Biology subjects

Zuo, X.-N.

Publications and source records attributed to Zuo, X.-N..

At least 19 recordsLinked to original sources

bioRxiv2026

Human brain white matter charts in international populations

Brain charts provide normative references for identifying neurodevelopmental milestones and clinically meaningful deviations. Existing white matter (WM) charts are derived predominantly from European and North American populations, limiting generalizability. Here, we established the first set of population-specific WM charts of 198 morphological and microstructural phenotypes for both Chinese (N=19,215) and Western (N=15,584) populations across the human lifespan. Developmental trajectories and spatial organization of WM maturation and degeneration were broadly conserved across the populations, whereas maturational timing varied across pathways and metrics. The Chinese population showed earlier maturation in several projection and sensorimotor pathways, but more protracted maturation in higher-order association and limbic pathways. Chinese population-matched charts more accurately predicted WM phenotypes and detected more extreme disease-related deviations in brain health in the Chinese population than Western-derived models. Individual deviation patterns further discriminated patients from healthy controls and predicted cognitive performance and plasma biomarkers. Our findings reveal population-specific WM maturation and show that reference-population mismatch can reduce the accuracy and clinical sensitivity of normative brain charts.

neuroscience↗

bioRxiv2026

The emergent mental operation of the learning brain

How does the learning brain give rise to emergent mental operations that enable skill internalization and generalization? Using five-year longitudinal tracking of children acquiring abacus-based mental calculation, we reveal how sustained practice progressively reconfigures whole-brain states. Learning induces nonlinear transitions from stable baseline configurations through a transient exploratory phase toward restabilized associative brain networks. This hierarchical remodeling cascades from perceptual systems to action and default networks, mediated by adaptive neural variability and selective connectivity reweighting, with the salience-parietal memory network acting as a dynamic relay. These brain-state transitions accompany an ordered cognitive transfer cascade, emerging within arithmetic and extending to visuospatial and executive functions. Our findings are reproducible and provide a systems-level account of how ecological learning sculpts large-scale networks to enable complex skill acquisition.

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bioRxiv2026

Temporal structure of task engagement organizes infra-slow BOLD dynamics

Task-based fMRI has traditionally characterized cognitive engagement through spatial patterns of BOLD activation, leaving open whether tasks also organize activity along the frequency dimension. Here we tested whether temporally structured task engagement reshapes infra-slow BOLD dynamics, asking whether task responses are simply superimposed onto ongoing activity or instead redistribute power across the infra-slow spectrum. Across three independent fMRI datasets, task timing systematically reorganized the BOLD power spectrum. In the main visual attention dataset, periodic stimulation produced sharp stimulus-locked peaks at 0.083 and 0.125 Hz; critically, these gains were matched by a reduction concentrated within the dominant intrinsic infra-slow range (0.01-0.04 Hz), indicating redistribution rather than a simple local increase. Independent auditory and visual-motion datasets showed analogous task-frequency responses at their respective timescales, indicating that this reorganization generalizes across modalities and stimulation frequencies. The peaks were spatially related to GLM-derived activation but persisted after removal of the GLM-modeled response, indicating that this frequency-specific organization is not fully reducible to conventional task-evoked activation. These features were functionally informative: task-imposed frequencies distinguished cognitive states, predicted individual reaction time, and preserved subject-specific signatures across runs more effectively than broader spectral ranges or GLM-derived features. Finally, Jansen-Rit simulations coupled to a Balloon-Windkessel model reproduced the peaks, showing that their expression depends on balanced excitatory, inhibitory, and filter-gain regimes rather than simple amplification of input. Together, these findings indicate that task-fMRI reveals not only where cognition is localized, but how temporally structured engagement redistributes infra-slow dynamics across timescales.

neuroscience↗

bioRxiv2026

Nonlinear trajectories of language network development

How the human brain organizes complex cognitive functions remains unresolved, particularly regarding the debate between localized and distributed architectures. Here, we show that the language network undergoes a non-linear developmental reorganization that reconciles these views. Using multimodal neuroimaging and behavioral measures, we identify a three-stage trajectory: early localization, a transiently distributed state during adolescence marked by a connectivity dip, and a return to refined localization in adulthood. This adolescent dip is behaviorally meaningful and contributes to integrative network architecture. Convergent shifts in functional connectivity and brain-behavior relationships identify adolescence as a critical window for large-scale network remodeling. Our findings provide a unifying framework for language network development and suggest that transient redistribution may represent a general principle of human brain maturation.

