Greater male than female variability in regional brain structure across the lifespan.

For many traits, males show greater variability than females, with possible implications for understanding sex differences in health and disease. Here, the ENIGMA (Enhancing Neuro Imaging Genetics through Meta-Analysis) Consortium presents the largest-ever mega-analysis of sex differences in variability of brain structure, based on international data spanning nine decades of life. Subcortical volumes, cortical surface area and cortical thickness were assessed in MRI data of 16,683 healthy individuals 1-90 years old (47% females). We observed significant patterns of greater male than female between-subject variance for all subcortical volumetric measures, all cortical surface area measures, and 60% of cortical thickness measures. This pattern was stable across the lifespan for 50% of the subcortical structures, 70% of the regional area measures, and nearly all regions for thickness. Our findings that these sex differences are present in childhood implicate early life genetic or gene-environment interaction mechanisms. The findings highlight the importance of individual differences within the sexes, that may underpin sex-specific vulnerability to disorders.

Inter-individual variability in structural brain development from late childhood to young adulthood.

A fundamental task in neuroscience is to characterize the brain's developmental course. While replicable group-level models of structural brain development from childhood to adulthood have recently been identified, we have yet to quantify and understand individual differences in structural brain development. The present study examined inter-individual variability and sex differences in changes in brain structure, as assessed by anatomical MRI, across ages 8.0-26.0 years in 269 participants (149 females) with three time points of data (807 scans), drawn from three longitudinal datasets collected in the Netherlands, Norway, and USA. We further investigated the relationship between overall brain size and developmental changes, as well as how females and males differed in change variability across development. There was considerable inter-individual variability in the magnitude of changes observed for all examined brain measures. The majority of individuals demonstrated decreases in total gray matter volume, cortex volume, mean cortical thickness, and white matter surface area in mid-adolescence, with more variability present during the transition into adolescence and the transition into early adulthood. While most individuals demonstrated increases in white matter volume in early adolescence, this shifted to a majority demonstrating stability starting in mid-to-late adolescence. We observed sex differences in these patterns, and also an association between the size of an individual's brain structure and the overall rate of change for the structure. The present study provides new insight as to the amount of individual variance in changes in structural morphometrics from late childhood to early adulthood in order to obtain a more nuanced picture of brain development. The observed individual- and sex-differences in brain changes also highlight the importance of further studying individual variation in developmental patterns in healthy, at-risk, and clinical populations.

The nature of the self: Neural analyses and heritability estimates of self-evaluations in middle childhood.

How neural correlates of self-concept are influenced by environmental versus genetic factors is currently not fully understood. We investigated heritability estimates of behavioral and neural correlates of self-concept in middle childhood since this phase is an important time window for taking on new social roles in academic and social contexts. To do so, a validated self-concept fMRI task was applied in a twin sample of 345 participants aged between 7 and 9 years. In the self-concept condition, participants were asked to indicate whether academic and social traits applied to them whereas the control condition required trait categorization. The self-processing activation analyses (n = 234) revealed stronger medial prefrontal cortex (mPFC) activation for self than for control conditions. This effect was more pronounced for social-self than academic self-traits, whereas stronger dorsolateral prefrontal cortex (DLPFC) activation was observed for academic versus social self-evaluations. Behavioral genetic modeling (166 complete twin pairs) revealed that 25-52% of the variation in academic self-evaluations was explained by genetic factors, whereas 16-49% of the variation in social self-evaluations was explained by shared environmental factors. Neural genetic modeling (91 complete twin pairs) for variation in mPFC and anterior prefrontal cortex (PFC) activation for academic self-evaluations confirmed genetic and unique environmental influences, whereas anterior PFC activation for social self-evaluations was additionally influenced by shared environmental influences. This indicates that environmental context possibly has a larger impact on the behavioral and neural correlates of social self-concept at a young age. This is the first study demonstrating in a young twin sample that self-concept depends on both genetic and environmental factors, depending on the specific domain.

Sex Effects on Development of Brain Structure and Executive Functions: Greater Variance than Mean Effects.

