Longitudinal neurocognitive trajectories and risk factors in the first three months following pediatric concussion.

OBJECTIVE: To identify differential trajectories of neurocognitive outcomes following pediatric concussion and investigate predictors associated with patterns of recovery up to 3 months. METHODS: 74 participants aged 8-17 years completed attention/working memory, processing speed, and executive function measures at 2 weeks, 1 month, and 3 months post-injury. We used principal component analysis to generate a composite of information processing. Group-based trajectory modeling identified latent trajectories. Multinominal logistic regression was used to examine associations between risk factors and trajectory groups. RESULTS: We identified three trajectories of neurocognitive outcomes. The medium (54.6%) and high improving groups (35.8%) showed ongoing increase in information processing, while the low persistent group showed limited change 3 months post-injury. This group recorded below average scores on Digit Span Forward and Backward at 3 months. History of pre-injury headache was significantly associated with the persistent low scoring group, relative to the medium improving (p = 0.03) but not the high improving group (p = 0.09). CONCLUSIONS: This study indicates variability in neurocognitive outcomes according to three differential trajectories, with groups partially distinguished by preexisting child factors (history of frequent headaches). Modelling that accounts for heterogeneity in individual outcomes is essential to identify clinically meaningful indices that are indicative of children requiring intervention.

Fatigue recovery and connected factors following paediatric concussion.

OBJECTIVE: Using a biopsychosocial framework and the three-factor fatigue model, we aimed to (1) plot recovery of fatigue over the 3 months following paediatric concussion and (2) explore factors associated with persisting fatigue during the first 3 months postconcussion. METHODS: 240 children and adolescents aged 5-18 years (M=11.64, SD=3.16) completed assessments from time of injury to 3 months postinjury. Separate linear mixed effects models were conducted for child and parent ratings on the PedsQL-Multidimensional Fatigue Scale to plot recovery across domains (General, Cognitive, Sleep/Rest) and Total fatigue, from 1 week to 3 months postinjury. Two-block hierarchical regression analyses were then conducted for parent and child ratings of fatigue at each time point, with age, sex and acute symptoms in block 1 and child and parent mental health variables added to block 2. RESULTS: There was a significant reduction in both child and parent ratings across the 3 months postinjury for all fatigue domains (all p<0.001). For both child and parent fatigue ratings, child mental health was the most significant factor associated with fatigue at all time points. Adding child and parent mental health variables in the second block of the regression substantially increased the variance explained for both child and parent ratings of fatigue. CONCLUSION: Our findings confirm that fatigue improves during the first 3 months postconcussion and highlights the importance of considering child and parent mental health screening when assessing patients with persisting postconcussive symptoms.

What lies beneath: White matter microstructure in pediatric myalgic encephalomyelitis/chronic fatigue syndrome using diffusion MRI.

Recent studies in adults with myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) suggest that changes in brain white matter microstructural organization may correlate with core ME/CFS symptoms, and represent a potential biomarker of disease. However, this has yet to be investigated in the pediatric ME/CFS population. We examined group differences in macrostructural and microstructural white matter properties, and their relationship with clinical measures, between adolescents recently diagnosed with ME/CFS and healthy controls. Forty-eight adolescents (25 ME/CFS, 23 controls, mean age 16 years) underwent brain diffusion MRI, and a robust multi-analytic approach was used to evaluate white and gray matter volume, regional brain volume, cortical thickness, fractional anisotropy, mean/axial/radial diffusivity, neurite dispersion and density, fiber density, and fiber cross section. From a clinical perspective, adolescents with ME/CFS showed greater fatigue and pain, poorer sleep quality, and poorer performance on cognitive measures of processing speed and sustained attention compared with controls. However, no significant group differences in white matter properties were observed, with the exception of greater white matter fiber cross section of the left inferior longitudinal fasciculus in the ME/CFS group compared with controls, which did not survive correction for intracranial volume. Overall, our findings suggest that white matter abnormalities may not be predominant in pediatric ME/CFS in the early stages following diagnosis. The discrepancy between our null findings and white matter abnormalities identified in the adult ME/CFS literature could suggest that older age and/or longer illness duration influence changes in brain structure and brain-behavior relationships that are not yet established in adolescence.

Brain tissue microstructural and free-water composition 13 years after very preterm birth.

