Biology subjects

Zhang, Z.

Publications and source records attributed to Zhang, Z..

At least 199 records · Page 11Linked to original sources

bioRxiv2026

A Dynamic Oligomerization Network Coordinates Hemagglutinin-Mediated Membrane Fusion on Influenza Virions

Influenza A virus (IAV) entry is mediated by the trimeric glycoprotein hemagglutinin (HA), which undergoes low-pH-triggered conformational rearrangements to drive membrane fusion. Although biochemical and biophysical studies have long suggested that HA trimers function cooperatively, the structural basis for this cooperativity on intact virions has remained unclear. Here, using cryo-electron tomography (cryo-ET) and subtomogram averaging (STA), we determine the in situ structure of prefusion HA on native virions at 3.58 [A] resolution, enabling atomic modeling of membrane-anchored HA. We show that neighbouring HA trimers engage in lateral interactions mediated by HA1 subunits, assembling into flexible dimeric, pentameric, and hexameric organizations that form locally ordered lattices on the viral surface. A 6.0 [A] reconstruction of the HA-dimer reveals the molecular interfaces underlying these assemblies. Disruption of the dominant HA1-HA1 interaction markedly impairs viral entry and slows membrane fusion kinetics. Together, these findings define a virion-level mechanism for coordinated HA activation and establish a structural framework for understanding cooperative membrane fusion by class-I viral fusion proteins.

biophysics↗

bioRxiv2026

The enhanced multi-tissue atlas of regulatory effects in cattle

Cattle are integral to global food security, yet the molecular architecture of their complex traits remains poorly understood. Here, we present the Cattle Genotype-Tissue Expression (CattleGTEx) Phase 1 resource (https://cattlegtex.farmgtex.org/), a substantial expansion of the pilot study. By leveraging 12,422 RNA-seq profiles across 43 tissues and 82 breeds, we characterized 433,972 primary and 161,428 non-primary regulatory effects spanning seven molecular phenotypes. This high-resolution atlas resolves 75% of GWAS signals for 44 complex traits, significantly addressing the "missing regulation" in livestock. We propose a genetic regulatory model demonstrating how variants across multiple biological layers interact with specific biological contexts to shape phenotypic variation. Furthermore, CattleGTEx elucidates mechanisms underlying adaptive evolution between Bos taurus and Bos indicus, as well as artificial selection in dairy and beef breeds. Finally, by mapping evolutionary constraints on these regulatory effects, we demonstrate the translational value of this resource for prioritizing causal variants in human complex diseases. Together, Phase 1 of CattleGTEx provides a transformative framework for functional genomics, precision breeding, and comparative genetics.

genetics↗

bioRxiv2026

Computational Fluid Particle Dynamics-Informed Machine Learning Prototype for a User-Centered Smart Inhaler Enabling Uniform Drug Delivery to Small Airways

