Serum cholesterol and variant in cholesterol-related gene CETP predict white matter microstructure

Several common genetic variants influence cholesterol levels, which play a key role in overall health. Myelin synthesis and maintenance are highly sensitive to cholesterol concentrations, and abnormal cholesterol levels increase the risk for various brain diseases, including Alzheimer's disease. We report significant associations between higher serum cholesterol (CHOL) and high-density lipoprotein levels and higher fractional anisotropy in 403 young adults (23.8 ± 2.4years) scanned with diffusion imaging and anatomic magnetic resonance imaging at 4Tesla. By fitting a multi-locus genetic model within white matter areas associated with CHOL, we found that a set of 18 cholesterol-related, single-nucleotide polymorphisms implicated in Alzheimer's disease risk predicted fractional anisotropy. We focused on the single-nucleotide polymorphism with the largest individual effects, CETP (rs5882), and found that increased G-allele dosage was associated with higher fractional anisotropy and lower radial and mean diffusivities in voxel-wise analyses of the whole brain. A follow-up analysis detected white matter associations with rs5882 in the opposite direction in 78 older individuals (74.3 ± 7.3years). Cholesterol levels may influence white matter integrity, and cholesterol-related genes may exert age-dependent effects on the brain.

Heritability analysis of surface-based cortical thickness estimation on a large twin cohort

The aim of this paper is to assess the heritability of cerebral cortex, based on measurements of grey matter (GM) thickness derived from structural MR images (sMRI). With data acquired from a large twin cohort (328 subjects), an automated method was used to estimate the cortical thickness, and EM-ICP surface registration algorithm was used to establish the correspondence of cortex across the population. An ACE model was then employed to compute the heritability of cortical thickness. Heritable cortical thickness measures various cortical regions, especially in frontal and parietal lobes, such as bilateral postcentral gyri, superior occipital gyri, superior parietal gyri, precuneus, the orbital part of the right frontal gyrus, right medial superior frontal gyrus, right middle occipital gyrus, right paracentral lobule, left precentral gyrus, and left dorsolateral superior frontal gyrus.

Cortical Abnormalities in Adults and Adolescents with Major Depression based on Brain Scans from 20 Cohorts Worldwide in the ENIGMA Major Depressive Disorder Working Group

The neuro-anatomical substrates of major depressive disorder (MDD) are still not well understood, despite many neuroimaging studies over the past few decades. Here we present the largest ever worldwide study by the ENIGMA (Enhancing Neuro Imaging Genetics through Meta-Analysis) Major Depressive Disorder Working Group on cortical structural alterations in MDD. Structural T1-weighted brain magnetic resonance imaging (MRI) scans from 2148 MDD patients and 7957 healthy controls were analysed with harmonized protocols at 20 sites around the world. To detect consistent effects of MDD and its modulators on cortical thickness and surface area estimates derived from MRI, statistical effects from sites were meta-analysed separately for adults and adolescents. Adults with MDD had thinner cortical gray matter than controls in the orbitofrontal cortex (OFC), anterior and posterior cingulate, insula and temporal lobes (Cohen’s d effect sizes: −0.10 to −0.14). These effects were most pronounced in first episode and adult-onset patients (>21 years). Compared to matched controls, adolescents with MDD had lower total surface area (but no differences in cortical thickness) and regional reductions in frontal regions (medial OFC and superior frontal gyrus) and primary and higher-order visual, somatosensory and motor areas (d: −0.26 to −0.57). The strongest effects were found in recurrent adolescent patients. This highly powered global effort to identify consistent brain abnormalities showed widespread cortical alterations in MDD patients as compared to controls and suggests that MDD may impact brain structure in a highly dynamic way, with different patterns of alterations at different stages of life.

