The restorative effect of sleep on waking brain activity remains poorly understood. Previous studies have compared overall neural network characteristics after normal sleep and sleep deprivation. To study whether sleep and sleep deprivation might differentially affect subsequent connectivity characteristics in different brain regions, we performed a within-subject study of resting state brain activity using the graph theory framework adapted for the individual electrode level.In balanced order, we obtained high-density resting state electroencephalography (EEG) in 8 healthy participants, during a day following normal sleep and during a day following total sleep deprivation. We computed topographical maps of graph theoretical parameters describing local clustering and path length characteristics from functional connectivity matrices, based on synchronization likelihood, in five different frequency bands. A non-parametric permutation analysis with cluster correction for multiple comparisons was applied to assess significance of topographical changes in clustering coefficient and path length.
The non-synonymous SNP rs2228145 in the IL6R gene on chromosome 1q21.3 is associated with a wide range of common diseases, including asthma, rheumatoid arthritis, type 1 diabetes and coronary heart disease. We examined the contribution of this functional IL6R gene polymorphism rs2228145 versus other genome-wide SNPs to the variance of sIL-6R levels in blood plasma in a large population-based sample (N ~5,000), and conducted an expression QTL analysis to identify SNPs associated with IL6R gene expression. Based on data from 2,360 twin families, the broad heritability of sIL-6R was estimated at 72 and 51% of the total variance was explained by the functional SNP rs2228145. Converging findings from GWAS, linkage, and GCTA analyses indicate that additional variance of sIL-6R levels can be explained by other variants in the IL6R region, including variants at the 3'-end of IL6R tagged by rs60760897 that are associated with IL6R RNA expression.
Human brain volumes change throughout life, are highly heritable, and have been associated with general cognitive functioning. Cross-sectionally, this association between volume and cognition can largely be attributed to the same genes influencing both traits. We address the question whether longitudinal changes in brain volume or in surface area in young adults are under genetic control and whether these changes are also related to general cognitive functioning. We measured change in brain volume and surface area over a 5-year interval in 176 monozygotic and dizygotic twins and their non-twin siblings aged 19 to 56, using magnetic resonance imaging. Results show that changes in volumes of total brain (mean = -6.4 ml; 0.5% loss), cerebellum (1.4 ml, 1.0% increase), cerebral white matter (4.4 ml, 0.9% increase), lateral ventricles (0.6 ml; 4.8% increase) and in surface area (-19.7 cm(2),1.1% contraction) are heritable (h(2) = 43%; 52%; 29%; 31%; and 33%, respectively). An association between IQ (available for 91 participants) and brain volume change was observed, which was attributed to genes involved in both the variation in change in brain volume and in intelligence. Thus, dynamic changes in brain structure are heritable and may have cognitive significance in adulthood.
We examined the genetic architecture of functional brain connectivity measures in resting state electroencephalographic (EEG) recordings. Previous studies in Dutch twins have suggested that genetic factors are a main source of variance in functional brain connectivity derived from EEG recordings. In addition, qualitative descriptors of the brain network derived from graph analysis - network clustering and average path length - are also heritable traits. Here we replicated previous findings for connectivity, quantified by the synchronization likelihood, and the graph theoretical parameters cluster coefficient and path length in an Australian sample of 16-year-old twins (879) and their siblings (93). Modeling of monozygotic and dizygotic twins and sibling resemblance indicated heritability estimates of the synchronization likelihood (27-74%) and cluster coefficient and path length in the alpha and theta band (40-44% and 23-40% respectively) and path length in the beta band frequency (41%). This corroborates synchronization likelihood and its graph theoretical derivatives cluster coefficient and path length as potential endophenotypes for behavioral traits and neurological disorders.
