Xiujuan Geng

Dynamic Development of Regional Cortical Thickness and Surface Area in Early Childhood

Cortical thickness (CT) and surface area (SA) are altered in many neuropsychiatric disorders and are correlated with cognitive functioning. Little is known about how these components of cortical gray matter develop in the first years of life. We studied the longitudinal development of regional CT and SA expansion in healthy infants from birth to 2 years. CT and SA have distinct and heterogeneous patterns of development that are exceptionally dynamic; overall CT increases by an average of 36.1%, while cortical SA increases 114.6%. By age 2, CT is on average 97% of adult values, compared with SA, which is 69%. This suggests that early identification, prevention, and intervention strategies for neuropsychiatric illness need to be targeted to this period of rapid postnatal brain development, and that SA expansion is the principal driving factor in cortical volume after 2 years of age.

Quantitative tract-based white matter development from birth to age 2years

Few large-scale studies have been done to characterize the normal human brain white matter growth in the first years of life. We investigated white matter maturation patterns in major fiber pathways in a large cohort of healthy young children from birth to age two using diffusion parameters fractional anisotropy (FA), radial diffusivity (RD) and axial diffusivity (RD). Ten fiber pathways, including commissural, association and projection tracts, were examined with tract-based analysis, providing more detailed and continuous spatial developmental patterns compared to conventional ROI based methods. All DTI data sets were transformed to a population specific atlas with a group-wise longitudinal large deformation diffeomorphic registration approach. Diffusion measurements were analyzed along the major fiber tracts obtained in the atlas space. All fiber bundles show increasing FA values and decreasing radial and axial diffusivities during development in the first 2years of life. The changing rates of the diffusion indices are faster in the first year than the second year for all tracts. RD and FA show larger percentage changes in the first and second years than AD. The gender effects on the diffusion measures are small. Along different spatial locations of fiber tracts, maturation does not always follow the same speed. Temporal and spatial diffusion changes near cortical regions are in general smaller than changes in central regions. Overall developmental patterns revealed in our study confirm the general rules of white matter maturation. This work shows a promising framework to study and analyze white matter maturation in a tract-based fashion. Compared to most previous studies that are ROI-based, our approach has the potential to discover localized development patterns associated with fiber tracts of interest.

Common Variants in Psychiatric Risk Genes Predict Brain Structure at Birth

Studies in adolescents and adults have demonstrated that polymorphisms in putative psychiatric risk genes are associated with differences in brain structure, but cannot address when in development these relationships arise. To determine if common genetic variants in disrupted-in-schizophrenia-1 (DISC1; rs821616 and rs6675281), catechol-O-methyltransferase (COMT; rs4680), neuregulin 1 (NRG1; rs35753505 and rs6994992), apolipoprotein E (APOE; ε3ε4 vs. ε3ε3), estrogen receptor alpha (ESR1; rs9340799 and rs2234693), brain-derived neurotrophic factor (BDNF; rs6265), and glutamate decarboxylase 1 (GAD1; rs2270335) are associated with individual differences in brain tissue volumes in neonates, we applied both automated region-of-interest volumetry and tensor-based morphometry to a sample of 272 neonates who had received high-resolution magnetic resonance imaging scans. ESR1 (rs9340799) predicted intracranial volume. Local variation in gray matter (GM) volume was significantly associated with polymorphisms in DISC1 (rs821616), COMT, NRG1, APOE, ESR1 (rs9340799), and BDNF. No associations were identified for DISC1 (rs6675281), ESR1 (rs2234693), or GAD1. Of note, neonates homozygous for the DISC1 (rs821616) serine allele exhibited numerous large clusters of reduced GM in the frontal lobes, and neonates homozygous for the COMT valine allele exhibited reduced GM in the temporal cortex and hippocampus, mirroring findings in adults. The results highlight the importance of prenatal brain development in mediating psychiatric risk.

