Genetics of brain fiber architecture and intellectual performance

The study is the first to analyze genetic and environmental factors that affect brain fiber architecture and its genetic linkage with cognitive function. We assessed white matter integrity voxelwise using diffusion tensor imaging at high magnetic field (4 Tesla), in 92 identical and fraternal twins. White matter integrity, quantified using fractional anisotropy (FA), was used to fit structural equation models (SEM) at each point in the brain, generating three-dimensional maps of heritability. We visualized the anatomical profile of correlations between white matter integrity and full-scale, verbal, and performance intelligence quotients (FIQ, VIQ, and PIQ). White matter integrity (FA) was under strong genetic control and was highly heritable in bilateral frontal (a2 = 0.55, p = 0.04, left; a2 = 0.74, p = 0.006, right), bilateral parietal (a2 = 0.85, p < 0.001, left; a2 = 0.84, p < 0.001, right), and left occipital (a2 = 0.76, p = 0.003) lobes, and was correlated with FIQ and PIQ in the cingulum, optic radiations, superior fronto-occipital fasciculus, internal capsule, callosal isthmus, and the corona radiata (p = 0.04 for FIQ and p = 0.01 for PIQ, corrected for multiple comparisons). In a cross-trait mapping approach, common genetic factors mediated the correlation between IQ and white matter integrity, suggesting a common physiological mechanism for both, and common genetic determination. These genetic brain maps reveal heritable aspects of white matter integrity and should expedite the discovery of single-nucleotide polymorphisms affecting fiber connectivity and cognition

Genetic architecture of the white matter connectome of the human brain

White matter tracts form the structural basis of large-scale functional networks in the human brain. We applied brain-wide tractography to diffusion images from 30,810 adult participants (UK Biobank), and found significant heritability for 90 regional connectivity measures and 851 tract-wise connectivity measures. Multivariate genome- wide association analyses identified 355 independently associated lead SNPs across the genome, of which 77% had not been previously associated with human brain metrics. Enrichment analyses implicated neurodevelopmental processes including neurogenesis, neural differentiation, neural migration, neural projection guidance, and axon development, as well as prenatal brain expression especially in stem cells, astrocytes, microglia and neurons. We used the multivariate association profiles of lead SNPs to identify 26 genomic loci implicated in structural connectivity between core regions of the left-hemisphere language network, and also identified 6 loci associated with hemispheric left-right asymmetry of structural connectivity. Polygenic scores for schizophrenia, bipolar disorder, autism spectrum disorder, attention-deficit hyperactivity disorder, left-handedness, Alzheimer’s disease, amyotrophic lateral sclerosis, and epilepsy showed significant multivariate associations with structural connectivity, each implicating distinct sets of brain regions with trait-relevant functional profiles. This large-scale mapping study revealed common genetic contributions to the structural connectome of the human brain in the general adult population, highlighting links with polygenic disposition to brain disorders and behavioural traits

Structural Neuroimaging of Anorexia Nervosa: Future Directions in the Quest for Mechanisms Underlying Dynamic Alterations.

Anorexia nervosa (AN) is a serious eating disorder characterized by self-starvation and extreme weight loss. Pseudoatrophic brain changes are often readily visible in individual brain scans, and AN may be a valuable model disorder to study structural neuroplasticity. Structural magnetic resonance imaging studies have found reduced gray matter volume and cortical thinning in acutely underweight patients to normalize following successful treatment. However, some well-controlled studies have found regionally greater gray matter and persistence of structural alterations following long-term recovery. Findings from diffusion tensor imaging studies of white matter integrity and connectivity are also inconsistent. Furthermore, despite the severity of AN, the number of existing structural neuroimaging studies is still relatively low, and our knowledge of the underlying cellular and molecular mechanisms for macrostructural brain changes is rudimentary. We critically review the current state of structural neuroimaging in AN and discuss the potential neurobiological basis of structural brain alterations in the disorder, highlighting impediments to progress, recent developments, and promising future directions. In particular, we argue for the utility of more standardized data collection, adopting a connectomics approach to understanding brain network architecture, employing advanced magnetic resonance imaging methods that quantify biomarkers of brain tissue microstructure, integrating data from multiple imaging modalities, strategic longitudinal observation during weight restoration, and large-scale data pooling. Our overarching objective is to motivate carefully controlled research of brain structure in eating disorders, which will ultimately help predict therapeutic response and improve treatment

A genetic perspective on the developing brain: electrophysiological indices of neural functioning in young and adolescent twins.

Changes in genetic and environmental influences on electroencephalographic (EEG) and event-related potential (ERP) indices of neural development were studied in two large cohorts of young (N = 418) and adolescent (N = 426) twins. Individual differences in these indices were largely influenced by genetic factors, and throughout development, the stable part of the variance was mainly genetic. Both EEG power (which describes the amount of variability in brain electrical potentials that can be attributed to different frequencies) and long-distance EEG coherence (which is the squared cross-correlation between two EEG signals at different scalp locations and can be regarded as an index for cortico-cortical connectivity) were highly heritable. ERP-P300 latencies and amplitudes were low to moderately heritable. Clear differences between young children and adolescents could be observed in the heritabilities of EEG and ERP indices. The heritabilities of EEG power and EEG coherence were higher in adolescents than in children, whereas the heritabilities of P300 latencies were lower. Both cohorts (young children and adolescents) were measured twice: The children were tested when they were 5 and again at 7 years, the adolescents when they were 16 and again at 18 years. Therefore, within these age ranges a more detailed analysis of age-related changes in heritabilities and in the emergence of new genetic influences could be studied. The heritabilities of EEG powers and P300 amplitudes and latencies did not change much from age 5 to age 7 and from age 16 to 18 years. The heritabilities of a substantial number of connections within the cortex, however, as indexed by EEG coherence, changed significantly from age 5 to age 7, though not from age 16 to 18. The only changes in the heritabilities in adolescents were connections within the prefrontal cortex, which is in agreement with theories of adolescent development. These age-related changes in the heritabilities may reflect a larger impact of maturation on cortico-cortical connectivity in childhood than in adolescence. Evidence was found for qualitative changes in brain electrophysiology in young children: New genetic factors emerged at age 7 for posterior EEG coherences and for P300 latency at some scalp locations. This supports theories of qualitative stage transitions in this age range, as previously suggested using behavioral and EEG data

