Explore the vast range of titles available.

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.

Structural covariance conceptualizes how morphologic properties of brain regions are related to one another (across individuals). It can provide unique information to cortical structure (e.g., thickness) about the development of functionally meaningful networks. The current study investigated how structural covariance networks develop during the transition from childhood to adolescence, a period characterized by marked structural re-organization. Participants (N = 192; scans = 366) completed MRI assessments between 8.5 and 14.5 years of age. A sliding window approach was used to create “age-bins”, and structural covariance networks (based on cortical thickness) were created for each bin. Next, generalized additive models were used to characterize trajectories of age-related changes in network properties. Results revealed nonlinear trajectories with “peaks” in mean correlation and global density that are suggestive of a period of convergence in anatomical properties across the cortex during early adolescence, prior to regional specialization. “Hub” regions in sensorimotor cortices were present by late childhood, but the extent and strength of association cortices as “hubs” increased into mid-adolescence. Moreover, these regional changes were found to be related to rates of thinning across the cortex. In the context of neurocognitive networks, the frontoparietal, default mode, and attention systems exhibited age-related increases in within-network and between-network covariance. These regional and modular developmental patterns are consistent with continued refinement of socioemotional and other complex executive functions that are supported by higher-order cognitive networks during early adolescence.

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.

Serial regional brain growth from the newborn period to adolescence has not been described. Here, we measured regional brain growth in 216 very preterm (VP) and 45 full-term (FT) children. Brain MRI was performed at term-equivalent age, 7 and 13 years in 82 regions. Brain volumes increased between term-equivalent and 7 years, with faster growth in the FT than VP group. Perinatal brain abnormality was associated with less increase in brain volume between term-equivalent and 7 years in the VP group. Between 7 and 13 years, volumes were relatively stable, with some subcortical and cortical regions increasing while others reduced. Notably, VP infants continued to lag, with overall brain size generally less than that of FT peers at 13 years. Parieto–frontal growth, mainly between 7 and 13 years in FT children, was associated with higher intelligence at 13 years. This study improves understanding of typical and atypical regional brain growth. In this longitudinal study, the authors tracked the course of brain development from birth to adolescence (age 13 years) and examined the effects of very preterm birth. Very preterm children showed slower brain growth from age 0 (term equivalent) to age 7.

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. 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 ​= ​138 VP and 32 FT children) and 13.3 (0.4) years (n ​= ​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. 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. VP birth and concomitant perinatal risk factors are associated with fibre tract-specific alterations to axonal development in childhood and adolescence. •We present a fixel-based analysis in preterm-born children relative to controls.•Preterms have widespread fibre density and cross-section reductions at age 7 and 13.•Longitudinally, preterms have slower development of commissural and motor fibres.•Gestational age, birth weight and brain abnormalities relate to fibre alterations.

Brain development is a dynamic process with tissue-specific alterations that reflect complex and ongoing biological processes taking place during childhood and adolescence. Accurate identification and modelling of these anatomical processes in vivo with MRI may provide clinically useful imaging markers of individual variability in development. In this study, we use manifold learning to build a model of age- and sex-related anatomical variation using multiple magnetic resonance imaging metrics. Using publicly available data from a large paediatric cohort (n = 768), we apply a multi-metric machine learning approach combining measures of tissue volume, cortical area and cortical thickness into a low-dimensional data representation. We find that neuroanatomical variation due to age and sex can be captured by two orthogonal patterns of brain development and we use this model to simultaneously predict age with a mean error of 1.5–1.6 years and sex with an accuracy of 81%. We validate this model in an independent developmental cohort. We present a framework for modelling anatomical development during childhood using manifold embedding. This model accurately predicts age and sex based on image-derived markers of cerebral morphology and generalises well to independent populations.

ObjectiveTo explore whether regional brain volume and white matter microstructure at term-equivalent age (TEA) are associated with development at 2 years of age in children born moderate–late preterm (MLPT).Study designA cohort of MLPT infants had brain MRI at approximately TEA (38–44 weeks’ postmenstrual age) and had a developmental assessment (Bayley Scales of Infant and Toddler Development and Infant Toddler Social Emotional Assessment) at 2 years’ corrected age. Relationships between cortical grey matter and white matter volumes and 2-year developmental outcomes were explored using voxel-based morphometry. Relationships between diffusion tensor measures of white matter microstructure (fractional anisotropy (FA) and axial (AD), radial (RD) and mean (MD) diffusivities) and 2-year developmental outcomes were explored using tract-based spatial statistics.Results189 MLPT children had data from at least one MRI modality (volumetric or diffusion) and data for at least one developmental domain. Larger cortical grey and white matter volumes in many brain regions, and higher FA and lower AD, RD and MD in several major white matter regions, were associated with better cognitive and language scores. There was little evidence that cortical grey matter and white matter volumes and white matter microstructure were associated with motor and behavioural outcomes.ConclusionsRegional cortical grey matter and white matter volumes and white matter microstructure are associated with cognitive and language development at 2 years of age in MLPT children. Thus, early alterations to brain volumes and microstructure may contribute to some of the developmental deficits described in MLPT children.

