Diet-Stimulated Marrow Adiposity Fails to Worsen Early, Age-Related Bone Loss.

INTRODUCTION: Longitudinal effect of diet-induced obesity on bone is uncertain. Prior work showed both no effect and a decrement in bone density or quality when obesity begins prior to skeletal maturity. We aimed to quantify long-term effects of obesity on bone and bone marrow adipose tissue (BMAT) in adulthood. METHODS: Skeletally mature, female C57BL/6 mice (n = 70) aged 12 weeks were randomly allocated to low-fat diet (LFD; 10% kcal fat; n = 30) or high-fat diet (HFD; 60% kcal fat; n = 30), with analyses at 12, 15, 18, and 24 weeks (n = 10/group). Tibial microarchitecture was analyzed by µCT, and volumetric BMAT was quantified via 9.4T MRI/advanced image analysis. Histomorphometry of adipocytes and osteoclasts, and qPCR were performed. RESULTS: Body weight and visceral white adipose tissue accumulated in response to HFD started in adulthood. Trabecular bone parameters declined with advancing experimental age. BV/TV declined 22% in LFD (p = 0.0001) and 17% in HFD (p = 0.0022) by 24 weeks. HFD failed to appreciably alter BV/TV and had negligible impact on other microarchitecture parameters. Both dietary intervention and age accounted for variance in BMAT, with regional differences: distal femoral BMAT was more responsive to diet, while proximal femoral BMAT was more attenuated by age. BMAT increased 60% in the distal metaphysis in HFD at 18 and 24 weeks (p = 0.0011). BMAT in the proximal femoral diaphysis, unchanged by diet, decreased 45% due to age (p = 0.0002). Marrow adipocyte size via histomorphometry supported MRI quantification. Osteoclast number did not differ between groups. Tibial qPCR showed attenuation of some adipose, metabolism, and bone genes. A regulator of fatty acid ß-oxidation, cytochrome C (CYCS), was 500% more abundant in HFD bone (p < 0.0001; diet effect). CYCS also increased due to age, but to a lesser extent. HFD mildly increased OCN, TRAP, and SOST. CONCLUSIONS: Long-term high fat feeding after skeletal maturity, despite upregulation of visceral adiposity, body weight, and BMAT, failed to attenuate bone microarchitecture. In adulthood, we found aging to be a more potent regulator of microarchitecture than diet-induced obesity.

A regional brain volume-based age prediction model for neonates and the derived brain maturation index.

OBJECTIVE: To develop a postmenstrual age (PMA) prediction model based on segmentation volume and to evaluate the brain maturation index using the proposed model. METHODS: Neonatal brain MRIs without clinical illness or structural abnormalities were collected from four datasets from the Developing Human Connectome Project, the Catholic University of Korea, Hammersmith Hospital (HS), and Dankook University Hospital (DU). T1- and T2-weighted images were used to train a brain segmentation model. Another model to predict the PMA of neonates based on segmentation data was developed. Accuracy was assessed using mean absolute error (MAE), root mean square error (RMSE), and mean error (ME). The brain maturation index was calculated as the difference between the PMA predicted by the model and the true PMA, and its correlation with postnatal age was analyzed. RESULTS: A total of 247 neonates (mean gestation age 37 ± 4 weeks; range 24-42 weeks) were included. Thirty-one features were extracted from each neonate and the three most contributing features for PMA prediction were the right lateral ventricle, left caudate, and corpus callosum. The predicted and true PMA were positively correlated (coefficient = 0.88, p < .001). MAE, RMSE, and ME of the external dataset of HS and DU were 1.57 and 1.33, 1.79 and 1.37, and 0.37 and 0.06 weeks, respectively. The brain maturation index negatively correlated with postnatal age (coefficient = - 0.24, p < .001). CONCLUSION: A model that calculates the regional brain volume can predict the PMA of neonates, which can then be utilized to show the brain maturation degree. CLINICAL RELEVANCE STATEMENT: A brain maturity index based on regional volume of neonate's brain can be used to measure brain maturation degree, which can help identify the status of early brain development. KEY POINTS: • Neonatal brain MRI segmentation model could be used to assess neonatal brain maturation status. • A postmenstrual age (PMA) prediction model was developed based on a neonatal brain MRI segmentation model. • The brain maturation index, derived from the PMA prediction model, enabled the estimation of the neonatal brain maturation status.

