Robust 3D segmentation of primary and permanent teeth in cone-beam CT (CBCT) is critical for pediatric and orthodontic care. We propose a fully automatic deep-learning pipeline built on the self-configuring nnU-Net v2 framework, tailored for high-fidelity dental shape modeling. Our approach learns fine-scale tooth geometries directly from volumetric data, eliminating manual tuning. On a pediatric CBCT cohort (369 training, 93 validation, 55 test scans), our model attains a mean Dice score of 0.87 across 55 dental and supporting anatomical structures. Key components include adaptive preprocessing (isotropic resampling, automatic craniofacial cropping, intensity normalization), on-the-fly 3D augmentations, and lightweight postprocessing to remove spurious segment. The resulting segmentations are consistent and clinically actionable, supporting advanced 3D morphometric analysis and digital treatment planning. By extending state-of-the-art volumetric segmentation to mixed dentition CBCT data, our work facilitates integration of AI-driven geometric learning into routine pediatric dentistry workflows.

Congenital and early-life Zika virus (ZIKV) infection can result in neurologic deficits. Precise mechanisms of injury, especially in the more subtle presentation of postnatal infection, are not fully elucidated. Here, we defined the effects of ZIKV on the developing brain using single cell transcriptomics, histopathology and design-based stereology, diffusion MRI, and neurobehavioral assessments in infant rhesus macaques. ZIKV upregulated interferon-stimulated genes in activated microglia and cell death pathways in neurons and downregulated metabolism and differentiation genes in mature oligodendrocytes. Abnormal micro-organization of the corpus collosum and limbic white matter tracts was seen on diffusion weighted imaging. A curated gene set associated with autism spectrum disorder risk was negatively enriched in inhibitory and excitatory neurons from ZIKV-infected infants, with increased emotional reactivity already evident two weeks following infection. From single cells to organism-level behaviors, these results define the pathways and processes disrupted by early-life ZIKV infection.

White matter tracts are bundles of myelinated nerve fibers that connect different regions of the brain, facilitating communication between them. These tracts play an important role in the cognitive and behavioral functioning of the brain. Understanding the structure and connectivity of white matter tracts is crucial for studying brain function and diagnosing neurological disorders. In this study, we propose a new method to map fiber tract information onto the cortical regions via fiber to cortex minimal distances. Diffusion properties of the fiber tracts weighted by these distances can then be incorporated as adjacency weights in a graph convolutional neural network. Our approach provides a multi-modality framework that integrates structural and diffusion MRI, providing a comprehensive view of the brain’s architecture. We evaluate this framework in two longitudinal studies, predicting later cognitive outcomes.

Maternal depressive symptoms during pregnancy have consequences for offspring brain development, likely mediated via biological signals. However, gestational biological correlates of maternal depression may differ depending on childhood maltreatment (CM) history. We investigated the association of maternal depressive symptoms in pregnancy and CM history with newborn global white matter microstructure. In a sample of N = 90 mother-infant dyads from two cohorts, maternal depressive symptoms were assessed with the Edinburgh Postnatal Depression Scale. CM was assessed with the Childhood Trauma Questionnaire or the Adverse Childhood Experiences scale. Diffusion-weighted imaging was performed in the infants within 90 days of birth. Fiber profiles of fractional anisotropy (FA), axial diffusivity (AD), and radial diffusivity (RD) were determined, and a global mean for each metric was computed. In adjusted models, there was a significant interaction effect of maternal depression and CM on newborn global FA (β = -0.523, p = .029) and RD (β = 0.590, p = .014) but not AD (β = 0.367, p = .120). In infants of women with CM history, maternal depressive symptoms were correlated negatively with FA and positively with RD. In contrast, infants of women without CM exhibited the reverse pattern of associations between depressive symptoms and diffusion metrics. These findings suggest that the impact of prenatal exposures, such as maternal depressive symptoms, on offspring brain development may be conditional on the presence or absence of maltreatment history. These findings highlight the importance of assessing trauma history and monitoring psychosocial well-being during pregnancy.

