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.

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.

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.

Mutations in the SNCA gene encoding α-synuclein underlie familial early-onset Parkinson's disease. Pathological α-synuclein deposition may commence decades prior to the emergence of cardinal motor symptoms. Long-term investigation of brain and behavioural development in an SNCA-A53T transgenic macaque model offers critical insights into Parkinson's disease progression. In this study, we systematically characterized SNCA-A53T transgenic rhesus monkeys through multimodal assessments. Our results showed that these transgenic monkeys exhibited phosphorylated α-synuclein aggregation patterns and dopaminergic degeneration resembling Parkinson's disease patients. Progressive motor and cognitive deficits were observed in transgenic monkeys with ageing. Polysomnographic analysis revealed rapid eye movement sleep behaviour disorder manifestations in transgenic animals. Four-year longitudinal MRI tracking demonstrated abnormal developmental patterns of cortical surface area alongside alterations in thickness and volume. The single-cell transcriptome revealed that astrocyte-specific gene dysregulation and cell loss contribute to brain atrophy in transgenic monkeys. Cortical and subcortical grey matter regions showing volume reduction were functionally associated with behavioural deficits and differentiated transgenic animals from wild-type controls. Collectively, this comprehensive study provides evidence that SNCA-A53T transgenic monkeys recapitulate Parkinson's disease pathophysiology while demonstrating the utility of longitudinal monitoring in genetically engineered non-human primates for tracking neurodegenerative disease progression.

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.

Cortical folds encode the architecture of human cognition, yet the mechanisms that transform the smooth fetal cortex into its convoluted geometry remain elusive. Biophysical modeling enables mechanistic insight into cortical morphogenesis, but existing models often lack anatomical realism and fail to capture key hallmarks and morphometrics of dynamic cortical folding in the developing human brain. Here, we introduce a novel whole-brain developmental framework that integrates region-specific, data-driven growth laws with anatomically accurate cortical geometry to enable realistic and biologically interpretable modeling of cortical morphogenesis during gestation. Growth fields derived from large-scale prenatal magnetic resonance imaging data capture spatiotemporal variations in cortical expansion and thickness across parcellated regions. Incorporating this heterogeneous growth yields anatomically faithful folding patterns that closely match qualitative landmarks and quantitative morphometrics from human imaging. Systematic perturbations of geometry and growth attributes delineate control parameters that produce realistic morphological variability and replicate clinically atypical brain phenotypes consistent with lissencephaly, pachygyria, and polymicrogyria. This framework provides a quantitative foundation for elucidating the mechanisms of typical and atypical fetal brain development and can serve as a promising generative engine for high-fidelity, longitudinal synthetic brain datasets to advance AI-driven developmental neuroscience and clinical translation.

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.

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.

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.

Functional connectivity (FC) derived from functional MRI (fMRI) shows significant promise in predicting behavior and demographics using deep learning techniques. Incorporating vertex-wise FC maps, which capture fine-grained spatial details of neural activity, offers the potential to enhance FC-based prediction accuracy. However, fMRI data is inherently limited and noisy, challenging neural networks to reliably identify patterns within high-dimensional cortical vertices. Therefore, we design a novel Masked Momentum Contrastive Dynamic Transformer, which utilizes masked momentum contrastive pre-training to explore subject-specific features and enhances prediction accuracy by leveraging the temporal dynamics of FCs with a dynamic transformer. Specifically, our framework 1) learns effective subject-specific representations by treating vertex-wise FCs from different runs of an individual as distinct views and maximizing their affinity, and 2) employs a vertex-wise masking strategy to promote learning from limited data. Extensive experiments on gender classification and cognition prediction validate its superior performance on the Human Connectome Project dataset.

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 cortical 3-hinge gyrus (3HG) and its network (GyralNet) play key roles in understanding the regularity and variability of brain structure and function. However, existing cortical surface registration methods overlook these features, resulting in suboptimal alignment across subjects. Currently, no 3HG and GyralNet atlas exist for registration, and generation of the corresponding atlas requires extensive runtime using traditional methods. To enable better registration of these features, we introduce an unsupervised learning framework to jointly develop 3HGs and GyralNet atlas and register the individual cortical features onto the atlas. To incorporate the graph structure of 3HGs and GyralNet into the registration network, we convert them into surface distance maps, facilitating effective integration. To effectively learn large deformations, a multi-level spherical registration network based on spherical U-Net is introduced to perform registration in a coarse-to-fine manner. Experiments demonstrate our approach's ability to generate 3HGs and GyralNet atlas with detailed patterns and effectively improve registration accuracy.

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.

Resting-state functional MRI (rs-fMRI) data analysis provides essential insights into early neurodevelopment through longitudinal assessment of functional connectivity (FC) patterns in infant brains, which may help uncover critical biomarkers for developmental monitoring. However, due to challenges in acquiring high-quality functional MRI (fMRI) data in infants, such as strong motion artifacts, short scan durations, and participant compliance, longitudinal FC of infants remain scarce, which significantly hampers the capacity to systematically investigate early functional brain development. To address this challenge, we propose MAD-Net, a novel diffusion model that predicts longitudinal FC from morphometric features derived from structural MRI (sMRI). Our framework integrates classifier-free guidance with a cross-modal attention mechanism, enabling the dynamic fusion of morphometric features and developmental age constraints during the diffusion process. A shared triplet encoder learns robust feature representations from longitudinal data, while a U-Net-based architecture ensures precise conditioning on individual morphometry and target age. We evaluate MAD-Net on 386 longitudinal infant fMRI scans and demonstrate its superior performance in FC prediction compared to state-of-the-art methods. By integrating diffusion-based learning, structural priors, and age-dependent constraints, MAD-Net represents a significant advancement in neuroimaging-based functional network reconstruction. The code is available at https://github.com/IPMI-NWU/MAD-Net.