Structural and functional connectome relationships in early childhood.

There is strong evidence that the functional connectome is highly related to the white matter connectome in older children and adults, though little is known about structure-function relationships in early childhood. We investigated the development of cortical structure-function coupling in children longitudinally scanned at 1, 2, 4, and 6 years of age (N = 360) and in a comparison sample of adults (N = 89). We also applied a novel graph convolutional neural network-based deep learning model with a new loss function to better capture inter-subject heterogeneity and predict an individual's functional connectivity from the corresponding structural connectivity. We found regional patterns of structure-function coupling in early childhood that were consistent with adult patterns. In addition, our deep learning model improved the prediction of individual functional connectivity from its structural counterpart compared to existing models.

Extracellular free water elevations are associated with brain volume and maternal cytokine response in a longitudinal nonhuman primate maternal immune activation model.

Maternal infection has emerged as an important environmental risk factor for neurodevelopmental disorders, including schizophrenia and autism spectrum disorders. Animal model systems of maternal immune activation (MIA) suggest that the maternal immune response plays a significant role in the offspring's neurodevelopment and behavioral outcomes. Extracellular free water is a measure of freely diffusing water in the brain that may be associated with neuroinflammation and impacted by MIA. The present study evaluates the brain diffusion characteristics of male rhesus monkeys (Macaca mulatta) born to MIA-exposed dams (n = 14) treated with a modified form of the viral mimic polyinosinic:polycytidylic acid at the end of the first trimester. Control dams received saline injections at the end of the first trimester (n = 10) or were untreated (n = 4). Offspring underwent diffusion MRI scans at 6, 12, 24, 36, and 45 months. Offspring born to MIA-exposed dams showed significantly increased extracellular free water in cingulate cortex gray matter starting as early as 6 months of age and persisting through 45 months. In addition, offspring gray matter free water in this region was significantly correlated with the magnitude of the maternal IL-6 response in the MIA-exposed dams. Significant correlations between brain volume and extracellular free water in the MIA-exposed offspring also indicate converging, multimodal evidence of the impact of MIA on brain development. These findings provide strong evidence for the construct validity of the nonhuman primate MIA model as a system of relevance for investigating the pathophysiology of human neurodevelopmental psychiatric disorders. Elevated free water in individuals exposed to immune activation in utero could represent an early marker of a perturbed or vulnerable neurodevelopmental trajectory.

Mapping the evolution of regional brain network efficiency and its association with cognitive abilities during the first twenty-eight months of life.

Human brain undergoes rapid growth during the first few years of life. While previous research has employed graph theory to study early brain development, it has mostly focused on the topological attributes of the whole brain. However, examining regional graph-theory features may provide unique insights into the development of cognitive abilities. Utilizing a large and longitudinal rsfMRI dataset from the UNC/UMN Baby Connectome Project, we investigated the developmental trajectories of regional efficiency and evaluated the relationships between these changes and cognitive abilities using Mullen Scales of Early Learning during the first twenty-eight months of life. Our results revealed a complex and spatiotemporally heterogeneous development pattern of regional global and local efficiency during this age period. Furthermore, we found that the trajectories of the regional global efficiency at the left temporal occipital fusiform and bilateral occipital fusiform gyri were positively associated with cognitive abilities, including visual reception, expressive language, receptive language, and early learning composite scores (P < 0.05, FDR corrected). However, these associations were weakened with age. These findings offered new insights into the regional developmental features of brain topologies and their associations with cognition and provided evidence of ongoing optimization of brain networks at both whole-brain and regional levels.