Small airways are the primary sites of airflow obstruction in chronic obstructive pulmonary disease. Effective delivery of aerosolized drug particles to these regions is crucial to maximize treatment efficacy while minimizing side effects. However, conventional inhalation therapy approaches (i.e., full-mouth particle release and inhalation (FMD)) typically result in insufficient drug deposition in the small airways and an uneven distribution across the five lung lobes. To address such deficiencies, the goals of this study are triple folds: (1) to develop a fast and accurate framework to secure target drug delivery (TDD) nozzle diameter and location based on the conventional computational fluid particle dynamics (CFPD)-FMD simulations, (2) to develop a CFPD-informed machine learning (ML) inverse-design framework that predicts optimal inhaler nozzle parameters based on patient-specific breathing patterns and drug properties, and (3) to demonstrate the feasibility of embedding this framework into a user-centered smart inhaler prototype to improve uniform TTD to the small airways across all five lung lobes. Specifically, a subject-specific mouth-to-generation-10 human respiratory system was employed, and 108 high-fidelity CFPD-FMD simulations were performed under varied physiological and design parameters, including tidal volume, particle diameter, release location, and release timing. Particle release maps generated from those CFPD-FMD simulations via backtracking identified optimal nozzle diameters and locations that promote uniform multi-lobe drug delivery while limiting off-target deposition. Accordingly, a dataset was compiled with inputs (i.e., flow rate, particle size, release z-coordinate, release time) and targets (i.e., nozzle center x- and y-coordinates, nozzle diameter). These inputs and targets form the CFPD-TDD dataset, on which 16 ML models were trained to learn inverse mapping from patient- and drug-specific inputs to optimal nozzle design parameters. Performance was evaluated using mean squared error (MSE) and mean absolute error (MAE) overall and per target feature. Parametric analysis using CFPD-FMD simulations was conducted to determine how patient-specific and drug-specific factors affect pulmonary air-particle transport dynamics and to explain why achieving CFPD-TDD in small airways with CFPD-FMD strategies remains challenging. Furthermore, the ML evaluation in this feasibility study demonstrated robust learning of the inverse mapping from patient-specific inputs to optimal nozzle parameters. Four top-performing models showed consistently low MSE/MAE across cases, and an ensemble (i.e., mixed model (MixModel)) combining their strengths was formulated. Independent CFPD-TDD simulations beyond the training and testing datasets were used as the ground truth to validate ML-predicted nozzle configurations. Compared with conventional CFPD-FMD strategies, ML-guided nozzle designs significantly improved inter-lobar deposition uniformity and reduced off-target deposition in the upper airways, demonstrating the feasibility of ML-enabled TDD to the small airways. Overall, this study establishes a CFPD-informed ML inverse-design framework as a viable algorithmic foundation for user-centered smart inhalers, enabling adaptive, patient-specific TDD to the small airways with improved deposition uniformity across all five lung lobes. By integrating first-principle-based CFPD with ML, this work provides a methodological pathway toward next-generation smart inhalers for more effective treatment of small airway diseases.

bioengineering↗

bioRxiv2026

Mapping Individualized Dual-Axis Network Topology in Focal Epilepsy: Divergent Alterations in System Integrity, Integration, and Clinical Correlates

Focal epilepsy is increasingly recognized as a disorder of distributed brain systems, yet characterizations of patient-specific alterations in network organization that are clinically meaningful remain limited. Here, we used resting-state functional MRI to introduce an overlap-permitting individualized network-estimation framework anchored to normative references to derive two complementary, system-level axes of topology: (1) network correspondence, indexing the expression of canonical networks and the fidelity of intrinsic system boundaries, and (2) k-hubness, indexing cross-system integration and multi-functionality through multi-network participation. We used a large presurgical focal epilepsy cohort (305 patients, including temporal and extratemporal epilepsy; 224 healthy participants) and an independent multi-syndrome validation cohort spanning focal and common generalized and childhood epilepsy syndromes (903 patients; 666 healthy participants), to show: (1) correspondence disruption of both canonical and non-normative networks constitutes a broadly shared system-level endpoint that displays heterogeneity across individuals and differential impacts on cognition, (2) k-hubness reconfigurations reflect lateralized and syndrome-dependent redistributions of cross-system integration, demonstrating epilepsys impact on the functional multiplicity inherent to specific brain areas. These complementary axes shared variance yet showed dissociable clinical associations, with correspondence preferentially relating to neurocognitive deficits and k-hubness aligning with epilepsy clinical features. Accordingly, these axes demonstrate the presence of distinct topological phenotypes, providing clinically relevant, reproducible patient-level signatures that explain both the syndrome-specific and common effects of seizures on the organization and integration of canonical and individualized brain systems.

neuroscience↗

bioRxiv2026

Spatio-temporal mapping of immune cell dynamics during human sequential lymph node metastasis

