[This corrects the article DOI: 10.1016/j.ynirp.2026.100336.].

Functional magnetic resonance imaging (fMRI) has greatly advanced our understanding of neurodevelopment. However, head motion during fMRI acquisition remains a significant challenge, especially for pediatric subjects. Excessive head motion can introduce substantial artifacts into fMRI scans, degrading the accuracy of subsequent analyses. Although motion correction methods have been proposed to directly eliminate motion effects from fMRI signals, the resulting functional connectivity (FC), the key component in fMRI studies, still contains substantial motion-induced artifacts. Effective motion correction methods applicable to FC are therefore highly desirable but remain unexplored. To address this gap, given the complementary information provided by different brain atlas parcellation schemes, we propose a novel Multi-Atlas Representation Alignment Transformer (MARA-Former) for joint motion correction of FCs derived from multiple atlases by leveraging their intrinsic relationship. Specifically, (1) we develop an FC-specified conditional Transformer architecture. By conditioning our model on both the input high-motion degree and the target low-motion degree, it can adaptively extract motion-aware features and generate low-motion results, thereby enhancing its ability in handling different motion degrees. (2) We employ optimal transport to align the representations of different atlases and design an atlas fusion block to enable comprehensive information exchange and joint learning across atlases, thereby effectively integrating complementary information and intrinsic relationships across atlases to jointly correct multi-atlas high-motion FCs. Extensive experiments on 1,289 resting-state fMRI scans of infants demonstrate the superiority of our MARA-Former in generating low-motion FCs from varying high-motion inputs. Moreover, downstream experiments further validate its effectiveness in FC-based infant brain development analysis.

Layer-dependent functional magnetic resonance imaging (fMRI) is a promising yet challenging approach for investigating layer-specific functional connectivity (FC). Achieving a brain-wide mapping of layer-specific FC requires several technical advancements, including sub-millimeter spatial resolution, sufficient temporal resolution, functional sensitivity, global brain coverage, and high spatial specificity. Although gradient echo (GE)-based echo planar imaging (EPI) is commonly used for rapid fMRI acquisition, it faces significant challenges due to the draining-vein contamination. In this study, we addressed these limitations by integrating velocity-nulling (VN) gradients into a GE-BOLD fMRI sequence to suppress vascular signals from the vessels with fast-flowing velocity. The extravascular contamination from pial veins was mitigated using a GE-EPI sequence at 3T rather than 7T, combined with phase regression methods. Additionally, we incorporated advanced techniques, including simultaneous multi-slice (SMS) acceleration and NOise Reduction with DIstribution Corrected principal component analysis (NORDIC PCA) denoising, to improve temporal resolution, spatial coverage, and signal sensitivity. This resulted in a VN fMRI sequence with 0.9 mm isotropic spatial resolution, a repetition time (TR) of 4 s, and brain-wide coverage. The VN gradient strength was determined based on results from a button-pressing task. Using resting-state data, we validated layer-specific FC through seed-based analyses, identifying distinct connectivity patterns in the superficial and deep layers of the primary motor cortex (M1), with significant inter-layer differences. Further analyses with a seed in the primary sensory cortex (S1) demonstrated the reliability of the method. Brain-wide layer-dependent FC analyses yielded results consistent with prior literature, reinforcing the efficacy of VN fMRI in resolving layer-specific functional connectivity. Given the widespread availability of 3T scanners, this technical advancement has the potential for significant impact across multiple domains of neuroscience research.

