Association between structural brain network efficiency and intelligence increases during adolescence.

Adolescence represents an important period during which considerable changes in the brain take place, including increases in integrity of white matter bundles, and increasing efficiency of the structural brain network. A more efficient structural brain network has been associated with higher intelligence. Whether development of structural network efficiency is related to intelligence, and if so to which extent genetic and environmental influences are implicated in their association, is not known. In a longitudinal study, we mapped FA-weighted efficiency of the structural brain network in 310 twins and their older siblings at an average age of 10, 13, and 18 years. Age-trajectories of global and local FA-weighted efficiency were related to intelligence. Contributions of genes and environment were estimated using structural equation modeling. Efficiency of brain networks changed in a non-linear fashion from childhood to early adulthood, increasing between 10 and 13 years, and leveling off between 13 and 18 years. Adolescents with higher intelligence had higher global and local network efficiency. The dependency of FA-weighted global efficiency on IQ increased during adolescence (rph =0.007 at age 10; 0.23 at age 18). Global efficiency was significantly heritable during adolescence (47% at age 18). The genetic correlation between intelligence and global and local efficiency increased with age; genes explained up to 87% of the observed correlation at age 18. In conclusion, the brain's structural network differentiates depending on IQ during adolescence, and is under increasing influence of genes that are also associated with intelligence as it develops from late childhood to adulthood.

Development of the brain's structural network efficiency in early adolescence: A longitudinal DTI twin study.

The brain is a network and our intelligence depends in part on the efficiency of this network. The network of adolescents differs from that of adults suggesting developmental changes. However, whether the network changes over time at the individual level and, if so, how this relates to intelligence, is unresolved in adolescence. In addition, the influence of genetic factors in the developing network is not known. Therefore, in a longitudinal study of 162 healthy adolescent twins and their siblings (mean age at baseline 9.9 [range 9.0-15.0] years), we mapped local and global structural network efficiency of cerebral fiber pathways (weighted with mean FA and streamline count) and assessed intelligence over a three-year interval. We find that the efficiency of the brain's structural network is highly heritable (locally up to 74%). FA-based local and global efficiency increases during early adolescence. Streamline count based local efficiency both increases and decreases, and global efficiency reorganizes to a net decrease. Local FA-based efficiency was correlated to IQ. Moreover, increases in FA-based network efficiency (global and local) and decreases in streamline count based local efficiency are related to increases in intellectual functioning. Individual changes in intelligence and local FA-based efficiency appear to go hand in hand in frontal and temporal areas. More widespread local decreases in streamline count based efficiency (frontal cingulate and occipital) are correlated with increases in intelligence. We conclude that the teenage brain is a network in progress in which individual differences in maturation relate to level of intellectual functioning.

Longitudinal development of hormone levels and grey matter density in 9 and 12-year-old twins.

Puberty is characterized by major changes in hormone levels and structural changes in the brain. To what extent these changes are associated and to what extent genes or environmental influences drive such an association is not clear. We acquired circulating levels of luteinizing hormone, follicle stimulating hormone (FSH), estradiol and testosterone and magnetic resonance images of the brain from 190 twins at age 9 [9.2 (0.11) years; 99 females/91 males]. This protocol was repeated at age 12 [12.1 (0.26) years] in 125 of these children (59 females/66 males). Using voxel-based morphometry, we tested whether circulating hormone levels are associated with grey matter density in boys and girls in a longitudinal, genetically informative design. In girls, changes in FSH level between the age of 9 and 12 positively associated with changes in grey matter density in areas covering the left hippocampus, left (pre)frontal areas, right cerebellum, and left anterior cingulate and precuneus. This association was mainly driven by environmental factors unique to the individual (i.e. the non-shared environment). In 12-year-old girls, a higher level of circulating estradiol levels was associated with lower grey matter density in frontal and parietal areas. This association was driven by environmental factors shared among the members of a twin pair. These findings show a pattern of physical and brain development going hand in hand.

Genetic associations between intelligence and cortical thickness emerge at the start of puberty.

Cognitive abilities are related to (changes in) brain structure during adolescence and adulthood. Previous studies suggest that associations between cortical thickness and intelligence may be different at different ages. As both intelligence and cortical thickness are heritable traits, the question arises whether the association between cortical thickness development and intelligence is due to genes influencing both traits. We study this association in a longitudinal sample of young twins. Intelligence was assessed by standard IQ tests at age 9 in 224 twins, 190 of whom also underwent structural magnetic resonance imaging (MRI). Three years later at age 12, 177/125 twins returned for a follow-up measurement of intelligence/MRI scanning, respectively. We investigated whether cortical thickness was associated with intelligence and if so, whether this association was driven by genes. At age 9, there were no associations between cortical thickness and intelligence. At age 12, a negative relationship emerged. This association was mainly driven by verbal intelligence, and manifested itself most prominently in the left hemisphere. Cortical thickness and intelligence were explained by the same genes. As a post hoc analysis, we tested whether a specific allele (rs6265; Val66Met in the BDNF gene) contributed to this association. Met carriers showed lower intelligence and a thicker cortex, but only the association between the BDNF genotype and cortical thickness in the left superior parietal gyrus reached significance. In conclusion, it seems that brain areas contributing to (verbal) intellectual performance are specializing under the influence of genes around the onset of puberty.

