Sex differences in brain-behavior relationships in the first two years of life

Background

Evidence for sex differences in cognition in childhood is established, but less is known about the underlying neural mechanisms for these differences. Recent findings suggest the existence of brain-behavior relationship heterogeneities during infancy; however, it remains unclear whether sex underlies these heterogeneities during this critical period when sex-related behavioral differences arise.

Methods

A sample of 316 infants was included with resting-state functional magnetic resonance imaging scans at neonate (3 weeks), 1, and 2 years of age. We used multiple linear regression to test interactions between sex and resting-state functional connectivity on behavioral scores of working memory, inhibitory self-control, intelligence, and anxiety collected at 4 years of age.

Results

We found six age-specific, intra-hemispheric connections showing significant and robust sex differences in functional connectivity-behavior relationships. All connections are either with the prefrontal cortex or the temporal pole, which has direct anatomical pathways to the prefrontal cortex. Sex differences in functional connectivity only emerge when associated with behavior, and not in functional connectivity alone. Furthermore, at neonate and 2 years of age, these age-specific connections displayed greater connectivity in males and lower connectivity in females in association with better behavioral scores.

Conclusions

Taken together, we critically capture robust and conserved brain mechanisms that are distinct to sex and are defined by their relationship to behavioral outcomes. Our results establish brain-behavior mechanisms as an important feature in the search for sex differences during development.

Associations between early trajectories of amygdala development and later school-age anxiety in two longitudinal samples

Amygdala function is implicated in the pathogenesis of autism spectrum disorder (ASD) and anxiety. We investigated associations between early trajectories of amygdala growth and anxiety and ASD outcomes at school age in two longitudinal studies: high- and low-familial likelihood for ASD, Infant Brain Imaging Study (IBIS, n = 257) and typically developing (TD) community sample, Early Brain Development Study (EBDS, n = 158). Infants underwent MRI scanning at up to 3 timepoints from neonate to 24 months. Anxiety was assessed at 6-12 years. Linear multilevel modeling tested whether amygdala volume growth was associated with anxiety symptoms at school age. In the IBIS sample, children with higher anxiety showed accelerated amygdala growth from 6 to 24 months. ASD diagnosis and ASD familial likelihood were not significant predictors. In the EBDS sample, amygdala growth from birth to 24 months was associated with anxiety. More anxious children had smaller amygdala volume and slower rates of amygdala growth. We explore reasons for the contrasting results between high-familial likelihood for ASD and TD samples, grounding results in the broader literature of variable associations between early amygdala volume and later anxiety. Results have the potential to identify mechanisms linking early amygdala growth to later anxiety in certain groups.

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.

Early Childhood Development of Node Centrality in the White Matter Connectome and Its Relationship to IQ at Age 6 Years

BACKGROUND

The white matter (WM) connectome is important for cognitive development and intelligence and is altered in neuropsychiatric illnesses. Little is known about how the WM connectome develops or its relationship to IQ in early childhood.

METHODS

The development of node centrality in the WM connectome was studied in a longitudinal cohort of 226 (123 female) children from the University of North Carolina Early Brain Development Study. Structural and diffusion-weighted images were acquired after birth and at 1, 2, 4, and 6 years, and IQ was assessed at 6 years. Eigenvector centrality, betweenness centrality, and the global graph metrics of global efficiency, small worldness, and modularity were determined at each age.

RESULTS

The greatest developmental change in eigenvector centrality and betweenness centrality occurred during the first year of life, with relative stability between ages 1 and 6 years. Most of the high-centrality hubs at age 6 were also high-centrality hubs at 1 year, and many were already high-centrality hubs at birth. There were generally small but significant changes in global efficiency and modularity from birth to 6 years, while small worldness increased between 2 and 4 years. Individual node centrality was not significantly correlated with IQ at 6 years.

CONCLUSIONS

Node centrality in the WM connectome is established very early in childhood and is relatively stable from age 1 to 6 years. Many high-centrality hubs are established before birth, and most are present by age 1.