Phthalates and replacement plasticizers (PRPs) are ubiquitous exposures in daily life across all age ranges. Exposure to phthalates has been linked to changes in cognitive and behavioral development and associated with increased risk of some developmental disabilities. We examined the extent to which early life exposure to PRPs was associated with changes in connection strength of resting state functional networks or impacted structural morphologies of cortical regions of interest that underlie basic and higher order cognitions. We utilized the UNC Chapel Hill enrollment in the Baby Connectome Project, a longitudinal study of normative brain development of children between 2 weeks and 5 years of age. Non-sedated structural and resting state functional magnetic resonance imaging of the brain during natural sleep were obtained longitudinally, along with urine samples that were analyzed for 17 PRP metabolites. Using kernal weighted estimating equations and generalized linear models, we identified multiple PRP metabolites were associated with alterations in within-network connection strengths in the executive control and dorsal attention networks, with directionality often differing between boys and girls. PRP exposure among boys tended to be associated with lower functional connectivity, whereas PRP exposure among girls tended to be associated with higher functional connectivity. Among girls, MiBP metabolite concentrations were also significantly associated with cortical thinning in several regions of interest in the temporal lobe. Our results indicate that exposure to PRPs in early life has a measurable impact on the developmental trajectory of brain maturation, with potentially important differences by child sex.

Background and purpose Early infancy is characterized by rapid and regionally specific changes in white matter microstructure, which can be quantified using diffusion tensor imaging (DTI). We examined whether diffusion-derived microstructural measures during early postnatal life are associated with emerging cognitive functions. Material and methods Twenty-three infants from the UNC Brain Connectome Project underwent 3T MRI between 2 weeks and 3 months of age. Fractional anisotropy (FA), axial diffusivity (AD), and radial diffusivity (RD) were extracted from major white matter tracts using atlas-based tractography. Cognitive performance was assessed using the Mullen Scales of Early Learning (MSEL). Associations between tract-specific DTI metrics and cognitive scores were evaluated. Results Tract-specific associations were observed between white matter microstructure and cognitive outcomes. Diffusion metrics in the corticothalamic tract and fornix were associated with gross and fine motor performance, while FA, AD, and RD in the optic tract and optic radiation were related to visual reception. Language measures were associated with diffusion properties of the arcuate and uncinate fasciculi. Several corpus callosum subdivisions also showed significant associations with early learning outcomes. Conclusion These findings demonstrate that early postnatal white matter microstructure is selectively associated with emerging cognitive functions, supporting the utility of DTI for investigating brain–behavior relationships during early infancy.

The brain morphological connectome derived from structural MRI reflects inter-regional morphological relationships, providing a powerful representation for characterizing individual variability and detecting abnormalities across the lifespan. However, these abnormal alterations are subtle and complex, posing significant challenges for accurate and generalizable diagnosis using machine learning. Here, we present FedFound, the first federated foundation model inspired by the structured educational and residency training pathway of radiologists, designed for robust and scalable analysis of lifespan brain morphological connectomes. Integrating heterogeneous neuroimaging datasets across sites and disorders (22,911 subjects aged 0 to 100 years), FedFound combines self-supervised pre-training and supervised federated disease-specific refinement, supporting multidisciplinary knowledge aggregation through distributed optimization. Across nine diagnostic tasks spanning neurodevelopmental, neuropsychiatric, and neurodegenerative disorders, FedFound demonstrates superior performance and interpretability, revealing both shared and disorder-specific morphological patterns across etiologies. FedFound provides a robust foundation for lifespan neuroimage-based diagnosis that complements clinical expertise, while establishing a scalable and generalizable paradigm for integrating heterogeneous neuroimaging data across institutions, populations, and diseases to advance medical foundation models.

Functional Connectivity Representation (FCR) free of brain parcellations, such as diffusion maps of functional connectivity, provides critical insights into brain functional organization, making it essential in neuroimaging studies. However, site-specific variability in multi-site fMRI datasets caused by scanner and imaging protocol differences introduces non-biological variability, necessitating harmonization for reproducible and generalizable cross-cohort studies. Despite existing efforts, previous methods typically overlook some critical properties during harmonization, such as local functional, longitudinal, and population-affinity relation, limiting the reliability and interpretability of downstream analyses. Therefore, we propose Relation-Preserving Functional Connectivity Representation Harmonization (RP-FCRH), a novel framework that removes site effects while preserving biologically meaningful variability in FCR. For site effect removal, RP-FCRH is built upon a Cycle-Consistent Adversarial Autoencoder (CAA), which enforces site-invariance in the latent space via a discriminator and utilizes cycle consistency to generate realistic predictions. To preserve biological variability, we introduce three relation-preserving constraints based on CAA: (1) Local Functional Stability: RP-FCRH maintains the spatial organization of local functional connectivity patterns by preserving local gradient structures, ensuring fine-grained functional relationships remain intact. (2) Longitudinal Trend Consistency: RP-FCRH preserves individual-specific developmental trajectories by minimizing deviations in longitudinal trajectories before and after harmonization. (3) Population-Level Similarity: RP-FCRH maintains similarity relationships among individuals within a cohort by constraining inter-subject distances, preventing artificial alterations in within-group characteristics. Extensive experiments on 4 fMRI datasets (1,206 scans) highlight the superiority of RP-FCRH in reducing site-specific variability while preserving critical functional connectivity relationships, demonstrating its potential in enabling more robust and generalizable cross-cohort fMRI studies.

