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Chen, Y.

Publications and source records attributed to Chen, Y..

At least 271 records · Page 15Linked to original sources

Peptide signaling in the paraventricular thalamus contributes to disrupted adult reward behaviors after early-life adversity

Early-life adversity (ELA) is strongly linked to emotional disorders characterized by dysregulated reward behaviors including diminished motivation for and enjoyment of rewards (anhedonia). However, how transient ELA leads to enduring disruptions of reward behaviors remains unclear. Using activity-dependent genetic labeling during ELA, we previously found that the paraventricular thalamic nucleus (PVT) uniquely distinguishes ELA from typical rearing, and inhibiting ELA-activated PVT cells in adult ELA mice rescued reward behaviors in a sex-specific manner. However, the cellular mechanisms by which ELA alters the function of PVT neurons enduringly, to impact adult reward behaviors is unresolved. Here, we probed potential gene expression mechanisms by assessing reward-induced gene translation selectively in early-life activated (TRAPed) PVT cells, and identified genes regulated by reward in a sex-specific way. Combining data- and hypothesis-driven approaches, we targeted the corticotropin-releasing hormone receptor type 1 (CRHR1) as potentially mediating ELA-induced disruptions of reward behaviors. We find that CRISPR-Cas9-mediated Crhr1 deletion in TRAPed-PVT cells restores typical reward behaviors in ELA mice. Our results provide insight into the developmental origin of mental illness and identify potentially powerful molecular targets for prevention or mitigation of the consequences of ELA on reward behaviors germane to human mental illness.

neuroscience↗

Near-complete, haplotype-resolved genome assembly of common buckwheat (Fagopyrum esculentum Moench)

Common buckwheat (Fagopyrum esculentum Moench) is a globally cultivated pseudocereal with a high nutritional quality and economic value. Due to its self-incompatibility, common buckwheat exhibits a high level of heterozygosity, making genome assembly challenging. Consequently, reference-level haplotype-resolved assemblies of common buckwheat are scarce, hindering research and genomics-assisted breeding. Here, we present a near-complete, chromosome-level, haplotype-resolved assembly of a common buckwheat F1 genotype (named Tuka), generated using a trio-binning approach that integrated parental Illumina short-read data with PacBio HiFi and Hi-C data from Tuka. The Tuka assembly comprises two haplomes, Tuka_h1 and Tuka_h2, both showing high contiguity (contig N50 of 76.68 Mb and 84.57 Mb, respectively), high completeness (assembly sizes of 1.28 Gb and 1.23 Gb with BUSCO scores of 96.9% and 96.8%, respectively), high base-level accuracy (QV of 59.08 and 63.03, respectively), and few gaps (35 and 30, respectively). This near-complete assembly of Tuka serves as a valuable genomic resource for common buckwheat, enabling advanced genomic analyses and accelerating research and breeding using state-of-the-art genomic tools.

genomics↗

Mouse behavioral genomics identifies Creld1 as a gatekeeper of somatosensation

Somatosensation enables the perception of touch, temperature, pain, and itch. These sensory modalities are mediated by primary sensory neurons in the dorsal root ganglion (DRG), which convert physical and chemical stimuli into electrical signals using specialized molecular sensors, including the touch sensor PIEZO2 (Ref1,2) and temperature sensors such as TRPV1 (Ref3,4), coupled with downstream voltage-gated sodium channels (Nav) like Nav1.7 (Ref5,6). However, the full repertoire of molecular components involved in somatosensory processing remains incompletely identified. Here, we developed an efficient postnatal CRISPR-Cas9 knockout platform to screen DRG-expressed genes via AAV9-sgRNA delivery. Combining this approach with behavioral assays of somatosensory responses, we validated the roles of PIEZO2 and TRPV1 in sensing gentle touch and noxious heat, respectively, and the broad involvement of Nav1.7 in distinct somatosensory modalities. Remarkably, a targeted screen of 20 DRG-expressed genes identified the Cysteine-rich with EGF-like domains 1 (Creld1) as a master regulator of somatosensation. Either sgRNA-mediated postnatal knockout or tamoxifen-induced Cre-mediated deletion of Creld1 in DRG neurons resulted in profound behavioral deficits in touch, temperature, pain, and itch perception, while its overexpression enhanced touch and thermal responses. Mechanistically, Creld1 functions as a novel auxiliary regulator of Nav for controlling the excitability of DRG neurons. The biochemical interaction and functional modulation of Nav by mouse Creld1 are mediated via its C-terminal transmembrane region. Notably, this domain is conserved in mouse Creld1 and some isoforms of human CRELD1, resulting in an isoform-dependent regulation of Nav1.7. Together, this work establishes a robust postnatal screening platform for somatosensory genomics and identifies Creld1 as a master regulator of somatosensory function, and provides novel therapeutic strategy for pain and itch treatment.