Human behavior is imperfect. This is notably clear during repetitive tasks in which sequences of errors or deviations from perfect performance result. These errors are not random, but show patterned fluctuations with long-range temporal correlations that are well described using power-law spectra P(f)?1/f(?), where ? is the power-law scaling exponent describing the decay in temporal correlations. The neural basis of temporal correlations in such behaviors is not known. Interestingly, long-range temporal correlations are a hallmark of amplitude fluctuations in resting-state neuronal oscillations. Here, we investigated whether the temporal dynamics in brain and behavior are related. Thirty-nine subjects eyes-open rest EEG was measured. Next, subjects reproduced without feedback a 1 s interval by tapping with their right index finger. In line with previous reports, we found evidence for the presence of long-range temporal correlations both in the amplitude modulation of resting-state oscillations in multiple frequency bands and in the timing-error sequences. Frequency scaling exponents of finger tapping and amplitude modulation of oscillations exhibited large individual differences. Neuronal dynamics of resting-state alpha-band oscillations (9-13 Hz) recorded at precentral sites strongly predicted scaling exponents of tapping behavior. The results suggest that individual variation in resting-state brain dynamics offer a neural explanation for individual variation in the error dynamics of human behavior.
The effects of inbreeding on the health of offspring can be studied by measuring genome-wide autozygosity as the proportion of the genome in runs of homozygosity (F roh) and relate F roh to outcomes such as psychiatric phenotypes. To successfully conduct these studies, the main patterns of variation for genome-wide autozygosity between and within populations should be well understood and accounted for. Within population variation was investigated in the Dutch population by comparing autozygosity between religious and non-religious groups. The Netherlands have a history of societal segregation and assortment based on religious affiliation, which may have increased parental relatedness within religious groups. Religion has been associated with several psychiatric phenotypes, such as major depressive disorder (MDD). We investigated whether there is an association between autozygosity and MDD, and the extent to which this association can be explained by religious affiliation. All F roh analyses included adjustment for ancestry-informative principal components (PCs) and geographic factors. Religious affiliation was significantly associated with autozygosity, showing that F roh has the ability to capture within population differences that are not captured by ancestry-informative PCs or geographic factors. The non-religious group had significantly lower F roh values and significantly more MDD cases, leading to a nominally significant negative association between autozygosity and depression. After accounting for religious affiliation, MDD was not associated with F roh, indicating that the relation between MDD and inbreeding was due to stratification. This study shows how past religious assortment and recent secularization can have genetic consequences in a relatively small country. This warrants accounting for the historical social context and its effects on genetic variation in association studies on psychiatric and other related traits.
The child brain is a small-world network, which is hypothesized to change toward more ordered configurations with development. In graph theoretical studies, comparing network topologies under different conditions remains a critical point. Constructing a minimum spanning tree (MST) might present a solution, since it does not require setting a threshold and uses a fixed number of nodes and edges. In this study, the MST method is introduced to examine developmental changes in functional brain network topology in young children. Resting-state electroencephalography was recorded from 227 children twice at 5 and 7 years of age. Synchronization likelihood (SL) weighted matrices were calculated in three different frequency bands from which MSTs were constructed, which represent constructs of the most important routes for information flow in a network. From these trees, several parameters were calculated to characterize developmental change in network organization. The MST diameter and eccentricity significantly increased, while the leaf number and hierarchy significantly decreased in the alpha band with development. Boys showed significant higher leaf number, betweenness, degree and hierarchy and significant lower SL, diameter, and eccentricity than girls in the theta band. The developmental changes indicate a shift toward more decentralized line-like trees, which supports the previously hypothesized increase toward regularity of brain networks with development. Additionally, girls showed more line-like decentralized configurations, which is consistent with the view that girls are ahead of boys in brain development. MST provides an elegant method sensitive to capture subtle developmental changes in network organization without the bias of network comparison.
Human neuronal circuits undergo life-long functional reorganization with profound effects on cognition and behavior. Well documented prolonged development of anatomical brain structures includes white and gray matter changes that continue into the third decade of life. We investigated resting-state EEG oscillations in 1433 subjects from 5 to 71 years. Neuronal oscillations exhibit scale-free amplitude modulation as reflected in power-law decay of autocorrelations--also known as long-range temporal correlations (LRTC)--which was assessed by detrended fluctuation analysis. We observed pronounced increases in LRTC from childhood to adolescence, during adolescence, and even into early adulthood (?25 years of age) after which the temporal structure stabilized. A principal component analysis of the spatial distribution of LRTC revealed increasingly uniform scores across the scalp. Together, these findings indicate that the scale-free modulation of resting-state oscillations reflects brain maturation, and suggests that scaling analysis may prove useful as a biomarker of pathophysiology in neurodevelopmental disorders such as attention deficit hyperactivity disorder and schizophrenia.