neuroscience↗

bioRxiv2026

Reliability and validity of multi-band multi-echo fMRI

Precision mapping of individual differences in human brain spontaneous activity is critical for characterizing brain function and guiding clinical translation. Multi-echo resting-state functional MRI (ME-rfMRI) has the potential to provide more reliable mapping of inter-individual differences in intrinsic brain function than conventional single-echo rfMRI (SE-rfMRI). However, the psychometric benefits of ME-rfMRI for mapping individual differences in spontaneous brain activity (SBA) have not been systematically investigated, which is fundamental for understanding the human brain in health and disease. To quantify the psychometric performance of ME-rfMRI in precisely mapping inter-individual variations in SBA, we employed a psychometric design. We scanned 27 healthy adults with both ME-rfMRI and SE-rfMRI to assess short-term (minutes) test-retest reliability, as well as validity across eyes-open (EO) and eyes-closed (EC) resting states. Our results demonstrate that ME-rfMRI improves reliability relative to SE-rfMRI by 10.8% for amplitude of low-frequency fluctuation (ALFF), 4.4% for regional homogeneity (ReHo), and 8.0% for voxel-mirrored homotopic connectivity (VMHC) in cortical regions, and by 12.5% for ALFF, 8.3% for ReHo, and 4.0% for VMHC in subcortical regions. This enhancement is achieved by increasing inter-individual variability while reducing intra-individual variability. Notably, ME-rfMRI accentuates inter-individual variability in the unimodal cortex and clearly delineates two parallel systems with distinct variability patterns within the motor cortex. Concurrently, ME-rfMRI increases effect sizes (4%-8%) for detecting individual differences between EO and EC resting states. Interestingly, ME-rfMRI reveals differential effects of eye closure on two intertwined systems in the primary motor and sensory cortices, extending previous findings. Computational simulations indicate that, compared to SE-rfMRI, ME-rfMRI facilitates experimental designs with reduced costs (i.e., 4%-11% smaller sample sizes) for precise mapping of individual differences. These findings establish, for the first time, the psychometric performance of ME-rfMRI measurements, supporting its potential utility in personalized neuroscience applications, including neurodevelopment and brain disorders.

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bioRxiv2025

A multivariate pattern metric of individualized hemispheric functional asymmetry

Functional asymmetry is a fundamental feature of human brain organization, yet existing measures of functional connectivity asymmetry rely mainly on univariate indices that miss distributed pattern structure. We introduce the Pattern Dissimilarity of Hemispheric Functional Connectivity (PDHC), a multivariate metric that quantifies the pattern-level distance between each individuals left- and right-hemisphere connectivity architecture. In over one thousand adults, PDHC showed high test-retest reliability, cross-atlas robustness, and strong individual specificity. Network analyses indicated that hemispheric similarity is anchored in conserved sensorimotor and subcortical systems, whereas higher-order networks drive divergence. Developmental data from infancy through early adulthood revealed a characteristic trajectory: dissimilarity declines sharply early in life and increases modestly thereafter. Twin and Turner syndrome samples further demonstrated moderate heritability and sensitivity to X-chromosome dosage. PDHC thus provides a reliable and individualized metric of hemispheric functional architecture, offering a scalable tool for probing lateralization across development, genetics, cognition, and clinical conditions.

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bioRxiv2025

The Human Brain's Action Network Affinity

This study proposes a spatiotemporal connectome-based framework to characterize the human brains action network. Unlike temporal connectivity (TPC), this approach leverages full functional connectivity profiles derived from large-scale neural wave dynamics, termed spatial connectivity (SPC). We applied SPC to map the action network for the first time in a non-Western young adult cohort. Our results delineate the networks detailed functional architecture across the cerebral cortex, cerebellum, and subcortical nuclei. Compared to TPC, SPC is more robust to global signal regression when characterizing anticorrelation between the action and default networks. Critically, this intrinsic antagonism reflects a fundamental energy-saving balance in the brains dynamical system. Moreover, SPC reveals that the salience/parietal memory network resides at the interface of this antagonism, exhibiting weak but highly variable connectivity, a position that enables rapid state switching between external action and internal contemplation. These findings offer a mechanistic view of the brains resting "dark energy" and suggest a blueprint for brain-inspired AI. Embedding a dual-system opponent architecture, balanced by a flexible switching hub, may foster adaptive, energy-efficient intelligence. All derived high-resolution SPC patterns and computational code are publicly shared to promote open science (https://ccndc.scidb.cn/en).