Regional lymph node (LN) metastasis critically influences distant metastatic progression, anti-tumour immunity, and patient prognosis. While tumour-induced immune modulation in tumour-draining LNs (TDLNs) has been extensively studied using murine models, the systematic reconstruction of the immune system from primary tumours through TDLNs and subsequent lymph nodes in human cancer progression remains understudied. Here, we utilised integrated multi-omics approaches, including imaging mass cytometry, single-cell RNA sequencing, Visium and Xenium spatial transcriptomics, and multi-colour immunofluorescence to systematically characterise immune cell dynamics across 147 paired primary tumours, sentinel TDLNs (S-TDLNs), and secondary axillary LNs (ALNs) obtained from 50 treatment-naive triple-negative breast cancer patients with different progression statuses. Our comprehensive profiling revealed critical immune alterations, such as decreased type-2 conventional dendritic cells (cDC2), naive T cells, and B cells, along with an increase in immunosuppressive macrophages. Developing a novel single-cell transformer model, we identified substantial alterations in various immune cell populations, notably MARCO+ macrophages, which strongly correlated with breast cancer patient survival outcomes. Spatial analysis combined with our newly integrated cell-cell interaction platform revealed diminished immune cell communication and impaired priming interactions among dendritic cells, B cells, and T cells within metastatic lymph nodes and primary tumour sites. In an independent neoadjuvant immunotherapy cohort of 36 TNBC patients with 52 lymph node samples, we found preservation of CD1c cDC2 in lymph nodes predicted pathological complete response and longer event-free survival, highlighting cDC2 as a potential biomarker and therapeutic target. Collectively, this systematic mapping of immune landscape alterations during human sequential LN metastasis provides essential insights for understanding cancer metastasis mechanisms and paves the way for innovative immunotherapeutic strategies.

immunology↗

bioRxiv2026

A circRNA-based uricase replacement therapy for sustained treatment of hyperuricemia

Hyperuricemia, a major risk factor for gout and kidney disease, arises from the evolutionary loss of human uricase and remains a significant medical challenge due to its high prevalence. However, limited therapeutic options are available for refractory hyperuricemia that typically require long-term treatment. Here we developed a circRNA-based uricase replacement strategy and evaluated its efficacy in uricase-knockout mice as a model for severe hyperuricemia. Lipid nanoparticle-mediated delivery of circRNA enabled efficient in vivo expression of an engineered human-like uricase, which rapidly reduced serum urate levels after a single injection and maintained the urate-lowering effect for up to 10 days. Repeated administration led to sustained urate reduction for 10 weeks, mitigated renal injury, and exhibited favorable biosafety. These findings highlight the therapeutic potential of circRNA-based uricase replacement for the long-term treatment of hyperuricemia and its associated complications.

molecular biology↗

bioRxiv2026

Bacillus velezensis-derived muropeptide promotes growth of zebrafish via NOD2-mediated induction of IGF1 signaling

The role of gut microbiome in regulating vertebrate metabolism has been well-recognized. However, the effects of gut bacteria on growth have been less studied. Bacillus is a prevalent genus in the gut microbiota of human and animals. In this study, the effect of gut-derived Bacillus velezensis T23 on growth was investigated in zebrafish. B. velezensis T23 improved the growth of zebrafish and promoted IGF1 production in the liver and muscle, with a concomitant activation of the AKT/mTOR signaling pathway. The growth-promoting effect of B. velezensis T23 was not dependent on lipopeptides and polyketides. Cell wall peptidoglycan isolated from B. velezensis T23, as well as muramyl dipeptide (MDP), was sufficient to stimulate IGF1 signaling and growth. Further, the effect of B. velezensis T23 on growth and IGF1 production was abrogated in nod2-/- zebrafish, confirming that B. velezensis T23 promoted growth via MDP-NOD2 signaling. Gut transcriptomic analysis indicated that B. velezensis T23 promoted renewal and differentiation of intestinal cells, suggesting an involvement of gut-liver axis in the effect of B. velezensis T23 on systemic IGF1 production. Together, our results revealed an effect of gut Bacillus-derived muropeptide on growth via NOD2-IGF1 signaling, and provided novel mechanistic insights in the beneficial effect of Bacillus spp. as probiotics.