Longitudinal developmental trajectories of functional connectivity reveal regional distribution of distinct age effects in infancy

Prior work has shown that different functional brain networks exhibit different maturation rates, but little is known about whether and how different brain areas may differ in the exact shape of longitudinal functional connectivity growth trajectories during infancy. We used resting-state functional magnetic resonance imaging (fMRI) during natural sleep to characterize developmental trajectories of different regions using a longitudinal cohort of infants at 3 weeks (neonate), 1 year, and 2 years of age (n = 90; all with usable data at three time points). A novel whole brain heatmap analysis was performed with four mixed-effect models to determine the best fit of age-related changes for each functional connection: (i) growth effects: positive-linear-age, (ii) emergent effects: positive-log-age, (iii) pruning effects: negative-quadratic-age, and (iv) transient effects: positive-quadratic-age. Our results revealed that emergent (logarithmic) effects dominated developmental trajectory patterns, but significant pruning and transient effects were also observed, particularly in connections centered on inferior frontal and anterior cingulate areas that support social learning and conflict monitoring. Overall, unique global distribution patterns were observed for each growth model indicating that developmental trajectories for different connections are heterogeneous. All models showed significant effects concentrated in association areas, highlighting the dominance of higher-order social/cognitive development during the first 2 years of life.

Genetic and environmental factors influencing neonatal resting-state functional connectivity

Functional magnetic resonance imaging has been used to identify complex brain networks by examining the correlation of blood-oxygen-level-dependent signals between brain regions during the resting state. Many of the brain networks identified in adults are detectable at birth, but genetic and environmental influences governing connectivity within and between these networks in early infancy have yet to be explored. We investigated genetic influences on neonatal resting-state connectivity phenotypes by generating intraclass correlations and performing mixed effects modeling to estimate narrow-sense heritability on measures of within network and between-network connectivity in a large cohort of neonate twins. We also used backwards elimination regression and mixed linear modeling to identify specific demographic and medical history variables influencing within and between network connectivity in a large cohort of typically developing twins and singletons. Of the 36 connectivity phenotypes examined, only 6 showed narrow-sense heritability estimates greater than 0.10, with none being statistically significant. Demographic and obstetric history variables contributed to between- and within-network connectivity. Our results suggest that in early infancy, genetic factors minimally influence brain connectivity. However, specific demographic and medical history variables, such as gestational age at birth and maternal psychiatric history, may influence resting-state connectivity measures.

Sex differences in resting state functional connectivity across the first two years of life

Sex differences in behavior have been reported from infancy through adulthood, but little is known about sex effects on functional circuitry in early infancy. Moreover, the relationship between early sex effects on the functional architecture of the brain and later behavioral performance remains to be elucidated. In this study, we used resting-state fMRI and a novel heatmap analysis to examine sex differences in functional connectivity with cross-sectional and longitudinal mixed models in a large cohort of infants (n = 319 neonates, 1-, and 2-year-olds). An adult dataset (n = 92) was also included for comparison. We investigated the relationship between sex differences in functional circuitry and later measures of language (collected in 1- and 2-year-olds) as well as indices of anxiety, executive function, and intelligence (collected in 4-year-olds). Brain areas showing the most significant sex differences were age-specific across infancy, with two temporal regions demonstrating consistent differences. Measures of functional connectivity showing sex differences in infancy were significantly associated with subsequent behavioral scores of language, executive function, and intelligence. Our findings provide insights into the effects of sex on dynamic neurodevelopmental trajectories during infancy and lay an important foundation for understanding the mechanisms underlying sex differences in health and disease.

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.

Genetic Influences on Longitudinal Trajectories of Cortical Thickness and Surface Area during the First 2 Years of Life

Genetic influences on cortical thickness (CT) and surface area (SA) are known to vary across the life span. Little is known about the extent to which genetic factors influence CT and SA in infancy and toddlerhood. We performed the first longitudinal assessment of genetic influences on variation in CT and SA in 501 twins who were aged 0-2 years. We observed substantial additive genetic influences on both average CT (0.48 in neonates, 0.37 in 1-year-olds, and 0.44 in 2-year-olds) and total SA (0.59 in neonates, 0.74 in 1-year-olds, and 0.73 in 2-year-olds). In addition, we found strong heritability of the change in average CT (0.49) from neonates to 1-year-olds, but not from 1- to 2-year-olds. Moreover, we found strong genetic correlations for average CT (rG = 0.92) between 1- and 2-year-olds and strong genetic correlations for total SA across all timepoints (rG = 0.96 between neonates and 1-year-olds, rG = 1 between 1- and 2-year-olds). In addition, we found CT and SA are strongly genetic correlated at birth, but weaken over time. Overall, results suggest a dynamic genetic relationship between CT and SA during first 2 years of life and provide novel insights into how genetic influences shape the cortical structure during early brain development.

Developmental heatmaps of brain functional connectivity from newborns to 6-year-olds

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Heatmaps quantify degrees of developmental changes in functional connectivity.

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More changes are observed in the first than second postnatal year, driven by girls.

