription loop-mediated isothermal amplification (RT-LAMP) for highly sensitive and specific target amplification. Reaction reagents are compartmentalized into numerous nanofluidic reservoirs, enabling highly quantitative detection while minimizing contamination risks. Using a single chip, we successfully detect both SARS-CoV-2 and Influenza H3 RNA with a detection limit of 10 fM, demonstrating the Nano-dChips potential as a rapid, low-cost, and scalable diagnostic platform for timely outbreak control.

bioengineering↗

bioRxiv2026

RNASTOP: A Deep Learning Framework for mRNA Chemical Stability Prediction and Optimization

Messenger RNA (mRNA) vaccines offer promising therapeutics for combating various diseases, yet their inherent chemical instability hampers their long-term efficacy. Although several methods have been developed to predict mRNA degradation, they exhibit limited accuracy and lack the capability for rational sequence optimization. Here, we propose RNASTOP, a novel framework integrating deep learning with heuristic search to simultaneously predict and optimize mRNA chemical stability. RNASTOP achieves a 13% accuracy improvement over the top-performing model on the Stanford OpenVaccine competition dataset and demonstrates robust generalization in predicting full-length mRNA degradation. Applied to mRNA codon optimization, RNASTOP reduces the minimum free energy of the Varicella-Zoster Virus vaccine sequence by 75.73% while maintaining high translation efficiency. Overall, RNASTOP serves as a powerful tool for predicting and optimizing mRNA chemical stability, poised to expedite the development of mRNA therapeutics. The source code of RNASTOP can be accessed at https://github.com/xlab-BioAI/RNASTOP.

bioinformatics↗

bioRxiv2026

X-Cell: Scaling Causal Perturbation Prediction Across Diverse Cellular Contexts via Diffusion Language Models

Causal models of cellular systems hold the promise to empower broad biological discovery, including the systematic identification of novel targets for drug discovery. Predicting how genetic and pathway perturbations reshape gene expression across diverse cellular contexts is a prerequisite for building generalizable cellular foundation models. However, current methods typically fail to extrapolate beyond their training distributions because they rely predominantly on observational expression atlases rather than interventional perturbation data. We present X-Atlas/Pisces, the largest genome-wide CRISPRi Perturb-seq compendium to date, comprising 25.6 million perturbed single-cell transcriptomes across 16 biologically diverse contexts, including widely used cell lines, induced pluripotent stem cells (iPSCs), resting and CD3/CD28 activated Jurkat T lymphoma cells, and multi-lineage differentiating iPSCs. Leveraging this resource, we develop X-Cell, a diffusion language model that predicts perturbation responses by iteratively refining control-to-perturbed state transitions through cross-attention to multi-modal biological priors derived from natural language, protein language models, interaction networks, genetic dependency maps, and morphological profiles. X-Cell outperforms existing state-of-the-art models by up to five-fold on key metrics such as Pearson{Delta} (correlation between predicted and observed perturbation-induced log-fold changes), and demonstrates zero-shot prediction of T cell inactivating perturbations in stimulated Jurkat cells. We scale X-Cell to 4.9 billion parameters (X-Cell-Ultra), the largest causal perturbation model to date. We demonstrate for the first time that perturbation prediction follows power-law scaling with an exponent matching large language models. X-Cell-Ultra demonstrates zero-shot generalization to novel biological contexts, including unseen iPSC-derived melanocyte progenitors and primary human CD4+ T cells from multiple donors, and outperforms all baselines after self-supervised test-time adaptation. These results demonstrate that coordinated scaling of causal perturbation data and model capacity yields foundation models capable of generalizable perturbation prediction across cellular contexts, with potential applications for improving computational target identification, validation, and context-specific therapeutic prioritization.

systems biology↗

bioRxiv2026

Individuals with Intermittent Explosive Disorder Exhibit Idiosyncratic Neural Responses during Social-emotional Processing

Intermittent explosive disorder (IED) is associated with impulsive aggression in ambiguous social contexts. Prior neuroimaging studies have treated IED as a homogenous group, but identical social situations may elicit divergent responses across IED individuals. Here, we test the hypothesis that IED is characterized by idiosyncratic neural responses to social cues during naturalistic social-emotional processing. IED individuals and healthy controls completed a validated paradigm where they were presented with video vignettes of interpersonal interactions while undergoing fMRI. We computed the intersubject correlation (ISC) in neural time courses between pairs of participants to quantify neural similarity, and assessed whether similarity differed between Healthy-Healthy and IED-IED dyads using Bayesian multilevel models, controlling for self-reported emotional responses and intention attributions for each vignette. Healthy-Healthy dyads showed significantly higher ISC than IED-IED dyads, indicating that neural responses to the videos were similar among healthy participants, but idiosyncratic in IED individuals. These effects were observed in regions in the default mode and salience networks, including the precuneus, medial prefrontal cortex, superior temporal sulcus, insula, and dorsal anterior cingulate cortex. Individuals with IED exhibited idiosyncratic neural responses during naturalistic social-emotional processing, even after accounting for differences in emotional reaction and intention attribution. This neural idiosyncrasy may reflect atypical integration of social cues, giving rise to maladaptive interpretations and impulsive aggression. Assessing neural synchrony during ecologically valid paradigms offers a promising tool for identifying neural markers of interpersonal dysfunction and informing targeted interventions.