microbiology↗

bioRxiv2026

Cross-Propagative Graph Learning Reveals Spatial Tissue Domains in Multi-Modal Spatial Transcriptomics

Spatial transcriptomics enables in situ characterization of tissue organization by jointly profiling gene expression profiles and spatial coordinates, with histological images as complementary contextual information. However, effectively integrating these heterogeneous modalities remains challenging due to differences in statistical properties and structural patterns. We propose st-Xprop, a cross-propagative graph network with dual-graph embedding coupling for spatial domain identification. st-Xprop constructs modality-specific graphs for gene expression and histological features, and performs alternating cross-modal propagation to explicitly model inter-modal heterogeneity while enabling complementary information exchange. Through dual-graph embedding coupling, the framework progressively learns a unified low-dimensional representation that integrates multimodal signals and preserves spatial coherence. Evaluations on multiple real spatial transcriptomics datasets demonstrate that st-Xprop consistently improves clustering accuracy and robustness, particularly in weak-signal or structurally complex regions, yielding spatial domains that are more stable and biologically meaningful.

bioinformatics↗

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

STEVE: Single-cell Transcriptomics Expression Visualization and Evaluation

Single-cell RNA sequencing (scRNA-seq) has become a key technology for characterizing cell-type heterogeneity in complex tissues. However, its utility depends on accurate and reproducible cell-type annotation, which remains a major analytical challenge. Although hundreds of computational tools have been developed for automated annotation, there is currently no systematic framework to evaluate annotation robustness in a dataset-specific manner or within the context of complete analytical pipelines. Here, we present STEVE (Single-cell Transcriptomics Expression Visualization and Evaluation), a quantitative framework designed to assess the accuracy, robustness, and reproducibility of cell-type annotation in scRNA-seq studies. STEVE implements three complementary in silico evaluation modules: (i) Subsampling Evaluation to quantify annotation stability under varying reference sizes and data partitions; (ii) Novel Cell Evaluation to assess the ability to detect previously unseen cell types; and (iii) Annotation Benchmarking to compare alternative annotation tools against ground-truth labels. In addition, STEVE includes a Reference Transfer Annotation module that enables cross-dataset cell-type mapping using external reference datasets. All modules are built upon a unified probabilistic framework that provides consistent confidence estimation across evaluation scenarios. We evaluated STEVE across four independent scRNA-seq datasets with experimentally defined or expert-curated cell-type labels. Our results show that annotation robustness is strongly influenced by the annotation method, biological separability, dataset complexity, and batch effects. STEVE provides a practical framework for quantifying annotation uncertainty and improving reproducibility in single-cell transcriptomic analyses. STEVE is freely available at GitHub (https://github.com/XiaoDongLab/STEVE).

bioinformatics↗

bioRxiv2026

Immune response to spiral ganglion neuron death in rats during development and after kanamycin-induced deafening

AO_SCPLOWBSTRACTC_SCPLOWSpiral ganglion neurons (SGNs) constitute the sole afferent connection between cochlear hair cells and central auditory nuclei. SGNs die during postnatal developmental pruning, and also following hair cell death, which can be triggered by ototoxic agents such as aminoglycoside antibiotics, including kanamycin. After hair cell loss, animal models show extensive SGN degeneration occurring gradually over a period of weeks to months. Here, we compared spatial and temporal patterns of SGN loss and immune cell involvement in these two cases of cell death in rats. Developmental SGN pruning occurred from postnatal day 5 (P5) to P8 in the basal half of the cochlea, and from P5 to P12 in the apical half. This was accompanied by a transient increase in spiral ganglion macrophages temporally and spatially correlated with SGN death, consistent with a role clearing degenerating neurons. After deafening neonatal rats with kanamycin injections, SGN death became evident at approximately 5.5 weeks of age and persisted throughout the ganglion, with greatest loss in the middle regions; less in the base and apex. Macrophage numbers also increased but neither temporally nor spatially correlated with SGN death. Rather, increased macrophage number and activation began approximately three weeks before SGN death and was highest in the apex. Additionally, T-cells and NK cells appeared in the ganglion concurrently with SGN degeneration. These observations suggest fundamentally different roles for macrophages post-deafening than during developmental pruning and, with prior observations that anti-inflammatory drugs reduce SGN death, support a causal role for immune responses in SGN death post-deafening.