There have been many studies demonstrating children born very preterm exhibit brain white matter microstructural alterations, which have been related to neurodevelopmental difficulties. These prior studies have often been based on diffusion MRI modelling and analysis techniques, which commonly focussed on white matter microstructural properties in children born very preterm. However, there have been relatively fewer studies investigating the free-water content of the white matter, and also the microstructure and free-water content of the cortical grey matter, in children born very preterm. These biophysical properties of the brain change rapidly during fetal and neonatal brain development, and therefore such properties are likely also adversely affected by very preterm birth. In this study, we investigated the relationship of very preterm birth (<30 weeks' gestation) to both white matter and cortical grey matter microstructure and free-water content in childhood using advanced diffusion MRI analyses. A total of 130 very preterm participants and 45 full-term control participants underwent diffusion MRI at age 13 years. Diffusion tissue signal fractions derived by Single-Shell 3-Tissue Constrained Spherical Deconvolution were used to investigate brain tissue microstructural and free-water composition. The tissue microstructural and free-water composition metrics were analysed using a voxel-based analysis and cortical region-of-interest analysis approach. Very preterm 13-year-olds exhibited reduced white matter microstructural density and increased free-water content across widespread regions of the white matter compared with controls. Additionally, very preterm 13-year-olds exhibited reduced microstructural density and increased free-water content in specific temporal, frontal, occipital and cingulate cortical regions. These brain tissue composition alterations were strongly associated with cerebral white matter abnormalities identified in the neonatal period, and concurrent adverse cognitive and motor outcomes in very preterm children. The findings demonstrate brain microstructural and free-water alterations up to thirteen years from neonatal brain abnormalities in very preterm children that relate to adverse neurodevelopmental outcomes.

Individual Differences in Intrinsic Brain Networks Predict Symptom Severity in Autism Spectrum Disorders.

The neurobiology of heterogeneous neurodevelopmental disorders such as Autism Spectrum Disorders (ASD) is still unknown. We hypothesized that differences in subject-level properties of intrinsic brain networks were important features that could predict individual variation in ASD symptom severity. We matched cases and controls from a large multicohort ASD dataset (ABIDE-II) on age, sex, IQ, and image acquisition site. Subjects were matched at the individual level (rather than at group level) to improve homogeneity within matched case-control pairs (ASD: n = 100, mean age = 11.43 years, IQ = 110.58; controls: n = 100, mean age = 11.43 years, IQ = 110.70). Using task-free functional magnetic resonance imaging, we extracted intrinsic functional brain networks using projective non-negative matrix factorization. Intrapair differences in strength in subnetworks related to the salience network (SN) and the occipital-temporal face perception network were robustly associated with individual differences in social impairment severity (T = 2.206, P = 0.0301). Findings were further replicated and validated in an independent validation cohort of monozygotic twins (n = 12; 3 pairs concordant and 3 pairs discordant for ASD). Individual differences in the SN and face-perception network are centrally implicated in the neural mechanisms of social deficits related to ASD.

Individual variation underlying brain age estimates in typical development.

Typical brain development follows a protracted trajectory throughout childhood and adolescence. Deviations from typical growth trajectories have been implicated in neurodevelopmental and psychiatric disorders. Recently, the use of machine learning algorithms to model age as a function of structural or functional brain properties has been used to examine advanced or delayed brain maturation in healthy and clinical populations. Termed 'brain age', this approach often relies on complex, nonlinear models that can be difficult to interpret. In this study, we use model explanation methods to examine the cortical features that contribute to brain age modelling on an individual basis. In a large cohort of n = 768 typically-developing children (aged 3-21 years), we build models of brain development using three different machine learning approaches. We employ SHAP, a model-agnostic technique to identify sample-specific feature importance, to identify regional cortical metrics that explain errors in brain age prediction. We find that, on average, brain age prediction and the cortical features that explain model predictions are consistent across model types and reflect previously reported patterns of regions brain development. However, while several regions are found to contribute to brain age prediction error, we find little spatial correspondence between individual estimates of feature importance, even when matched for age, sex and brain age prediction error. We also find no association between brain age error and cognitive performance in this typically-developing sample. Overall, this study shows that, while brain age estimates based on cortical development are relatively robust and consistent across model types and preprocessing strategies, significant between-subject variation exists in the features that explain erroneous brain age predictions on an individual level.