Maternal diabetes and obesity are established risk factors for adverse offspring health. Emerging evidence suggests that these fetal programming effects vary by sex, yet it remains unclear whether these factors independently or interactively influence early brain development. This prospective study included 1,965 infants from six international cohorts. Infant MRI was used to derive subcortical volumes (thalamus, amygdala, hippocampus, pallidum, putamen, caudate). ComBat harmonization was applied. Multiple linear regression tested main and interaction effects of maternal obesity, maternal diabetes, and sex, controlling for covariates with false discovery rate (FDR) corrections. Of the sample, 46

ABSTRACT Introduction Down syndrome (DS), arising from Trisomy 21, is the most common genetic condition associated with intellectual disability. While smaller total brain volumes have been consistently observed in DS, no longitudinal neuroimaging studies have examined volumetric brain development in DS during infancy, a period of rapid neural growth when interventions may have the greatest impact. Method High-resolution T1- and T2-weighted images were acquired during natural sleep in a multisite longitudinal cohort of 44 infants with DS and 39 control infants without DS at ages 6 and 12 months. Neuroimaging data were harmonized to reduce batch effects, and a novel deep-learning, repeated-measures segmentation approach was applied to optimize neuroanatomical segmentations. Total intracranial volume (ICV) and bilateral absolute subcortical volumes (amygdala, caudate, hippocampus, pallidum, putamen, thalamus) were first directly compared in infants with and without DS at 6 and 12 months. Hierarchical linear modeling (HLM) evaluated longitudinal group differences for each structure, accounting for sex, gestational age, and laterality. Subcortical group differences estimated by HLM were also compared to group differences in total ICV. Results ICV in infants with DS was lower than controls at 6 months (12.6%; p< .001) and 12 months (16.3%; p< .001). Subcortical structures displayed a range of lower volumes (6.9%-13.1%; p’s ≤.003) in infants with DS, although the caudate and putamen were exceptions. Caudate volumes were on average lower in DS but not significantly different from controls, while putamen volumes were on average higher in DS but not significantly different from controls, except for the right putamen, which was significantly larger (5.3%; p =.018) at 6 months. In HLM, ICV and all subcortical structures showed slower growth in DS from 6 to 12 months, except for the amygdala and putamen, which displayed similar growth rates to controls. DS-associated reductions in subcortical volumes were similar in magnitude to ICV, although 12-month caudate and 6- and 12-month putamen volumes were enlarged relative to ICV. Conclusion Infants with DS exhibited substantially reduced ICV and widespread reductions in subcortical volumes and growth from 6-12 months. Across a range of volumetric differences, findings were most distinct in the basal ganglia, for which volume reductions were attenuated in the caudate, while the putamen was uniquely enlarged with comparable growth to controls. These observations support early regional specificity in the neural impact of Trisomy 21 and underscore the utility of infant neuroimaging to inform biologically based interventions and clinical trial readiness in DS.

Abnormally increased extra-axial cerebrospinal fluid (EA-CSF) volume is present as early as 6 months in infants later diagnosed with autism and is associated with symptom severity at the age of diagnosis, but it is unknown whether early EA-CSF enlargement has long-term impacts on other clinical domains. Executive function (EF) deficits are frequently observed in children with autism and are linked with worse academic outcomes, higher anxiety, and lower adaptive functioning. The current study examines the association between EA-CSF volume at infancy and EF at school age in a longitudinally phenotyped cohort of children with either high (HL) or low (LL) familial likelihood for autism. In this prospective study, 239 infants underwent MRI scans during natural sleep at 6, 12, and 24 months of age. HL was defined as having an older sibling with autism. The sample was divided into three diagnostic groups: HL infants who were diagnosed with autism at 24 months (HL+, n = 34), HL infants not diagnosed with autism (HL-, n = 131), and LL infants without autism (LL-, n = 74). Two parent-rating scales of EF were collected at the school-age follow-up (Mage= 10.4 years ± 1.34): the Behavior Rating Inventory of Executive Function (BRIEF) and Conners Parent Short Form. ANOVA was used to test for diagnostic group differences in EF. Longitudinal mixed-effects models utilized EA-CSF volumes in infancy to predict EF at school-age, while controlling for IQ, sex, total cerebral volume, and diagnostic group. Consistent with previous literature, HL + participants had greater EF deficits than both HL- and LL- on the BRIEF (F = 18.46, p < .0001) and greater EF deficits than LL- on the Conners (F = 8.55, p < .001). Further, higher EA-CSF volumes during infancy were associated with poorer executive function eight years later on both BRIEF (ß=0.18, p < .001) and Conners (ß=0.18, p < .001). This association between EA-CSF and executive function was observed across familial likelihood and diagnostic groups. Our study indicates that elevations in EA-CSF volumes during infancy, even in those who do not have autism, have long-term associations beyond autism symptomatology. These findings underscore the potential link between infant CSF physiology and future behavioral domains such as executive function at school age.