neuroscience↗

Sequence-Independent In Situ Imaging of GlycoRNA in Living Cells and Tissues based on COMPASS

Current methods for the imaging of glycosylated RNAs (glycoRNAs) focus on limited types with identified RNA sequences, making the distribution of global glycoRNAs at single-cell and tissue levels largely unexplored. To address this gap, we developed a co-metabolic proximity labeling assay (COMPASS) for sequence-independent imaging of glycoRNAs. COMPASS employs metabolic chemical reporters (MCRs) to label glycans and RNAs, followed by click-chemistry-based dye conjugation to generate Forster resonance energy transfer (FRET) signals. The MCR-enabled RNA labeling allows for sequence-independent imaging, and FRET ensures accurate localization of glycoRNA distributions. COMPASS facilitates glycoRNA imaging across multiple cell lines, cerebral organoids, and mouse tissues. We observed the downregulation of glycoRNAs in metastatic lung nodules and upregulation in original regions of brain development in cerebral organoids, suggesting their potential association with carcinogenesis and neurogenesis. COMPASS illuminates previously invisible glycoRNAs and offers a comprehensive profile of glycoRNA distribution for studying their functions.

bioengineering↗

Modeling gene regulatory perturbations via deep learning from high-throughput reporter assays

Assessing likely variant effects on phenotypes is of critical importance in diagnostic settings, and while much progress has been made in interpreting genic mutations based on our understanding of coding sequence, noncoding variants can be much more challenging to reliably interpret based on DNA sequence alone. High-throughput reporter assays such as STARR-seq and MPRA have shown utility in experimentally measuring regulatory effects of noncoding variants present in samples but provide no readout for variants not present in the assay inputs. However, whole-genome reporter assays provide copious data that can be used to train predictive models for prioritizing variants not directly observed in the experiment. We describe a retrainable predictive modeling framework, BlueSTARR, for this task, and present results of training several models with this framework on whole-genome STARR-seq data from two cell lines and one drug treatment. Using these models, we uncover a global signature across the human genome consistent with purifying selection against both loss-of-function and gain-of-function regulatory variants, with the latter showing a significant bias consistent with selection against gains of cis regulatory function in closed chromatin proximal to genes. By testing the model on synthetic enhancers with binding motifs for transcription factors GR and AP-1, we find that when trained on drug perturbation data, the model is able to learn distance-dependent and treatment-dependent binding patterns and their resulting reporter gene activation. These results demonstrate that lightweight, easily retrainable models such as ours have utility in probing latent signals present in novel experimental data. Finally, we find only modest differences in performance between different deep-learning architectures when trained on this single data modality, and while somewhat greater predictive accuracy can be achieved with much larger models trained at great expense on many terabytes of data, there is still copious room for improvement even for industrial strength, state-of-the-art models.

bioinformatics↗

Transcriptional landscape of cardiac-specific Gpx4 deletion recapitulates human cardiomyopathy

Glutathione peroxidase 4 (GPX4) is an antioxidant enzyme important for the reduction of toxic lipid peroxide products. Previous studies revealed the importance of mouse Gpx4 in protecting cardiomyocytes from ferroptosis and, subsequently, the development of cardiovascular disease. In this paper, we investigate the transcriptional consequences of cardiac-specific deletion of Gpx4 in mice and compare this response with that observed in human cardiomyopathy. The findings in this study highlight the importance of GPX4 in maintaining both structural and functional stability of the heart and identify key pathway changes resulting from excessive ferroptosis in cardiac tissue. By overlapping common transcriptional programs perturbed in this animal model and human cardiomyopathy, our findings identify putative mechanisms through which ferroptosis contributes to the development and progression of heart disease. These studies may help guide future cardiovascular therapeutics targeting ferroptosis-dependent pathways.