Although male brains have consistently reported to be 8-10% larger than female brains, it remains not well understood whether there are differences between sexes (average or variance) in developmental trajectories. Furthermore, if sex differences in average brain growth or variance are observed, it is unknown whether these sex differences have behavioral relevance. The present longitudinal study aimed to unravel sex effects in cortical brain structure, development, and variance, in relation to the development of educationally relevant cognitive domains and executive functions (EFs). This was assessed with three experimental tasks including working memory, reading comprehension, and fluency. In addition, real-life aspects of EF were assessed with self- and parent-reported Behavior Rating Inventory of Executive Function scores. The full data set included 271 participants (54% female) aged between 8 and 29 years of which three waves were collected at 2-year intervals, resulting in 680 T1-weighted MRI scans and behavioral measures. Analyses of average trajectories confirmed general age-related patterns of brain development but did not support the hypothesis of sex differences in brain development trajectories, except for left banks STS where boys had a steeper decline in surface area than girls. Also, our brain age prediction model (including 270 brain measures) did not indicate delayed maturation in boys compared with girls. Interestingly, support was found for greater variance in male brains than female brains in both structure and development, consistent with prior cross-sectional studies. Behaviorally, boys performed on average better on a working memory task with a spatial aspect and girls performed better on a reading comprehension task, but there was no relation between brain development and cognitive performance, neither for average brain measures, brain age, or variance measures. Taken together, we confirmed the hypothesis of greater males within-group variance in brain structures compared with females, but these were not related to EF. The sex differences observed in EF were not related to brain development, possibly suggesting that these are related to experiences and strategies rather than biological development.

Qoala-T: A supervised-learning tool for quality control of FreeSurfer segmented MRI data.

Performing quality control to detect image artifacts and data-processing errors is crucial in structural magnetic resonance imaging, especially in developmental studies. Currently, many studies rely on visual inspection by trained raters for quality control. The subjectivity of these manual procedures lessens comparability between studies, and with growing study sizes quality control is increasingly time consuming. In addition, both inter-rater as well as intra-rater variability of manual quality control is high and may lead to inclusion of poor quality scans and exclusion of scans of usable quality. In the current study we present the Qoala-T tool, which is an easy and free to use supervised-learning model to reduce rater bias and misclassification in manual quality control procedures using FreeSurfer-processed scans. First, we manually rated quality of N = 784 FreeSurfer-processed T1-weighted scans acquired in three different waves in a longitudinal study. Different supervised-learning models were then compared to predict manual quality ratings using FreeSurfer segmented output data. Results show that the Qoala-T tool using random forests is able to predict scan quality with both high sensitivity and specificity (mean area under the curve (AUC) = 0.98). In addition, the Qoala-T tool was also able to adequately predict the quality of two novel unseen datasets (total N = 872). Finally, analyses of age effects showed that younger participants were more likely to have lower scan quality, underlining that scan quality might confound findings attributed to age effects. These outcomes indicate that this procedure could further help to reduce variability related to manual quality control, thereby benefiting the comparability of data quality between studies.

A three-wave longitudinal study of subcortical-cortical resting-state connectivity in adolescence: Testing age- and puberty-related changes.

Adolescence is the transitional period between childhood and adulthood, characterized by substantial changes in reward-driven behavior. Although reward-driven behavior is supported by subcortical-medial prefrontal cortex (PFC) connectivity, the development of these circuits is not well understood. Particularly, while puberty has been hypothesized to accelerate organization and activation of functional neural circuits, the relationship between age, sex, pubertal change, and functional connectivity has hardly been studied. Here, we present an analysis of resting-state functional connectivity between subcortical structures and the medial PFC, in 661 scans of 273 participants between 8 and 29 years, using a three-wave longitudinal design. Generalized additive mixed model procedures were used to assess the effects of age, sex, and self-reported pubertal status on connectivity between subcortical structures (nucleus accumbens, caudate, putamen, hippocampus, and amygdala) and cortical medial structures (dorsal anterior cingulate, ventral anterior cingulate, subcallosal cortex, frontal medial cortex). We observed an age-related strengthening of subcortico-subcortical and cortico-cortical connectivity. Subcortical-cortical connectivity, such as, between the nucleus accumbens-frontal medial cortex, and the caudate-dorsal anterior cingulate cortex, however, weakened across age. Model-based comparisons revealed that for specific connections pubertal development described developmental change better than chronological age. This was particularly the case for changes in subcortical-cortical connectivity and distinctively for boys and girls. Together, these findings indicate changes in functional network strengthening with pubertal development. These changes in functional connectivity may maximize the neural efficiency of interregional communication and set the stage for further inquiry of biological factors driving adolescent functional connectivity changes.