Objectives: Brain tissue segmentation of infant magnetic resonance (MR) images is important for studying typical and atypical brain development. The infant brain undergoes rapid changes throughout the first years of postnatal life, making tissue segmentation difficult for most existing algorithms. We introduce a deep neural network BIBSNet (Baby and Infant Brain Segmentation Neural Network), an open-source model for robust and generalizable brain tissue segmentation leveraging data augmentation and a large sample size of manually annotated images. Experimental design: Model training included MR brain images from 90 participants with an age range of 0-8 months (median age 4.6 months). Using manually annotated real images along with synthetic segmentation images produced using SynthSeg, the model was trained using a 10-fold procedure. Model performance was assessed by comparing BIBSNet, and joint label fusion (JLF) inferred segmentations to ground truth segmentations, and an ad-hoc analysis with iBeat inferred segmentation, using Dice Similarity Coefficient (DSC). Additionally, MR data along with the FreeSurfer compatible segmentations were processed with the DCAN labs infant-ABCD-BIDS processing pipeline from ground truth, JLF, and BIBSNet to produce anatomical and resting state functional derivatives to further assess model performance on processed derivatives. Principal observations: BIBSNet outperforms JLF based on DSC comparisons especially with gray matter (BIBSNet = 0.849, JLF = 0.713) and white matter (BIBSNet = 0.862, JLF = 0.791). Additionally, with processed derived metrics, BIBSNet inferred segmentations outperforms JLF inferred segmentations across nearly all anatomical and functional metrics. Ad-hoc analyses of cortical segmentations-iBeat does not perform subcortical segmentations-showed that there is no significant difference between iBeat and BIBSNet segmentation for infants 0-5 months, but iBeat performed significantly better for infants 6-8 months. Conclusions: BIBSNet shows marked improvement over JLF across all age groups analyzed. The BIBSNet model is 600x faster compared to JLF at segmentation inference, produces FreeSurfer-compatible segmentation labels, and can be easily included in other processing pipelines. BIBSNet provides a viable alternative for segmenting the brain in the earliest stages of development.

Although congenital Zika virus (ZIKV) syndrome is well-characterized, the neurodevelopmental consequences of postnatal infection are less understood. Here we used a rhesus macaque model to investigate the developmental consequences of ZIKV infection during infancy on the brain and behavior, building on our prior research. Male and female infant rhesus macaques infected with ZIKV at 1 month of age were compared to sex-, age-, and rearing-matched uninfected controls and infants treated with the TLR3 agonist PolyIC as a control for activation of the innate immune system. Longitudinal behavioral assessments revealed alterations in emotional regulation following ZIKV exposure, including poor state control scores obtained from the Infant Neurobehavioral Assessment Scale early after ZIKV infection and longer-term displays of increased hostility during an acute stressor. While attachment bonds to caregivers were preserved, ZIKV-infected infants showed sex-specific alterations in behavioral regulation during caregiver separation compared to controls. At 3 months of age, MRI scans revealed larger total cerebrospinal fluid (CSF) volume and reduced volumes in visual processing regions in ZIKV-infected infants compared to controls. Postnatal ZIKV exposure also resulted in sex-specific brain structural alterations with males exhibiting amygdala hypertrophy, whereas ZIKV-infected females had volumetric reductions in temporal-limbic and temporal-auditory cortices. These findings demonstrate that postnatal ZIKV infection disrupts the development of sensory, social and emotion-regulatory systems and CSF function, highlighting the critical need for long-term monitoring of exposed children.

Background:While perinatal factors are known to influence brain development, their long-term impact on white matter microstructure remains incompletely understood. Previous studies using tract-based spatial statistics (TBSS) have shown limited associations between neonatal measures and later white matter development. Methods:We investigated associations between perinatal factors (birth weight [BW], gestational age [GA], and head circumference at birth [HC]) and white matter microstructure in 117 children aged 8-10 years from the UNC Early Brain Development Study cohort. Diffusion tensor imaging (DTI) data were analyzed using a fiber tract-based framework examining 54 major white matter tracts. Statistical analyses were performed using a functional analysis of fiber tract profiles. Results:GA and BW showed widespread patterns of significant associations with white matter microstructure (38 and 36 out of 54 tracts, respectively), whereas HC showed limited associations (3 out of 54 tracts). Post hoc univariate analyses revealed stronger associations with axial diffusivity (AD) compared to radial diffusivity (RD) or fractional anisotropy (FA). AD associations with BW, GA, and HC were observed in 30, 31, and 8 tracts, respectively. Conclusion:Using a fiber tract-based analysis approach, we found that GA and BW were associated with widespread patterns of differences in white matter organization at school age, whereas HC showed limited associations. Associations involving AD were most consistently observed across tracts, suggesting that these perinatal factors may be related to variation in axonal characteristics of white matter. Overall, our findings indicate that early-life biological measures are related to later white matter microstructure, although further work is needed to clarify the developmental mechanisms underlying these associations.