Heritability of volumetric brain changes and height in children entering puberty.

The human brain undergoes structural changes in children entering puberty, while simultaneously children increase in height. It is not known if brain changes are under genetic control, and whether they are related to genetic factors influencing the amount of overall increase in height. Twins underwent magnetic resonance imaging brain scans at age 9 (N = 190) and 12 (N = 125). High heritability estimates were found at both ages for height and brain volumes (49-96%), and high genetic correlation between ages were observed (r(g) > 0.89). With increasing age, whole brain (+1.1%), cerebellum (+4.2%), cerebral white matter (+5.1%), and lateral ventricle (+9.4%) volumes increased, and third ventricle (-4.0%) and cerebral gray matter (-1.6%) volumes decreased. Children increased on average 13.8 cm in height (9.9%). Genetic influences on individual difference in volumetric brain and height changes were estimated, both within and across traits. The same genetic factors influenced both cerebral (20% heritable) and cerebellar volumetric changes (45%). Thus, the extent to which changes in cerebral and cerebellar volumes are heritable in children entering puberty are due to the same genes that influence change in both structures. The increase in height was heritable (73%), and not associated with cerebral volumetric change, but positively associated with cerebellar volume change (r(p) = 0.24). This association was explained by a genetic correlation (r(g) = 0.48) between height and cerebellar change. Brain and body each expand at their own pace and through separate genetic pathways. There are distinct genetic processes acting on structural brain development, which cannot be explained by genetic increase in height.

Genetic control of functional brain network efficiency in children.

The human brain is a complex network of interconnected brain regions. In adulthood, the brain's network was recently found to be under genetic influence. However, the extent to which genes influence the functional brain network early in development is not yet known. We report on the heritability of functional brain efficiency during early brain development. Using a twin design, young children underwent resting-state functional magnetic resonance imaging brain scans (N=86 from 21MZ and 22DZ twin-pairs, age=12 years). Functional connectivity, defined as the temporal dependency of neuronal activation patterns of anatomically separated brain regions, was explored using graph theory and its heritability was examined using structural equation modeling. Our findings suggest that 'global efficiency of communication' among brain regions is under genetic control (h² lambda=42%), irrespectively of the total number of brain connections (connectivity density). In addition, no influence of genes or common environment to local clustering (gamma) was found, suggesting a less pronounced effect of genes on local information segregation. Thus our findings suggest that a set of genes is shaping the underlying architecture of functional brain communication during development.

Brain SCALE: brain structure and cognition: an adolescent longitudinal twin study into the genetic etiology of individual differences.

From childhood into adolescence, the child's brain undergoes considerable changes in both structure and function. Twin studies are of great value to explore to what extent genetic and environmental factors explain individual differences in brain development and cognition. In The Netherlands, we initiated a longitudinal study in which twins, their siblings and their parents are assessed at three year intervals. The participants were recruited from The Netherlands Twin Register (NTR) and at baseline consisted of 112 families, with 9-year-old twins and an older sibling. Three years later, 89 families returned for follow-up assessment. Data collection included psychometric IQ tests, a comprehensive neuropsychological testing protocol, and parental and self-ratings of behavioral and emotional problems. Physical maturation was measured through assessment of Tanner stages. Hormonal levels (cortisol, luteinizing hormone, follicle-stimulating hormone, testosterone, and estrogens) were assessed in urine and saliva. Brain scans were acquired using 1.5 Tesla Magnetic Resonance Imaging (MRI), which provided volumetric measures and measures of cortical thickness. Buccal swabs were collected for DNA isolation for future candidate gene and genome-wide analysis studies. This article gives an overview of the study and the main findings. Participants will return for a third assessment when the twins are around 16 years old. Longitudinal twin-sibling studies that map brain development and cognitive function at well-defined ages aid in the understanding of genetic influences on normative brain development.

White matter development in early puberty: a longitudinal volumetric and diffusion tensor imaging twin study.