Pregnancy and adolescence entail similar neuroanatomical adaptations: A comparative analysis of cerebral morphometric changes.

Mapping the impact of pregnancy on the human brain is essential for understanding the neurobiology of maternal caregiving. Recently, we found that pregnancy leads to a long-lasting reduction in cerebral gray matter volume. However, the morphometric features behind the volumetric reductions remain unexplored. Furthermore, the similarity between these reductions and those occurring during adolescence, another hormonally similar transitional period of life, still needs to be investigated. Here, we used surface-based methods to analyze the longitudinal magnetic resonance imaging data of a group of 25 first-time mothers (before and after pregnancy) and compare them to those of a group of 25 female adolescents (during 2 years of pubertal development). For both first-time mothers and adolescent girls, a monthly rate of volumetric reductions of 0.09 mm3 was observed. In both cases, these reductions were accompanied by decreases in cortical thickness, surface area, local gyrification index, sulcal depth, and sulcal length, as well as increases in sulcal width. In fact, the changes associated with pregnancy did not differ from those that characterize the transition during adolescence in any of these measures. Our findings are consistent with the notion that the brain morphometric changes associated with pregnancy and adolescence reflect similar hormonally primed biological processes.

A Key Characteristic of Sex Differences in the Developing Brain: Greater Variability in Brain Structure of Boys than Girls.

In many domains, including cognition and personality, greater variability is observed in males than in females in humans. However, little is known about how variability differences between sexes are represented in the brain. The present study tested whether there is a sex difference in variance in brain structure using a cohort of 643 males and 591 females aged between 3 and 21 years. The broad age-range of the sample allowed us to test if variance differences in the brain differ across age. We observed significantly greater male than female variance for several key brain structures, including cerebral white matter and cortex, hippocampus, pallidum, putamen, and cerebellar cortex volumes. The differences were observed at both upper and lower extremities of the distributions and appeared stable across development. These findings move beyond mean levels by showing that sex differences were pronounced for variability, thereby providing a novel perspective on sex differences in the developing brain.

A multisample study of longitudinal changes in brain network architecture in 4-13-year-old children.

Recent advances in human neuroimaging research have revealed that white-matter connectivity can be described in terms of an integrated network, which is the basis of the human connectome. However, the developmental changes of this connectome in childhood are not well understood. This study made use of two independent longitudinal diffusion-weighted imaging data sets to characterize developmental changes in the connectome by estimating age-related changes in fractional anisotropy (FA) for reconstructed fibers (edges) between 68 cortical regions. The first sample included 237 diffusion-weighted scans of 146 typically developing children (4-13 years old, 74 females) derived from the Pediatric Longitudinal Imaging, Neurocognition, and Genetics (PLING) study. The second sample included 141 scans of 97 individuals (8-13 years old, 62 females) derived from the BrainTime project. In both data sets, we compared edges that had the most substantial age-related change in FA to edges that showed little change in FA. This allowed us to investigate if developmental changes in white matter reorganize network topology. We observed substantial increases in edges connecting peripheral and a set of highly connected hub regions, referred to as the rich club. Together with the observed topological differences between regions connecting to edges showing the smallest and largest changes in FA, this indicates that changes in white matter affect network organization, such that highly connected regions become even more strongly imbedded in the network. These findings suggest that an important process in brain development involves organizing patterns of inter-regional interactions. Hum Brain Mapp 39:157-170, 2018. © 2017 Wiley Periodicals, Inc.

Longitudinal structural brain development and externalizing behavior in adolescence.