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Most change is observed from ages 2–4 compared with any other age span.

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Limbic and subcortical areas show more changes than primary sensory regions.

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Consistent trajectories of functional connectivity are found across validations.

Different functional networks exhibit distinct longitudinal trajectories throughout development, but the timeline of the dynamics of functional connectivity across the whole brain remains to be elucidated. Here we used resting-state fMRI to investigate the development of voxel-level changes in functional connectivity across the first six years of life. Globally, we found that developmental changes in functional connectivity are nonlinear with more changes during the first postnatal year than the second, followed by most significant changes from ages 2–4 and from ages 4–6. However, the overall global difference observed between the first and second year appears to have been driven by girls. Limbic and subcortical areas consistently demonstrated the most substantial changes, whereas primary sensory areas were the most stable. These patterns were consistent in full-term and preterm subgroups. Validation on randomly divided subsamples as well as in an independent cross-sectional sample revealed global patterns consistent with the main results. Overall, the derived developmental heatmaps reveal novel dynamics underlying functional circuit development during the first 6 years of life.

Cortical Structure and Cognition in Infants and Toddlers

Cortical structure has been consistently related to cognitive abilities in children and adults, yet we know little about how the cortex develops to support emergent cognition in infancy and toddlerhood when cortical thickness (CT) and surface area (SA) are maturing rapidly. In this report, we assessed how regional and global measures of CT and SA in a sample (N = 487) of healthy neonates, 1-year-olds, and 2-year-olds related to motor, language, visual reception, and general cognitive ability. We report novel findings that thicker cortices at ages 1 and 2 and larger SA at birth, age 1, and age 2 confer a cognitive advantage in infancy and toddlerhood. While several expected brain-cognition relationships were observed, overlapping cortical regions were also implicated across cognitive domains, suggesting that infancy marks a period of plasticity and refinement in cortical structure to support burgeoning motor, language, and cognitive abilities. CT may be a particularly important morphological indicator of ability, but its impact on cognition is relatively weak when compared with gestational age and maternal education. Findings suggest that prenatal and early postnatal cortical developments are important for cognition in infants and toddlers but should be considered in relation to other child and demographic factors.

Altered Brain Structure in Infants with Turner Syndrome

Turner syndrome (TS) is a genetic disorder affecting approximately 1:2000 live-born females. It results from partial or complete X monosomy and is associated with a range of clinical issues including a unique cognitive profile and increased risk for certain behavioral problems. Structural neuroimaging studies in adolescents, adults, and older children with TS have revealed altered neuroanatomy but are unable to identify when in development differences arise. In addition, older children and adults have often been exposed to years of growth hormone and/or exogenous estrogen therapy with potential implications for neurodevelopment. The study presented here is the first to test whether brain structure is altered in infants with TS. Twenty-six infants with TS received high-resolution structural MRI scans of the brain at 1 year of age and were compared to 47 typically developing female and 39 typically developing male infants. Results indicate that the typical neuroanatomical profile seen in older individuals with TS, characterized by decreased gray matter volumes in premotor, somatosensory, and parietal-occipital cortex, is already present at 1 year of age, suggesting a stable phenotype with origins in the prenatal or early postnatal period.

Hippocampal functional connectivity development during the first two years indexes 4-year working memory performance

The hippocampus is a key limbic region involved in higher-order cognitive processes including learning and memory. Although both typical and atypical functional connectivity patterns of the hippocampus have been well-studied in adults, the developmental trajectory of hippocampal connectivity during infancy and how it relates to later working memory performance remains to be elucidated. Here we used resting state fMRI (rsfMRI) during natural sleep to examine the longitudinal development of hippocampal functional connectivity using a large cohort (N = 202) of infants at 3 weeks (neonate), 1 year, and 2 years of age. Next, we used multivariate modeling to investigate the relationship between both cross-sectional and longitudinal growth in hippocampal connectivity and 4-year working memory outcome. Results showed robust local functional connectivity of the hippocampus in neonates with nearby limbic and subcortical regions, with dramatic maturation and increasing connectivity with key default mode network (DMN) regions resulting in adult-like topology of the hippocampal functional connectivity by the end of the first year. This pattern was stabilized and further consolidated by 2 years of age. Importantly, cross-sectional and longitudinal measures of hippocampal connectivity in the first year predicted subsequent behavioral measures of working memory at 4 years of age. Taken together, our findings provide insight into the development of hippocampal functional circuits underlying working memory during this early critical period.