Subcortical segmentation of infant brain MR images is fundamental for studying neurodevelopment but remains challenging due to rapid brain development, low tissue contrast, and ambiguous anatomical boundaries. Conventional age-specific methods typically segment longitudinal scans independently, which ignores crucial temporal guidance and frequently leads to longitudinally inconsistent results. To address these issues, we propose C2FSRnet, a longitudinally-consistent coarse-to-fine network for joint subcortical segmentation and registration. Inspired by the reciprocal benefits between these tasks, we develop a framework to simultaneously perform affine/deformable registration and segmentation of longitudinal scans. In the coarse stage, the registration and segmentation branches share a joint encoder, enabling them to assist each other in capturing more informative and generalized features. Specifically, we jointly input multiple longitudinal scans from each subject to train the framework to learn within-subject anatomical correspondences, providing explicit longitudinal guidance and ensuring consistency. Additionally, signed distance maps are incorporated as spatial contextual guidance into the fine-stage network in a multi-path manner to achieve refined segmentation and registration. We validated C2FSRnet on the UNC/UMN Baby Connectome Project (393 scans from 124 subjects) and the developing Human Connectome Project (20 scans from 10 subjects) datasets. Compared to 15 state-of-the-art methods, our framework consistently achieves superior accuracy and longitudinal consistency in both tasks, demonstrating its robust performance for characterizing early subcortical development in both healthy and clinical populations.

The hippocampus is implicated in a myriad of crucial functions, particularly centered around memory and emotion, with distinct or multiple subdivisions fulfilling specific roles. However, its heterogeneity is multidimensional, given that the functional connectivity and gene expression-based parcellation along its long axis differs from histology-based parcellation along medial-lateral axis. The rapid nonuniform surface expansion of the hippocampus during early development reflects underlying changes of microstructure and functional establishment, providing important clues. Furthermore, the thin and convoluted properties bring out hippocampal maturity largely in the form of expanding surface area. We thus unprecedentedly explore the development-based surface area regionalization and patterns of the hippocampus by leveraging 513 high-quality longitudinal MRI scans during the first two postnatal years. Our findings imply two discrete hippocampal developmental patterns, featuring one pattern of subdivisions along anterior-posterior axis (head, regions 1 and 5; body, regions 2, 4, 6, and 7; tail, region 3) and the other one along medial-lateral axis (subiculum, regions 4, 5, and 6; CA fields, regions 1, 2, and 7). The resulting 7 subdivisions exhibit region-specific and nonlinear spatiotemporal surface area expansion patterns. These results provide important references for exploring fine-grained organization and development of the hippocampus and its intricate cognitions.