BACKGROUND: Cross-sectional studies report relations between externalizing behavior and structural abnormalities in cortical thickness of prefrontal regions and volume reductions in subcortical regions. To understand how these associations emerge and develop, longitudinal designs are pivotal. METHOD: In the current longitudinal study, a community sample of children, adolescents and young adults (N = 271) underwent magnetic resonance imaging (MRI) in three biennial waves (680 scans). At each wave, aspects of externalizing behavior were assessed with parent-reported aggression and rule-breaking scores (Child Behavior Checklist), and self-reported aggression scores (Buss-Perry Aggression Questionnaire). Regions of interest (ROIs) were selected based on prior research: dorsolateral prefrontal (dlPFC), orbitofrontal (OFC), anterior cingulate cortex (ACC), insula, and parahippocampal cortex, as well as subcortical regions. Linear mixed models were used to assess the longitudinal relation between externalizing behavior and structural brain development. Structural covariance analyses were employed to identify whether longitudinal relations between ROIs (maturational coupling) were associated with externalizing behavior. RESULTS: Linear mixed model analyses showed a negative relation between parent-reported aggression and right hippocampal volume. Moreover, this longitudinal relation was driven by change in hippocampal volume and not initial volume of hippocampus at time point 1. Exploratory analyses showed that stronger maturational coupling between prefrontal regions, the limbic system, and striatum was associated with both low and high externalizing behavior. CONCLUSIONS: Together, these findings reinforce the hypothesis that altered structural brain development coincides with development of more externalizing behavior. These findings may guide future research on normative and deviant development of externalizing behavior.

What can Cortical Development in Attention-Deficit/Hyperactivity Disorder Teach us About the Early Developmental Mechanisms Involved?

Studies of Attention-Deficit/Hyperactivity Disorder (ADHD) have shown developmental changes in the cortical mantle. Different dimensions of cortical morphology, such as surface area and thickness, relate to different neurodevelopmental mechanisms. As such, studying multiple dimensions may inform us about the developmental origins of ADHD. Furthermore, results from existing longitudinal samples await replication. Therefore, we conducted a longitudinal study of multiple cortical dimensions in a sizable, independent ADHD sample. We analyzed 297 anatomical MRI scans from two matched groups of 94 subjects with ADHD and 94 controls, aged 6-28 years. We estimated the developmental trajectories of cortical volume, surface, thickness and gyrification for 68 regions using mixed-effects regression analysis. Subjects with ADHD had smaller overall cortical volume, predominantly driven by decreases in frontal lobe volume that were associated with reduced surface area and gyrification. Nearly all decreases were stable across development. Only a few decreases survived stringent Bonferroni correction for multiple comparisons, with the smallest detectable Cohen's d |0.43|. There were no between-group differences in cortical thickness, or in subcortical volumes. Our results suggest that ADHD is associated with developmentally persistent reductions in frontal cortical volume, surface area, and gyrification. This may implicate early neurodevelopmental mechanisms regulating cortical expansion and convolution in ADHD.

The development of brain network architecture.

Brain connectivity shows protracted development throughout childhood and adolescence, and, as such, the topology of brain networks changes during this period. The complexity of these changes with development is reflected by regional differences in maturation. This study explored age-related changes in network topology and regional developmental patterns during childhood and adolescence. We acquired two sets of Diffusion Weighted Imaging-scans and anatomical T1-weighted scans. The first dataset included 85 typically developing individuals (53 males; 32 females), aged between 7 and 23 years and was acquired on a Philips Achieva 1.5 Tesla scanner. A second dataset (N = 38) was acquired on a different (but identical) 1.5 T scanner and was used for independent replication of our results. We reconstructed whole brain networks using tractography. We operationalized fiber tract development as changes in mean diffusivity and radial diffusivity with age. Most fibers showed maturational changes in mean and radial diffusivity values throughout childhood and adolescence, likely reflecting increasing white matter integrity. The largest age-related changes were observed in association fibers within and between the frontal and parietal lobes. Furthermore, there was a simultaneous age-related decrease in average path length (P < 0.0001), increase in node strength (P < 0.0001) as well as network clustering (P = 0.001), which may reflect fine-tuning of topological organization. These results suggest a sequential maturational model where connections between unimodal regions strengthen in childhood, followed by connections from these unimodal regions to association regions, while adolescence is characterized by the strengthening of connections between association regions within the frontal and parietal cortex. Hum Brain Mapp 37:717-729, 2016. © 2015 Wiley Periodicals, Inc.

Unique developmental trajectories of cortical thickness and surface area.