neuroscience↗

bioRxiv2026

Frequency-domain kernels enable atlas-scale detection of spatially variable genes

Spatial transcriptomics links gene expression to tissue architecture, but detecting spatially variable genes at atlas scale remains difficult because biologically relevant patterns are multiscale, sparse, and often non-parametric. Here we show that FlashS, a frequency-domain kernel test using random Fourier features, sparse sketching, and a kurtosis-corrected null, retains Gaussian-kernel flexibility at near-linear per-gene cost without permutation. Across 50 benchmark datasets spanning 9 spatial transcriptomics platforms, FO_SCPLOWLASHC_SCPLOWS achieves the highest ranking accuracy among 14 compared methods while maintaining calibrated inference under simulation and full-atlas permutation. In human heart tissue, it recovers a mitochondrial biogenesis program that co-localizes with ventricular cardiomyocytes. On the Allen Brain MERFISH atlas containing 3.94 million cells, FO_SCPLOWLASHC_SCPLOWS completes in minutes on a single workstation and separates biological signal from negative-control barcodes even when p-values saturate.

bioinformatics↗

bioRxiv2026

Nanosecond laser-driven proton FLASH spares normal tissue cells by sustaining mitochondrial homeostasis and attenuating ferroptosis

Radiotherapys clinical utility remains fundamentally constrained by the collateral damage to healthy tissues. Ultra-high-dose-rate (UHDR) irradiation, or, FLASH-radiotherapy (FLASH-RT) has emerged as a transformative paradigm to mitigate such toxicity. However, the biological effects of FLASH-RT on the high-efficiency of tumor killing and normal tissue sparing remain poorly understood. In this work, we utilized a petawatt-class laser-plasma acceleration (LPA) platform to deliver discrete 12.9-nanosecond proton pulses at an extreme instantaneous dose rate of 1.94x107 Gy/s. This temporal singularity achieved a profound sparing effect in normal bronchial epithelial cells, evidenced by a nine-fold reduction in the lethal coefficient (from 0.47 to 0.05 Gy-1), while maintaining full tumoricidal potency against lung adenocarcinoma. Mechanistically, we demonstrated that LPA-FLASH could effectively bypass the ATF3-mediated stress response and circumvent the subsequent ferroptotic cascade. This molecular evasion could preserve the mitochondrial cristae integrity and trigger an adaptive bioenergetic ATP surge--a hallmark of metabolic resilience exclusively in healthy tissue cells. Therefore, our findings identify ferroptosis-mediated mitochondrial integrity as a unifying framework for selective normal-tissue protection at the physical limits of radiation delivery, and establish LPA-FLASH-RT as a potent, compact modality for next-generation oncology.

cell biology↗

bioRxiv2026

Programming Biomolecular Interactions with All-Atom Generative Model

Biomolecular interactions lie at the core of cellular life, spanning diverse molecular modalities from small molecules to nucleic acids and proteins. Nevertheless, design strategies remain separated despite shared physicochemical principles of molecular recognition. Here we present AnewOmni, a unified generative framework trained on more than 5 million biomolecular complexes, that enables transferable molecular design across molecular scales by assembling chemically meaningful building blocks at atomic resolution. We further introduce programmable graph prompts to support user-defined chemical, topological, and geometric steering during generation, exploring hybrid and unconventional chemistries beyond canonical structures. We demonstrate that transferable learning of interaction patterns and physical constraints across molecular modalities is possible, via an atom-to-block latent space capturing both atomic details and structural priors. The framework successfully designed small molecules, peptides, and nanobodies targeting the challenging KRAS G12D switch II pocket, as well as orthosteric peptides and allosteric small-molecule inhibitors for PCSK9 in the absence of known binding site, achieving 23%-75% success with only low-throughput validation, bypassing modality-specific high-throughput screening. AnewOmni is the first to succeed in functional molecular design across all scales, from small chemical entities to large biologics, and represents a stepstone towards general molecular reasoning engines, advocating a generative foundation model for biomolecular interactions to enter regimes where data and human intuition remain limited.

bioengineering↗

bioRxiv2026

Regimen-dependent glucocorticoid effects improve muscle performance without altering CNS physiology in mdx mice