Knowledge-Guided Robust MRI Brain Extraction for Diverse Large-Scale Neuroimaging Studies on Humans and Non-Human Primates

Accurate and robust brain extraction is a critical step in most neuroimaging analysis pipelines. In particular, for the large-scale multi-site neuroimaging studies involving a significant number of subjects with diverse age and diagnostic groups, accurate and robust extraction of the brain automatically and consistently is highly desirable. In this paper, we introduce population-specific probability maps to guide the brain extraction of diverse subject groups, including both healthy and diseased adult human populations, both developing and aging human populations, as well as non-human primates. Specifically, the proposed method combines an atlas-based approach, for coarse skull-stripping, with a deformable-surface-based approach that is guided by local intensity information and population-specific prior information learned from a set of real brain images for more localized refinement. Comprehensive quantitative evaluations were performed on the diverse large-scale populations of ADNI dataset with over 800 subjects (55∼90 years of age, multi-site, various diagnosis groups), OASIS dataset with over 400 subjects (18∼96 years of age, wide age range, various diagnosis groups), and NIH pediatrics dataset with 150 subjects (5∼18 years of age, multi-site, wide age range as a complementary age group to the adult dataset). The results demonstrate that our method consistently yields the best overall results across almost the entire human life span, with only a single set of parameters. To demonstrate its capability to work on non-human primates, the proposed method is further evaluated using a rhesus macaque dataset with 20 subjects. Quantitative comparisons with popularly used state-of-the-art methods, including BET, Two-pass BET, BET-B, BSE, HWA, ROBEX and AFNI, demonstrate that the proposed method performs favorably with superior performance on all testing datasets, indicating its robustness and effectiveness.

White matter heritability using diffusion tensor imaging in neonatal brains

Understanding genetic and environmental effects on white matter development in the first years of life is of great interest, as it provides insights into the etiology of neurodevelopmental disorders. In this study, the genetic and environmental effects on white matter were estimated using data from 173 neonatal twin subjects. Diffusion tensor imaging scans were acquired around 40 days after birth and were non-rigidly registered to a group-specific atlas and parcellated into 98 ROIs. A model of additive genetic, and common and specific environmental variance components was used to estimate overall and regional genetic and environmental contributions to diffusion parameters of fractional anisotropy, radial diffusivity, and axial diffusivity. Correlations between the regional heritability values and diffusion parameters were also examined. Results indicate that individual differences in overall white matter microstructure, represented by the average diffusion parameters over the whole brain, are heritable, and estimates are higher than found in studies in adults. Estimates of genetic and environmental variance components vary considerably across different white matter regions. Significant positive correlations between radial diffusivity heritability and radial diffusivity values are consistent with regional genetic variation being modulated by maturation status in the neonatal brain: the more mature the region is, the less genetic variation it shows. Common environmental effects are present in a few regions that tend to be characterized by low radial diffusivity. Results from the joint diffusion parameter analysis suggest that multivariate modeling approaches might be promising to better estimate maturation status and its relationship with genetic and environmental effects.

Structural and Maturational Covariance in Early Childhood Brain Development

Abstract Brain structural covariance networks (SCNs) composed of regions with correlated variation are altered in neuropsychiatric disease and change with age. Little is known about the development of SCNs in early childhood, a period of rapid cortical growth. We investigated the development of structural and maturational covariance networks, including default, dorsal attention, primary visual and sensorimotor networks in a longitudinal population of 118 children after birth to 2 years old and compared them with intrinsic functional connectivity networks. We found that structural covariance of all networks exhibit strong correlations mostly limited to their seed regions. By Age 2, default and dorsal attention structural networks are much less distributed compared with their functional maps. The maturational covariance maps, however, revealed significant couplings in rates of change between distributed regions, which partially recapitulate their functional networks. The structural and maturational covariance of the primary visual and sensorimotor networks shows similar patterns to the corresponding functional networks. Results indicate that functional networks are in place prior to structural networks, that correlated structural patterns in adult may arise in part from coordinated cortical maturation, and that regional co‐activation in functional networks may guide and refine the maturation of SCNs over childhood development.

Impact of Sex and Gonadal Steroids on Neonatal Brain Structure

There are numerous reports of sexual dimorphism in brain structure in children and adults, but data on sex differences in infancy are extremely limited. Our primary goal was to identify sex differences in neonatal brain structure. Our secondary goal was to explore whether brain structure was related to androgen exposure or sensitivity. Two hundred and ninety-three neonates (149 males) received high-resolution structural magnetic resonance imaging scans. Sensitivity to androgen was measured using the number of cytosine, adenine, guanine (CAG) triplets in the androgen receptor gene and the ratio of the second to fourth digit, provided a proxy measure of prenatal androgen exposure. There was a significant sex difference in intracranial volume of 5.87%, which was not related to CAG triplets or digit ratios. Tensor-based morphometry identified extensive areas of local sexual dimorphism. Males had larger volumes in medial temporal cortex and rolandic operculum, and females had larger volumes in dorsolateral prefrontal, motor, and visual cortices. Androgen exposure and sensitivity had minor sex-specific effects on local gray matter volume, but did not appear to be the primary determinant of sexual dimorphism at this age. Comparing our study with the existing literature suggests that sex differences in cortical structure vary in a complex and highly dynamic way across the human lifespan.