Gestational age at birth and birth size are major risk factors for early life behavioral/cognitive problems, but their impact on functional brain network dynamics during this period is not understood. Our objective was to conduct an exploratory study to evaluate associations of birth measures with longitudinal early life functional connectivity. The Baby Connectome Project used resting-state functional magnetic resonance imaging to assess connectivity within seven canonical brain networks (Yeo atlas): dorsal attention, salience, limbic, frontoparietal, default mode, visual, and sensorimotor. For 254 children <3 years old (contributing 583 observations), birth weight, birth length, and gestational age at birth were self-reported or abstracted from medical records, and we calculated weight-to-length ratio. Using covariate-adjusted multiple linear mixed models, we evaluated overall and sex-specific associations of birth measures as continuous variables and in tertiles with each network, which were Fisher r-to-z-transformed. Most children (54% female) were born to non-Hispanic White (80%) and college-educated (83%) mothers, were delivered ≥37 weeks gestation (97%), and had birth weights ≥2.5 kg (98%). Only birth size measures were associated with brain network connectivity. Compared with that in tertile 2, frontoparietal network connectivity was higher in birth weight tertile 1 (β: 0.02; 95% CI: 0.00, 0.04) and tertile 3 (β: 0.03; 95% CI: 0.01, 0.05). Also, compared with birth size tertile 2, birth size tertile 3 was associated with higher limbic (birth length β: 0.03; 95% CI: 0.00, 0.07) and default mode (birth length β: 0.02; 95% CI: 0.00, 0.03), but decreased sensorimotor (birth weight β: -0.03; 95% CI: -0.05, 0.00; birth length β: -0.03; 95% CI: -0.05, 0.00) network connectivity. Compared with birth size tertile 2, birth size tertile 1 was associated with lower limbic (birth weight β: -0.04; 95% CI: -0.08, 0.00) and default mode (weight-to-length ratio β: -0.02; 95% CI: -0.04, 0.00). In sex-stratified models, birth size was associated with frontoparietal and default mode networks in both sexes; sensorimotor, limbic, and dorsal attention networks in males; and salience and visual networks in females. Associations followed a U-shaped pattern in females, whereas those in males appeared at only the lowest or highest tertile. In this non-clinical sample, birth size was sex-specifically associated with early life brain network dynamics. This may have implications for later neurodevelopment.

Constructing a temporally continuous fetal brain atlas with anatomically realistic cortical geometry remains challenging due to rapid cortical folding during gestation. Deep learning, especially Implicit Neural Representation (INR) based methods, improves continuity but often fails to ensure cortical alignment due to the absence of explicit geometric constraints. We propose a cortical surface-constrained joint learning framework that integrates a registration network with an INR for continuous atlas representation. The registration network learns accurate deformation fields, while surface constraints guide alignment to preserve cortical topology. Through joint optimization, our method constructs a temporally continuous fetal brain atlas that captures smooth developmental transitions and preserves anatomically faithful cortical folding. Experiments on the dHCP dataset demonstrate that our method produces anatomically consistent and temporally smooth 4D atlases, achieving superior label-propagation (atlas-based) segmentation accuracy and biologically plausible developmental trajectories.

Large-scale gradients of functional connectivity between brain areas organize the human neocortex, linking brain topography to the texture of cognition1,2. In adults, three dominant axes-sensory-association, visual-somatosensory and modulation-representation-run, respectively, from primary sensory to transmodal association areas, from visual to body-centred systems and from control and attention networks to default mode and sensory areas1-4. These gradients provide a compact description of large-scale cortical hierarchies that underlie distinct modes of information processing. However, how these gradients and their multiscale biological and cognitive correlates evolve across the lifespan is unknown. Here we establish a continuous normative reference of functional organization from birth to 100 years of age, revealing complex, nonlinear developmental trajectories. Gradient architecture is anchored by primary sensory systems in infancy, differentiates along association and control axes during childhood and adolescence and gradually dedifferentiates during ageing. The importance of this functional architecture is corroborated by biology and behaviour: gradient metrics predict cognitive performance across development; structure-function coupling varies by axis and age; and distinct transcriptomic signatures are strongest early in life and weaken with age, consistent with a transient genetic scaffold for gradient architecture. Our lifespan gradients unify diverse research into developmental brain connectivity and provide a shared multimodal reference for future studies.

