Rhoshel Lenroot

Intellectual ability and cortical development in children and adolescents

Children who are adept at any one of the three academic ‘Rs (reading, writing and arithmetic) tend to be good at the others, and grow into adults who are similarly skilled at diverse intellectually demanding activities. Determining the neuroanatomical correlates of this relatively stable individual trait of general intelligence has proved difficult, particularly in the rapidly developing brains of children and adolescents. Here we demonstrate that the trajectory of change in the thickness of the cerebral cortex, rather than cortical thickness itself, is most closely related to level of intelligence. Using a longitudinal design, we find a marked developmental shift from a predominantly negative correlation between intelligence and cortical thickness in early childhood to a positive correlation in late childhood and beyond. Additionally, level of intelligence is associated with the trajectory of cortical development, primarily in frontal regions implicated in the maturation of intelligent activity. More intelligent children demonstrate a particularly plastic cortex, with an initial accelerated and prolonged phase of cortical increase, which yields to equally vigorous cortical thinning by early adolescence. This study indicates that the neuroanatomical expression of intelligence in children is dynamic.

Brain development in children and adolescents: Insights from anatomical magnetic resonance imaging

Advances in neuroimaging have ushered in a new era of developmental neuroscience. Magnetic resonance imaging (MRI) is particularly well suited for pediatric studies because it does not use ionizing radiation which enables safe longitudinal scans of healthy children. Key findings related to brain anatomical changes during childhood and adolescent are increases in white matter volumes throughout the brain and regionally specific inverted U-shaped trajectories of gray matter volumes. Brain morphometric measures are highly variable across individuals and there is considerable overlap amongst groups of boys versus girls, typically developing versus neuropsychiatric populations, and young versus old. Studies are ongoing to explore the influences of genetic and environmental factors on developmental trajectories.

Sexual dimorphism of brain developmental trajectories during childhood and adolescence

Human total brain size is consistently reported to be approximately 8-10% larger in males, although consensus on regionally specific differences is weak. Here, in the largest longitudinal pediatric neuroimaging study reported to date (829 scans from 387 subjects, ages 3 to 27 years), we demonstrate the importance of examining size-by-age trajectories of brain development rather than group averages across broad age ranges when assessing sexual dimorphism. Using magnetic resonance imaging (MRI) we found robust male/female differences in the shapes of trajectories with total cerebral volume peaking at age 10.5 in females and 14.5 in males. White matter increases throughout this 24-year period with males having a steeper rate of increase during adolescence. Both cortical and subcortical gray matter trajectories follow an inverted U shaped path with peak sizes 1 to 2 years earlier in females. These sexually dimorphic trajectories confirm the importance of longitudinal data in studies of brain development and underline the need to consider sex matching in studies of brain development.

Mapping anatomical correlations across cerebral cortex (MACACC) using cortical thickness from MRI

We introduce MACACC-Mapping Anatomical Correlations Across Cerebral Cortex-to study correlated changes within and across different cortical networks. The principal topic of investigation is whether the thickness of one area of the cortex changes in a statistically correlated fashion with changes in thickness of other cortical regions. We further extend these methods by introducing techniques to test whether different population groupings exhibit significantly varying MACACC patterns. The methods are described in detail and applied to a normal childhood development population (n = 292), and show that association cortices have the highest correlation strengths. Taking Brodmann Area (BA) 44 as a seed region revealed MACACC patterns strikingly similar to tractography maps obtained from diffusion tensor imaging. Furthermore, the MACACC map of BA 44 changed with age, older subjects featuring tighter correlations with BA 44 in the anterior portions of the superior temporal gyri. Lastly, IQ-dependent MACACC differences were investigated, revealing steeper correlations between BA 44 and multiple frontal and parietal regions for the higher IQ group, most significantly (t = 4.0) in the anterior cingulate.

Disrupted modularity and local connectivity of brain functional networks in childhood-onset schizophrenia.

