D.J.A. Smit

Heritability of "small-world" networks in the brain: A graph theoretical analysis of resting-state EEG functional connectivity.

Recent studies have shown that resting‐state functional networks as studied with fMRI, EEG, and MEG may be so‐called small‐world networks. We investigated to what extent the characteristic features of small‐world networks are genetically determined. To represent functional connectivity between brain areas, we measured resting EEG in 574 twins and their siblings and calculated the synchronization likelihood between each pair of electrodes. We applied a threshold to obtain a binary graph from which we calculated the clustering coefficient C (describing local interconnectedness) and average path length L (describing global interconnectedness) for each individual. Modeling of MZ and DZ twin and sibling resemblance indicated that across various frequency bands 46–89% of the individual differences in C and 37–62% of the individual differences in L are heritable. It is asserted that C, L, and a small‐world organization are viable markers of genetic differences in brain organization. Hum Brain Mapp, 2008.

Network Analysis of Resting State EEG in the Developing Young Brain: Structure Comes With Maturation

During childhood, brain structure and function changes substantially. Recently, graph theory has been introduced to model connectivity in the brain. Small‐world networks, such as the brain, combine optimal properties of both ordered and random networks, i.e., high clustering and short path lengths. We used graph theoretical concepts to examine changes in functional brain networks during normal development in young children. Resting‐state eyes‐closed electroencephalography (EEG) was recorded (14 channels) from 227 children twice at 5 and 7 years of age. Synchronization likelihood (SL) was calculated in three different frequency bands and between each pair of electrodes to obtain SL‐weighted graphs. Mean normalized clustering index, average path length and weight dispersion were calculated to characterize network organization. Repeated measures analysis of variance tested for time and gender effects. For all frequency bands mean SL decreased from 5 to 7 years. Clustering coefficient increased in the alpha band. Path length increased in all frequency bands. Mean normalized weight dispersion decreased in beta band. Girls showed higher synchronization for all frequency bands and a higher mean clustering in alpha and beta bands. The overall decrease in functional connectivity (SL) might reflect pruning of unused synapses and preservation of strong connections resulting in more cost‐effective networks. Accordingly, we found increases in average clustering and path length and decreased weight dispersion indicating that normal brain maturation is characterized by a shift from random to more organized small‐world functional networks. This developmental process is influenced by gender differences early in development. Hum Brain Mapp, 2011.

The Young Netherlands Twin Register (YNTR): Longitudinal Twin and Family Studies in Over 70,000 Children

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.

Growing trees in child brains: graph theoretical analysis of electroencephalography-derived minimum spanning tree in 5- and 7-year-old children reflects brain maturation.

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.

Genetic contributions to long-range temporal correlations in ongoing oscillations

The amplitude fluctuations of ongoing oscillations in the electroencephalographic (EEG) signal of the human brain show autocorrelations that decay slowly and remain significant at time scales up to tens of seconds. We call these long-range temporal correlations (LRTC). Abnormal LRTC have been observed in several brain pathologies, but it has remained unknown whether genetic factors influence the temporal correlation structure of ongoing oscillations. We recorded the ongoing EEG during eyes-closed rest in 390 monozygotic and dizygotic twins and investigated the temporal structure of ongoing oscillations in the alpha- and beta-frequency bands using detrended fluctuation analysis (DFA). The strength of LRTC was more highly correlated in monozygotic than in dizygotic twins. Statistical analysis attributed up to ∼60% of the variance in DFA to genetic factors, indicating a high heritability for the temporal structure of amplitude fluctuations in EEG oscillations. Importantly, the DFA and EEG power were uncorrelated. LRTC in ongoing oscillations are robust, heritable, and independent of power, suggesting that LRTC and oscillation power are governed by distinct biophysical mechanisms and serve different functions in the brain. We propose that the DFA method is an important complement to classical spectral analysis in fundamental and clinical research on ongoing oscillations.

The relation between frontal EEG asymmetry and the risk for anxiety and depression.