Language exposure during infancy is negatively associated with white matter microstructure in the arcuate fasciculus.

Decades of research have established that the home language environment, especially quality of caregiver speech, supports language acquisition during infancy. However, the neural mechanisms behind this phenomenon remain under studied. In the current study, we examined associations between the home language environment and structural coherence of white matter tracts in 52 typically developing infants from English speaking homes in a western society. Infants participated in at least one MRI brain scan when they were 3, 6, 12, and/or 24 months old. Home language recordings were collected when infants were 9 and/or 15 months old. General linear regression models indicated that infants who heard the most adult words and participated in the most conversational turns at 9 months of age also had the lowest fractional anisotropy in the left posterior parieto-temporal arcuate fasciculus at 24 months. Similarly, infants who vocalized the most at 9 months also had the lowest fractional anisotropy in the same tract at 6 months of age. This is one of the first studies to report significant associations between caregiver speech collected in the home and white matter structural coherence in the infant brain. The results are in line with prior work showing that protracted white matter development during infancy confers a cognitive advantage.

Genetic Influences on the Developing Young Brain and Risk for Neuropsychiatric Disorders.

Imaging genetics provides an opportunity to discern associations between genetic variants and brain imaging phenotypes. Historically, the field has focused on adults and adolescents; very few imaging genetics studies have focused on brain development in infancy and early childhood (from birth to age 6 years). This is an important knowledge gap because developmental changes in the brain during the prenatal and early postnatal period are regulated by dynamic gene expression patterns that likely play an important role in establishing an individual's risk for later psychiatric illness and neurodevelopmental disabilities. In this review, we summarize findings from imaging genetics studies spanning from early infancy to early childhood, with a focus on studies examining genetic risk for neuropsychiatric disorders. We also introduce the Organization for Imaging Genomics in Infancy (ORIGINs), a working group of the ENIGMA (Enhancing NeuroImaging Genetics through Meta-Analysis) consortium, which was established to facilitate large-scale imaging genetics studies in infancy and early childhood.

Increased Striatal Presynaptic Dopamine in a Nonhuman Primate Model of Maternal Immune Activation: A Longitudinal Neurodevelopmental Positron Emission Tomography Study With Implications for Schizophrenia.

BACKGROUND: Epidemiological studies suggest that maternal immune activation (MIA) is a significant risk factor for future neurodevelopmental disorders, including schizophrenia (SZ), in offspring. Consistent with findings in SZ research and work in rodent systems, preliminary cross-sectional findings in nonhuman primates suggest that MIA is associated with dopaminergic hyperfunction in young adult offspring. METHODS: In this unique prospective longitudinal study, we used [18F]fluoro-l-m-tyrosine positron emission tomography to examine the developmental time course of striatal presynaptic dopamine synthesis in male rhesus monkeys born to dams (n = 13) injected with a modified form of the inflammatory viral mimic, polyinosinic:polycytidylic acid [poly(I:C)], in the late first trimester. Striatal (caudate, putamen, and nucleus accumbens) dopamine from these animals was compared with that of control offspring born to dams that received saline (n = 10) or no injection (n = 4). Dopamine was measured at 15, 26, 38, and 48 months of age. Prior work with this cohort found decreased prefrontal gray matter volume in MIA offspring versus controls between 6 and 45 months of age. Based on theories of the etiology and development of SZ-related pathology, we hypothesized that there would be a delayed (relative to the gray matter decrease) increase in striatal fluoro-l-m-tyrosine signal in the MIA group versus controls. RESULTS: [18F]fluoro-l-m-tyrosine signal showed developmental increases in both groups in the caudate and putamen. Group comparisons revealed significantly greater caudate dopaminergic signal in the MIA group at 26 months. CONCLUSIONS: These findings are highly relevant to the known pathophysiology of SZ and highlight the translational relevance of the MIA model in understanding mechanisms by which MIA during pregnancy increases risk for later illness in offspring.