Modularity is a fundamental concept in systems neuroscience, referring to the formation of local cliques or modules of densely intra-connected nodes that are sparsely inter-connected with nodes in other modules. Topological modularity of brain functional networks can quantify theoretically anticipated abnormality of brain network community structure – so-called dysmodularity – in developmental disorders such as childhood-onset schizophrenia (COS). We used graph theory to investigate topology of networks derived from resting-state fMRI data on 13 COS patients and 19 healthy volunteers. We measured functional connectivity between each pair of 100 regional nodes, focusing on wavelet correlation in the frequency interval 0.05–0.1 Hz, then applied global and local thresholding rules to construct graphs from each individual association matrix over the full range of possible connection densities. We show how local thresholding based on the minimum spanning tree facilitates group comparisons of networks by forcing the connectedness of sparse graphs. Threshold-dependent graph theoretical results are compatible with the results of a k-means unsupervised learning algorithm and a multi-resolution (spin glass) approach to modularity, both of which also find community structure but do not require thresholding of the association matrix. In general modularity of brain functional networks was significantly reduced in COS, due to a relatively reduced density of intra-modular connections between neighboring regions. Other network measures of local organization such as clustering were also decreased, while complementary measures of global efficiency and robustness were increased, in the COS group. The group differences in complex network properties were mirrored by differences in simpler statistical properties of the data, such as the variability of the global time series and the internal homogeneity of the time series within anatomical regions of interest.

Sex differences in the adolescent brain

Adolescence is a time of increased divergence between males and females in physical characteristics, behavior, and risk for psychopathology. Here we will review data regarding sex differences in brain structure and function during this period of the lifespan. The most consistent sex difference in brain morphometry is the 9-12% larger brain size that has been reported in males. Individual brain regions that have most consistently been reported as different in males and females include the basal ganglia, hippocampus, and amygdala. Diffusion tensor imaging and magnetization transfer imaging studies have also shown sex differences in white matter development during adolescence. Functional imaging studies have shown different patterns of activation without differences in performance, suggesting male and female brains may use slightly different strategies for achieving similar cognitive abilities. Longitudinal studies have shown sex differences in the trajectory of brain development, with females reaching peak values of brain volumes earlier than males. Although compelling, these sex differences are present as group averages and should not be taken as indicative of relative capacities of males or females.

Differences in Genetic and Environmental Influences on the Human Cerebral Cortex Associated With Development During Childhood and Adolescence

In this report, we present the first regional quantitative analysis of age‐related differences in the heritability of cortical thickness using anatomic MRI with a large pediatric sample of twins, twin siblings, and singletons (n = 600, mean age 11.1 years, range 5–19). Regions of primary sensory and motor cortex, which develop earlier, both phylogenetically and ontologically, show relatively greater genetic effects earlier in childhood. Later developing regions within the dorsal prefrontal cortex and temporal lobes conversely show increasingly prominent genetic effects with maturation. The observation that regions associated with complex cognitive processes such as language, tool use, and executive function are more heritable in adolescents than children is consistent with previous studies showing that IQ becomes increasingly heritable with maturity(Plomin et al. 1997 : Psychol Sci 8:442–447). These results suggest that both the specific cortical region and the age of the population should be taken into account when using cortical thickness as an intermediate phenotype to link genes, environment, and behavior. Hum Brain Mapp, 2009.

Cerebellum development during childhood and adolescence: a longitudinal morphometric MRI study.

In addition to its well-established role in balance, coordination, and other motor skills, the cerebellum is increasingly recognized as a prominent contributor to a wide array of cognitive and emotional functions. Many of these capacities undergo dramatic changes during childhood and adolescence. However, accurate characterization of co-occurring anatomical changes has been hindered by lack of longitudinal data and methodologic challenges in quantifying subdivisions of the cerebellum. In this study we apply an innovative image analysis technique to quantify total cerebellar volume and 11 subdivisions (i.e. anterior, superior posterior, and inferior posterior lobes, corpus medullare, and three vermal regions) from anatomic brain MRI scans from 25 healthy females and 25 healthy males aged 5-24 years, each of whom was scanned at least three times at approximately 2-year intervals. Total cerebellum volume followed an inverted U shaped developmental trajectory peaking at age 11.8 years in females and 15.6 years in males. Cerebellar volume was 10% to 13% larger in males depending on the age of comparison and the sexual dimorphism remained significant after covarying for total brain volume. Subdivisions of the cerebellum had distinctive developmental trajectories with more phylogenetically recent regions maturing particularly late. The cerebellums unique protracted developmental trajectories, sexual dimorphism, preferential vulnerability to environmental influences, and frequent implication in childhood onset disorders such as autism and ADHD make it a prime target for pediatric neuroimaging investigations.