Spatiotemporal (4D) atlases of fetal brains are essential for quantitative analysis of the dynamic morphological changes and developmental patterns of the prenatal brain. An essential step in atlas construction is image registration, wherein accurate alignment of anatomical structures across subjects is essential for generating meaningful atlases. In existing learning-based atlas construction frameworks, registration is mainly driven by the matching of intensity images. However, due to the inherently low and spatiotemporally varying tissue contrasts and dynamic gyrification during prenatal brain development, enforcing anatomical constraints solely via intensity images often leads to inaccurate alignment, especially for the complex, folded cortical regions. To address this issue, we propose a novel learning-based framework which leverages accurate inter-subject cortical anatomical correspondences established by surface registration to improve deformation predictions. The proposed method leads us to construct continuous, high-quality, and anatomically meaningful 4D volumetric atlases. Specifically, given gestational age (GA) and two randomly selected subjects at this GA, the atlas synthesis network generates an atlas based on the input GA. To supervise this generation, the image registration network then deforms the generated atlas to the two input subjects under anatomically meaningful guidance. This guidance is implemented by (1) minimizing the distance between corresponding cortical vertices of the two subjects in the age-specific atlas space and (2) maximizing the overlap between the warped atlas tissue probability maps (TPMs) and those of each subject. Compared with 4D atlases built by state-of-the-art methods, our atlases exhibit sharper and anatomically more meaningful patterns, allowing better alignment of brain anatomical structures.

Despite the cerebellum's crucial role in brain function, its early development, particularly in relation to the cerebrum, remains poorly understood. Here, we examine cerebellocortical connectivity using over 1000 high-quality resting-state functional MRI scans from children between birth and five years of age. By mapping cerebellar topography with fine temporal granularity, we unveil the hierarchical organization of cerebellocortical functional connectivity from infancy. We observe dynamic shifts in cerebellar functional topography, which become more focal with age while largely maintaining stable anchor regions similar to adults, highlighting the cerebellum's evolving yet organized role in functional integration during early development. Our findings demonstrate cerebellar connectivity to higher-order networks at birth, which generally strengthen with age, emphasizing the cerebellum's early role in cognitive processing beyond sensory and motor functions. Our study provides insights into early cerebellocortical interactions, reveals functional asymmetry and sex-specific patterns in cerebellar development, and lays the groundwork for future research on cerebellum-related disorders in children.

Predicting the development of functional connectivity (FC) derived from resting-state functional MRI is pivotal for elucidating the intrinsic brain functional organization and modeling its dynamic development during infancy. Existing deep learning methods typically predict FC at a target timepoint from each available FC independently, yielding inconsistent predictions and overlooking longitudinal dependencies, which introduce ambiguity in practical applications. Furthermore, the scarcity and irregular distribution of longitudinal rsfMRI data pose significant challenges in accurately predicting and delineating the trajectories of early brain functional development. To address these issues, we propose a novel Triplet Cycle-Consistent Masked Autoencoder (TC-MAE) for the trajectory prediction of the development of infant FC. Our TC-MAE has the capability to traverse FC over an extended period, extract unique individual characteristics, and predict target FC at any given age in infancy with longitudinal consistency. Extensive experiments on 368 longitudinal infant rs-fMRI scans demonstrate the superior performance of the proposed method in longitudinal FC prediction compared with state-of-the-art approaches.

Despite the cerebellum's crucial role in brain functions, its early development, particularly in relation to the cerebrum, remains poorly understood. Here, we examine cerebellocortical connectivity using over 1,000 high-quality resting-state functional MRI scans of children from birth to 60 months. By mapping cerebellar topography with fine temporal detail for the first time, we show the hierarchical organization of cerebellocortical functional connectivity from infancy. We observe dynamic shifts in cerebellar network gradients, which become more focal with age while generally maintaining stable anchor points similar to adults, highlighting the cerebellum's evolving yet stable role in functional integration during early development. Our findings provide the first evidence of cerebellar connections to higher-order networks at birth, which generally strengthen with age, emphasizing the cerebellum's early role in cognitive processing beyond sensory and motor functions. Our study provides insights into early cerebellocortical interactions, reveals functional asymmetry and sex-specific patterns in cerebellar development, and lays the groundwork for future research on cerebellum-related disorders in children.