Cognitive neuroscience: the evolution of a discipline

Preface. The evolution of cognitive neuroscience has been spurred by the development of increasingly sophisticated investigative techniques to study human cognition. In Methods in Mind, experts examine the wide variety of tools available to cognitive neuroscientists, paying particular attention to the ways in which different methods can be integrated to strengthen empirical findings and how innovative uses for established techniques can be developed. The book will be a uniquely valuable resource for the researcher seeking to expand his or her repertoire of investigative techniques. Each chapter explores a different approach. These include transcranial magnetic stimulation, cognitive neuropsychiatry, lesion studies in nonhuman primates, computational modeling, psychophysiology, single neurons and primate behavior, grid computing, eye movements, fMRI, electroencephalography, imaging genetics, magnetoencephalography, neuropharmacology, and neuroendocrinology. As mandated, authors focus on convergence and innovation in their fields; chapters highlight such cross-method innovations as the use of the fMRI signal to constrain magnetoencephalography, the use of electroencephalography (EEG) to guide rapid transcranial magnetic stimulation at a specific frequency, and the successful integration of neuroimaging and genetic analysis. Computational approaches depend on increased computing power, and one chapter describes the use of distributed or grid computing to analyze massive datasets in cyberspace. Each chapter author is a leading authority in the technique discussed.

Methods in mind

The evolution of cognitive neuroscience has been spurred by the development of increasingly sophisticated investigative techniques to study human cognition. In Methods in Mind, experts examine the wide variety of tools available to cognitive neuroscientists, paying particular attention to the ways in which different methods can be integrated to strengthen empirical findings and how innovative uses for established techniques can be developed. The book will be a uniquely valuable resource for the researcher seeking to expand his or her repertoire of investigative techniques. Each chapter explores a different approach. These include transcranial magnetic stimulation, cognitive neuropsychiatry, lesion studies in nonhuman primates, computational modeling, psychophysiology, single neurons and primate behavior, grid computing, eye movements, fMRI, electroencephalography, imaging genetics, magnetoencephalography, neuropharmacology, and neuroendocrinology. As mandated, authors focus on convergence and innovation in their fields; chapters highlight such cross-method innovations as the use of the fMRI signal to constrain magnetoencephalography, the use of electroencephalography (EEG) to guide rapid transcranial magnetic stimulation at a specific frequency, and the successful integration of neuroimaging and genetic analysis. Computational approaches depend on increased computing power, and one chapter describes the use of distributed or grid computing to analyze massive datasets in cyberspace. Each chapter author is a leading authority in the technique discussed.

25th RCOphth Congress, President's Session paper:25 years of progress in medical retina

The quarter century since the foundation of the Royal College of Ophthalmologists has coincided with immense change in the subspecialty of medical retina, which has moved from being the province of a few dedicated enthusiasts to being an integral, core part of ophthalmology in every eye department. In age-related macular degeneration, there has been a move away from targeted, destructive laser therapy, dependent on fluorescein angiography to intravitreal injection therapy of anti-growth factor agents, largely guided by optical coherence tomography. As a result of these changes, ophthalmologists have witnessed a marked improvement in visual outcomes for their patients with wet age-related macular degeneration (AMD), while at the same time developing and enacting entirely novel ways of delivering care. In the field of diabetic retinopathy, this period also saw advances in laser technology and a move away from highly destructive laser photocoagulation treatment to gentler retinal laser treatments. The introduction of intravitreal therapies, both steroids and anti-growth factor agents, has further advanced the treatment of diabetic macular oedema. This era has also seen in the United Kingdom the introduction of a coordinated national diabetic retinopathy screening programme, which offers an increasing hope that the burden of blindness from diabetic eye disease can be lessened. Exciting future advances in retinal imaging, genetics, and pharmacology will allow us to further improve outcomes for our patients and for ophthalmologists specialising in medical retina, the future looks very exciting but increasingly busy.

Brain fiber architecture, genetics, and intelligence: a high angular resolution diffusion imaging (HARDI) study

We developed an analysis pipeline enabling population studies of HARDI data, and applied it to map genetic influences on fiber architecture in 90 twin subjects. We applied tensor-driven 3D fluid registration to HARDI, resampling the spherical fiber orientation distribution functions (ODFs) in appropriate Riemannian manifolds, after ODF regularization and sharpening. Fitting structural equation models (SEM) from quantitative genetics, we evaluated genetic influences on the Jensen-Shannon divergence (JSD), a novel measure of fiber spatial coherence, and on the generalized fiber anisotropy (GFA) a measure of fiber integrity. With random-effects regression, we mapped regions where diffusion profiles were highly correlated with subjects' intelligence quotient (IQ). Fiber complexity was predominantly under genetic control, and higher in more highly anisotropic regions; the proportion of genetic versus environmental control varied spatially. Our methods show promise for discovering genes affecting fiber connectivity in the brain.