A longitudinal analysis of puberty-related cortical development.

The brain undergoes extensive structural changes during adolescence, concurrent to puberty-related physical and hormonal changes. While animal research suggests these biological processes are related to one another, our knowledge of brain development in humans is largely based on age-related processes. Thus, the current study characterized puberty-related changes in human brain structure, by combining data from two longitudinal neuroimaging cohorts. Beyond normative changes in cortical thickness, we examined whether individual differences in the rate of pubertal maturation (or "pubertal tempo") was associated with variations in cortical trajectories. Participants (N = 192; scans = 366) completed up to three waves of MRI assessments between 8.5 and 14.5 years of age, as well as questionnaire assessments of pubertal stage at each wave. Generalized additive mixture models were used to characterize trajectories of cortical development. Results revealed widespread linear puberty-related changes across much of the cortex. Many of these changes, particularly within the frontal and parietal cortices, were independent of age-related development. Males exhibiting faster pubertal tempo demonstrated greater thinning in the precuneus and frontal cortices than same-aged and -sex peers. Findings suggest that the unique influence of puberty on cortical development may be more extensive than previously identified, and also emphasize important individual differences in the coupling of these developmental processes.

Towards understanding neurocognitive mechanisms of parenting: Maternal behaviors and structural brain network organization in late childhood.

A substantial body of knowledge suggests that exposure to adverse family environments - including violence and neglect - influences many aspects of brain development. Relatively less attention has been directed toward the influence of "normative" differences in parenting behaviors. Given the rapid brain reorganization during late childhood, parenting behaviors are particularly likely to impact the structure of the brain during this time. This study investigated associations between maternal parenting behaviors and the organization of structural brain networks in late childhood, as measured by structural covariance. One hundred and forty-five typically developing 8-year-olds and their mothers completed questionnaire measures and two observed interaction tasks; magnetic resonance imaging (MRI) scans were obtained from the children. Measures of maternal negative, positive, and communicative behavior were derived from the interaction tasks. Structural covariance networks based on partial correlations between cortical thickness estimates were constructed and estimates of modularity were obtained using graph theoretical analysis. High levels of negative maternal behavior were associated with low modularity. Minimal support was found for an association between positive maternal behaviors and modularity and between maternal communicative behaviors and modularity. Our findings suggest that variation in negative maternal behavior is associated with the structural organization of brain networks in children.

Investigating the brain structural connectome following working memory training in children born extremely preterm or extremely low birth weight.

Children born extremely preterm (EP, <28 weeks' gestation) or extremely low birth weight (ELBW, <1,000 g) are a vulnerable population at high risk of working memory impairments. We aimed to examine changes in the brain structural connectivity networks thought to underlie working memory performance, after completion of a working memory training program (Cogmed) compared with a placebo program in EP/ELBW children. This was a double-blind, placebo-controlled randomized trial (the Improving Memory in a Preterm Randomised Intervention Trial). Children born EP/ELBW received either the Cogmed or placebo program at 7 years of age (n = 91). A subset of children had magnetic resonance imaging of the brain immediately pre- and 2 weeks post-training (Cogmed n = 28; placebo n = 27). T1 -weighted and diffusion-weighted images were used to perform graph theoretical analysis of structural connectivity networks. Changes from pre-training to post-training in structural connectivity metrics were generally similar between randomized groups. There was little evidence that changes in structural connectivity metrics were related to changes in working memory performance from pre- to post-training. Overall, our results provide little evidence that the Cogmed working memory training program has training-specific effects on structural connectivity networks in EP/ELBW children.

A systematic review of brain MRI findings in monogenic disorders strongly associated with autism spectrum disorder.

