Sex and mental health are related to subcortical brain microstructure.

Some mental health problems such as depression and anxiety are more common in females, while others such as autism and attention deficit/hyperactivity (AD/H) are more common in males. However, the neurobiological origins of these sex differences are poorly understood. Animal studies have shown substantial sex differences in neuronal and glial cell structure, while human brain imaging studies have shown only small differences, which largely reflect overall body and brain size. Advanced diffusion MRI techniques can be used to examine intracellular, extracellular, and free water signal contributions and provide unique insights into microscopic cellular structure. However, the extent to which sex differences exist in these metrics of subcortical gray matter structures implicated in psychiatric disorders is not known. Here, we show large sex-related differences in microstructure in subcortical regions, including the hippocampus, thalamus, and nucleus accumbens in a large sample of young adults. Unlike conventional T1-weighted structural imaging, large sex differences remained after adjustment for age and brain volume. Further, diffusion metrics in the thalamus and amygdala were associated with depression, anxiety, AD/H, and antisocial personality problems. Diffusion MRI may provide mechanistic insights into the origin of sex differences in behavior and mental health over the life course and help to bridge the gap between findings from experimental, epidemiological, and clinical mental health research.

FEMA: Fast and efficient mixed-effects algorithm for large sample whole-brain imaging data.

The linear mixed-effects model (LME) is a versatile approach to account for dependence among observations. Many large-scale neuroimaging datasets with complex designs have increased the need for LME; however LME has seldom been used in whole-brain imaging analyses due to its heavy computational requirements. In this paper, we introduce a fast and efficient mixed-effects algorithm (FEMA) that makes whole-brain vertex-wise, voxel-wise, and connectome-wide LME analyses in large samples possible. We validate FEMA with extensive simulations, showing that the estimates of the fixed effects are equivalent to standard maximum likelihood estimates but obtained with orders of magnitude improvement in computational speed. We demonstrate the applicability of FEMA by studying the cross-sectional and longitudinal effects of age on region-of-interest level and vertex-wise cortical thickness, as well as connectome-wide functional connectivity values derived from resting state functional MRI, using longitudinal imaging data from the Adolescent Brain Cognitive DevelopmentSM Study release 4.0. Our analyses reveal distinct spatial patterns for the annualized changes in vertex-wise cortical thickness and connectome-wide connectivity values in early adolescence, highlighting a critical time of brain maturation. The simulations and application to real data show that FEMA enables advanced investigation of the relationships between large numbers of neuroimaging metrics and variables of interest while considering complex study designs, including repeated measures and family structures, in a fast and efficient manner. The source code for FEMA is available via: https://github.com/cmig-research-group/cmig_tools/.

Sex-Dependent Associations among Maternal Depressive Symptoms, Child Reward Network, and Behaviors in Early Childhood.

Maternal depression is associated with disrupted neurodevelopment in offspring. This study examined relationships among postnatal maternal depressive symptoms, the functional reward network and behavioral problems in 4.5-year-old boys (57) and girls (65). We employed canonical correlation analysis to evaluate whether the resting-state functional connectivity within a reward network, identified through an activation likelihood estimation (ALE) meta-analysis of fMRI studies, was associated with postnatal maternal depressive symptoms and child behaviors. The functional reward network consisted of three subnetworks, that is, the mesolimbic, mesocortical, and amygdala-hippocampus reward subnetworks. Postnatal maternal depressive symptoms were associated with the functional connectivity of the mesocortical subnetwork with the mesolimbic and amygdala-hippocampus complex subnetworks in girls and with the functional connectivity within the mesocortical subnetwork in boys. The functional connectivity of the amygdala-hippocampus subnetwork with the mesocortical and mesolimbic subnetworks was associated with both internalizing and externalizing problems in girls, while in boys, the functional connectivity of the mesocortical subnetwork with the amygdala-hippocampus complex and the mesolimbic subnetworks was associated with the internalizing and externalizing problems, respectively. Our findings suggest that the functional reward network might be a promising neural phenotype for effects of maternal depression and potential intervention to nurture child behavioral development.