Imaging the genetics of executive function

Recent advances in neuroimaging technologies have allowed ever more detailed studies of the human brain. The combination of neuroimaging techniques with genetics may provide a more sensitive measure of the influence of genetic variants on cognitive function than behavioural measures alone. Here we present a review of functional magnetic resonance imaging (fMRI) studies of genetic links to executive functions, focusing on sustained attention, working memory and response inhibition. In addition to studies in the normal population, we also address findings from three clinical populations: schizophrenia, ADHD and autism spectrum disorders. While the findings in the populations studied do not always converge, they all point to the usefulness of neuroimaging techniques such as fMRI as potential endophenotypes for parsing the genetic aetiology of executive function. (C) 2007 Elsevier B.V. All rights reserved.

Genetic and environmental influences on neuroimaging phenotypes: A meta-analytical perspective on twin imaging studies

Because brain structure and function are affected in neurological and psychiatric disorders, it is important to disentangle the sources of variation in these phenotypes. Over the past 15 years, twin studies have found evidence for both genetic and environmental influences on neuroimaging phenotypes, but considerable variation across studies makes it difficult to draw clear conclusions about the relative magnitude of these influences. Here we performed the first meta-analysis of structural MRI data from 48 studies on >1,250 twin pairs, and diffusion tensor imaging data from 10 studies on 444 twin pairs. The proportion of total variance accounted for by genes (A), shared environment (C), and unshared environment (E), was calculated by averaging A, C, and E estimates across studies from independent twin cohorts and weighting by sample size. The results indicated that additive genetic estimates were significantly different from zero for all metaanalyzed phenotypes, with the exception of fractional anisotropy (FA) of the callosal splenium, and cortical thickness (CT) of the uncus, left parahippocampal gyrus, and insula. For many phenotypes there was also a significant influence of C. We now have good estimates of heritability for many regional and lobar CT measures, in addition to the global volumes. Confidence intervals are wide and number of individuals small for many of the other phenotypes. In conclusion, while our meta-analysis shows that imaging measures are strongly influenced by genes, and that novel phenotypes such as CT measures, FA measures, and brain activation measures look especially promising, replication across independent samples and demographic groups is necessary.

Genetics of white matter development: A DTI study of 705 twins and their siblings aged 12 to 29

White matter microstructure is under strong genetic control, yet it is largely unknown how genetic influences change from childhood into adulthood. In one of the largest brain mapping studies ever performed, we determined whether the genetic control over white matter architecture depends on age, sex, socioeconomic status (SES), and intelligence quotient (IQ). We assessed white matter integrity voxelwise using diffusion tensor imaging at high magnetic field (4-Tesla), in 705 twins and their siblings (age range 12-29; 290. M/415. F). White matter integrity was quantified using a widely accepted measure, fractional anisotropy (FA). We fitted gene-environment interaction models pointwise, to visualize brain regions where age, sex, SES and IQ modulate heritability of fiber integrity. We hypothesized that environmental factors would start to outweigh genetic factors during late childhood and adolescence. Genetic influences were greater in adolescence versus adulthood, and greater in males than in females. Socioeconomic status significantly interacted with genes that affect fiber integrity: heritability was higher in those with higher SES. In people with above-average IQ, genetic factors explained over 80% of the observed FA variability in the thalamus, genu, posterior internal capsule, and superior corona radiata. In those with below-average IQ, however, only around 40% FA variability in the same regions was attributable to genetic factors. Genes affect fiber integrity, but their effects vary with age, sex, SES and IQ. Gene-environment interactions are vital to consider in the search for specific genetic polymorphisms that affect brain integrity and connectivity.

Meeting the challenges of neuroimaging genetics

As research encompassing neuroimaging and genetics gains momentum, extraordinary information will be uncovered on the genetic architecture of the human brain. However, there are significant challenges to be addressed first. Not the least of these challenges is to accomplish the sample size necessary to detect subtle genetic influences on the morphometry and function of the healthy brain. Aside from sample size, image acquisition and analysis methods need to be refined in order to ensure optimum sensitivity to genetic and complementary environmental influences. Then there is the vexing issue of interpreting the resulting data. We describe how researchers from the east coast of Australia and the west coast of America have embarked upon a collaboration to meet these challenges using data currently being collected from a large-scale twin study, and offer some opinions about future directions in the field.