FMEM: Functional mixed effects modeling for the analysis of longitudinal white matter Tract data

Many longitudinal imaging studies have collected repeated diffusion tensor magnetic resonance imaging data to understand white matter maturation and structural connectivity pattern in normal controls and diseased subjects. There is an urgent demand for the development of statistical methods for the analysis of diffusion properties along fiber tracts and clinical data obtained from longitudinal studies. Jointly analyzing repeated fiber-tract diffusion properties and covariates (e.g., age or gender) raises several major challenges including (i) infinite-dimensional functional response data, (ii) complex spatial-temporal correlation structure, and (iii) complex spatial smoothness. To address these challenges, this article is to develop a functional mixed effects modeling (FMEM) framework to delineate the dynamic changes of diffusion properties along major fiber tracts and their association with a set of covariates of interest and the structure of the variability of these white matter tract properties in various longitudinal studies. Our FMEM consists of a functional mixed effects model for addressing all three challenges, an efficient method for spatially smoothing varying coefficient functions, an estimation method for estimating the spatial-temporal correlation structure, a test procedure with local and global test statistics for testing hypotheses of interest associated with functional response, and a simultaneous confidence band for quantifying the uncertainty in the estimated coefficient functions. Simulated data are used to evaluate the finite sample performance of FMEM and to demonstrate that FMEM significantly outperforms the standard pointwise mixed effects modeling approach. We apply FMEM to study the spatial-temporal dynamics of white-matter fiber tracts in a clinical study of neurodevelopment.

A longitudinal functional analysis framework for analysis of white matter tract statistics

Many longitudinal imaging studies have been/are being widely conducted to use diffusion tensor imaging (DTI) to better understand white matter maturation in normal controls and diseased subjects. There is an urgent demand for the development of statistical methods for analyzing diffusion properties along major fiber tracts obtained from longitudinal DTI studies. Jointly analyzing fiber-tract diffusion properties and covariates from longitudinal studies raises several major challenges including (i) infinite-dimensional functional response data, (ii) complex spatial-temporal correlation structure, and (iii) complex spatial smoothness. To address these challenges, this article is to develop a longitudinal functional analysis framework (LFAF) to delineate the dynamic changes of diffusion properties along major fiber tracts and their association with a set of covariates of interest (e.g., age and group status) and the structure of the variability of these white matter tract properties in various longitudinal studies. Our LFAF consists of a functional mixed effects model for addressing all three challenges, an efficient method for spatially smoothing varying coefficient functions, an estimation method for estimating the spatial-temporal correlation structure, a test procedure with a global test statistic for testing hypotheses of interest associated with functional response, and a simultaneous confidence band for quantifying the uncertainty in the estimated coefficient functions. Simulated data are used to evaluate the finite sample performance of LFAF and to demonstrate that LFAF significantly outperforms a voxel-wise mixed model method. We apply LFAF to study the spatial-temporal dynamics of white-matter fiber tracts in a clinical study of neurodevelopment.

Multi-contrast diffusion tensor image registration with structural MRI

We present a diffeomorphic diffusion tensor image (DTI) registration technique with multi-contrast images extracted from DTI and conventional structural MRI data. DTI provides microstructure information in white matter. However due to the acquisition protocols used in many clinical studies, DTI has lower SNR and spatial resolution compared to structural MRI. Complementary information can be used to improve the registration of white and gray matter. The proposed registration framework is constructed by a vector-valued large deformation diffeomorphic demons approach. Fractional anisotropy (FA) and eigenvalues are included as DTI components. T1-weighted image serves as the structural MRI component. The performance of the proposed method is compared with DTI only multi-contrast and full tensor based registration methods. Incorporation of structural data reduces FA variance in white matter adjacent to cortical regions. Compared to tensor based registration, the multi-contrast methods generate smaller shape variance but less directional consistency. We also demonstrate that the proposed method reduces fiber tract variations across individuals and creates a denser fiber tract probability map compared to DTI based registrations.