Neural Transcription Correlates of Multimodal Cortical Phenotypes during Development.

During development, cellular events such as cell proliferation, migration, and synaptogenesis determine the structural organization of the brain. These processes are driven in part by spatiotemporally regulated gene expression. We investigated how the genetic signatures of specific neural cell types shape cortical organization of the human brain throughout infancy and childhood. Using a transcriptional atlas and in vivo magnetic resonance imaging (MRI) data, we demonstrated time-dependent associations between the expression levels of neuronal and glial genes and cortical macro- and microstructure. Neonatal cortical phenotypes were associated with prenatal glial but not neuronal gene expression. These associations reflect cell migration and proliferation during fetal development. Childhood cortical phenotypes were associated with neuronal and astrocyte gene expression related to synaptic signaling processes, reflecting the refinement of cortical connections. These findings indicate that sequential developmental stages contribute to distinct MRI measures at different time points. This helps to bridge the gap between the genetic mechanisms driving cellular changes and widely used neuroimaging techniques.

Do intrinsic brain functional networks predict working memory from childhood to adulthood?

Working memory (WM) is defined as the ability to maintain a representation online to guide goal-directed behavior. Its capacity in early childhood predicts academic achievements in late childhood and its deficits are found in various neurodevelopmental disorders. We employed resting-state fMRI (rs-fMRI) of 468 participants aged from 4 to 55 years and connectome-based predictive modeling (CPM) to explore the potential predictive power of intrinsic functional networks to WM in preschoolers, early and late school-age children, adolescents, and adults. We defined intrinsic functional networks among brain regions identified by activation likelihood estimation (ALE) meta-analysis on existing WM functional studies (ALE-based intrinsic functional networks) and intrinsic functional networks generated based on the whole brain (whole-brain intrinsic functional networks). We employed the CPM on these networks to predict WM in each age group. The CPM using the ALE-based and whole-brain intrinsic functional networks predicted WM of individual adults, while the prediction power of the ALE-based intrinsic functional networks was superior to that of the whole-brain intrinsic functional networks. Nevertheless, the CPM using the whole-brain but not the ALE-based intrinsic functional networks predicted WM in adolescents. And, the CPM using neither the ALE-based nor whole-brain networks predicted WM in any of the children groups. Our findings showed the trend of the prediction power of the intrinsic functional networks to cognition in individuals from early childhood to adulthood.

A tract-specific approach to assessing white matter in preterm infants.

Diffusion-weighted imaging (DWI) is becoming an increasingly important tool for studying brain development. DWI analyses relying on manually-drawn regions of interest and tractography using manually-placed waypoints are considered to provide the most accurate characterisation of the underlying brain structure. However, these methods are labour-intensive and become impractical for studies with large cohorts and numerous white matter (WM) tracts. Tract-specific analysis (TSA) is an alternative WM analysis method applicable to large-scale studies that offers potential benefits. TSA produces a skeleton representation of WM tracts and projects the group's diffusion data onto the skeleton for statistical analysis. In this work we evaluate the performance of TSA in analysing preterm infant data against results obtained from native space tractography and tract-based spatial statistics. We evaluate TSA's registration accuracy of WM tracts and assess the agreement between native space data and template space data projected onto WM skeletons, in 12 tracts across 48 preterm neonates. We show that TSA registration provides better WM tract alignment than a previous protocol optimised for neonatal spatial normalisation, and that TSA projects FA values that match well with values derived from native space tractography. We apply TSA for the first time to a preterm neonatal population to study the effects of age at scan on WM tracts around term equivalent age. We demonstrate the effects of age at scan on DTI metrics in commissural, projection and association fibres. We demonstrate the potential of TSA for WM analysis and its suitability for infant studies involving multiple tracts.

Microstructural development from 9 to 14 years: Evidence from the ABCD Study.