Differential effects of genes and environment can contribute to etiological heterogeneity in schizophrenia. Twins concordant and discordant for schizophrenia may differ in genetic predisposition to schizophrenia with concordant twins having a higher genetic liability and discordant twins having a lower genetic liability to schizophrenia. We aimed to investigate whether P300 amplitude (which has been postulated as a genetic marker for schizophrenia) reflected this heterogeneity.
We assessed the heritability of head circumference, an approximation of brain size, in twin-sib families of different ages. Data from the youngest participants were collected a few weeks after birth and from the oldest participants around age 50 years. In nearly all age groups the largest part of the variation in head circumference was explained by genetic differences. Heritability estimates were 90% in young infants (4 to 5 months), 85-88% in early childhood, 83-87% in adolescence, 75% in young and mid adulthood. In infants younger than 3 months, heritability was very low or absent. Quantitative sex differences in heritability were observed in 15- and 18-year-olds, but there was no evidence for qualitative sex differences, that is, the same genes were expressed in both males and females. Longitudinal analysis of the data between 5, 7, and 18 years of age showed high genetic stability (.78 > R(G) > .98). These results indicate that head circumference is a highly heritable biometric trait and a valid target for future GWA studies.
We investigated the relationship between three electrophysiological indices of response anticipation in a spatial delayed response task with a low and high memory load manipulation: a slow cortical potential (SCP), theta desynchronization, and upper alpha synchronization. Individual differences in these three measures were examined in 531 adult twins and siblings. Heritability of the SCP at occipital-parietal leads varied from 30% to 43%. Heritability of upper alpha synchronization (35% to 65%) and theta desynchronization (31% to 50%) was significant at all leads. Theta desynchronization and upper alpha synchronization were significantly correlated (r approximately 43%), but SCP was not correlated with either. The effect of working memory load on all three measures was not heritable. Response anticipation reliably evokes an SCP, upper alpha synchronization and theta desynchronization, but variation in these measures reflects different (genetic) sources.
We examined sex differences in familial resemblance for a broad range of behavioral, psychiatric and health related phenotypes (122 complex traits) in children and adults. There is a renewed interest in the importance of genotype by sex interaction in, for example, genome-wide association (GWA) studies of complex phenotypes. If different genes play a role across sex, GWA studies should consider the effect of genetic variants separately in men and women, which affects statistical power. Twin and family studies offer an opportunity to compare resemblance between opposite-sex family members to the resemblance between same-sex relatives, thereby presenting a test of quantitative and qualitative sex differences in the genetic architecture of complex traits. We analyzed data on lifestyle, personality, psychiatric disorder, health, growth, development and metabolic traits in dizygotic (DZ) same-sex and opposite-sex twins, as these siblings are perfectly matched for age and prenatal exposures. Sample size varied from slightly over 300 subjects for measures of brain function such as EEG power to over 30,000 subjects for childhood psychopathology and birth weight. For most phenotypes, sample sizes were large, with an average sample size of 9027 individuals. By testing whether the resemblance in DZ opposite-sex pairs is the same as in DZ same-sex pairs, we obtain evidence for genetic qualitative sex-differences in the genetic architecture of complex traits for 4% of phenotypes. We conclude that for most traits that were examined, the current evidence is that same the genes are operating in men and women.