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bioRxiv2025

Spacetime concordance in the primate cortex

Understanding the functional organization of the primate cortex requires metrics that capture both the temporal and topological dimensions of functional connectivity. Here we propose the spatial connectivity for local homogeneity in cortex (SoHo), a vertex-wise, continuous metric that quantifies the degree to which a cortical vertex and its immediate neighbors share similar spatial profiles of whole-brain functional connectivity. We validated SoHo using large-scale wakeful resting-state fMRI datasets from the Human Connectome Project (HCP) and the NIH Marmoset Brain Mapping Project. In humans, SoHo values showed a striking correspondence with the parcellation boundaries of the HCP multimodal atlas, with low-value regions consistently aligning with areal boundaries. Higher-order association areas exhibited lower SoHo values (functional diversity), while primary sensorimotor areas demonstrated higher values (functional uniformity). Cross-species SoHo mapping revealed that this primary-to-association gradient is evolutionarily conserved across primates, alongside species-specific adaptations in frontoparietal and motor regions. By capturing the local concordance of spatial fingerprints of whole-brain connectivity, SoHo bridges discrete parcellation schemes and continuous models of brain function, offering new insights into primate brain organization and evolution.

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bioRxiv2025

Frequency-specific maturation of cortical organization and social-cognitive links from childhood to adolescence

The human brain unfolds its functional architecture over multiple timescales during childhood and adolescence, yet we know little about how these developmental trajectories vary between individuals. Slow brain signals at different frequencies are thought to support distinct functional processes, but their contribution to shaping large-scale cortical organization across development remains unclear. Here, we map the maturation of cortical organization by decomposing spontaneous slow brain activity into multiple frequency bands in participants aged 6-19 years. We identify a reproducible three-stage progression - child-like, transitional, and adult-like configurations - whose timing depends on frequency: faster dynamics mature earliest, intermediate frequencies develop gradually, and lower-frequency dynamics reorganize abruptly around puberty. Individual cognitive and social traits modulate these trajectories: children with higher social anxiety or higher IQ reach adult-like configurations earlier in specific frequency bands, whereas lower-IQ children show generalized delays. These results reveal that cortical organization matures along multiple, frequency-specific timescales and that intellectual and socio-emotional factors shape its pace. Our multi-frequency framework provides a new perspective on hierarchical brain development and may inform biomarkers for atypical neurodevelopment.

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bioRxiv2025

Human cortex organizes dynamic co-fluctuations along sensation-association axis

The human brain dynamically organizes its activity through coordinated fluctuations, whose spatiotemporal interactions form the foundation of functional networks. While large-scale co-fluctuations are well-studied, the principles governing their amplitude-dependent transitions--particularly across high, intermediate, and low-amplitude regimes--remain unknown. We introduce a co-fluctuation score to quantify how instantaneous functional interactions reorganize with global amplitude dynamics. Using resting-state fMRI data, we identified amplitude-dependent co-fluctuation transitions between functional systems hierarchically aligned with the sensorimotor-association (SA) axis: sensorimotor networks dominated high-amplitude co-fluctuations, associative systems prevailed during intermediate amplitudes, and limbic system preferentially engaged in low-amplitude states. This hierarchy underwent developmental refinement from childhood to adulthood and adaptively reconfigured under external stimuli. Replicated across four independent samples including 7T fMRI, these findings establish the SA axis as infrastructure for amplitude-dependent transitions of co-fluctuation states. Our framework bridges transient coordination and stable functional architecture, demonstrating how brain networks balance external processing (high-amplitude states) with internal cognition/emotion (mid-to-low amplitude states) through amplitude-stratified interactions.

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bioRxiv2025

Domain-General Brain Networks Support Language Development

Understanding the neural basis of verbal intelligence across development requires disentangling the contributions of domain-general and language-selective brain systems. Although language is often considered a domain-specific function, complex language tasks also engage domain-general networks, such as the Default-Mode (DM) and Multiple-Demand (MD) systems. Yet how these systems contribute to the maturation of verbal competence remains poorly understood. Here, we examined this question using gray matter volume measures in an accelerated longitudinal dataset of children and adolescents from Beijing (N = 170), using the Verbal Comprehension Index (VCI) from the Wechsler Intelligence Scale as a benchmark for verbal abilities. We observed that individual differences in VCI were more strongly associated with structural maturation of domain-general networks (DM and MD) than with the language-selective network, and that these effects varied with age. Targeted validation in an independent cohort from Chongqing (N = 150) confirmed significant contributions of domain-general networks in adolescence (13-15 years), highlighting the robustness of these developmental effects. These findings suggest that domain-general cortical systems play a critical and previously underappreciated role in the emergence of verbal intelligence during adolescence, with implications for understanding how large-scale brain networks support the development of abstract verbal reasoning.