Neonatal brain connectivity outliers identify over forty percent of IQ outliers at 4 years of age

BACKGROUND

Defining reliable brain markers for the prediction of abnormal behavioral outcomes remains an urgent but extremely challenging task in neuroscience research. This is particularly important for infant studies given the most dramatic brain and behavioral growth during infancy.

METHODS

In this study, we proposed a novel prediction scheme through abstracting individual newborn's whole-brain functional connectivity pattern to three outlier measures (Triple O) and tested the hypothesis that neonates identified as "brain outliers" based on Triple O were more likely to develop as IQ outliers at 4 years of age. Without need for training with behavioral data, Triple O represents a novel proof-of-concept approach to predict later IQ outcomes based on neonatal brain data.

RESULTS

Triple O correctly identified 42.1% true IQ outliers among a mixed cohort of 175 newborns with different term, twin, and maternal disorder statuses. Triple O also reached a high level of specificity (96.2%) and overall accuracy (90.3%). Further incorporating a demographic information indicator, the enhanced Triple O+ could further differentiate between high and low 4YR IQ outliers. Validation tests against seven independent reference samples revealed highly consistent results and a minimum sample size of ~50 for robust performance.

CONCLUSIONS

Considering that postnatal brain growth and various environmental factors likely also contribute to 4YR IQ, the fact that Triple O, based purely on neonatal functional connectivity data, could identify >40% of 4YR IQ outliers is striking. Together with the very high level of specificity, each outlier predicted by Triple O represents a meaningful risk but future efforts are needed to explore ways to identify the rest of outliers. Overall, with no need for training, a high level of robustness, and a minimal requirement on sample size, the proposed Triple O approach demonstrates great potential to predict later outlying IQ performances using neonatal functional connectivity data.

The Subgrouping Structure of Newborns with Heterogenous Brain-Behavior Relationships

The presence of heterogeneity/subgroups in infants and older populations against single-domain brain or behavioral measures has been previously characterized. However, few attempts have been made to explore heterogeneity at the brain-behavior relationship level. Such a hypothesis posits that different subgroups of infants may possess qualitatively different brain-behavior relationships that could ultimately contribute to divergent developmental outcomes even with relatively similar brain phenotypes. In this study, we aimed to explore such relationship-level heterogeneity and delineate the subgrouping structure of newborns with differential brain-behavior associations based on a typically developing sample of 81 infants with 3-week resting-state functional magnetic resonance imaging scans and 4-year intelligence quotient (IQ) measures. Our results not only confirmed the existence of relationship-level heterogeneity in newborns but also revealed divergent developmental outcomes associated with two subgroups showing similar brain functional connectivity but contrasting brain-behavior relationships. Importantly, further analyses unveiled an intriguing pattern that the subgroup with higher 4-year IQ outcomes possessed brain-behavior relationships that were congruent to their functional connectivity pattern in neonates while the subgroup with lower 4-year IQ not, providing potential explanations for the observed IQ differences. The characterization of heterogeneity at the brain-behavior relationship level may not only improve our understanding of the patterned intersubject variability during infancy but could also pave the way for future development of heterogeneity-inspired, personalized, subgroup-specific models for better prediction.

Individual Variation of Human Cortical Structure Is Established in the First Year of Life

BACKGROUND

Individual differences in cortical gray matter (GM) structure are associated with cognitive function and psychiatric disorders with developmental origins. Identifying when individual differences in cortical structure are established in childhood is critical for understanding the timing of abnormal cortical development associated with neuropsychiatric disorders.

METHODS

We studied the development of cortical GM and white matter volume, cortical thickness, and surface area using structural magnetic resonance imaging in two unique cohorts of singleton (121 male and 131 female) and twin (99 male and 83 female) children imaged longitudinally from birth to 6 years.

RESULTS

Cortical GM volume increases rapidly in the first year of life, with more gradual growth thereafter. Between ages 1 and 6 years, total surface area expands 29%, while average cortical thickness decreases about 3.5%. In both cohorts, a large portion of individual variation in cortical GM volume (81%-87%) and total surface area (73%-83%) at age 6 years is present by age 1 year. Regional heterogeneity of cortical thickness observed at age 6 is largely in place at age 1.

CONCLUSIONS

These findings indicate that individual differences in cortical GM structure are largely established by the end of the first year of life, following a period of rapid postnatal GM growth. This suggests that alterations in GM structure associated with psychiatric disorders with developmental origins may largely arise in the first year of life and that interventions to normalize or mitigate abnormal GM development may need to be targeted to very early childhood.