Reducing structural variation to determine the genetics of white matter integrity across hemispheres - a dti study of 100 twins

Studies of cerebral asymmetry can open doors to understanding the functional specialization of each brain hemisphere, and how this is altered in disease. Here we examined hemispheric asymmetries in fiber architecture using diffusion tensor imaging (DTI) in 100 subjects, using high-dimensional fluid warping to disentangle shape differences from measures sensitive to myelination. Confounding effects of purely structural asymmetries were reduced by using co-registered structural images to fluidly warp 3D maps of fiber characteristics (fractional and geodesic anisotropy) to a structurally symmetric minimal deformation template (MDT). We performed a quantitative genetic analysis on 100 subjects to determine whether the sources of the remaining signal asymmetries were primarily genetic or environmental. A twin design was used to identify the heritable features of fiber asymmetry in various regions of interest, to further assist in the discovery of genes influencing brain micro-architecture and brain lateralization. Genetic influences and left/right asymmetries were detected in the fiber architecture of the frontal lobes, with minor differences depending on the choice of registration template.

Genetics of anisotropy asymmetry: Registration and sample size effects

Brain asymmetry has been a topic of interest for neuroscientists for many years. The advent of diffusion tensor imaging (DTI) allows researchers to extend the study of asymmetry to a microscopic scale by examining fiber integrity differences across hemispheres rather than the macroscopic differences in shape or structure volumes. Even so, the power to detect these microarchitectural differences depends on the sample size and how the brain images are registered and how many subjects are studied. We fluidly registered 4 Tesla DTI scans from 180 healthy adult twins (45 identical and fraternal pairs) to a geometrically-centered population mean template. We computed voxelwise maps of significant asymmetries (left/right hemisphere differences) for common fiber anisotropy indices (FA, GA). Quantitative genetic models revealed that 47-62% of the variance in asymmetry was due to genetic differences in the population. We studied how these heritability estimates varied with the type of registration target (T1- or T2-weighted) and with sample size. All methods consistently found that genetic factors strongly determined the lateralization of fiber anisotropy, facilitating the quest for specific genes that might influence brain asymmetry and fiber integrity.

Labeling white matter tracts in hardi by fusing multiple tract atlases with applications to genetics

Accurate identification of white matter structures and segmentation of fibers into tracts is important in neuroimaging and has many potential applications. Even so, it is not trivial because whole brain tractography generates hundreds of thousands of streamlines that include many false positive fibers. We developed and tested an automatic tract labeling algorithm to segment anatomically meaningful tracts from diffusion weighted images. Our multi-atlas method incorporates information from multiple hand-labeled fiber tract atlases. In validations, we showed that the method outperformed the standard ROI-based labeling using a deformable, parcellated atlas. Finally, we show a high-throughput application of the method to genetic population studies. We use the sub-voxel diffusion information from fibers in the clustered tracts based on 105-gradient HARDI scans of 86 young normal twins. The whole workflow shows promise for larger population studies in the future.

The multivariate A/C/E model and the genetics of fiber architecture

We present a new algorithm to compute the voxel-wise genetic contribution to brain fiber microstructure using diffusion tensor imaging (DTI) in a dataset of 25 monozygotic (MZ) twins and 25 dizygotic (DZ) twin pairs (100 subjects total). First, the structural and DT scans were linearly co-registered. Structural MR scans were nonlinearly mapped via a 3D fluid transformation to a geometrically centered mean template, and the deformation fields were applied to the DTI volumes. After tensor re-orientation to realign them to the anatomy, we computed several scalar and multivariate DT-derived measures including the geodesic anisotropy (GA), the tensor eigenvalues and the full diffusion tensors. A covariance-weighted distance was measured between twins in the Log-Euclidean framework [2], and used as input to a maximum-likelihood based algorithm to compute the contributions from genetics (A), common environmental factors (C) and unique environmental ones (E) to fiber architecture. Quanititative genetic studies can take advantage of the full information in the diffusion tensor, using covariance weighted distances and statistics on the tensor manifold.