The Netherlands Twin Register (NTR) began in 1987 with data collection in twins and their families, including families with newborn twins and triplets. Twenty-five years later, the NTR has collected at least one survey for 70,784 children, born after 1985. For the majority of twins, longitudinal data collection has been done by age-specific surveys. Shortly after giving birth, mothers receive a first survey with items on pregnancy and birth. At age 2, a survey on growth and achievement of milestones is sent. At ages 3, 7, 9/10, and 12 parents and teachers receive a series of surveys that are targeted at the development of emotional and behavior problems. From age 14 years onward, adolescent twins and their siblings report on their behavior problems, health, and lifestyle. When the twins are 18 years and older, parents are also invited to take part in survey studies. In sub-groups of different ages, in-depth phenotyping was done for IQ, electroencephalography , MRI, growth, hormones, neuropsychological assessments, and cardiovascular measures. DNA and biological samples have also been collected and large numbers of twin pairs and parents have been genotyped for zygosity by either micro-satellites or sets of short nucleotide polymorphisms and repeat polymorphisms in candidate genes. Subject recruitment and data collection is still ongoing and the longitudinal database is growing. Data collection by record linkage in the Netherlands is beginning and we expect these combined longitudinal data to provide increased insights into the genetic etiology of development of mental and physical health in children and adolescents.
A new genetic factor model for multivariate phenotypic time series, iFACE, is presented which allows for the estimation of subject-specific model parameters of genetic and environmental factors. The iFACE was applied to multivariate EEG registrations obtained with single dizygotic twin pairs. The results showed evidence for considerable subject-specificity in heritabilities and environmental effects. The assumption that the population is homogeneous (i.e., that each case in the population obeys the same parametric model), does not hold for these psychophysiological data, and its use should be critically reconsidered. We conclude that the iFACE provides a powerful new methodology to assess heterogeneity (subject-specificity) based on phenotypic observations.
The human electroencephalogram (EEG) consists of oscillations that reflect the summation of postsynaptic potentials at the dendritic tree of cortical neurons. The strength of the oscillations (EEG power) is a highly genetic trait that has been related to individual differences in many phenotypes, including intelligence and liability for psychopathology. Here, we investigated whether brain anatomy underlies these EEG power differences by correlating it to gray and white matter volumes (GMV, WMV), and additionally investigated whether this association can be attributed to genes or environmental factors. EEG was measured in a sample of 405 young adult twins and their siblings, and power in the theta (~4 Hz), alpha (~10 Hz), and beta (~20 Hz) frequency bands determined. A subset of 121 subjects were also scanned in a 1.5 T MRI scanner, and gray and white matter volumes defined as the total of cortical and subcortical volumes, excluding cerebellum. Both MRI-based volumes and EEG power spectra were highly heritable. GMV and WMV correlated .25 to .29 with EEG power for the slower oscillations (theta, alpha). Moreover, these phenotypic correlations largely reflected genetic covariation, irrespective of oscillation frequency and volume type. Genetic correlations (.31 < rA < .43) revealed that only moderate proportions of the heritable variance overlapped between MRI volumes and EEG power. The results suggest that MRI volumes and EEG power share genetic sources of variation, which may reflect such processes as myelination, synaptic density, and dendritic outgrowth.
We investigated the development of the brains functional connectivity throughout the life span (ages 5 through 71 years) by measuring EEG activity in a large population-based sample. Connectivity was established with Synchronization Likelihood. Relative randomness of the connectivity patterns was established with Watts and Strogatz (1998) graph parameters C (local clustering) and L (global path length) for alpha (~10 Hz), beta (~20 Hz), and theta (~4 Hz) oscillation networks. From childhood to adolescence large increases in connectivity in alpha, theta and beta frequency bands were found that continued at a slower pace into adulthood (peaking at ~50 yrs). Connectivity changes were accompanied by increases in L and C reflecting decreases in network randomness or increased order (peak levels reached at ~18 yrs). Older age (55+) was associated with weakened connectivity. Semi-automatically segmented T1 weighted MRI images of 104 young adults revealed that connectivity was significantly correlated to cerebral white matter volume (alpha oscillations: r = 33, p<01; theta: r = 22, p<05), while path length was related to both white matter (alpha: max. r = 38, p<001) and gray matter (alpha: max. r = 36, p<001; theta: max. r = 36, p<001) volumes. In conclusion, EEG connectivity and graph theoretical network analysis may be used to trace structural and functional development of the brain.
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