BACKGROUND: Research on monogenic forms of autism spectrum disorder (autism) can inform our understanding of genetic contributions to the autism phenotype; yet, there is much to be learned about the pathways from gene to brain structure to behavior. This systematic review summarizes and evaluates research on brain magnetic resonance imaging (MRI) findings in monogenic conditions that have strong association with autism. This will improve understanding of the impact of genetic variability on brain structure and related behavioral traits in autism. METHODS: The search strategy for this systematic review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Risk of bias (ROB) assessment was completed on included studies using the Newcastle-Ottawa Scales. RESULTS: Of 4,287 studies screened, 69 were included pertaining to 13 of the top 20 genes with the strongest association with autism. The greatest number of studies related to individuals with PTEN variants and autism. Brain MRI abnormalities were reported for 12 of the 13 genes studied, and in 51.7% of participants across all 13 genes, including 100% of participants with ARID1B variants. Specific MRI findings were highly variable, with no clear patterns emerging within or between the 13 genes, although white matter abnormalities were the most common. Few studies reported specific details about methods for acquisition and processing of brain MRI, and descriptors for brain abnormalities were variable. ROB assessment indicated high ROB for all studies, largely due to small sample sizes and lack of comparison groups. CONCLUSIONS: Brain abnormalities are common in this population of individuals, in particular, children; however, a range of different brain abnormalities were reported within and between genes. Directions for future neuroimaging research in monogenic autism are suggested.

Adrenarcheal hormone-related development of white matter during late childhood.

The aim of the current study was to longitudinally examine how adrenarcheal hormones influence the development of white matter structure from age 8.5 to 10 years. Participants were 120 children (66 female; mean age 8.45 years at Time 1 and 9.97 years at Time 2) who completed two diffusion-weighted imaging scans 1.5 years apart. Morning saliva samples were taken at both assessment time points to measure levels of dehydroepiandrosterone (DHEA), its sulphate (DHEAS), and testosterone. Fixel-based analysis was performed to examine how changes in white matter fibre density (FD) and cross-section (FC) over time were associated with initial levels of hormones, and changes in hormone levels over time. Both FD and FC increased over time in a wide range of white matter tracts. Increases in testosterone over time were related to relatively weaker increases in FC in the inferior fronto-occipital fasciculus. Levels and change in DHEA and DHEAS were not related to FD or FC changes. The results demonstrated development of white matter fibre density and cross-section from age 8.5 to 10 years. Changes in adrenarcheal hormone levels showed limited, localized associations with development of white matter FC. Future research should examine the relevance of adrenarcheal hormone-related white matter development for cognitive functioning; as well as directly compare analysis techniques of white matter structure.

Long-term development of white matter fibre density and morphology up to 13 years after preterm birth: A fixel-based analysis.

BACKGROUND: It is well documented that infants born very preterm (VP) are at risk of brain injury and altered brain development in the neonatal period, however there is a lack of long-term, longitudinal studies on the effects of VP birth on white matter development over childhood. Most previous studies were based on voxel-averaged, non-fibre-specific diffusion magnetic resonance imaging (MRI) measures, such as fractional anisotropy. In contrast, the novel diffusion MRI analysis framework, fixel-based analysis (FBA), enables whole-brain analysis of microstructural and macrostructural properties of individual fibre populations at a sub-voxel level. We applied FBA to investigate the long-term implications of VP birth and associated perinatal risk factors on fibre development in childhood and adolescence. METHODS: Diffusion images were acquired for a cohort of VP (born <30 weeks' gestation) and full-term (FT, ≥37 weeks' gestation) children at two timepoints: mean (SD) 7.6 (0.2) years (n â€‹= â€‹138 VP and 32 FT children) and 13.3 (0.4) years (n â€‹= â€‹130 VP and 45 FT children). 103 VP and 21 FT children had images at both ages for longitudinal analysis. At every fixel (individual fibre population within an image voxel) across the white matter, we compared FBA metrics (fibre density (FD), cross-section (FC) and a combination of these properties (FDC)) between VP and FT groups cross-sectionally at each timepoint, and longitudinally between timepoints. We also examined associations between known perinatal risk factors and FBA metrics in the VP group. RESULTS: Compared with FT children, VP children had lower FD, FC and FDC throughout the white matter, particularly in the corpus callosum, tapetum, inferior fronto-occipital fasciculus, fornix and cingulum at ages 7 and 13 years, as well as the corticospinal tract and anterior limb of the internal capsule at age 13 years. VP children also had slower FDC development in the corpus callosum and corticospinal tract between ages 7 and 13 years compared with FT children. Within VP children, earlier gestational age at birth, lower birth weight z-score, and neonatal brain abnormalities were associated with lower FD, FC and FDC throughout the white matter at both ages. CONCLUSIONS: VP birth and concomitant perinatal risk factors are associated with fibre tract-specific alterations to axonal development in childhood and adolescence.

Increased power by harmonizing structural MRI site differences with the ComBat batch adjustment method in ENIGMA.