neuroscience↗

bioRxiv2026

A nicotine biosensor derived from microbial screening

Physiologically relevant biosensors are in increasingly high demand, yet existing ones are severely limited in the number and type of biomarkers that are detected. The lack of biorecognition elements for most medically relevant biomarkers restricts the development of next generation single and continuous use monitors. Over billions of years, microbes have evolved a vast array of proteins to sense and metabolize small molecules, including those pertinent to human health. Of particular interest to us is the identification and subsequent integration of new microbial redox enzymes into electronic biosensors building off the established electrochemical technology of the continuous glucose monitor. Here we deploy genomic screening to identify analyte specific redox enzymes for biosensor development. As a proof of concept, we report the first electrochemical enzyme-based nicotine biosensor from a novel microbial enzyme, and use a variant with improved catalytic performance to enhance sensor performance. The biosensor detects nicotine over 0.4-100 M, a range relevant to nicotine concentrations present in active smoker sweat, saliva, gastric juice, and urine. This microbial mining approach for discovering redox enzymes expands the sensing parts toolbox available over conventional antibodies and aptamers.

bioengineering↗

bioRxiv2026

Single-cell transcriptomic atlas of mouse oocyte development from growth to ovulation

Oocyte growth and maturation depend on tightly coordinated programs within oocytes and their surrounding granulosa cells, yet defining the transcriptional continuum of growing oocytes has been challenging due to their large size and the limitations of current droplet-based and spatial transcriptomic platforms. Here, we optimized a high-density array-based platform called Stereo-cell for high-throughput dual-modality profiling of large mouse oocytes, allowing for both the preservation of morphology and transcript capture. By integrating unsupervised transcriptomic clustering with cell morphological features, we delineated successive temporal windows from growing oocytes to metaphase II and uncovered stage-linked shifts from early programs toward later programs. We also profiled the ovarian somatic fraction, reconstructed granulosa-cell subtype relationships, and placed ovarian cell states in tissue context using single-cell-resolution spatial data.

developmental biology↗

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↗

bioRxiv2026

A DNA foundation model predicts osteoporosis risk genes without proximity bias

Targets supported by human genetic associations are more than twice as likely to progress from clinical development to approval. Genome-wide association studies are the largest source of genetic evidence for disease risk but linking non-coding variants to effector genes remains a significant barrier to identifying causal targets. Current gene-mapping approaches suffer from proximity bias, largely ignoring distal genes. Here we introduce Rosalind, a DNA foundation model fine-tuned on human genetic variation from GTEx, that directly predicts variant-gene regulatory relationships from sequence without relying on nearest-gene heuristics. We demonstrate Rosalinds accuracy through extensive benchmarking, apply it to multiple complex traits to establish broad utility, and provide experimental validation in osteoporosis using a translational osteoblast assay. We demonstrate that genes distal to osteoporosis risk variants were significantly more likely to alter a bone formation phenotype than nearest genes. Together, these results highlight deep learning-based regulatory models as a general and scalable framework for translating novel genetic insights to drug discovery.

genetics↗

bioRxiv2026

Therapeutic scheduling of WEE1 inhibition preserves T cell function and promotes immune control of HPV⁺ tumors

