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The brain is a complex dynamic system of functionally connected regions. Graph theory has been successfully used to describe the organization of such dynamic systems. Recent resting-state fMRI studies have suggested that inter-regional functional connectivity shows a small-world topology, indicating an organization of the brain in highly clustered sub-networks, combined with a high level of global connectivity. In addition, a few studies have investigated a possible scale-free topology of the human brain, but the results of these studies have been inconclusive. These studies have mainly focused on inter-regional connectivity, representing the brain as a network of brain regions, requiring an arbitrary definition of such regions. However, using a voxel-wise approach allows for the model-free examination of both inter-regional as well as intra-regional connectivity and might reveal new information on network organization. Especially, a voxel-based study could give information about a possible scale-free organization of functional connectivity in the human brain. Resting-state 3 Tesla fMRI recordings of 28 healthy subjects were acquired and individual connectivity graphs were formed out of all cortical and sub-cortical voxels with connections reflecting inter-voxel functional connectivity. Graph characteristics from these connectivity networks were computed. The clustering-coefficient of these networks turned out to be much higher than the clustering-coefficient of comparable random graphs, together with a short average path length, indicating a small-world organization. Furthermore, the connectivity distribution of the number of inter-voxel connections followed a power-law scaling with an exponent close to 2, suggesting a scale-free network topology. Our findings suggest a combined small-world and scale-free organization of the functionally connected human brain. The results are interpreted as evidence for a highly efficient organization of the functionally connected brain, in which voxels are mostly connected with their direct neighbors forming clustered sub-networks, which are held together by a small number of highly connected hub-voxels that ensure a high level of overall connectivity.

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.

Genetic influences on behavior are complex and, as such, the effect of any single gene is likely to be modest. Neuroimaging measures may serve as a biological intermediate phenotype to investigate the effect of genes on human behavior. In particular, it is possible to constrain investigations by prior knowledge of gene characteristics and by including samples of subjects where the distribution of phenotypic variance is both wide and under heritable influences. Here, we use this approach to show a dissociation between the effects of two dopamine genes that are differentially expressed in the brain. We show that the DAT1 gene, a gene expressed predominantly in the basal ganglia, preferentially influences caudate volume, whereas the DRD4 gene, a gene expressed predominantly in the prefrontal cortex, preferentially influences prefrontal gray matter volume in a sample of subjects including subjects with ADHD, their unaffected siblings, and healthy controls. This demonstrates that, by constraining our investigations by prior knowledge of gene expression, including samples in which the distribution of phenotypic variance is wide and under heritable influences, and by using intermediate phenotypes, such as neuroimaging, we may begin to map out the pathways by which genes influence behavior.

During development from childhood to adulthood the human brain undergoes considerable thinning of the cerebral cortex. Whether developmental cortical thinning is influenced by genes and if independent genetic factors influence different parts of the cortex is not known. Magnetic resonance brain imaging was done in twins at age 9 (N=190) and again at age 12 (N=125; 113 repeated measures) to assess genetic influences on changes in cortical thinning. We find considerable thinning of the cortex between over this three year interval (on average 0.05mm; 1.5%), particularly in the frontal poles, and orbitofrontal, paracentral, and occipital cortices. Cortical thinning was highly heritable at age 9 and age 12, and the degree of genetic influence differed for the various areas of the brain. One genetic factor affected left inferior frontal (Broca's area), and left parietal (Wernicke's area) thinning; a second factor influenced left anterior paracentral (sensory–motor) thinning. Two factors influenced cortical thinning in the frontal poles: one of decreasing influence over time, and another independent genetic factor emerging at age 12 in left and right frontal poles. Thus, thinning of the cerebral cortex is heritable in children between the ages 9 and 12. Furthermore, different genetic factors are responsible for variation in cortical thickness at ages 9 and 12, with independent genetic factors acting on cortical thickness across time and between various brain areas during childhood brain development. ►A large number of twins were scanned at age 9 (N=190) and again at 12 (N=125). ►Genetic factors acting on cortical thickness were independent across ages 9 and 12. ►Considerable thinning of the cortex was found between the ages 9 and 12. ►Cortical thinning was under genetic control. ►Genetic factors acting on cortical change were independent across brain areas.

To study the underpinnings of individual differences in subjective well-being (SWB), we tested for associations of SWB with subcortical brain volumes in a dataset of 724 twins and siblings. For significant SWB-brain associations we probed for causal pathways using Mendelian Randomization (MR) and estimated genetic and environmental contributions from twin modeling. Another independent measure of genetic correlation was obtained from linkage disequilibrium (LD) score regression on published genome-wide association summary statistics. Our results indicated associations of SWB with hippocampal volumes but not with volumes of the basal ganglia, thalamus, amygdala, or nucleus accumbens. The SWB-hippocampus relations were nonlinear and characterized by lower SWB in subjects with relatively smaller hippocampal volumes compared to subjects with medium and higher hippocampal volumes. MR provided no evidence for an SWB to hippocampal volume or hippocampal volume to SWB pathway. This was in line with twin modeling and LD-score regression results which indicated non-significant genetic correlations. We conclude that low SWB is associated with smaller hippocampal volume, but that genes are not very important in this relationship. Instead other etiological factors, such as exposure to stress and stress hormones, may exert detrimental effects on SWB and the hippocampus to bring about the observed association.