There is evidence that the timing of developmental changes in cortical volume and thickness varies across the brain, although the processes behind these differences are not well understood. In contrast to volume and thickness, the regional developmental trajectories of cortical surface area have not yet been described. The present study used a combined cross-sectional and longitudinal design with 201 MRI-scans (acquired at 1.5-T) from 135 typically developing children and adolescents. Scans were processed using FreeSurfer software and the Desikan-Killiany atlas. Developmental trajectories were estimated using mixed model regression analysis. Within most regions, cortical thickness showed linear decreases with age, whereas both cortical volume and surface area showed curvilinear trajectories. On average, maximum surface area occurred later in development than maximum volume. Global gender differences were more pronounced in cortical volume and surface area than in average thickness. Our findings suggest that developmental trajectories of surface area and thickness differ across the brain, both in their pattern and their timing, and that they also differ from the developmental trajectory of global cortical volume. Taken together, these findings indicate that the development of surface area and thickness is driven by different processes, at least in part.

Typical development of basal ganglia, hippocampus, amygdala and cerebellum from age 7 to 24.

Developmental imaging studies show that cortical grey matter decreases in volume during childhood and adolescence. However, considerably less research has addressed the development of subcortical regions (caudate, putamen, pallidum, accumbens, thalamus, amygdala, hippocampus and the cerebellar cortex), in particular not in longitudinal designs. We used the automatic labeling procedure in FreeSurfer to estimate the developmental trajectories of the volume of these subcortical structures in 147 participants (age 7.0-24.3years old, 94 males; 53 females) of whom 53 participants were scanned twice or more. A total of 223 magnetic resonance imaging (MRI) scans (acquired at 1.5-T) were analyzed. Substantial diversity in the developmental trajectories was observed between the different subcortical gray matter structures: the volume of caudate, putamen and nucleus accumbens decreased with age, whereas the volume of hippocampus, amygdala, pallidum and cerebellum showed an inverted U-shaped developmental trajectory. The thalamus showed an initial small increase in volume followed by a slight decrease. All structures had a larger volume in males than females over the whole age range, except for the cerebellum that had a sexually dimorphic developmental trajectory. Thus, subcortical structures appear to not yet be fully developed in childhood, similar to the cerebral cortex, and continue to show maturational changes into adolescence. In addition, there is substantial heterogeneity between the developmental trajectories of these structures.

Mood variability during adolescent development and its relation to sleep and brain development.

Mood swings, or mood variability, are associated with negative mental health outcomes. Since adolescence is a time when mood disorder onset peaks, mood variability during this time is of significant interest. Understanding biological factors that might be associated with mood variability, such as sleep and structural brain development, could elucidate the mechanisms underlying mood and anxiety disorders. Data from the longitudinal Leiden self-concept study (N = 191) over 5 yearly timepoints was used to study the association between sleep, brain structure, and mood variability in healthy adolescents aged 11-21 at baseline in this pre-registered study. Sleep was measured both objectively, using actigraphy, as well as subjectively, using a daily diary self-report. Negative mood variability was defined as day-to-day negative mood swings over a period of 5 days after an MRI scan. It was found that negative mood variability peaked in mid-adolescence in females while it linearly increased in males, and average negative mood showed a similar pattern. Sleep duration (subjective and objective) generally decreased throughout adolescence, with a larger decrease in males. Mood variability was not associated with sleep, but average negative mood was associated with lower self-reported energy. In addition, higher thickness in the dorsolateral prefrontal cortex (dlPFC) compared to same-age peers, suggesting a delayed thinning process, was associated with higher negative mood variability in early and mid-adolescence. Together, this study provides an insight into the development of mood variability and its association with brain structure.

Recommendations for a Better Understanding of Sex and Gender in the Neuroscience of Mental Health.

There are prominent sex/gender differences in the prevalence, expression, and life span course of mental health and neurodiverse conditions. However, the underlying sex- and gender-related mechanisms and their interactions are still not fully understood. This lack of knowledge has harmful consequences for those with mental health problems. Therefore, we set up a cocreation session in a 1-week workshop with a multidisciplinary team of 25 researchers, clinicians, and policy makers to identify the main barriers in sex and gender research in the neuroscience of mental health. Based on this work, here we provide recommendations for methodologies, translational research, and stakeholder involvement. These include guidelines for recording, reporting, analysis beyond binary groups, and open science. Improved understanding of sex- and gender-related mechanisms in neuroscience may benefit public health because this is an important step toward precision medicine and may function as an archetype for studying diversity.

Normative modelling of brain morphometry across the lifespan with CentileBrain: algorithm benchmarking and model optimisation.