Puberty-related influences on brain development

Puberty is a time of striking changes in cognition and behavior. To indirectly assess the effects of puberty-related influences on the underlying neuroanatomy of these behavioral changes we will review and synthesize neuroimaging data from typically developing children and adolescents and from those with anomalous hormone or sex chromosome profiles. The trajectories (size by age) of brain morphometry differ between boys and girls, with girls generally reaching peak gray matter thickness 1-2 years earlier than boys. Both boys and girls with congenital adrenal hyperplasia (characterized by high levels of intrauterine testosterone), have smaller amygdala volume but the brain morphometry of girls with CAH did not otherwise significantly differ from controls. Subjects with XXY have gray matter reductions in the insula, temporal gyri, amygdala, hippocampus, and cingulate-areas consistent with the language-based learning difficulties common in this group.

Anatomical Brain Magnetic Resonance Imaging of Typically Developing Children and Adolescents.

Many psychiatric disorders, including some with adult onset such as schizophrenia, are increasingly being conceptualized as stemming from anomalies of neurodevelopment. To explore neurodevelopmental hypotheses of illness, it is useful to have well-characterized data regarding typical maturation to serve as a “yardstick” from which to assess possible deviations. Studies of typical development, and the influences on that development, may also unveil the timing and mechanisms of brain maturation guiding the way for novel interventions. In this overview, we will touch on methodological issues relevant to magnetic resonance imaging (MRI) studies of brain anatomy, summarize MRI findings of neuroanatomic changes during childhood and adolescence, and discuss possible influences on brain development trajectories. As indicated in the previous articles of this series, one of the first steps in measuring brain morphometric characteristics in a conventional anatomic MRI is to classify (or “segment”) individual voxels (the smallest elements of different MRI signals—usually approximately 1 mL) as corresponding to CSF, white matter (WM), or gray matter (GM). Once categorized by tissue type, various parcellations can be performed to derive volumes at the level of lobes (e.g., frontal, temporal, parietal, occipital); regions defined by gyral, sulcal, or GM, WM, and CSF boundaries (e.g., caudate nucleus); or individual voxels. Segmentation and parcellation of MRIs was originally exclusively done by trained individuals outlining particular regions of interest (frequently abbreviated as ROIs) by hand. Although having a highly trained individual manually identify brain regions is considered the closest thing to a “gold standard” available, the time and anatomic expertise necessary for training raters and performing this type of analysis can be prohibitive. This has motivated many laboratories to develop computer algorithms capable of automatically classifying regions of MRI images as belonging to different tissue types and anatomic regions. The rapid progress in this area has made it feasible to perform the type of large scale studies necessary to capture many of the changes associated with typical and atypical brain development. Automated methods have also opened the door to innovative ways of looking at brain structure, such as analyzing the shape and thickness of the cortical sheet. However, the fidelity of automated methods depends on the clarity of the borders between structures, which in turn is determined by a combination of the anatomy of a particular structure and the quality of the MRI image. For example, the amygdala and hippocampus are difficult for automated methods to separate properly because they represent adjacent GM structures. In cases such as these, hand measurements may still be the best approach, although even human raters may need considerable experience before they can consistently identify the borders of such structures on conventional MRI. The data for this overview are largely derived from 387 typically developing subjects (829 scans) participating in an ongoing longitudinal study at the Child Psychiatry Branch of the National Institute of Mental Health. Begun in 1989 by Markus Kruesi, M.D., and Judith Rapoport, M.D., the study design is for participants aged 3 to 30 years to come to the National Institutes of Health at approximately 2-year intervals for brain imaging, psychological and behavioral assessment, and collection of DNA. The emphasis on this single source is not to devalue the many excellent contributions of other investigators but to provide an integrated account from the world’s largest collection of child and adolescent brain MRI scans with data acquired using uniform screening/assessment batteries, the same scanner, and the same methods of image analyses. We have supplemented with references to studies by other laboratories, although a complete review of the field is beyond the scope of this article.