A common limitation of neuroimaging studies is their small sample sizes. To overcome this hurdle, the Enhancing Neuro Imaging Genetics through Meta-Analysis (ENIGMA) Consortium combines neuroimaging data from many institutions worldwide. However, this introduces heterogeneity due to different scanning devices and sequences. ENIGMA projects commonly address this heterogeneity with random-effects meta-analysis or mixed-effects mega-analysis. Here we tested whether the batch adjustment method, ComBat, can further reduce site-related heterogeneity and thus increase statistical power. We conducted random-effects meta-analyses, mixed-effects mega-analyses and ComBat mega-analyses to compare cortical thickness, surface area and subcortical volumes between 2897 individuals with a diagnosis of schizophrenia and 3141 healthy controls from 33 sites. Specifically, we compared the imaging data between individuals with schizophrenia and healthy controls, covarying for age and sex. The use of ComBat substantially increased the statistical significance of the findings as compared to random-effects meta-analyses. The findings were more similar when comparing ComBat with mixed-effects mega-analysis, although ComBat still slightly increased the statistical significance. ComBat also showed increased statistical power when we repeated the analyses with fewer sites. Results were nearly identical when we applied the ComBat harmonization separately for cortical thickness, cortical surface area and subcortical volumes. Therefore, we recommend applying the ComBat function to attenuate potential effects of site in ENIGMA projects and other multi-site structural imaging work. We provide easy-to-use functions in R that work even if imaging data are partially missing in some brain regions, and they can be trained with one data set and then applied to another (a requirement for some analyses such as machine learning).

White matter extension of the Melbourne Children's Regional Infant Brain atlas: M-CRIB-WM.

Brain atlases providing standardised identification of neonatal brain regions are key in investigating neurological disorders of early childhood. Our previously developed Melbourne Children's Regional Infant Brain (M-CRIB) and M-CRIB 2.0 neonatal brain atlases provide standardised parcellation of 100 brain regions including cortical, subcortical, and cerebellar regions. The aim of this study was to extend M-CRIB atlas coverage to include 54 white matter (WM) regions. Participants were 10 healthy term-born neonates that were used to create the initial M-CRIB atlas. WM regions were manually segmented based on T2 images and co-registered diffusion tensor imaging-based, direction-encoded colour maps. Our labelled regions imitate the Johns Hopkins University neonatal atlas, with minor anatomical modifications. All segmentations were reviewed and approved by a paediatric radiologist and a neurosurgery research fellow for anatomical accuracy. The resulting neonatal WM atlas comprises 54 WM regions: 24 paired regions, and six unpaired regions comprising five corpus callosum subdivisions, and one pontine crossing tract. Detailed protocols for manual WM parcellations are provided, and the M-CRIB-WM atlas is presented together with the existing M-CRIB cortical, subcortical, and cerebellar parcellations in 10 individual neonatal MRI data sets. The novel M-CRIB-WM atlas, along with the M-CRIB cortical and subcortical atlases, provide neonatal whole brain MRI coverage in the first multi-subject manually parcellated neonatal atlas compatible with atlases commonly used at older time points. The M-CRIB-WM atlas is publicly available, providing a valuable tool that will help facilitate neuroimaging research into neonatal brain development in both healthy and diseased states.

Working memory training and brain structure and function in extremely preterm or extremely low birth weight children.

This study in children born extremely preterm (EP; <28 weeks' gestational age) or extremely low birth weight (ELBW; <1,000 g) investigated whether adaptive working memory training using Cogmed® is associated with structural and/or functional brain changes compared with a placebo program. Ninety-one EP/ELBW children were recruited at a mean (standard deviation) age of 7.8 (0.4) years. Children were randomly allocated to Cogmed or placebo (45-min sessions, 5 days a week over 5-7 weeks). A subset had usable magnetic resonance imaging (MRI) data pretraining and 2 weeks posttraining (structural, n = 48; diffusion, n = 43; task-based functional, n = 18). Statistical analyses examined whether cortical morphometry, white matter microstructure and blood oxygenation level-dependent (BOLD) signal during an n-back working memory task changed from pretraining to posttraining in the Cogmed and placebo groups separately. Interaction analyses between time point and group were then performed. There was a significant increase in neurite density in several white matter regions from pretraining to posttraining in both the Cogmed and placebo groups. BOLD signal in the posterior cingulate and precuneus cortices during the n-back task increased from pretraining to posttraining in the Cogmed but not placebo group. Evidence for group-by-time interactions for the MRI measures was weak, suggesting that brain changes generally did not differ between Cogmed and placebo groups. Overall, while some structural and functional MRI changes between the pretraining and posttraining period in EP/ELBW children were observed, there was little evidence of training-induced neuroplasticity, with changes generally identified in both groups. Trial registration Australian New Zealand Clinical Trials Registry, anzctr.org.au; ACTRN12612000124831.