During late childhood behavioral changes, such as increased risk-taking and emotional reactivity, have been associated with the maturation of cortico-cortico and cortico-subcortical circuits. Understanding microstructural changes in both white matter and subcortical regions may aid our understanding of how individual differences in these behaviors emerge. Restriction spectrum imaging (RSI) is a framework for modelling diffusion-weighted imaging that decomposes the diffusion signal from a voxel into hindered, restricted, and free compartments. This yields greater specificity than conventional methods of characterizing diffusion. Using RSI, we quantified voxelwise restricted diffusion across the brain and measured age associations in a large sample (n = 8086) from the Adolescent Brain and Cognitive Development (ABCD) study aged 9-14 years. Older participants showed a higher restricted signal fraction across the brain, with the largest associations in subcortical regions, particularly the basal ganglia and ventral diencephalon. Importantly, age associations varied with respect to the cytoarchitecture within white matter fiber tracts and subcortical structures, for example age associations differed across thalamic nuclei. This suggests that age-related changes may map onto specific cell populations or circuits and highlights the utility of voxelwise compared to ROI-wise analyses. Future analyses will aim to understand the relevance of this microstructural developmental for behavioral outcomes.

Multivariate genome-wide association study on tissue-sensitive diffusion metrics highlights pathways that shape the human brain.

The molecular determinants of tissue composition of the human brain remain largely unknown. Recent genome-wide association studies (GWAS) on this topic have had limited success due to methodological constraints. Here, we apply advanced whole-brain analyses on multi-shell diffusion imaging data and multivariate GWAS to two large scale imaging genetic datasets (UK Biobank and the Adolescent Brain Cognitive Development study) to identify and validate genetic association signals. We discover 503 unique genetic loci that have impact on multiple regions of human brain. Among them, more than 79% are validated in either of two large-scale independent imaging datasets. Key molecular pathways involved in axonal growth, astrocyte-mediated neuroinflammation, and synaptogenesis during development are found to significantly impact the measured variations in tissue-specific imaging features. Our results shed new light on the biological determinants of brain tissue composition and their potential overlap with the genetic basis of neuropsychiatric disorders.

Neonatal White Matter Microstructure and Emotional Development during the Preschool Years in Children Who Were Born Very Preterm.

Children born very preterm (<33 weeks of gestation) are at a higher risk of developing socio-emotional difficulties compared with those born at term. In this longitudinal study, we tested the hypothesis that diffusion characteristics of white matter (WM) tracts implicated in socio-emotional processing assessed in the neonatal period are associated with socio-emotional development in 151 very preterm children previously enrolled into the Evaluation of Preterm Imaging study (EudraCT 2009-011602-42). All children underwent diffusion tensor imaging at term-equivalent age and fractional anisotropy (FA) was quantified in the uncinate fasciculus (UF), inferior fronto-occipital fasciculus (IFOF), inferior longitudinal fasciculus (ILF), and superior longitudinal fasciculus (SLF). Children's socio-emotional development was evaluated at preschool age (median = 4.63 years). Exploratory factor analysis conducted on the outcome variables revealed a three-factor structure, with latent constructs summarized as: "emotion moderation," "social function," and "empathy." Results of linear regression analyses, adjusting for full-scale IQ and clinical and socio-demographic variables, showed an association between lower FA in the right UF and higher "emotion moderation" scores (ß = -0.280; p < 0.001), which was mainly driven by negative affectivity scores (ß = -0.281; p = 0.001). Results further showed an association between higher full-scale IQ and better social functioning (ß = -0.334, p < 0.001). Girls had higher empathy scores than boys (ß = -0.341, p = 0.006). These findings suggest that early alterations of diffusion characteristics of the UF could represent a biological substrate underlying the link between very preterm birth and emotional dysregulation in childhood and beyond.

Maternal Prenatal Stress Is Associated With Altered Uncinate Fasciculus Microstructure in Premature Neonates.