Our recently published M-CRIB atlas comprises 100 neonatal brain regions including 68 compatible with the widely-used Desikan-Killiany adult cortical atlas. A successor to the Desikan-Killiany atlas is the Desikan-Killiany-Tourville atlas, in which some regions with unclear boundaries were removed, and many existing boundaries were revised to conform to clearer landmarks in sulcal fundi. Our first aim here was to modify cortical M-CRIB regions to comply with the Desikan-Killiany-Tourville protocol, in order to offer: (a) compatibility with this adult cortical atlas, (b) greater labeling accuracy due to clearer landmarks, and (c) optimisation of cortical regions for integration with surface-based infant parcellation pipelines. Secondly, we aimed to update subcortical regions in order to offer greater compatibility with subcortical segmentations produced in FreeSurfer. Data utilized were the T2-weighted MRI scans in our M-CRIB atlas, for 10 healthy neonates (post-menstrual age at MRI 40-43 weeks, four female), and corresponding parcellated images. Edits were performed on the parcellated images in volume space using ITK-SNAP. Cortical updates included deletion of frontal and temporal poles and 'Banks STS,' and modification of boundaries of many other regions. Changes to subcortical regions included the addition of 'ventral diencephalon,' and deletion of 'subcortical matter' labels. A detailed updated parcellation protocol was produced. The resulting whole-brain M-CRIB 2.0 atlas comprises 94 regions altogether. This atlas provides comparability with adult Desikan-Killiany-Tourville-labeled cortical data and FreeSurfer-labeed subcortical data, and is more readily adaptable for incorporation into surface-based neonatal parcellation pipelines. As such, it offers the ability to help facilitate a broad range of investigations into brain structure and function both at the neonatal time point and developmentally across the lifespan.

The long-term effect of regular cannabis use on brain function underlying cognitive control remains equivocal. Cognitive control abilities are thought to have a major role in everyday functioning, and their dysfunction has been implicated in the maintenance of maladaptive drug-taking patterns. In this study, the Multi-Source Interference Task was employed alongside functional magnetic resonance imaging and psychophysiological interaction methods to investigate functional interactions between brain regions underlying cognitive control. Current cannabis users with a history of greater than 10 years of daily or near-daily cannabis smoking (n=21) were compared with age, gender, and IQ-matched non-using controls (n=21). No differences in behavioral performance or magnitude of task-related brain activations were evident between the groups. However, greater connectivity between the prefrontal cortex and the occipitoparietal cortex was evident in cannabis users, as compared with controls, as cognitive control demands increased. The magnitude of this connectivity was positively associated with age of onset and lifetime exposure to cannabis. These findings suggest that brain regions responsible for coordinating behavioral control have an increased influence on the direction and switching of attention in cannabis users, and that these changes may have a compensatory role in mitigating cannabis-related impairments in cognitive control or perceptual processes.

Extremely preterm (EP) survivors have smaller brains, lower IQ, and worse educational achievement than their term-born peers. The contribution of smaller brain size to the IQ and educational disadvantages of EP is unknown. This study aimed (i) to compare brain volumes from multiple brain tissues and structures between EP-born (< 28 weeks) and term-born (≥ 37 weeks) control adolescents, (ii) to explore the relationships of brain tissue volumes with IQ and basic educational skills and whether this differed by group, and (iii) to explore how much total brain tissue volume explains the underperformance of EP adolescents compared with controls. Longitudinal cohort study of 148 EP and 132 term controls born in Victoria, Australia in 1991-92. At age 18, magnetic resonance imaging-determined brain volumes of multiple tissues and structures were calculated. IQ and educational skills were measured using the Wechsler Abbreviated Scale of Intelligence (WASI) and the Wide Range Achievement Test(WRAT-4), respectively. Brain volumes were smaller in EP adolescents compared with controls (mean difference [95% confidence interval] of -5.9% [-8.0, -3.7%] for total brain tissue volume). The largest relative differences were noted in the thalamus and hippocampus. The EP group had lower IQs(-11.9 [-15.4, -8.5]), spelling(-8.0 [-11.5, -4.6]), math computation(-10.3 [-13.7, -6.9]) and word reading(-5.6 [-8.8, -2.4]) scores than controls; all p-values<0.001. Volumes of total brain tissue and other brain tissues and structures correlated positively with IQ and educational skills, a relationship that was similar for both the EP and controls. Total brain tissue volume explained between 20-40% of the IQ and educational outcome differences between EP and controls. EP adolescents had smaller brain volumes, lower IQs and poorer educational performance than controls. Brain volumes of multiple tissues and structures are related to IQ and educational outcomes. Smaller total brain tissue volume is an important contributor to the cognitive and educational underperformance of adolescents born EP.