White matter microstructure and volume show synchronous developmental patterns in children. White matter volume increases considerably during development. Fractional anisotropy, a measure for white matter microstructural directionality, also increases with age. Development of white matter volume and development of white matter microstructure seem to go hand in hand. The extent to which the same or different genetic and/or environmental factors drive these two aspects of white matter maturation is currently unknown. We mapped changes in white matter volume, surface area and diffusion parameters in mono- and dizygotic twins who were scanned at age 9 (203 individuals) and again at age 12 (126 individuals). Over the three-year interval, white matter volume (+6.0%) and surface area (+1.7%) increased, fiber bundles expanded (most pronounced in the left arcuate fasciculus and splenium), and fractional anisotropy increased (+3.0%). Genes influenced white matter volume (heritability ~85%), surface area (~85%), and fractional anisotropy (locally 7% to 50%) at both ages. Finally, volumetric white matter growth was negatively correlated with fractional anisotropy increase (r = -0.62) and this relationship was driven by environmental factors. In children who showed the most pronounced white matter growth, fractional anisotropy increased the least and vice-versa. Thus, white matter development in childhood may reflect a process of both expansion and fiber optimization.

Heritability of verbal and performance intelligence in a pediatric longitudinal sample.

The longitudinal stability of IQ is well-documented as is its increasing heritability with age. In a longitudinal twin study, we addressed the question to what extent heritability and stability differ for full scale (FSIQ), verbal (VIQ), and performance IQ (PIQ) in childhood (age 9-11 years), and early adolescence (age 12-14 years). Genetic and environmental influences and correlations over time were evaluated in an extended twin design, including Dutch twins and their siblings. Intelligence was measured by the Wechsler Intelligence Scale for children - Third version (WISC III). Heritability in childhood was 34% for FSIQ, 37% for VIQ, and 64% for PIQ, and increased up to 65%, 51%, and 72% in early adolescence. The influence of common environment decreased between childhood and early adolescence from explaining 43% of the phenotypic variance for FSIQ to 18% and from 42% for VIQ to 26%. For PIQ common environmental influences did not play a role, either in childhood or in early adolescence. The stability in FSIQ and VIQ across the 3-year interval (r(p)) was .72 for both measures and was explained by genetic and common environmental correlations across time (FSIQ, r(g) = .96, r(c) = 1.0; VIQ, r(g) =.78, r(c) = 1.0). Stability of PIQ (r(p) =.56) was lower and was explained by genetic influences (r(g) = .90). These results confirm the robust findings of increased heritability of general cognitive abilities during the transition from childhood to adolescence. Interestingly, results for PIQ differ from those for FSIQ and VIQ, in that no significant contribution of environment shared by siblings from the same family was detected.

Genetic influence demonstrated for MEG-recorded somatosensory evoked responses.

We tested for a genetic influence on magnetoencephalogram (MEG)-recorded somatosensory evoked fields (SEFs) in 20 monozygotic (MZ) and 14 dizygotic (DZ) twin pairs. Previous electroencephalogram (EEG) studies that demonstrated a genetic contribution to evoked responses generally focused on characteristics of representative brain potentials. Here we demonstrate significantly smaller amplitude differences within MZ compared to DZ twin pairs for the complete SEF time series (across left and right hand SEFs: 0.37 vs. 0.60 pT(2) and 0.28 vs. 0.39 pT(2) for primary [SI] and secondary [SII] sensory cortex activation) and higher MZ than DZ wave shape correlations (.71 vs. .44 and .52 vs. .35 for SI and SII activation). Our findings indicate a genetic influence on MEG-recorded evoked brain activity and also confirm our recent conclusion (van 't Ent, van Soelen, Stam, De Geus, & Boomsma, 2009) that higher MZ resemblance for EEG amplitudes is not trivially reflecting greater MZ concordance in intervening biological tissues.

Effects of gestational age and birth weight on brain volumes in healthy 9 year-old children.

OBJECTIVE: To assess the effects of gestational age and birth weight on brain volumes in a population-based sample of normal developing children at the age of 9 years. STUDY DESIGN: A total of 192 children from twin births were included in the analyses. Data on gestational age and birth weight were reported shortly after birth. Total brain, cerebellum, cerebrum, gray and white matter, and lateral ventricle volumes were assessed with structural magnetic resonance imaging. The Wechsler Intelligence Scale for Children-III was administered to assess general cognitive abilities. Structural equation modeling was used to analyze the effects of gestational age and birth weight on brain volumes. RESULTS: Shorter gestational age was associated with a relatively smaller cerebellar volume (P = .002). This effect was independent of IQ scores. Lower birth weight was associated with lower IQ score (P = .03). Birth weight was not associated with brain volumes. CONCLUSION: The effect of gestational age on cerebellar volume is not limited to children with very premature birth or very low birth weight, but is also present in children born >32 weeks of gestation and with birth weight >1500 g.

Strong resemblance in the amplitude of oscillatory brain activity in monozygotic twins is not caused by "trivial" similarities in the composition of the skull.

Previous twin studies have shown strong heritability of electroencephalogram amplitude characteristics, such as power spectra. However, it has been suggested that these high heritabilities may reflect "trivial" twin resemblance in intervening tissues such as the skull. Here we demonstrate strong monozygotic twin correlation (0.79 < r < 0.88) of eyes-closed resting-state magnetoencephalogram power, which is insensitive to intervening tissues. These results confirm that brain activity itself is highly heritable.