Assessment of systemic AAV-microdystrophin gene therapy in the GRMD model of Duchenne muscular dystrophy.

Duchenne muscular dystrophy (DMD) is a progressive muscle wasting disease caused by the absence of dystrophin, a membrane-stabilizing protein encoded by the DMD gene. Although mouse models of DMD provide insight into the potential of a corrective therapy, data from genetically homologous large animals, such as the dystrophin-deficient golden retriever muscular dystrophy (GRMD) model, may more readily translate to humans. To evaluate the clinical translatability of an adeno-associated virus serotype 9 vector (AAV9)-microdystrophin (µDys5) construct, we performed a blinded, placebo-controlled study in which 12 GRMD dogs were divided among four dose groups [control, 1 × 1013 vector genomes per kilogram (vg/kg), 1 × 1014 vg/kg, and 2 × 1014 vg/kg; n = 3 each], treated intravenously at 3 months of age with a canine codon-optimized microdystrophin construct, rAAV9-CK8e-c-µDys5, and followed for 90 days after dosing. All dogs received prednisone (1 milligram/kilogram) for a total of 5 weeks from day -7 through day 28. We observed dose-dependent increases in tissue vector genome copy numbers; µDys5 protein in multiple appendicular muscles, the diaphragm, and heart; limb and respiratory muscle functional improvement; and reduction of histopathologic lesions. As expected, given that a truncated dystrophin protein was generated, phenotypic test results and histopathologic lesions did not fully normalize. All administrations were well tolerated, and adverse events were not seen. These data suggest that systemically administered AAV-microdystrophin may be dosed safely and could provide therapeutic benefit for patients with DMD.

Maternal adverse childhood experiences and infant subcortical brain volume.

Background: A large body of research supports the deleterious effects of adverse childhood experiences (ACEs) on disease susceptibility and health for both the exposed individual and the next generation. It is likely that there is an intergenerational transmission of risk from mother to child; however, the mechanisms through which such risk is conferred remain unknown. The current study evaluated the association between maternal ACEs, neonatal brain development of the amygdala and hippocampus, and later infant negative emotionality at six months of age. Methods: The sample included 85 mother-infant dyads (44 female infants) from a longitudinal study. Maternal ACEs were assessed with the Adverse Childhood Experiences Questionnaire (ACE-Q) and neonatal hippocampal and amygdala volume was assessed using structural magnetic resonance imaging (MRI). Infant negative emotionality was assessed at 6 months using the Infant Behavior Questionnaire (IBQ). Results: Multivariate analyses demonstrated that maternal ACEs were associated with bilateral amygdala volume (F(2,78) = 3.697,p = .029). Specifically, higher maternal ACEs were associated with smaller left (ß = -0.220, t(79) = -2.661, p = .009, R2 = 0.494, and right (ß = -0.167, t(79) = -2.043, p = .044, R2 = 0.501) amygdala volume. No significant association between maternal ACEs and bilateral hippocampal volume (F(2,78) = 0.215,p = .0807) was found. Follow-up regression analyses demonstrated that both high maternal ACEs and smaller left amygdala volume were associated with higher infant negative emotionality at six months of age (ß = .232, p = .040, R2 = 0.094, and ß = -0.337, p = .022, R2 = 0.16, respectively) although statistically significant mediation of this effect was not observed (Indirect effect = 0.0187, 95% CI [-0.0016-0.0557]). Conclusions: Maternal ACEs are associated with both newborn amygdala volume and subsequent infant negative emotionality. These findings linking maternal adverse childhood experiences and infant brain development and temperament provide evidence to support the intergenerational transmission of adversity from mother to child.

Longitudinal and prospective assessment of prenatal maternal sleep quality and associations with newborn hippocampal and amygdala volume.