Human induced pluripotent stem cell (iPSC) derived cortical organoids (hCOs) model neurogenesis on an individual's genetic background. The degree to which hCO phenotypes recapitulate the brain growth of the participants from which they were derived is not well established. We generated up to 3 iPSC clones from each of 18 participants in the Infant Brain Imaging Study, who have undergone longitudinal brain imaging during infancy. We identified consistent hCO morphology and cortical cell types across clones from the same participant. hCO cross-sectional area and production of cortical hem cells were associated with in vivo cortical growth rates. Cell cycle associated genes expression in early progenitors at the crux of fate decision trajectories were correlated with cortical growth rate from 6-12 months of age, and were enriched in microcephaly and neurodevelopmental disorder genes. Our data suggest the hCOs capture inter-individual variation in cortical cell types influencing infant cortical surface area expansion.

Temporomandibular degenerative joint disease (TM DJD) is a multifactorial condition with complex clinical presentations. This study presents a multimodal framework centered on structured summarization of clinical text, supported by imaging information from automatically registered MRI and CBCT scans. Two large language models, BART and DeepSeek-R1, were fine-tuned on 1,813 annotated text segments from 500 TM DJD patient records to extract 56 clinical indicators, including pain severity, jaw function, imaging findings, and sleep disturbances. The models converted narrative notes into structured data fields for use in clinical dashboards enabling patient-specific and population-level analyses. BART outperformed DeepSeek in clinical field extraction accuracy, precision, and recall, despite DeepSeek achieving slightly higher ROUGE metrics based on word-level overlap. A parallel automated MRI-to-CBCT registration pipeline achieved submillimeter accuracy and a 98.75

Down syndrome (DS) is the most common genetic cause of intellectual disability, but our understanding of white matter microstructure in children with DS remains limited. Previous studies have reported reductions in white matter integrity, but nearly all studies to date have been conducted in adults or relied solely on diffusion tensor imaging (DTI), which lacks the ability to disentangle underlying properties of white matter organization. This study examined white matter microstructural differences in 7- to 12-year-old children with DS (n = 23), autism (n = 27), and typical development (n = 50) using DTI as well as High Angular Resolution Diffusion Imaging, and Neurite Orientation and Dispersion Imaging. There was a spatially specific pattern of results that showed a dissociation between intra- and inter-hemispheric pathways. Intra-hemispheric pathways (e.g., inferior fronto-occipital fasciculus, superior longitudinal fasciculus) exhibited reduced organization and structural integrity. Inter-hemispheric pathways (e.g., corpus callosum projections) and motor pathways (e.g., corticospinal tract) showed denser neurite packing and lower neurite dispersion. The current findings provide early insight into white matter development in school-aged children with DS and have the potential to further elucidate microstructural differences and inform more targeted clinical trials than what has previously been observed through DTI models alone.

fcMRI correlates of autism spectrum disorder (ASD) diagnosis and familial liability were studied in 24-month-olds at high (older affected sibling) and low familial likelihood for ASD. fcMRI comparisons of high-familial-likelihood (HL) ASD-positive (HLP, N = 23) and ASD-negative (HLN, N = 91), and low-likelihood ASD-negative (LLN, N = 27) 24-month-olds from the Infant Brain Imaging Study (IBIS) Network were conducted, employing object oriented data analysis (OODA), support vector machine (SVM) classification, and network-level fcMRI enrichment analyses. OODA (alpha = 0.0167, 3 comparisons) revealed differences in HLP and LLN fcMRI matrices (p = 0.012), but none for HLP versus HLN (p = 0.047) nor HLN versus LLN (p = 0.225). SVM distinguished HLP from HLN (accuracy = 99