genomics↗

CoLa-VAE: Cell-Cell Communication-aware Variational Autoencoder with Dynamic Graph Laplacian Constraints

Single-cell RNA sequencing provides a powerful view of cellular heterogeneity, but its sparsity and dropout noise remain major obstacles for recovering biologically meaningful gene expression programs and for downstream analyses that depend on reliable expression measurements. Ligand-receptor-based cell-cell communication inference is such analysis, missing ligand or receptor expression can cause substantial false negatives in sparse single-cell data. Here, we present CoLa-VAE, a cell-cell communication-aware variational autoencoder that jointly learns latent representations and denoised expression profiles by incorporating ligand-receptor-derived communication topology through dynamic graph Laplacian regularization. Rather than treating denoising as a secondary output of representation learning, CoLa-VAE uses denoised expression to iteratively refine communication estimates and uses the resulting communication structure to guide both latent organization and expression reconstruction. In addition to improving latent space organization and producing robust denoised expression matrices, CoLa-VAE-denoised matrices also improved downstream biological analyses, including the detection of robust differential cell-cell communication programs, mitigation of batch-associated variation and enhanced spatial transcriptomic deconvolution when spatially constrained communication structure was incorporated. Together, these results establish CoLa-VAE as a communication-guided denoising and representation learning framework that recovers biologically meaningful expression signals from sparse single-cell and spatial transcriptomic data, enabling more sensitive and reliable downstream analysis.

bioinformatics↗

The diploid reference genome of a human embryonic stem cell line

Advances in DNA sequencing and assembly technologies are spurring a shift from haploid reference genomes to sample-specific diploid assemblies. Here, we generated the first telomere-to-telomere (T2T) diploid reference for the widely used human embryonic stem cell (hESC) line, H9 (WAe009-A). This haplotype-resolved assembly is highly accurate with comprehensive annotation of genes, segmental duplications, methylation, and chromatin conformation. Pangenomic and phased-locus inference point to H9s mixed ancestry with a predominant European component. H9-specific genomic features include near-perfect telomeres [~]1.65-fold longer than other T2T assemblies, consistent with telomerase activity during pluripotency; chromosome 17 inversions that can predispose offspring to neurological syndromes; and expansions of ncRNA clusters, with overall genomic stability maintained despite extensive culturing. Mapping multi-omic datasets to the genome, we demonstrate the power of this resource for allele-specific, high-precision transcriptomic, genetic, and epigenetic analyses, with far-reaching implications for human development and disease.

genomics↗

ALPINE: A Scalable Pipeline for Comprehensive Classification of Gene-Editing Outcomes from Long-Read Amplicon Sequencing

CRISPR genome editing has enabled precise genetic modification for gene and cell therapies, but edits often produce heterogeneous on-target outcomes, including homology-directed repair (HDR) knock-ins, DNA repair template integrations, and structural variants. Existing tools are frequently limited to short reads or lack viral vector-specific integration categories needed for therapeutic development. Here, we present ALPINE (Amplicon Long-read Pipeline for INtegration Evaluation), a scalable and reproducible pipeline for classifying and quantifying gene-editing outcomes from long-read amplicon sequencing. ALPINE classifies reads into 10+ categories, including DNA repair vector integration subtypes, and performs variant calling near the gene-edited site with batch, multi-sample reporting. Uniquely, ALPINE can distinguish between cells treated with multiple DNA repair vectors and identify distinct molecular features, such as inverted terminal repeats (ITRs), enabling comprehensive characterization of complex gene editing outcomes. Benchmarking on simulated datasets showed high accuracy, and application to edited T cell samples demonstrated comprehensive gene-editing outcome profiling. Supplementary data are available online. AvailabilityALPINE is implemented in Python and distributed as Docker containers with Common Workflow Language (CWL) support for cloud deployment. The pipeline is available under MIT license at https://github.com/Maggi-Chen/ALPINE.