BACKGROUND: The rapid maturation of the fetal brain renders the fetus susceptible to prenatal environmental signals. Prenatal maternal sleep quality is known to have important health implications for newborns including risk for preterm birth, however, the effect on the fetal brain is poorly understood. METHOD: Participants included 94 pregnant participants and their newborns (53% female). Pregnant participants (Mage = 30; SDage= 5.29) reported on sleep quality three times throughout pregnancy. Newborn hippocampal and amygdala volumes were assessed using structural magnetic resonance imaging. Multilevel modeling was used to test the associations between trajectories of prenatal maternal sleep quality and newborn hippocampal and amygdala volume. RESULTS: The overall trajectory of prenatal maternal sleep quality was associated with hippocampal volume (left: b = 0.00003, p = 0.013; right: b = 0.00003, p = .008). Follow up analyses assessing timing of exposure indicate that poor sleep quality early in pregnancy was associated with larger hippocampal volume bilaterally (e.g., late gestation left: b = 0.002, p = 0.24; right: b = 0.004, p = .11). Prenatal sleep quality was not associated with amygdala volume. CONCLUSION: These findings highlight the implications of poor prenatal maternal sleep quality and its role in contributing to newborn hippocampal development.

Subcortical Brain Development in Autism and Fragile X Syndrome: Evidence for Dynamic, Age- and Disorder-Specific Trajectories in Infancy.

OBJECTIVE: Previous research has demonstrated that the amygdala is enlarged in children with autism spectrum disorder (ASD). However, the precise onset of this enlargement during infancy, how it relates to later diagnostic behaviors, whether the timing of enlargement in infancy is specific to the amygdala, and whether it is specific to ASD (or present in other neurodevelopmental disorders, such as fragile X syndrome) are all unknown. METHODS: Longitudinal MRIs were acquired at 6-24 months of age in 29 infants with fragile X syndrome, 58 infants at high likelihood for ASD who were later diagnosed with ASD, 212 high-likelihood infants not diagnosed with ASD, and 109 control infants (1,099 total scans). RESULTS: Infants who developed ASD had typically sized amygdala volumes at 6 months, but exhibited significantly faster amygdala growth between 6 and 24 months, such that by 12 months the ASD group had significantly larger amygdala volume (Cohen's d=0.56) compared with all other groups. Amygdala growth rate between 6 and 12 months was significantly associated with greater social deficits at 24 months when the infants were diagnosed with ASD. Infants with fragile X syndrome had a persistent and significantly enlarged caudate volume at all ages between 6 and 24 months (d=2.12), compared with all other groups, which was significantly associated with greater repetitive behaviors. CONCLUSIONS: This is the first MRI study comparing fragile X syndrome and ASD in infancy, demonstrating strikingly different patterns of brain and behavior development. Fragile X syndrome-related changes were present from 6 months of age, whereas ASD-related changes unfolded over the first 2 years of life, starting with no detectable group differences at 6 months. Increased amygdala growth rate between 6 and 12 months occurs prior to social deficits and well before diagnosis. This gradual onset of brain and behavior changes in ASD, but not fragile X syndrome, suggests an age- and disorder-specific pattern of cascading brain changes preceding autism diagnosis.

Local Spatiotemporal Representation Learning for Longitudinally-consistent Neuroimage Analysis.

Recent self-supervised advances in medical computer vision exploit the global and local anatomical self-similarity for pretraining prior to downstream tasks such as segmentation. However, current methods assume i.i.d. image acquisition, which is invalid in clinical study designs where follow-up longitudinal scans track subject-specific temporal changes. Further, existing self-supervised methods for medically-relevant image-to-image architectures exploit only spatial or temporal self-similarity and do so via a loss applied only at a single image-scale, with naive multi-scale spatiotemporal extensions collapsing to degenerate solutions. To these ends, this paper makes two contributions: (1) It presents a local and multi-scale spatiotemporal representation learning method for image-to-image architectures trained on longitudinal images. It exploits the spatiotemporal self-similarity of learned multi-scale intra-subject image features for pretraining and develops several feature-wise regularizations that avoid degenerate representations; (2) During finetuning, it proposes a surprisingly simple self-supervised segmentation consistency regularization to exploit intra-subject correlation. Benchmarked across various segmentation tasks, the proposed framework outperforms both well-tuned randomly-initialized baselines and current self-supervised techniques designed for both i.i.d. and longitudinal datasets. These improvements are demonstrated across both longitudinal neurodegenerative adult MRI and developing infant brain MRI and yield both higher performance and longitudinal consistency.