Extra-axial Cerebrospinal Fluid Relationships to Infant Brain Structure, Cognitive Development, and Risk for Schizophrenia

BACKGROUND

Increased volume of extra-axial cerebrospinal fluid (EA-CSF) is associated with autism spectrum disorder diagnosis in young children. However, little is known about EA-CSF development in typically developing (TD) children or in children at risk for schizophrenia (SCZHR).

METHODS

3T magnetic resonance imaging scans were obtained in TD children (n = 105) and in SCZHR children (n = 38) at 1 and 2 years of age. EA-CSF volume and several measures of brain structure were generated, including global tissue volumes, cortical thickness, and surface area. Cognitive and motor abilities at 1 and 2 years of age were assessed using the Mullen Scales of Early Learning.

RESULTS

In the TD children, EA-CSF volume was positively associated with total brain volume, gray and white matter volumes, and total surface area at 1 and 2 years of age. In contrast, EA-CSF volume was negatively associated with average cortical thickness. Lower motor ability was associated with increased EA-CSF volume at 1 year of age. EA-CSF was not significantly increased in SCZHR children compared with TD children.

CONCLUSIONS

EA-CSF volume is positively associated with overall brain size and cortical surface area but negatively associated with cortical thickness. Increased EA-CSF is associated with delayed motor development at 1 year of age, similar to studies of children at risk for autism, suggesting that increased EA-CSF may be an early biomarker of abnormal brain development in infancy. Infants in the SCZHR group did not exhibit significantly increased EA-CSF, suggesting that increased EA-CSF could be specific to neurodevelopmental disorders with an earlier onset, such as autism.

White matter development in infants at risk for schizophrenia

BACKGROUND

Schizophrenia is considered a neurodevelopmental disorder with a pathophysiology that likely begins long before the onset of clinical symptoms. White matter abnormalities have been observed in schizophrenia and we hypothesized that the first 2 years of life is a period in which white matter abnormalities associated with schizophrenia risk may emerge.

METHODS

38 infants at high risk for schizophrenia and 202 healthy controls underwent diffusion tensor MRIs after birth and at 1 and 2 years of age. Quantitative tractography was used to determine diffusion properties (fractional anisotropy (FA), axial diffusivity (AD), and radial diffusivity (RD)) of 18 white matter tracts and a general linear model was used to analyze group differences at each age.

RESULTS

Adjusting gestational age at birth, postnatal age at MRI, gender, MRI scanner type, and maternal education, neonates at high risk had significantly lower FA (p = 0.02) and AD (p = 0.03) in the superior segment of the left cingulate, and higher RD in the hippocampal segment of the left cingulate (p = 0.04). High risk one year olds had significantly lower FA (p < 0.01) and AD (p = 0.02) in the hippocampal segment of the left cingulate. High risk two year olds had significantly lower FA in the left prefrontal cortico-thalamic tract (p = 0.04) and higher RD in the right uncinate fasciculus (p = 0.04). None of the tract differences remained significant after correction for multiple comparisons.

CONCLUSIONS

There is evidence of abnormal white matter development in young children at risk for schizophrenia, especially in the hippocampal segment of left cingulum. These results support the neurodevelopmental theory of schizophrenia and indicate that impaired white matter may be present in early childhood.

White matter microstructural development and cognitive ability in the first 2 years of life

White matter (WM) integrity has been related to cognitive ability in adults and children, but it remains largely unknown how WM maturation in early life supports emergent cognition. The associations between tract-based measures of fractional anisotropy (FA) and axial and radial diffusivity (AD, RD) shortly after birth, at age 1, and at age 2 and cognitive measures at 1 and 2 years were investigated in 447 healthy infants. We found that generally higher FA and lower AD and RD across many WM tracts in the first year of life were associated with better performance on measures of general cognitive ability, motor, language, and visual reception skills at ages 1 and 2, suggesting an important role for the overall organization, myelination, and microstructural properties of fiber pathways in emergent cognition. RD in particular was consistently related to ability, and protracted development of RD from ages 1 to 2 years in several tracts was associated with higher cognitive scores and better language performance, suggesting prolonged plasticity may confer cognitive benefits during the second year of life. However, we also found that cognition at age 2 was weakly associated with WM properties across infancy in comparison to child and demographic factors including gestational age and maternal education. Our findings suggest that early postnatal WM integrity across the brain is important for infant cognition, though its role in cognitive development should be considered alongside child and demographic factors.