BACKGROUND: Maternal prenatal stress exposure (PNSE) increases risk for adverse psychiatric and behavioral outcomes in offspring. The biological basis for this elevated risk is poorly understood but may involve alterations to the neurodevelopmental trajectory of white matter tracts within the limbic system, particularly the uncinate fasciculus. Additionally, preterm birth is associated with both impaired white matter development and adverse developmental outcomes. In this study we hypothesized that higher maternal PNSE was associated with altered uncinate fasciculus microstructure in offspring. METHODS: In this study, 251 preterm infants (132 male, 119 female) (median gestational age = 30.29 weeks [range, 23.57-32.86 weeks]) underwent brain magnetic resonance imaging including diffusion-weighted imaging around term-equivalent age (median = 42.43 weeks [range, 37.86-45.71 weeks]). Measures of white matter microstructure were calculated for the uncinate fasciculus and the inferior longitudinal fasciculus, a control tract that we hypothesized was not associated with maternal PNSE. Multiple regressions were used to investigate the relationship among maternal trait anxiety scores, stressful life events, and white matter microstructure indices in the neonatal brain. RESULTS: Adjusting for gestational age at birth, postmenstrual age at scan, maternal age, socioeconomic status, sex, and number of days on parenteral nutrition, higher stressful life events scores were associated with higher axial diffusivity (ß = .177, q = .007), radial diffusivity (ß = .133, q = .026), and mean diffusivity (ß = .149, q = .012) in the left uncinate fasciculus, and higher axial diffusivity (ß = .142, q = .026) in the right uncinate fasciculus. CONCLUSIONS: These findings suggest that PNSE is associated with altered development of specific frontolimbic pathways in preterm neonates as early as term-equivalent age.

Fixel-based analysis of the preterm brain: Disentangling bundle-specific white matter microstructural and macrostructural changes in relation to clinical risk factors.

Diffusion MRI (dMRI) studies using the tensor model have identified abnormal white matter development associated with perinatal risk factors in preterm infants studied at term equivalent age (TEA). However, this model is an oversimplification of the underlying neuroanatomy. Fixel-based analysis (FBA) is a novel quantitative framework, which identifies microstructural and macrostructural changes in individual fibre populations within voxels containing crossing fibres. The aim of this study was to apply FBA to investigate the relationship between fixel-based measures of apparent fibre density (FD), fibre bundle cross-section (FC), and fibre density and cross-section (FDC) and perinatal risk factors in preterm infants at TEA. We studied 50 infants (28 male) born at 24.0-32.9 (median 30.4) weeks gestational age (GA) and imaged at 38.6-47.1 (median 42.1) weeks postmenstrual age (PMA). dMRI data were acquired in non-collinear directions with b-value 2500 s/mm2 on a 3 Tesla system sited on the neonatal intensive care unit. FBA was performed to assess the relationship between FD, FC, FDC and PMA at scan, GA at birth, days on mechanical ventilation, days on total parenteral nutrition (TPN), birthweight z-score, and sex. FBA reveals fibre population-specific alterations in FD, FC and FDC associated with clinical risk factors. FD was positively correlated with GA at birth and was negatively correlated with number of days requiring ventilation. FC was positively correlated with GA at birth, birthweight z-scores and was higher in males. FC was negatively correlated with number of days on ventilation and days on TPN. FDC was positively correlated with GA at birth and birthweight z-scores, negatively correlated with days on ventilation and days on TPN and higher in males. We demonstrate that these relationships are fibre-specific even within regions of crossing fibres. These results show that aberrant white matter development involves both microstructural changes and macrostructural alterations.

Recent advances in diffusion neuroimaging: applications in the developing preterm brain.

Measures obtained from diffusion-weighted imaging provide objective indices of white matter development and injury in the developing preterm brain. To date, diffusion tensor imaging (DTI) has been used widely, highlighting differences in fractional anisotropy (FA) and mean diffusivity (MD) between preterm infants at term and healthy term controls; altered white matter development associated with a number of perinatal risk factors; and correlations between FA values in the white matter in the neonatal period and subsequent neurodevelopmental outcome. Recent developments, including neurite orientation dispersion and density imaging (NODDI) and fixel-based analysis (FBA), enable white matter microstructure to be assessed in detail. Constrained spherical deconvolution (CSD) enables multiple fibre populations in an imaging voxel to be resolved and allows delineation of fibres that traverse regions of fibre-crossings, such as the arcuate fasciculus and cerebellar-cortical pathways. This review summarises DTI findings in the preterm brain and discusses initial findings in this population using CSD, NODDI, and FBA.