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bioRxiv2025

Convergent and divergent brain-cognition relationships during development revealed by cross-sectional and longitudinal analyses in the ABCD Study

How brain networks and cognition co-evolve during development remains poorly understood. Here, we use resting-state functional magnetic resonance imaging (rs-fMRI) and cognitive data at baseline and Year 2 of 2,949 individuals in the Adolescent Brain Cognitive Development (ABCD) Study to examine how stable and changing features of brain network organization predict cognitive development during early adolescence. We find that baseline resting-state functional connectivity (FC) more strongly predicts future cognitive ability than baseline cognitive ability. Models trained on baseline FC to predict baseline cognition generalize better to Year 2 FC and cognition, suggesting that brain-cognition relationships strengthen over time. Intriguingly, baseline FC outperforms longitudinal FC change in predicting future cognitive ability. One potential reason is the lower reliability of FC change compared to baseline FC: ICC = 0.24 vs. 0.56. However, reducing baseline FCs reliability by shortening scan duration only partially narrows the predictive gap, suggesting reliability alone cannot be the full explanation. Furthermore, neither baseline FC nor FC change meaningfully predicts longitudinal change in cognitive ability. We also identify converging and diverging predictive network features across cross-sectional and longitudinal models of brain-cognition relationships, revealing a multivariate twist on Simpsons paradox. Together, these findings suggest that during early adolescence, stable individual differences in brain functional network organization play a more critical role than dynamic changes in shaping future cognitive outcomes.

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bioRxiv2025

Intellectual ability and cortical homotopy development in children and adolescents

Functional homotopy, defined as the similarity between the corresponding regions of the two hemispheres, is a critical feature of interhemispheric communication and cognitive integration. Throughout development, the brain transitions from broadly connected networks in early childhood to more specialized configurations in adoles-cence, accompanied by increased hemispheric differentiation and integration. Using longitudinal data and a novel metric of functional homotopy, homotopic functional affinity (HFA), we investigated the developmental patterns of functional homotopy and its relationship with intelligence. Our findings indicate a significant decrease in HFA with age, particularly in higher-order association networks. In addition, adoles-cents demonstrate stronger, predominantly negative correlations between HFA and intelligence, in contrast to younger children. In particular, individuals with superior intellectual ability experience accelerated decreases in HFA, indicating greater neural efficiency based on advanced hemispheric specialization and differentiation. These findings provide evidence of the neural mechanisms that underlie cognitive development, emphasizing the dynamic interaction between hemispheric organization and intelligence. Our work may have implications for the design of customized educational/clinical interventions to optimize individual developmental strategies. HighlightsO_LISubstantial decreases in functional homotopy along the unimodal-transmodal axis observed from childhood to adolescence, with associative areas experiencing a more pronounced decrease. C_LIO_LIDynamic shifts in the correlation between functional homotopy and intelligence in developmental stages. C_LIO_LIIndividuals with a higher IQ demonstrate a significantly faster development of hemi- spheric specialization or differentiation. C_LIO_LIThe distinct patterns in the development of functional homotopy in different IQ- groups underscore the complex interaction between brain maturation and intelligence. C_LI

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bioRxiv2025

Reproducible Brain Charts: An open data resource for mapping brain development and its associations with mental health

Major mental disorders are increasingly understood as disorders of brain development. Large and heterogeneous samples are required to define generalizable links between brain development and psychopathology. To this end, we introduce the Reproducible Brain Charts (RBC), an open data resource that integrates data from 5 large studies of brain development in youth from three continents (N=6,346; 45% Female). Confirmatory bifactor models were used to create harmonized psychiatric phenotypes that capture major dimensions of psychopathology. Following rigorous quality assurance, neuroimaging data were carefully curated and processed using consistent pipelines in a reproducible manner with DataLad, the Configurable Pipeline for the Analysis of Connectomes (C-PAC), and FreeSurfer. Initial analyses of RBC data emphasize the benefit of careful quality assurance and data harmonization in delineating developmental effects and associations with psychopathology. Critically, all RBC data - including harmonized psychiatric phenotypes, unprocessed images, and fully processed imaging derivatives - are openly shared without a data use agreement via the International Neuroimaging Data-sharing Initiative. Together, RBC facilitates large-scale, reproducible, and generalizable research in developmental and psychiatric neuroscience.