Frontal asymmetry of EEG alpha power (FA) may index the risk for anxiety and depression. Evidence linking FA to the underlying biological mechanisms is scarce. This is unfortunate because FA has potential as a biological marker to support gene finding in anxiety and depression. We examined the heritability of FA in 732 twins and their singleton siblings, and established the genetic and environmental contribution to the relation between FA and the risk for anxiety and depression. Multivariate models showed that FA is heritable only in young adults (males 32% and females 37%) but not in middle-aged adults. A significant relation between FA and the risk for anxiety and depression was only found in young adult females. This relation was explained by shared genes influencing both EEG and disease risk. Future studies on asymmetry of left and right frontal brain activation should carefully consider the effects of sex and age.

Working Memory in Schizophrenia : An EEG Study Using Power Spectrum and Coherence Analysis to Estimate Cortical Activation and Network Behavior

This study examined regional cortical activations and cortico-cortical connectivity in a group of 20 high-functioning patients with schizophrenia and 20 healthy controls matched for age and sex during a 0- and a 2-back working memory (WM) task. An earlier study comparing schizophrenia patients with education level-matched healthy controls revealed less “optimally” organized network during the 2-back task, whereas a second study with healthy volunteers had suggested that the degree of cortical organization may be inversely proportional to educational level (less optimal functional connectivity in better educated individuals interpreted as the result of higher efficiency). In the present study, both groups succeeded in the 2-back WM task although healthy individuals had generally attained a higher level of education. First absolute power spectrum of the different frequency bands corresponding to the electrodes of each lobe was calculated. Then the mean values of coherence were calculated as an index of the average synchronization to construct graphs in order to characterize local and large scale topological patterns of cortico-cortical connectivity. The power spectra analyses showed signs of hypofrontality in schizophrenics with an asymmetry. Additionally, differences between the groups with greater changes during WM in healthy individuals were visible in all lobes more on the left side. The graph parameter results indicated decreased small-world architecture i.e. less optimal cortico-cortical functional organization in patients as compared to controls. These findings are consistent with the notion of aberrant neural organization in schizophrenics which is nevertheless sufficient in supporting adequate task performance.

ENIGMA and the Individual: Predicting Factors that Affect the Brain in 35 Countries Worldwide

In this review, we discuss recent work by the ENIGMA Consortium (http://enigma.ini.usc.edu) – a global alliance of over 500 scientists spread across 200 institutions in 35 countries collectively analyzing brain imaging, clinical, and genetic data. Initially formed to detect genetic influences on brain measures, ENIGMA has grown to over 30 working groups studying 12 major brain diseases by pooling and comparing brain data. In some of the largest neuroimaging studies to date – of schizophrenia and major depression – ENIGMA has found replicable disease effects on the brain that are consistent worldwide, as well as factors that modulate disease effects. In partnership with other consortia including ADNI, CHARGE, IMAGEN and others1, ENIGMAs genomic screens – now numbering over 30,000 MRI scans – have revealed at least 8 genetic loci that affect brain volumes. Downstream of gene findings, ENIGMA has revealed how these individual variants – and genetic variants in general – may affect both the brain and risk for a range of diseases. The ENIGMA consortium is discovering factors that consistently affect brain structure and function that will serve as future predictors linking individual brain scans and genomic data. It is generating vast pools of normative data on brain measures – from tens of thousands of people – that may help detect deviations from normal development or aging in specific groups of subjects. We discuss challenges and opportunities in applying these predictors to individual subjects and new cohorts, as well as lessons we have learned in ENIGMAs efforts so far.

Sex Differences in Genetic Architecture of Complex Phenotypes

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.

Genetic components of functional connectivity in the brain: The heritability of synchronization likelihood

Cognitive functions require the integrated activity of multiple specialized, distributed brain areas. Such functional coupling depends on the existence of anatomical connections between the various brain areas as well as physiological processes whereby the activity in one area influences the activity in another area. Recently, the Synchronization Likelihood (SL) method was developed as a general method to study both linear and nonlinear aspects of coupling. In the present study the genetic architecture of the SL in different frequency bands was investigated. Using a large genetically informative sample of 569 subjects from 282 extended twin families we found that the SL is moderately to highly heritable (41–67%) especially in the alpha frequency (8–13 Hz) range. This index of functional connectivity of the brain has been associated with a number of pathological states of the brain. The significant heritability found here suggests that SL can be used to examine the genetic susceptibility to these conditions. Hum Brain Mapp, 2005.