Human papillomavirus-associated oropharyngeal squamous cell carcinoma (HPV OPC) is driven by viral E6 and E7 oncoproteins, which disrupt G1 checkpoint control and impose selective dependency on WEE1-mediated G2/M regulation. While this vulnerability confers sensitivity to WEE1 inhibition, its immunologic consequences remain poorly defined, and the challenge of eliciting antitumor immunity without compromising immune fitness has limited clinical translation. Here, we show that WEE1 inhibition elicits durable antitumor immunity in immunocompetent models of HPV OPC. Using murine and human preclinical systems, we demonstrate that the WEE1 inhibitor azenosertib (ZN-c3) mediates tumor control through both cell-autonomous cytotoxicity and immune-dependent mechanisms requiring T cells and conventional dendritic cells. Mechanistically, HPV tumor cells are deficient in STING signaling and fail to mount canonical type I interferon responses. Instead, tumor cell-intrinsic cGAS drives immune activation through STING-competent host cells within the tumor microenvironment, revealing a non-cell-autonomous relay that circumvents viral immune evasion. Intermittent WEE1 inhibition preserves T cell fitness while maintaining antitumor efficacy, and mice achieving complete responses develop immunologic memory capable of rejecting tumor rechallenge. These findings establish intermittent WEE1 inhibition as an immune-permissive therapeutic strategy that enables antigen-specific T cell responses in HPV-driven malignancies and provides a mechanistic rationale for combination with immunotherapy.

cancer biology↗

bioRxiv2026

Benchmarking zero-shot single-cell foundation model embeddings for cellular dynamics reconstruction

Reconstructing cellular trajectories from time-resolved single-cell transcriptomics is fundamental to understanding processes from embryonic development to cancer progression. While single-cell foundation models (scFMs) promise universal biological representations through large-scale pretraining, their capacity to capture the non-linear dynamics governing cell-fate decisions remains uncharacterized. Here we systematically benchmark multiple scFMs across challenging biomedical scenarios involving branching lineages and continuous state transitions. By coupling zero-shot scFM embeddings with dynamic optimal transport, we evaluated their performance against a traditional highly variable gene (HVG) baseline in backtracking progenitor states, interpolating transition intermediates, and extrapolating future fates. We find that zero-shot scFM embeddings underperform the HVG baseline across diverse biological systems, particularly in recovering the distributional complexity of unobserved cells. Mechanistic analysis reveals that current scFM architectures tend to over-compress subtle temporal signals, causing an artificial "linearization" of branched biological structures that may obscure critical divergence points in disease progression. Our findings suggest that while scFMs provide unified cell-state views, the HVG baseline remains more robust for trajectory inference, identifying a fundamental "temporal-compression" bottleneck that must be addressed to develop next-generation, dynamics-aware foundation models.

bioinformatics↗

bioRxiv2026

Digital Twin Brain simulation and manipulation of a functional brain network underlying mental illness

Linking synaptic-level perturbations to distributed brain-network dynamics remains a central challenge for understanding and treating mental illness. Although recent whole-brain models can reproduce individual brain activity patterns, they largely function as descriptive simulators rather than mechanistic, intervention-capable systems. Here we present an intervention-capable digital twin of the human brain, integrating individual neuroanatomy and task-evoked dynamics within a neuronal-scale framework. Individualised digital twin brains recapitulate a participant-specific compact cortico-subcortical network phenotype that captures transdiagnostic psychopathology across population and clinical cohorts. In silico modulation of excitatory and inhibitory synaptic conductance produces bidirectional, heterogeneous network responses across individuals. Population-scale simulations stratify individuals and predict longitudinal symptom trajectories from DTB-derived response profiles. Independent pharmacological functional MRI data further validate the predicted baseline-dependent network responses in vivo. Together, these findings establish digital brain models as experimental platforms for mechanistic perturbation, behavioural prediction and stratification, providing a foundation for precision neuroscience and psychiatry.

neuroscience↗