Neuroanatomical alterations in people with high and low cannabis dependence.

OBJECTIVES: We aimed to investigate whether severity of cannabis dependence is associated with the neuroanatomy of key brain regions of the stress and reward brain circuits. METHODS: To examine dependence-specific regional brain alterations, we compared the volumes of regions relevant to reward and stress, between high-dependence cannabis users (CD+, n = 25), low-dependence cannabis users (CD-, n = 20) and controls (n = 37). RESULTS: Compared to CD- and/or controls, the CD+ group had lower cerebellar white matter and hippocampal volumes, and deflation of the right hippocampus head and tail. CONCLUSION: These findings provide initial support for neuroadaptations involving stress and reward circuits that are specific to high-dependence cannabis users.

Structural covariance networks in children and their associations with maternal behaviors.

There is a substantial body of research documenting the influence of early adverse experience on brain development. In contrast, relatively little attention has been directed toward the influence of 'normative' variation in parenting behaviors. This study investigated associations between parenting behaviors and structural brain networks, as measured by structural covariance, in a community sample of children. One hundred and forty-five typically developing 8-year-olds and their mothers completed questionnaire measures and two observed parent-child interaction tasks. Structural MRI scans were also obtained from the children. Structural covariance networks based on partial correlation between cortical thickness estimates were constructed, and estimates of efficiency were obtained using graph theoretical analysis. Associations between affective and communicative maternal behaviors and these network metrics were investigated. High levels of observed negative affective and communicative maternal behaviors were associated with decreased local efficiency, whereas high levels of positive affective maternal behaviors were associated with increased local efficiency. The regions implicated (including the cingulate cortex, temporal pole, and temporo-parietal junction) are thought to be involved in the processing of social information. Minimal support was found for an association between global efficiency and maternal behaviors. Our findings suggest that variations in parenting behaviors are associated with structural organization of socio-emotional brain networks in children.

Interaction between hypothalamic-pituitary-adrenal axis genetic variation and maternal behavior in the prediction of amygdala connectivity in children.

High levels of negative, and low levels of positive parenting behaviors can increase the risk of internalizing symptoms in children, but the mechanisms underlying this association are still unclear. One possibility is that parenting behaviors affect the neural correlates of emotion processing in children. Further, genetic variants relevant to the function of the hypothalamic-pituitary-adrenal (HPA) axis are thought to moderate the effect of early experiences on the brain circuits underlying emotion processing, particularly those involving the amygdala. However, no studies have investigated the interactive effect of parenting behaviors and HPA axis-related genes on amygdala activity and connectivity during emotion processing, and in turn internalizing symptoms in children. Participants comprised 80 children (46 females, mean age = 10.0 years) from the community. Observational measures of maternal behavior were collected during mother-child interactions. Children underwent functional magnetic resonance imaging while performing an implicit emotion-processing task, and mothers and children completed measures of child internalizing symptoms. Genetic risk was calculated using an HPA genetic risk score. HPA genetic risk score was indirectly associated with greater child self-reported depressive symptoms via increased amygdala-precuneus connectivity during the emotion-processing task, and interacted with negative maternal parenting behavior to predict increased connectivity between amygdala and superior frontal gyrus, anterior cingulate cortex and parietal cortex. HPA-related genetic variation appears to moderate the effect of negative maternal parenting behavior on the neural underpinnings of emotion processing in children, and may confer risk for depressive symptoms via modulation of amygdala connectivity.

Early life predictors of brain development at term-equivalent age in infants born across the gestational age spectrum.