Background While perinatal factors are known to influence brain development, their long-term impact on white matter microstructure remains incompletely understood. Previous studies using tract-based spatial statistics (TBSS) have shown limited associations between neonatal measures and later white matter development. Methods We investigated associations between perinatal factors (birth weight [BW], gestational age [GA], and head circumference [HC]) and white matter microstructure in 117 children aged 8-10 years from the UNC Early Brain Development Study cohort. Diffusion tensor imaging (DTI) data were analyzed using a fiber tract-based framework examining 54 major white matter tracts. Statistical analysis was performed using a functional analysis of fiber tract profiles. Results GA and BW showed widespread significant associations with white matter microstructure (38 and 36 out of 54 tracts, respectively), while HC showed limited associations (3 out of 54 tracts). Post-hoc univariate analysis revealed stronger associations with axial diffusivity (AD) compared to radial diffusivity (RD) or fractional anisotropy (FA). AD associations with BW, GA, and HC were found in 30, 31, and 8 tracts, respectively. Conclusions Using a fiber tract-based analysis approach, we demonstrated that GA and BW are strongly predictive of white matter organization at school age, while HC showed limited predictive power. The predominant associations with AD suggest these perinatal factors primarily influence axonal organization rather than myelination. These findings enhance our understanding of how early life factors impact long-term brain development. ### Competing Interest Statement The authors have declared no competing interest.

Functional connectivity has been widely investigated to understand brain disease in clinical studies and imaging-based neuroscience, and analyzing changes in functional connectivity has proven to be valuable for understanding and computationally evaluating the effects on brain function caused by diseases or experimental stimuli. By using Mahalanobis data whitening prior to the use of dimensionality reduction algorithms, we are able to distill meaningful information from fMRI signals about subjects and the experimental stimuli used to prompt them. Furthermore, we offer an interpretation of Mahalanobis whitening as a two-stage de-individualization of data which is motivated by similarity as captured by the Bures distance, which is connected to quantum mechanics. These methods have potential to aid discoveries about the mechanisms that link brain function with cognition and behavior and may improve the accuracy and consistency of Alzheimer's diagnosis, especially in the preclinical stage of disease progression.

BACKGROUND:Perimenopause is associated with increases in depressive and vasomotor symptoms (VMS), which can be alleviated with transdermal estradiol (TE2) administration. Subcortical brain regions are commonly implicated in depression, are dense with E2 receptors and are susceptible to volumetric changes resulting from E2 regulation of synaptic density. No studies have examined linkages among TE2 administration, perimenopausal-onset major depression (PO-MDD) and subcortical brain volumes. METHODS:This is an exploratory data analysis of change in subcortical brain volumes measured via 3 T MRI before and after three-weeks of TE2 administration in 14 women with PO-MDD and 17 euthymic controls. Regions of interest were the hippocampus, amygdala, putamen, thalamus, and caudate nucleus. Multilevel models examined relations between baseline volumes and volumetric changes with symptom trajectories in the PO-MDD group. RESULTS:In the PO-MDD group, anhedonia (p < 0.004) and VMS (p < 0.001) significantly reduced following TE2 administration. There was a significant Group X Time interaction in the right hippocampus (p < 0.01), driven by volume increases in the control group (p < 0.001). In the PO-MDD group, change in right hippocampal volumes significantly predicted decreases in anhedonia trajectories from baseline to week 2 and week 3 (p's < 0.001) and decreases in VMS across all timepoints (p's < 0.001). DISCUSSION:Women with PO-MDD, who presented with more severe baseline anhedonia and VMS, experienced greater reductions in anhedonia, VMS, and hippocampal volumes, demonstrating a greater response to E2. Hippocampal volume change may be a candidate for predicting treatment response to E2 for anhedonia and vasomotor symptoms in women with PO-MDD. These findings should be validated with a placebo-controlled trial.

Prenatal stress exposure may negatively influence the development of the amygdala and hippocampus. Although there is significant income instability during pregnancy, and it can increase stress among pregnant parents, the impact of income instability on brain development is not well understood. The present study examined the association between household income losses during pregnancy and hippocampus and amygdala volumes in early infancy. A total of 63 infants from a prospective longitudinal study of pregnant individuals and their infants completed an MRI during natural sleep. The total number of negative month-to-month earnings shocks (defined as an arc percent change [APC] of -25 or greater) was significantly associated with smaller right hippocampal and right amygdala volumes. This suggests that household income losses during the perinatal period are associated with infant brain structure after birth. These findings provide support for the development of public programs that prioritize financial consistency, especially during sensitive periods like pregnancy.