Learning Strategies for Contrast-agnostic Segmentation via SynthSeg for Infant MRI data.

Longitudinal studies of infants' brains are essential for research and clinical detection of neurodevelopmental disorders. However, for infant brain MRI scans, effective deep learning-based segmentation frameworks exist only within small age intervals due to the large image intensity and contrast changes that take place in the early postnatal stages of development. However, using different segmentation frameworks or models at different age intervals within the same longitudinal data set would cause segmentation inconsistencies and age-specific biases. Thus, an age-agnostic segmentation model for infants' brains is needed. In this paper, we present "Infant-SynthSeg", an extension of the contrast-agnostic SynthSeg segmentation framework applicable to MRI data of infants at ages within the first year of life. Our work mainly focuses on extending learning strategies related to synthetic data generation and augmentation, with the aim of creating a method that employs training data capturing features unique to infants' brains during this early-stage development. Comparison across different learning strategy settings, as well as a more-traditional contrast-aware deep learning model (nnU-net) are presented. Our experiments show that our trained Infant-SynthSeg models show consistently high segmentation performance on MRI scans of infant brains throughout the first year of life. Furthermore, as the model is trained on ground truth labels at different ages, even labels that are not present at certain ages (such as cerebellar white matter at 1 month) can be appropriately segmented via Infant-SynthSeg across the whole age range. Finally, while Infant-SynthSeg shows consistent segmentation performance across the first year of life, it is outperformed by age-specific deep learning models trained for a specific narrow age range.

Influence of gonadal steroids on cortical surface area in infancy.

Sex differences in the human brain emerge as early as mid-gestation and have been linked to sex hormones, particularly testosterone. Here, we analyzed the influence of markers of early sex hormone exposure (polygenic risk score (PRS) for testosterone, salivary testosterone, number of CAG repeats, digit ratios, and PRS for estradiol) on the growth pattern of cortical surface area in a longitudinal cohort of 722 infants. We found PRS for testosterone and right-hand digit ratio to be significantly associated with surface area, but only in females. PRS for testosterone at the most stringent P value threshold was positively associated with surface area development over time. Higher right-hand digit ratio, which is indicative of low prenatal testosterone levels, was negatively related to surface area in females. The current work suggests that variation in testosterone levels during both the prenatal and postnatal period may contribute to cortical surface area development in female infants.

Maternal Immune Activation during Pregnancy Alters Postnatal Brain Growth and Cognitive Development in Nonhuman Primate Offspring.