BACKGROUND: It is well established that preterm infants have altered brain development compared with full-term (FT; ≥37 weeks' gestational age [GA]) infants, however the perinatal factors associated with brain development in preterm infants have not been fully elucidated. In particular, perinatal predictors of brain development may differ between very preterm infants (VP; <32 weeks' GA) and infants born moderate (MP; 32-33 weeks' GA) and late (LP; 34-36 weeks' GA) preterm, but this has not been studied. This study aimed to investigate the effects of early life predictors on brain volume and microstructure at term-equivalent age (TEA; 38-44 weeks), and whether these effects differ for GA groups (VP, MP, LP or FT). METHODS: Structural images from 328 infants (91 VP, 63 MP, 104 LP and 70 FT) were segmented into white matter, cortical grey matter, cerebrospinal fluid, subcortical grey matter, brainstem and cerebellum. Cortical grey matter and white matter images were analysed using voxel-based morphometry. Fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD) and radial diffusivity (RD) images from 361 infants (92 VP, 69 MP, 120 LP and 80 FT) were analysed using Tract-Based Spatial Statistics. Relationships between early life predictors (birthweight standard deviation score [BWSDS], multiple birth, sex, postnatal growth and social risk) and global brain volumes were analysed using linear regressions. Relationships between early life predictors and regional brain volumes and diffusion measures were analysed using voxelwise non-parametric permutation testing. RESULTS: Male sex was associated with higher global volumes of all tissues and higher regional volumes throughout much of the cortical grey matter and white matter, particularly in the FT group. Male sex was also associated with lower FA and higher AD, RD and MD in the optic radiation, external and internal capsules and corona radiata, and these associations were generally similar between GA groups. Higher BWSDS was associated with higher global volumes of all tissues and higher regional volumes in much of the cortical grey matter and white matter in all GA groups, as well as higher FA and lower RD and MD in many major tracts (corpus callosum, optic radiation, internal and external capsules and corona radiata), particularly in the MP and LP groups. Multiple birth and social risk also showed associations with global and regional volumes and regional diffusion values which varied by GA group, but these associations were not independent of the other early life predictors. Postnatal growth was not associated with brain volumes or diffusion values. CONCLUSION: Early life predictors of brain volumes and microstructure at TEA include sex, BWSDS, multiple birth and social risk, which have different effects based on GA group at birth. This study improves knowledge of the perinatal factors associated with brain abnormalities in infants born across the prematurity spectrum.

Changes in neonatal regional brain volume associated with preterm birth and perinatal factors.

BACKGROUND: Preterm birth is associated with altered brain development, with younger gestational age (GA) at birth often associated with greater brain volume reduction. Such volume alterations at term equivalent age (TEA) have been found with differing magnitude across different brain regions, although this has mostly been investigated with regards to whole tissue volumes and large-scale subdivisions. In addition to degree of prematurity, many other perinatal factors have been found to influence brain structure and development in infants born preterm. We aimed to clarify the relationships between degree of prematurity and regional brain volumes at TEA, and between perinatal factors and regional brain volumes at TEA, in finer spatial detail. METHODS: 285 preterm and term-born infants (GA at birth 24.6-42.1 weeks; 145 female; 59 born at term) were scanned at TEA. Data on perinatal factors were obtained by chart review, including sex, multiple birth, birthweight standard deviation (SD) score, postnatal growth and social risk. The Melbourne Children's Regional Infant Brain (M-CRIB) atlas was registered to the current sample, then 100 brain regions were labelled for volumetric analyses. Linear regressions with generalised estimating equations and likelihood ratio tests were performed to investigate whether GA at birth or perinatal factors were associated with regional volumes at TEA. RESULTS: Younger GA at birth was associated with smaller volumes at TEA in some regions including bilateral cerebral white matter, middle temporal gyri, amygdalae, pallidum and brainstem. In other regions, younger GA at birth was associated with larger volumes, including in primary visual, motor and somatosensory cortices. Positive associations between perinatal factors and regional volumes at TEA were found in many brain regions for birthweight SD score, and male sex, independent of GA at birth. These associations were seen on both univariable analyses, and multivariable analyses controlling for other perinatal factors. Social risk and multiple birth were generally not associated with regional brain volumes, and postnatal growth was associated with volume in many regions only after adjusting for other perinatal factors. CONCLUSIONS: These results elucidate regional brain volume differences associated with preterm birth and perinatal factors at a more detailed parcellated level than previously reported, and contribute to understanding of the complex array of correlates of preterm birth.