bioinformatics↗

Wnt/β-catenin signaling restrains developmental beiging and imprints long-term energy expenditure

Beige adipocytes that emerge during the peri-weaning period support sympathetic nervous system (SNS)-independent thermogenesis, yet the mechanisms governing this spontaneous beiging remain unclear. Here, by integrating transcriptomic profiling with adipocyte-targeted Ctnnb1 deletion in mice, we identify the canonical Wnt signaling as an endogenous brake on developmental beige thermogenesis. Peri-weaning inguinal fat from adipocyte Ctnnb1 knockout mice exhibits enhanced beige adipocyte biogenesis, with increased thermogenesis-related gene expression and mitochondrial oxidative capacity, which programs durable activation of adaptive thermogenesis and augmented whole-body energy expenditure into adulthood. Mechanistically, suppression of Wnt/{beta}-catenin signaling induces a non-canonical Wnt5a-Ca2-AMPK axis that promotes triglyceride lipolysis and subsequent PPAR-driven fatty acid oxidation, thereby fueling mitochondrial respiration. Genetic or pharmacological disruption of this axis blunts thermogenic responses induced by {beta}-catenin inhibition in both murine and human subcutaneous adipocytes, indicating that Wnt5a-Ca2-AMPK axis is required for the cell-autonomous activation of beige fat. These results reveal Wnt/{beta}-catenin signaling as a developmental constraint on beige adipocyte formation and suggest an SNS-independent route to sustainably raise energy expenditure and improve metabolic health.

physiology↗

EthoClaw: An Integrated AI Workflow Platform for Automated Analysis in Neuroethology

Computational methods have advanced the analysis of animal behavior, yet significant challenges remain in data standardization, analytical reproducibility, and workflow integration. Existing computational solutions often demand extensive programming proficiency or compel users to navigate a highly fragmented ecosystem of disconnected tools for tracking, statistical analysis, and visualization. Here, we present EthoClaw, an open-source, artificial intelligence-driven workflow platform built upon the OpenClaw agentic framework, functioning as a locally deployable AI assistant for behavioral research. EthoClaw provides an integrated computational infrastructure that seamlessly bridges the gap between raw behavioral video acquisitions and publishable scientific results. In this study, we demonstrate the platforms capacity to natively ingest video data via a dual-mode tracking architecture: utilizing ultra-fast image processing for rapid object detection, and leveraging the SuperAnimal methods for precise, markerless postural tracking. To ensure maximal interoperability, EthoClaw automatically converts various tracking data formats into DeepLabCut-compatible formats, enabling high-throughput phenotyping by generating publication-quality visualizations alongside rigorous multidimensional statistical profiling. Furthermore, the platform incorporates a large language model (LLM)-driven reporting module that dynamically synthesizes analytical documents, ensuring methodological transparency. Through an open field test, we validate the practical usability of EthoClaw while accelerating computational throughput by localizing heavy video processing to circumvent cloud bandwidth bottlenecks. Operating via an omnichannel natural language interface that integrates seamlessly with ubiquitous instant messaging software, EthoClaw democratizes advanced computational behavioral analysis, offering a holistic, highly efficient ecosystem that enforces experimental reproducibility and open science principles.

animal behavior and cognition↗

A Targeting Parameter Space for Personalized 4x1 HD-tES: Montage Description, Optimization and Application

BackgroundPersonalized optimization of 4x1 high-definition transcranial electrical stimulation (HD-tES) faces inherent trade-offs between montage flexibility, computational efficiency, and implementation accessibility. Conventional 10-10 electrode systems constrain placement to discrete landmark positions, while unconstrained optimization relies on stochastic algorithms that risk converging to local optima and requires neuronavigation equipment often unavailable in rehabilitation settings. Here we introduce a scalp geometry-based parameter space (SGP) that parameterizes 4x1 HD-tES montages using three intuitive scalp-defined parameters--position, radius, and orientation--and characterize parameter-performance regularities through exhaustive electric field simulations across 30 subjects and 624 cortical targets (>3.6 million configurations). ResultsPosition primarily determined proximity to optimal performance, radius governed the intensity-focality trade-off, and orientation served as fine-tuning. Exploiting these regularities, a minimal search space (SGP-MSS) was constructed that reduced computational complexity by over 90% while guaranteeing global optima identification. Compared with standard 10-10 montages, SGP-MSS achieved up to 99% higher targeting intensity and 126% higher focality (all p < 0.0001). Compared with lead-field-free optimization, SGP-MSS achieved comparable performance with greater cross-subject stability. ConclusionsThe SGP framework enables efficient individualized HD-tES optimization without neuronavigation. Its scalp-based parameterization supports electrode positioning via standard cranial landmark measurements, facilitating translation to routine clinical and home-based rehabilitation settings.