Human epidemiological studies implicate exposure to infection during gestation in the etiology of neurodevelopmental disorders. Animal models of maternal immune activation (MIA) have identified the maternal immune response as the critical link between maternal infection and aberrant offspring brain and behavior development. Here we evaluate neurodevelopment of male rhesus monkeys (Macaca mulatta) born to MIA-treated dams (n = 14) injected with a modified form of the viral mimic polyinosinic:polycytidylic acid at the end of the first trimester. Control dams received saline injections at the same gestational time points (n = 10) or were untreated (n = 4). MIA-treated dams exhibited a strong immune response as indexed by transient increases in sickness behavior, temperature, and inflammatory cytokines. Although offspring born to control or MIA-treated dams did not differ on measures of physical growth and early developmental milestones, the MIA-treated animals exhibited subtle changes in cognitive development and deviated from species-typical brain growth trajectories. Longitudinal MRI revealed significant gray matter volume reductions in the prefrontal and frontal cortices of MIA-treated offspring at 6 months that persisted through the final time point at 45 months along with smaller frontal white matter volumes in MIA-treated animals at 36 and 45 months. These findings provide the first evidence of early postnatal changes in brain development in MIA-exposed nonhuman primates and establish a translationally relevant model system to explore the neurodevelopmental trajectory of risk associated with prenatal immune challenge from birth through late adolescence.SIGNIFICANCE STATEMENT Women exposed to infection during pregnancy have an increased risk of giving birth to a child who will later be diagnosed with a neurodevelopmental disorder. Preclinical maternal immune activation (MIA) models have demonstrated that the effects of maternal infection on fetal brain development are mediated by maternal immune response. Since the majority of MIA models are conducted in rodents, the nonhuman primate provides a unique system to evaluate the MIA hypothesis in a species closely related to humans. Here we report the first longitudinal study conducted in a nonhuman primate MIA model. MIA-exposed offspring demonstrate subtle changes in cognitive development paired with marked reductions in frontal gray and white matter, further supporting the association between prenatal immune challenge and alterations in offspring neurodevelopment.

Longitudinal Prediction of Infant MR Images With Multi-Contrast Perceptual Adversarial Learning.

The infant brain undergoes a remarkable period of neural development that is crucial for the development of cognitive and behavioral capacities (Hasegawa et al., 2018). Longitudinal magnetic resonance imaging (MRI) is able to characterize the developmental trajectories and is critical in neuroimaging studies of early brain development. However, missing data at different time points is an unavoidable occurrence in longitudinal studies owing to participant attrition and scan failure. Compared to dropping incomplete data, data imputation is considered a better solution to address such missing data in order to preserve all available samples. In this paper, we adapt generative adversarial networks (GAN) to a new application: longitudinal image prediction of structural MRI in the first year of life. In contrast to existing medical image-to-image translation applications of GANs, where inputs and outputs share a very close anatomical structure, our task is more challenging as brain size, shape and tissue contrast vary significantly between the input data and the predicted data. Several improvements over existing GAN approaches are proposed to address these challenges in our task. To enhance the realism, crispness, and accuracy of the predicted images, we incorporate both a traditional voxel-wise reconstruction loss as well as a perceptual loss term into the adversarial learning scheme. As the differing contrast changes in T1w and T2w MR images in the first year of life, we incorporate multi-contrast images leading to our proposed 3D multi-contrast perceptual adversarial network (MPGAN). Extensive evaluations are performed to assess the qualityand fidelity of the predicted images, including qualitative and quantitative assessments of the image appearance, as well as quantitative assessment on two segmentation tasks. Our experimental results show that our MPGAN is an effective solution for longitudinal MR image data imputation in the infant brain. We further apply our predicted/imputed images to two practical tasks, a regression task and a classification task, in order to highlight the enhanced task-related performance following image imputation. The results show that the model performance in both tasks is improved by including the additional imputed data, demonstrating the usability of the predicted images generated from our approach.

Infant gut microbiome composition is associated with non-social fear behavior in a pilot study.

Experimental manipulation of gut microbes in animal models alters fear behavior and relevant neurocircuitry. In humans, the first year of life is a key period for brain development, the emergence of fearfulness, and the establishment of the gut microbiome. Variation in the infant gut microbiome has previously been linked to cognitive development, but its relationship with fear behavior and neurocircuitry is unknown. In this pilot study of 34 infants, we find that 1-year gut microbiome composition (Weighted Unifrac; lower abundance of Bacteroides, increased abundance of Veillonella, Dialister, and Clostridiales) is significantly associated with increased fear behavior during a non-social fear paradigm. Infants with increased richness and reduced evenness of the 1-month microbiome also display increased non-social fear. This study indicates associations of the human infant gut microbiome with fear behavior and possible relationships with fear-related brain structures on the basis of a small cohort. As such, it represents an important step in understanding the role of the gut microbiome in the development of human fear behaviors, but requires further validation with a larger number of participants.