Duchenne muscular dystrophy (DMD) is a multisystem disorder affecting striated muscle, metabolism, and the central nervous system (CNS). Although glucocorticoids remain the standard therapy, muscle-centric evaluations typically fail to capture how dosing regimen and compound selection affect CNS and metabolic phenotypes. Here, we compared daily and weekly dosing of prednisolone and vamorolone in juvenile mdx mice over six weeks to determine how these variables influence multisystem outcomes. Multiorgan efficacy and adverse effects were quantified across behavioural, endocrine, metabolic, cardiovascular, and muscle domains using behavioural assays, in vivo and functional muscle testing, haemodynamic evaluation and histopathology. Daily glucocorticoid dosing failed to improve muscle function or strength, whereas weekly vamorolone produced the most robust improvements in functional and in vivo muscle strength. Daily prednisolone reduced circulating creatine kinase levels, but this biochemical change did not translate into enhanced muscle function outcomes. Daily regimens also induced severe adrenal cortical atrophy, yet these endocrine alterations were dissociated from CNS stress and anxiety responses, which remained unchanged by treatment. In addition, daily dosing caused pronounced systemic metabolic consequences, whereas weekly regimens substantially attenuated these effects, identifying dosing frequency as a key determinant of safety. Together, these findings demonstrate that glucocorticoid regimen selection fundamentally reshapes the efficacy-adverse effect profile and underscores the value of integrated multiorgan evaluation in DMD. This work highlights the need to expand therapeutic assessments beyond muscle pathology and raises new questions about how glucocorticoid signalling differentially engages peripheral and central physiological systems.

physiology↗

bioRxiv2026

NeuroMark-SZ: A Holistic Resting-State-fMRI-Based Model for Divergent Functional Circuitry in Schizophrenia

BackgroundSchizophrenia is a severe neuropsychiatric disorder. Efforts to describe the underlying biology and establish diagnostic markers through non-invasive neuroimaging methods are ongoing, resulting in a range of theoretical brain-based frameworks. Prominent frameworks for aberrant schizophrenia-associated functional connectivity in resting-state functional magnetic resonance imaging (rsfMRI) include the dysconnectivity hypothesis, theory of cognitive dysmetria, and triple network theory. Although informative, prior work can be improved by increasing sample size, avoiding confirmation bias, and accounting for individual variability and the effects of medication and chronicity. MethodsWith these recommendations in mind, we employed a data-driven, whole-brain approach using a large multi-site rsfMRI dataset (N = 2,656; schizophrenia = 1,248). We used reference-guided independent component analysis to generate subject-specific whole-brain functional network connectivity (FNC) and extract imaging markers of similarity to schizophrenia patterns. We modeled the relationship between medication dosage, age of onset, chronicity, symptom severity, and cognitive performance and FNC. ResultsOur analysis identified a reliable schizophrenia-FNC signature characterized by aberrantly stronger negative cerebellothalamic and positive thalamocortical connectivity, implicating sensory, motor, and associative cortical circuits. While medication and chronicity were significantly associated with these signatures, the core cerebellothalamic disruptions remained a robust marker of schizophrenia. ConclusionsThis work represents the largest schizophrenia-specific rsfMRI study to date, refines existing theoretical frameworks with a more nuanced map of how clinical variables interact with brain connectivity, and provides a high-fidelity template of schizophrenia-related connectivity. We have released this template as an open-access resource to facilitate reproducibility and accelerate the development of reliable rsfMRI-based schizophrenia biomarkers.

neuroscience↗

bioRxiv2026

Connectome-wide mega-analysis identifies a reproducible functional network signature of temporal lobe epilepsy

Resting-state functional magnetic resonance imaging (MRI) studies have reported abnormal intrinsic functional connectivity (FC) across distributed circuits in patients with temporal lobe epilepsy (TLE), indicating a system-level impact of the disorder. However, findings remain inconsistent due to limited sample sizes and methodological heterogeneity, leaving the structural determinants and clinical relevance of FC alterations unresolved. To identify a robust and reproducible FC signature, we conducted a data-driven, connectome-wide mega-analysis in a large multicentre cohort of 652 participants (297 TLE, 73 disease controls, and 282 healthy controls) with multimodal 3T MRI and deep clinical phenotyping. We identified convergent FC reconfigurations at both group and individual levels that preferentially involved densely connected hubs, manifesting as hyperconnectivity in frontoparietal association systems and hypoconnectivity in temporal and paralimbic systems. Integrating structural cortical wiring features further revealed that these extensive alterations were constrained by corticocortical proximity, microstructural similarity, and white matter connectivity. Clinically, the FC phenotype tracked symptom burden and disease progression, informed postsurgical seizure outcome, and distinguished TLE from other focal epilepsies. Collectively, these findings systematically delineate a neurobiologically grounded, hub-centric pattern of intrinsic network disruption in TLE, anchored in temporolimbic and adjacent transmodal systems, with potential utility for individualized phenotypic stratification and outcome prognostication.

neuroscience↗