biophysics↗

Construction of a GnRH mRNA Immunocastration Vaccine and Evaluation of Its Immunogenicity and Safety in Mice and Cats

Immunocastration has emerged as an alternative to surgical and chemical castration for managing reproductive function in animals, yet the development of safe and effective vaccines remains challenging. This study aimed to develop a gonadotropin-releasing hormone (GnRH)-based messenger RNA (mRNA) vaccine and systematically evaluate its immunogenicity, reproductive suppression efficacy, long-term durability, and biosafety in mice and cats. GnRH epitopes were fused to three carrier proteins, Fc, Foldon, and lumazine synthase nanoparticles (pLS) via a flexible linker. After identifying pLS as the optimal scaffold, three mRNA vaccine candidates (GnRH-3, GnRH-4, and GnRH-5) were generated with one, five, or ten tandem GnRH repeats, encapsulated in lipid nanoparticles (LNPs), and assessed in rodent and feline models. Immunogenicity was determined by enzyme-linked immunosorbent assay, gonadal histopathology, hormone measurements, transcriptomic analysis, and mating trials. Among the fusion partners, the pLS-based vaccine (GnRH-3) induced the strongest antibody responses and most pronounced reproductive suppression. Further optimization showed that GnRH-4, containing five tandem GnRH repeats, elicited the highest antibody titers, induced severe gonadal atrophy, and reduced litter size by 93.8% in mice. Transcriptomic analysis revealed that differentially expressed genes in males were enriched in spermatogenesis and motility pathways, whereas those in females were associated with RNA splicing and immune responses. In cats, the optimal regimen was a twoLdose schedule with 50Lg per dose and a 21Lday interval, which induced robust antibody responses lasting at least 12 Lmonths and sustained reproductive suppression. HighLdose (500Lg) administration showed no clinical toxicity or histopathological abnormalities, confirming favorable biosafety. This study successfully developed a pLSLbased GnRH mRNA vaccine (GnRH-4) with five tandem GnRH epitopes that demonstrates strong immunogenicity, longLlasting contraceptive effects, and excellent safety in both rodent and feline models, supporting its potential for clinical application in immunocastration.

immunology↗

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↗

Coronavirus envelope protein drives iron sensing disorder by hijacking the TAp73-FDXR axis

Iron overload is increasingly recognized as a critical contributor to coronavirus pathogenesis1, yet the underlying induction mechanisms remain unclear. Here, we uncover a fundamental pathway by which coronavirus drives IRP1 RNA-binding activity to induce iron accumulation2 via targeting the TAp73-FDXR axis. Specifically, coronavirus infection represses transcription of FDXR (encoding the key rate-limiting enzyme in host iron-sulfur cluster synthesis3), thereby impairing host iron-sulfur cluster generation to trigger the functional conversion of the cytosolic aconitase 1 (ACO1) into iron-regulatory protein 1 (IRP1)4, ultimately leading to the hosts persistently false perception of iron deficiency. We identify TAp73 as the primary transcription factor governing FDXR expression, and demonstrate that the coronavirus envelope protein (CoV-E) orchestrates TAp73 nuclear export. Subsequently, CoV-E binds TAp73 through a critical valine residue within its C-terminal PBM domain, inducing the K48-linked ubiquitination and proteasomal degradation of TAp73. Furthermore, we developed a CoV-E-targeting molecule, DPTP-FC, which blocks CoV-E-TAp73 interaction via forming steric hindrance and effectively alleviates iron accumulation and tissue damage caused by PEDV, PDCoV, and SARS-CoV-2 infection. Our study reveals the central role of the TAp73-FDXR axis in CoV-induced iron accumulation, highlighting CoV-E as an attractive antiviral target and DPTP-FC as a promising therapeutic candidate.