How to harmonize site effects is a fundamental challenge in modern multi-site neuroimaging studies. Although many statistical models and deep learning methods have been proposed to mitigate site effects while preserving biological characteristics, harmonization schemes for multi-site resting-state functional magnetic resonance imaging (rs-fMRI), particularly for functional connectivity (FC), remain undeveloped. Moreover, statistical models, though effective for region-level data, are inherently unsuitable for capturing complex, nonlinear mappings required for FC harmonization. To address these issues, we develop a novel, flexible deep learning method, Mamba-based Residual Generative adversarial network (MR-GAN), to harmonize multi-site functional connectivities. Our method leverages the Mamba Block, which has been proven effective in traditional visual tasks, to define FC-specified sequential patterns and integrate them with a multi-task residual GAN to harmonize multi-site FC data. Experiments on 939 infant rs-fMRI scans from four sites demonstrate the superior performance of the proposed method in harmonization compared to other approaches.

Accurately characterizing brain morphological changes throughout human lifespan is crucial for understanding brain development, aging, and disorders. At the core of this endeavor lies cortical surface reconstruction (CSR), which underpins the computation of essential brain morphological features. However, existing CSR methods face two major limitations. First, cortical surfaces are typically reconstructed from 3D MRI data with high isotropic resolution, which is confined to research settings. In contrast, clinical MRI scans are collected with high in-plane but low through-plane resolution. Second, most CSR pipelines are designed either for adult or pediatric populations, restricting their applicability across the lifespan. To this end, we develop a deep learning framework that harnesses MRI super-resolution (SR) as a bridging mechanism, leveraging the complementary information SR provides to jointly perform SR and CSR with a coarse-to-fine strategy. Specifically, we introduce a dual-decoder age-conditioned temporal attention network (DATAN) with a shared encoder, which simultaneously performs CSR and SR from thick-slice clinical MRI. By jointly training on the SR task, the shared encoder captures richer cortical features, thereby enhancing CSR performance. Through a two-stage coarse-to-fine approach, incremental refinements in the SR output progressively restore fine-scale details otherwise lost in low-resolution scans, ultimately improving CSR fidelity. Furthermore, to facilitate accurate CSR across the lifespan, we exploit the age-conditioning module of our framework and train our model on a large, diverse MRI dataset spanning ages from 1 to 100 years. Experimental results demonstrate that our method, despite requiring only thick-slice clinical MRI scans, achieves consistently improved CSR performance across the entire human lifespan.

Constructing a high-resolution spatiotemporal fetal brain atlas is essential for understanding early neurodevelopmental processes. Many existing atlas building methods assume a predefined temporal distribution, like a Gaussian distribution, and use Gaussian kernel regression for smoothing. However, this limits the ability to capture complex, non-linear developmental trajectories. Meanwhile, these methods suffer from limited resolution dependence. In this work, we leverage implicit neural representations to model continuous functions and jointly optimize both registration and atlas representation for learning a continuous, high-resolution spatiotemporal fetal brain atlas. A key innovation of our method is the explicit incorporation of gestational age as an additional input, allowing the network to learn the continuous deformation of brain structures over time rather than relying on a predefined distribution. We integrate tissue segmentation maps into the learning process to further enhance anatomical accuracy during the construction of atlas, guiding the network to capture structural details better. Additionally, we introduce a set of regularization constraints on the deformation field to ensure anatomical and physical plausibility. Experimental results demonstrate that our method outperforms existing methods, achieving superior anatomical alignment and continuity in both spatial and temporal domains. Compared to other methods, our approach improves anatomical alignment, achieving a higher Dice score (93.8

OBJECTIVE:Prenatal phthalate exposure is associated with adverse neurodevelopmental outcomes, yet data on impacts of early life exposure remains limited. We investigated phthalate and replacement plasticizer exposures from 2 weeks to 7 years of age in relation to brain anatomical attributes, using serial structural magnetic resonance imaging (sMRI). MATERIAL AND METHODS:Children were enrolled after birth into the UNC Baby Connectome Project, a longitudinal neuroimaging study (North Carolina, USA; 2017-2020). Urine samples (n = 406) were collected at each visit and analyzed for 17 phthalate and replacement plasticizer metabolites. Among 157 children contributing 369 sMRIs, we calculated metabolite-specific average exposures across each individual's urine samples and used linear mixed models to estimate longitudinal associations of log transformed, specific gravity-adjusted average metabolite concentrations with gray and white matter volume, and cortical volume, thickness, and surface area. We examined sex-specific differences in these associations. RESULTS:Higher average metabolite concentration was associated with lower gray matter volume (MCPP: (-1.73 cm3, 95 % CI: -3.36, -0.10) and higher white matter volume (∑DEHP: 2.28 cm3, 95 % CI: 0.08, 4.48). Among boys (n = 72, 140 sMRIs), MEP (-2.97 cm3, 95 % CI: -5.85, -0.09) and MiBP (-2.40 cm3, 95 % CI: -4.64, -0.15) were also associated with lower gray matter volume. Among females (n = 85, 229 MRIs), higher ∑DINCH exposure was associated with higher white matter volume (2.27 cm3, 95 % CI: 0.29, 4.25). We observed significant sex interactions for ∑DEHP with gray matter (p-interaction = 0.03) and ∑DINCH with white matter volume (p-interaction = 0.001). CONCLUSION:Early life phthalate/plasticizer exposure may differentially impact various brain region volumes in early childhood, with potential downstream consequences on functional development.