The Predictive Value of Developmental Assessments at 1 and 2 for Intelligence Quotients at 6

Intelligence is an important individual difference factor related to mental health, academic achievement, and life success, yet there is a lack of research into its early cognitive predictors. This study investigated the predictive value of infant developmental assessment scores for school-age intelligence in a large, heterogeneous sample of single- and twin-born subjects (N = 521). We found that Early Learning Composite (ELC) scores from the Mullen Scales of Early Learning have similar predictive power to that of other infant tests. ELC scores at age 2 were predictive of Stanford-Binet abbreviated intelligence (ABIQ) scores at age 6 (r = 0.46) even after controlling for sex, gestation number, and parental education. ELC scores at age 1 were less predictive of 6-year ABIQ scores (r = 0.17). When the sample was split to test robustness of findings, we found that results from the full sample replicated in a subset of children born at ≥32 weeks gestation without birth complications (n = 405), though infant cognitive scores did not predict IQ in a subset born very prematurely or with birth complications (n = 116). Scores at age 2 in twins and singletons showed similar predictive ability for scores at age 6, though twins had particularly high correlations between ELC at age 1 and ABIQ at age 6.

Regression Models on Riemannian Symmetric Spaces

The aim of this paper is to develop a general regression framework for the analysis of manifold-valued response in a Riemannian symmetric space (RSS) and its association with multiple covariates of interest, such as age or gender, in Euclidean space. Such RSS-valued data arises frequently in medical imaging, surface modeling, and computer vision, among many others. We develop an intrinsic regression model solely based on an intrinsic conditional moment assumption, avoiding specifying any parametric distribution in RSS. We propose various link functions to map from the Euclidean space of multiple covariates to the RSS of responses. We develop a two-stage procedure to calculate the parameter estimates and determine their asymptotic distributions. We construct the Wald and geodesic test statistics to test hypotheses of unknown parameters. We systematically investigate the geometric invariant property of these estimates and test statistics. Simulation studies and a real data analysis are used to evaluate the finite sample properties of our methods.

Antenatal depression, treatment with selective serotonin reuptake inhibitors, and neonatal brain structure: A propensity-matched cohort study

The aim of this propensity-matched cohort study was to evaluate the impact of prenatal SSRI exposure and a history of maternal depression on neonatal brain volumes and white matter microstructure. SSRI-exposed neonates (n=27) were matched to children of mothers with no history of depression or SSRI use (n=54). Additionally, neonates of mothers with a history of depression, but no prenatal SSRI exposure (n=41), were matched to children of mothers with no history of depression or SSRI use (n=82). Structural magnetic resonance imaging and diffusion weighted imaging scans were acquired with a 3T Siemens Allegra scanner. Global tissue volumes were characterized using an automatic, atlas-moderated expectation maximization segmentation tool. Local differences in gray matter volumes were examined using deformation-based morphometry. Quantitative tractography was performed using an adaptation of the UNC-Utah NA-MIC DTI framework. SSRI-exposed neonates exhibited widespread changes in white matter microstructure compared to matched controls. Children exposed to a history of maternal depression but no SSRIs showed no significant differences in brain development compared to matched controls. No significant differences were found in global or regional tissue volumes. Additional research is needed to clarify whether SSRIs directly alter white matter development or whether this relationship is mediated by depressive symptoms during pregnancy.

Assessing temporal agreement between central and local progression-free survival times

In oncology clinical trials, progression-free survival (PFS), generally defined as the time from randomization until disease progression or death, has been a key endpoint to support licensing approval. In the U.S. Food and Drug Administration guidance for industry, May 2007, concerning the PFS as the primary or co-primary clinical trial endpoint, it is recommended to have tumor assessments verified by an independent review committee blinded to study treatments, especially in open-label studies. It is considered reassuring about the lack of reader-evaluation bias if treatment effect estimates from the investigators' and independent review committees' evaluations agree. The agreement between these evaluations may vary for subjects with short or long PFS, while there exist no such statistical quantities that can completely account for this temporal pattern of agreements. Therefore, in this paper, we propose a new method to assess temporal agreement between two time-to-event endpoints, while the two event times are assumed to have a positive probability of being identical. This method measures agreement in terms of the two event times being identical at a given time or both being greater than a given time. Overall scores of agreement over a period of time are also proposed. We propose a maximum likelihood estimation to infer the proposed agreement measures using empirical data, accounting for different censoring mechanisms, including reader's censoring (event from one reader dependently censored by event from the other reader). The proposed method is demonstrated to perform well in small samples via extensive simulation studies and is illustrated through a head and neck cancer trial.