microbiology↗

CombinGym: a benchmark platform for machine learning-assisted design of combinatorial protein variants

Combinatorial mutagenesis is essential for exploring protein sequence-function landscapes in engineering applications. However, while large-scale machine learning benchmarks exist for protein function prediction, they are primarily limited to single-mutant libraries, leaving a critical gap for combinatorial mutagenesis. Here we introduce CombinGym, a benchmarking platform featuring 14 curated combinatorial mutagenesis datasets spanning 9 proteins with diverse functional properties including binding affinity, fluorescence, and enzymatic activities. We evaluated nine machine learning algorithms from five methodological categories (alignment-based, protein language, structure-based, sequence-label, and substitution-based) across multiple prediction tasks, assessing both zero-shot and supervised learning performance using Spearmans {rho} and Normalized Discounted Cumulative Gain metrics. Our analysis reveals the substantial impact of measurement noise and data processing strategies on model performance. By implementing hierarchical dataset splits (0-vs-rest, 1-vs-rest, 2-vs-rest, and 3-vs-rest scenarios), we demonstrate the value of lower-order mutation data for empowering machine learning models to predict higher-order mutant properties. We validated this capacity through both in silico simulation (improving fluorescence brightness of an oxygen-independent fluorescent protein) and experimental validation (engineering enzyme substrate specificity), achieving a substantial increase in specific activity. All datasets, benchmarks, and metrics are available through an interactive website (https://www.combingym.org), facilitating collaborative dataset expansion and model development through integration with automated biofoundry platforms.

synthetic biology↗

Systems-level longitudinal immune profiling reveals individualized immunotypes and genetic associations

The human immune system exhibits substantial inter-individual variation, yet how this variability is coordinated across the system and persists over time remains incompletely defined. Here, we integrated whole-genome sequencing with longitudinal multi-omics immune profiling in 101 healthy individuals followed over two years, combining mass cytometry-based immune cell profiling, PBMC transcriptomics, plasma proteomics, and clinical measurements. Coordinated variation across cellular composition, gene expression, and circulating proteins revealed reproducible immune patterns within individuals across visits. Integrative analyses identified three major immunotypes: adaptive lymphoid (CD4 T cell and B cell enriched), myeloid-inflammation (monocyte and dendritic cell-driven), and cytotoxic (CD8 T cell-dominated), each associated with distinct metabolic and inflammatory profiles. Genome-wide association analyses identified cell-type-specific quantitative trait loci primarily affecting memory lymphocytes, and a polygenic score for memory B cells correlated with both cellular abundance and transcriptional activity. Together, these findings provide a systems-level view for understanding baseline immune heterogeneity in human populations. TeaserLongitudinally stable immune individuality reflects coordinated modules shaped by systemic physiology and genetics

systems biology↗

Pregistered movie-fMRI analyses reveal altered visual feature encoding in autism in pSTS

Sensory-perceptual differences are widely reported in autism, yet their underlying mechanisms remain unclear. We tested preregistered hypotheses using stacked encoding models applied to naturalistic movie-viewing fMRI from children and adolescents with and without an autism diagnosis from the Healthy Brain Network. We mapped cortical responsiveness to low- and high-level auditory and visual feature spaces. Contrary to enhanced perceptual functioning predictions, autism was not associated with increased low-level encoding in primary sensory cortices. Instead, autistic children and adolescents had reduced high-level visual representations and a relative shift toward low-level over high-level feature encoding in integration and social brain regions including the pSTS and adjacent face/social areas. In pSTS, this high-low weighting tracked Social Responsiveness Scale (SRS) scores. By contrast, audio-visual modality preference and sensory dominance were broadly conserved across groups. Developmentally, encoding exhibited strong, lateralized, modality-congruent age effects. Together, these findings favor weak central coherence accounts over early sensory enhancement, constrain mechanisms to altered visual feature weighting within social/multisensory networks, and demonstrate the value of naturalistic stimuli and encoding models for characterizing sensory-perceptual neurodevelopmental differences.

neuroscience↗