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bioRxiv2025

Profiling brain morphology for autism spectrum disorder with two cross-culture large-scale consortia

We explore neurodevelopmental heterogeneity in Autism Spectrum Disorder (ASD) through normative modeling of cross-cultural cohorts. By leveraging large-scale datasets from Autism Brain Imaging Data Exchange (ABIDE) and China Autism Brain Imaging Consortium (CABIC), our model identifies two ASD subgroups with distinct brain morphological abnormalities: subgroup "L" is characterized by generally smaller brain region volumes and higher rates of abnormality, while subgroup "H" exhibits larger volumes with less pronounced deviations in specific areas. Key areas, such as the isthmus cingulate and transverse temporal gyrus, were identified as critical for subgroup differentiation and ASD trait correlations. In subgroup H, the regional volume of the isthmus cingulate cortex showed a direct correlation with individuals autistic mannerisms, potentially corresponding to its slower post-peak volumetric declines during development. These findings offer insights into the biological mechanisms underlying ASD and support the advancement of subgroup-driven precision clinical practices.

neuroscience↗

bioRxiv2025

China Autism Brain Imaging Consortium: Charting Brain Growth in Chinese Children with Autism

Autism Spectrum Disorder (ASD) is a lifelong neurodevelopmental condition characterized by atypical brain growth. While advances in neuroimaging and openly sharing large-sample datasets such as the Autism Brain Imaging Data Exchange (ABIDE) have improved understanding of ASD, most studies focus on adolescents and adults, with early brain development-critical for diagnosis and intervention-remaining underexplored. Existing research predominantly involves Western samples, offering limited insight and generalizability into non-Caucasian populations. We introduce the China Autism Brain Imaging Consortium (CABIC) (https://php.bdnilab.com/resources/), a grassroots effort by researchers across the country to aggregate previously collected multi-site structural MRI datasets and phenotypic information from 1,451 autistic children and 1,119 typically developing children, covering an age range from early childhood to school age (1.0 - 12.92 years). Here, we present this resource and depict brain growth charts to push forward a more comprehensive understanding of the brain development in Chinese autism children. We constructed brain growth charts that reveal a developmental shift in autistic children, transitioning from early overgrowth to delayed maturation. Regional analyses identified distinct atypical trajectories across specific brain regions. Individual deviation scores quantified inter-subject variability, characterizing the heterogeneity of brain development in ASD. Comparative analyses between CABIC and ABIDE highlighted differences potentially attributable to ethnicity and culture, advancing our understanding of cross-population neurodevelopmental diversity. CABIC MRI datasets will be shared publicly to foster investigation of the potential neural mechanisms underlying ASD in non-Western populations and support efforts toward precision medicine for autistic individuals across diverse backgrounds.

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bioRxiv2024

Older is Order: Entropy reduction in cortical spontaneous activity marks healthy aging

Entropy trajectories remain unclear for the aging process of human brain system due to the lacking of longitudinal neuroimaging resource. We used open data from an accelerated longitudinal cohort (PREVENT-AD) that included 24 healthy aging participants followed by 4 years with 5 visits per participant to establish cortical entropy aging curves and distinguish with the effects of age and cohort. This reveals that global cortical entropy decreased with aging, while a significant cohort effect was detectable that people who were born earlier showed higher cortical entropy. Such entropy reductions were also evident for large-scale cortical networks, although with different rates of reduction for different networks. Specifically, the primary and intermediate networks reduce their entropy faster than the higher-order association networks. We conclude two specific characteristics of the entropy of the human cortex with aging: the shift of the complexity hierarchy and the diversity of complexity strengthen.

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bioRxiv2024

AmygdalaGo-BOLT3D: A boundary learning transformer for tracing human amygdala

Each year, thousands of brain MRI scans are collected to study structural development in children and adolescents. However, the amygdala, a particularly small and complex structure, remains difficult to segment reliably, especially in developing populations where its volume is even smaller. To address this challenge, we developed AmygdalaGo-BOLT, a boundary-aware deep learning model tailored for human amygdala segmentation. It was trained and validated using 854 manually labeled scans from pediatric datasets, with independent samples used to ensure performance generalizability. The model integrates multiscale image features, spatial priors, and self-attention mechanisms within a compact encoder-decoder architecture to enhance boundary detection. Validation across multiple imaging centers and age groups shows that AmygdalaGo-BOLT closely matches expert manual labels, improves processing efficiency, and outperforms existing tools in accuracy. This enables robust and scalable analysis of amygdala morphology in developmental neuroimaging studies where manual tracing is impractical. To support open and reproducible science, we publicly release both the labeled datasets and the full source code. TeaserAn AI model enables accurate amygdala segmentation across ages and centers, supporting large-scale brain imaging studies.

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