Proteobacteria abundance during nursing predicts physical growth and brain volume at one year of age in young rhesus monkeys.

Over the last decade, multiple studies have highlighted the essential role of gut microbiota in normal infant development. However, the sensitive periods during which gut bacteria are established and become associated with physical growth and maturation of the brain are still poorly defined. This study tracked the assembly of the intestinal microbiota during the initial nursing period, and changes in community structure after transitioning to solid food in infant rhesus monkeys (Macaca mulatta). Anthropometric measures and rectal swabs were obtained at 2-month intervals across the first year of life and bacterial taxa identified by 16S rRNA gene sequencing. At 12 months of age, total brain and cortical regions volumes were quantified through structural magnetic resonance imaging. The bacterial community structure was dynamic and characterized by discrete maturational phases, reflecting an early influence of breast milk and the later transition to solid foods. Commensal microbial taxa varied with diet similar to findings in other animals and human infants; however, monkeys differ in the relative abundances of Lactobacilli and Bifidobacteria, two taxa predominant in breastfed human infants. Higher abundances of taxa in the phylum Proteobacteria during nursing were predictive of slower growth trajectories and smaller brain volumes at one year of age. Our findings define discrete phases of microbial succession in infant monkeys and suggest there may be a critical period during nursing when endogenous differences in certain taxa can shift the community structure and influence the pace of physical growth and the maturational trajectory of the brain.

Robust Cortical Thickness Morphometry of Neonatal Brain and Systematic Evaluation Using Multi-Site MRI Datasets.

The human brain grows the most dramatically during the perinatal and early post-natal periods, during which pre-term birth or perinatal injury that may alter brain structure and lead to developmental anomalies. Thus, characterizing cortical thickness of developing brains remains an important goal. However, this task is often complicated by inaccurate cortical surface extraction due to small-size brains. Here, we propose a novel complex framework for the reconstruction of neonatal WM and pial surfaces, accounting for large partial volumes due to small-size brains. The proposed approach relies only on T1-weighted images unlike previous T2-weighted image-based approaches while only T1-weighted images are sometimes available under the different clinical/research setting. Deep neural networks are first introduced to the neonatal magnetic resonance imaging (MRI) pipeline to address the mis-segmentation of brain tissues. Furthermore, this pipeline enhances cortical boundary delineation using combined models of the cerebrospinal fluid (CSF)/GM boundary detection with edge gradient information and a new skeletonization of sulcal folding where no CSF voxels are seen due to the limited resolution. We also proposed a systematic evaluation using three independent datasets comprising 736 pre-term and 97 term neonates. Qualitative assessment for reconstructed cortical surfaces shows that 86.9% are rated as accurate across the three site datasets. In addition, our landmark-based evaluation shows that the mean displacement of the cortical surfaces from the true boundaries was less than a voxel size (0.532 ± 0.035 mm). Evaluating the proposed pipeline (namely NEOCIVET 2.0) shows the robustness and reproducibility across different sites and different age-groups. The mean cortical thickness measured positively correlated with post-menstrual age (PMA) at scan (p < 0.0001); Cingulate cortical areas grew the most rapidly whereas the inferior temporal cortex grew the least rapidly. The range of the cortical thickness measured was biologically congruent (1.3 mm at 28 weeks of PMA to 1.8 mm at term equivalent). Cortical thickness measured on T1 MRI using NEOCIVET 2.0 was compared with that on T2 using the established dHCP pipeline. It was difficult to conclude that either T1 or T2 imaging is more ideal to construct cortical surfaces. NEOCIVET 2.0 has been open to the public through CBRAIN (https://mcin-cnim.ca/technology/cbrain/), a web-based platform for processing brain imaging data.