The value of normative models in research and clinical practice relies on their robustness and a systematic comparison of different modelling algorithms and parameters; however, this has not been done to date. We aimed to identify the optimal approach for normative modelling of brain morphometric data through systematic empirical benchmarking, by quantifying the accuracy of different algorithms and identifying parameters that optimised model performance. We developed this framework with regional morphometric data from 37 407 healthy individuals (53% female and 47% male; aged 3-90 years) from 87 datasets from Europe, Australia, the USA, South Africa, and east Asia following a comparative evaluation of eight algorithms and multiple covariate combinations pertaining to image acquisition and quality, parcellation software versions, global neuroimaging measures, and longitudinal stability. The multivariate fractional polynomial regression (MFPR) emerged as the preferred algorithm, optimised with non-linear polynomials for age and linear effects of global measures as covariates. The MFPR models showed excellent accuracy across the lifespan and within distinct age-bins and longitudinal stability over a 2-year period. The performance of all MFPR models plateaued at sample sizes exceeding 3000 study participants. This model can inform about the biological and behavioural implications of deviations from typical age-related neuroanatomical changes and support future study designs. The model and scripts described here are freely available through CentileBrain.

Understanding the Dynamics of the Developing Adolescent Brain Through Team Science.

One of the major goals for research on adolescent development is to identify the optimal conditions for adolescents to grow up in a complex social world and to understand individual differences in these trajectories. Based on influential theoretical and empirical work in this field, achieving this goal requires a detailed understanding of the social context in which neural and behavioral development takes place, along with longitudinal measurements at multiple levels (e.g., genetic, hormonal, neural, behavioral). In this perspectives article, we highlight the promising role of team science in achieving this goal. To illustrate our point, we describe meso (peer relations) and micro (social learning) approaches to understand social development in adolescence as crucial aspects of adolescent mental health. Finally, we provide an overview of how our team has extended our collaborations beyond scientific partners to multiple societal partners for the purpose of informing and including policymakers, education and health professionals, as well as adolescents themselves when conducting and communicating research.

Beyond the average brain: individual differences in social brain development are associated with friendship quality.

We tested whether adolescents differ from each other in the structural development of the social brain and whether individual differences in social brain development predicted variability in friendship quality development. Adolescents (N = 299, Mage T1 = 13.98 years) were followed across three biannual waves. We analysed self-reported friendship quality with the best friend at T1 and T3, and bilateral measures of surface area and cortical thickness of the medial prefrontal cortex (mPFC), posterior superior temporal sulcus (pSTS), temporoparietal junction (TPJ) and precuneus across all waves. At the group level, growth curve models confirmed non-linear decreases of surface area and cortical thickness in social brain regions. We identified substantial individual differences in levels and change rates of social brain regions, especially for surface area of the mPFC, pSTS and TPJ. Change rates of cortical thickness varied less between persons. Higher levels of mPFC surface area and cortical thickness predicted stronger increases in friendship quality over time. Moreover, faster cortical thinning of mPFC surface area predicted a stronger increase in friendship quality. Higher levels of TPJ cortical thickness predicted lower friendship quality. Together, our results indicate heterogeneity in social brain development and how this variability uniquely predicts friendship quality development.

Sex differences and brain development during puberty and adolescence.

Sex differences in behavior, and whether these behavioral differences are related to sex differences in brain development, has been a longstanding topic of debate. Presumably, sex differences can provide critically important leads for explaining the etiology of various illnesses that show (i) large sex differences in prevalence and (ii) have an origin before or during adolescence. The general aim of this chapter is to provide an overview of scientific studies on sex differences in normative brain and behavioral development across puberty and adolescence, including the (sex) hormone-driven transition phase of puberty. Moreover, we describe the literature on brain and behavioral development in gender dysphoria, a severe and persistent incongruence between the self-identified gender and the assigned sex at birth. From the literature it becomes clear there is evidence for a specific link between pubertal maturation and developmental changes in arousal, motivation, and emotion. However, this link is rather similar between boys and girls. Moreover, although there is substantial evidence for sex differences in mean brain structure, these have not always been linked to sex differences in behavior, cognition, or psychopathology. Furthermore, there is little evidence for sex differences in brain development and thus, studies so far have been unable to explain sex differences in cognition. Suggestions for future research and methodologic considerations are provided.