Early childhood is crucial for brain functional development. Using advanced neuroimaging methods, characterizing functional connectivity has shed light on the developmental process in infants. However, insights into spatiotemporal functional maturation from birth to early childhood are substantially lacking. In this study, we aggregated 1,091 resting-state functional MRI scans of typically developing children from birth to 6 years of age, harmonized the cohort and imaging-state-related bias, and delineated developmental charts of functional connectivity within and between canonical brain networks. These charts revealed potential neurodevelopmental milestones and elucidated the complex development of brain functional integration, competition and transition processes. We further determined that individual deviations from normative growth charts are significantly associated with infant cognitive abilities. Specifically, connections involving the primary, default, control and attention networks were key predictors. Our findings elucidate early neurodevelopment and suggest that functional connectivity-derived brain charts may provide an effective tool to monitor normative functional development.

The human cortex undergoes immense change in the first years of life, doubling in thickness within the first year and evidencing the greatest change within the first 5 y. While substantial research has identified the early postnatal period as a sensitive period in cortical development, research to date lacks the temporal resolution necessary to identify which aspects of cortical change predict later neural and cognitive function. This study leveraged a rich longitudinal dataset of cortical thickness in 50 children who were scanned up to 11 times between birth and 6 y. We used nonlinear multilevel modeling to explore patterns of cortical change across the brain during this period and distinguish whether different phases of change would predict performance and brain activation during a working memory task children completed at approximately 9 y. Cortical thickness across the brain showed a large increase from birth through 12 mo, a decrease from 12 to 18 mo, and a small increase from 18 mo to 6 y, mirroring patterns of early neural proliferation, pruning, and sustained growth. Performance and neural activation during the working memory task were associated with smaller peak (i.e., 12 mo) thickness and a marginally less steep 12 to 18-mo decline in thickness in the middle frontal gyrus (MFG) of the frontal lobe, in line with evidence demonstrating concurrent links between frontal lobe structure and working memory. These findings validate theories of cortical growth developed in preclinical models using human data and demonstrate that prefrontal cortex development in infancy uniquely predicts neurocognitive function 9 y later.

Computational methods for prediction of the dynamic and complex development of the infant cerebral cortex are critical and highly desired for a better understanding of early brain development in health and disease. Although a few methods have been proposed, they are limited to predicting cortical surface maps at predefined ages and require a large amount of strictly paired longitudinal data at these ages for model training. However, longitudinal infant images are typically acquired at highly irregular and nonuniform scanning ages, thus leading to limited training data for these methods and low flexibility and accuracy. To address these issues, we propose a flexible framework for individualized prediction of cortical surface maps at arbitrary ages during infancy. The central idea is that a cortical surface map can be considered as an entangled representation of two distinct components: 1) the identity-related invariant features, which preserve the individual identity and 2) the age-related features, which reflect the developmental patterns. Our framework, called intensive triplet autoencoder, extracts the mixed latent feature and further disentangles it into two components with an attention-based module. Identity recognition and age estimation tasks are introduced as supervision for a reliable disentanglement. Thus, we can obtain the target individualized cortical property maps with disentangled identity-related information with specific age-related information. Moreover, an adversarial learning strategy is integrated to achieve a vivid and realistic prediction. Extensive experiments validate our method's superior capability in predicting early developing cortical surface maps flexibly and precisely, in comparison with existing methods.

In magnetic resonance imaging of the brain, an imaging-preprocessing step removes the skull and other non-brain tissue from the images. But methods for such a skull-stripping process often struggle with large data heterogeneity across medical sites and with dynamic changes in tissue contrast across lifespans. Here we report a skull-stripping model for magnetic resonance images that generalizes across lifespans by leveraging personalized priors from brain atlases. The model consists of a brain extraction module that provides an initial estimation of the brain tissue on an image, and a registration module that derives a personalized prior from an age-specific atlas. The model is substantially more accurate than state-of-the-art skull-stripping methods, as we show with a large and diverse dataset of 21,334 lifespans acquired from 18 sites with various imaging protocols and scanners, and it generates naturally consistent and seamless lifespan changes in brain volume, faithfully charting the underlying biological processes of brain development and ageing. A skull-stripping model for magnetic resonance images that leverages personalized priors from atlases of brain scans generates naturally consistent and seamless lifespan changes in brain volume.