Global brain abnormalities such as brain volume loss and grey- and white-matter deficits are consistently reported in first-episode schizophrenia patients and may already be detectable in the very early stages of the illness. Whether these changes are dependent on medication use or related to intelligence quotient (IQ) is still debated. Magnetic resonance imaging scans were obtained for 20 medication-naive patients with first-episode schizophrenia and 26 matched healthy subjects. Volume measures of total brain grey and white matter, third and lateral ventricles and cortical thickness/surface were obtained. Differences between the groups were investigated, taking into account the effect of intelligence. Medication-naive patients showed statistically significant reductions in whole-brain volume and cerebral grey- and white-matter volume together with lateral ventricle enlargement compared to healthy subjects. IQ was significantly lower in patients compared to controls and was positively associated with brain and white-matter volume in the whole group. No significant differences in cortical thickness were found between the groups but medication-naive patients had a significantly smaller surface in the left superior temporal pole, Heschl's gyrus and insula compared to controls. Our findings suggest that brain volume loss is present at illness onset, and can be explained by the reduced surface of the temporal and insular cortex. These abnormalities are not related to medication, but IQ.

Progressive brain volume changes have been reported in first-episode schizophrenia, but their relationship to the disease process or to other factors remains unclear. We examined such changes in the first year of illness, and related them to 5-year outcome. Progressive brain volume changes, in particular of grey matter, during the first year of illness were found to be significantly associated with clinical and functional outcome 5 years after the first episode. These findings suggest that early dynamic brain volume changes are related to the disease process and predict the longer-term outcome of schizophrenia.

The risk of developing bipolar disorder (BD) has been linked to structural brain abnormalities. The degree to which genes and environment influence the association of BD with cortical surface area remains to be elucidated. In this twin study, genetic and environmental contributions to the association between liability to develop BD and surface area, thickness and volume of the cortex were examined. The study cohort included 44 affected monozygotic (nine concordant, 12 discordant) and dizygotic (four concordant, 19 discordant) twin pairs, and seven twins from incomplete discordant monozygotic and dizygotic discordant twin pairs. In addition, 37 monozygotic and 24 dizygotic healthy control twin pairs, and six twins from incomplete monozygotic and dizygotic control pairs were included. Genetic liability to develop BD was associated with a larger cortical surface in limbic and parietal regions, and a thicker cortex in central and parietal regions. Environmental factors related to BD were associated with larger medial frontal, parietal and limbic, and smaller orbitofrontal surfaces. Furthermore, thinner frontal, limbic and occipital cortex, and larger frontal and parietal, and smaller orbitofrontal volumes were also associated with environmental factors related to BD. Our results suggest that unique environmental factors play a prominent role in driving the associations between liability to develop BD and cortical measures, particularly those involving cortical thickness. Further evaluation of their influence on the surface and thickness of the cortical mantle is recommended. In addition, cortical volume appeared to be primarily dependent on surface and not thickness.

Intellectual deficits are commonly found in schizophrenia patients. These intellectual deficits have been found to be heritable. However, whether the intellectual deficits change over time and, if so, whether the change is related with an increased genetic risk for the disease are not known. We investigated change of intelligence quotient (IQ) in a twin sample of chronically ill schizophrenia patients, the discordant co-twins and healthy controls during a follow-up period of 5 years. A total of 52 twins completed two IQ assessments: nine patients [three monozygotic (MZ) and six dizygotic (DZ)], 10 unaffected co-twins (three MZ and seven DZ) and 33 healthy control twins (21 MZ and 12 DZ). A significant interaction effect over time was found between IQ measurement and illness (F=4.22, df=1, p<0.05), indicating that change in IQ over time is significantly different between the groups. A stable course in IQ over time was found in the patients with schizophrenia (mean IQ from 109.78 at baseline to 108.44 at follow-up) relative to both the healthy control twins who showed a small increase (from 114.61 at baseline to 119.18 at follow-up) (t=2.06, p<0.05) and the unaffected co-twins (from 111.60 to 117.60, t=-2.32, p<0.05). IQ change in the unaffected co-twins of schizophrenia patients was comparable with that in healthy control twins (t=-0.49, p=0.63). Patients with schizophrenia in the chronic phase of the disease, but not the discordant co-twins, show a lack of increase in IQ, which is probably due to environmental (non-genetic) factors related to the disease.

Background. Structural brain volume abnormalities are among the most extensively studied endophenotypes in schizophrenia. Bivariate genetic model fitting (adjusted to account for selection) was used to quantify the genetic relationship between schizophrenia and brain volumes and to estimate the heritability of these volumes. Method. We demonstrated by simulation that the adjusted genetic model produced unbiased estimates for endophenotype heritability and the genetic and environmental correlations. The model was applied to brain volumes (whole brain, hippocampus, third and lateral ventricles) in a sample of 14 monozygotic (MZ) twin pairs concordant for schizophrenia, 10 MZ discordant pairs, 17 MZ control pairs, 22 discordant sibling pairs, three concordant sibling pairs, and 114 healthy control subjects. Results. Whole brain showed a substantial heritability (88%) and lateral ventricles substantial common environmental effects (67%). Whole brain showed a significant genetic correlation with schizophrenia, whereas lateral ventricles showed a significant individual specific correlation with schizophrenia. There were significant familial effects for hippocampus and third ventricle, but the analyses could not resolve whether these were genetic or environmental in origin (around 30% each). Conclusions. Using genetic model fitting on twin and sibling data we have demonstrated differential sources of covariation between schizophrenia and brain volumes, genetic in the case of whole brain volume and individual specific environment in the case of lateral ventricles.