Evaluating 35 Methods to Generate Structural Connectomes Using Pairwise Classification

There is no consensus on how to construct structural brain networks from diffusion MRI. How variations in pre-processing steps affect network reliability and its ability to distinguish subjects remains opaque. In this work, we address this issue by comparing 35 structural connectome-building pipelines. We vary diffusion reconstruction models, tractography algorithms and parcellations. Next, we classify structural connectome pairs as either belonging to the same individual or not. Connectome weights and eight topological derivative measures form our feature set. For experiments, we use three test-retest datasets from the Consortium for Reliability and Reproducibility (CoRR) comprised of a total of 105 individuals. We also compare pairwise classification results to a commonly used parametric test-retest measure, Intraclass Correlation Coefficient (ICC).Comment: Accepted for MICCAI 2017, 8 pages, 3 figure

Spleen histology in children with sickle cell disease and hereditary spherocytosis: Hints on the disease pathophysiology

open2Hereditary spherocytosis (HS) and sickle cell disease (SCD) are associated with splenomegaly and spleen dysfunction in pediatric patients. Scant data exist on possible correlations between spleen morphology and function in HS and SCD. This study aimed to assess the histological and morphometric features of HS and SCD spleens, in order to get possible correlations with disease pathophysiology. In a large series of spleens from SCD, HS and control patients the following parameters were considered: (i) macroscopic features; (ii) lymphoid follicle (LF) density; (iii) presence of peri-follicular marginal zones (MZs); (iv) presence of Gamna-Gandy bodies; (v) density of CD8-positive sinusoids; (vi) density of CD34-positive microvessels; (vii) presence/distribution of fibrosis and SMA-positive myoid cells; (viii) density of CD68-positive macrophages. SCD and HS spleens have similar macroscopic features. SCD spleens had lower LF density and fewer MZs than HS spleens and controls. SCD also showed lower CD8-positive sinusoid density, increased CD34-positive microvessel density and SMA-positive myoid cells, and higher prevalence of fibrosis and Gamna-Gandy bodies. HS had lower LF and CD8-positive sinusoid density than controls. No significant differences were noted in red pulp macrophages. By multivariate analysis, the majority of HS spleens clustered with controls, while SCD grouped separately. A multi-parametric score could predict the degree of spleen changes irrespective of the underlying disease. In conclusion, SCD spleens display greater histologic effacement than HS and SCD-related changes suggest impaired function due to vascular damage. These observations may contribute to guide the clinical management of patients.embargoed_20161128Alaggio, RitaAlaggio, Rita; Gamba, Piergiorgi

Meta-analysis identifies pleiotropic loci controlling phenotypic trade-offs in sorghum

Community association populations are composed of phenotypically and genetically diverse accessions. Once these populations are genotyped, the resulting marker data can be reused by different groups investigating the genetic basis of different traits. Because the same genotypes are observed and scored for a wide range of traits in different environments, these populations represent a unique resource to investigate pleiotropy. Here, we assembled a set of 234 separate trait datasets for the Sorghum Association Panel, a group of 406 sorghum genotypes widely employed by the sorghum genetics community. Comparison of genome-wide association studies (GWAS) conducted with two independently generated marker sets for this population demonstrate that existing genetic marker sets do not saturate the genome and likely capture only 35–43% of potentially detectable loci controlling variation for traits scored in this population. While limited evidence for pleiotropy was apparent in cross-GWAS comparisons, a multivariate adaptive shrinkage approach recovered both known pleiotropic effects of existing loci and new pleiotropic effects, particularly significant impacts of known dwarfing genes on root architecture. In addition, we identified new loci with pleiotropic effects consistent with known trade-offs in sorghum development. These results demonstrate the potential for mining existing trait datasets from widely used community association populations to enable new discoveries from existing trait datasets as new, denser genetic marker datasets are generated for existing community association populations

Imaging genetics: bio-informatics and bio-statistics challenges

International audienceThe IMAGEN study -- a very large European Research Project -- seeks to identify and characterize biological and environmental factors that in uence teenagers mental health. To this aim, the consortium plans to collect data for more than 2000 subjects at 8 neuroimaging centres. These data comprise neuroimaging data, behavioral tests (for up to 5 hours of testing), and also white blood samples which are collected and processed to obtain 650k single nucleotide polymorphisms (SNP) per subject. Data for more than 1000 subjects have already been collected. We describe the statistical aspects of these data and the challenges, such as the multiple comparison problem, created by such a large imaging genetics study (i.e., 650k for the SNP, 50k data per neuroimage).We also suggest possible strategies, and present some rst investigations using uni or multi-variate methods in association with re-sampling techniques. Specically, because the number of variables is very high, we rst reduce the data size and then use multivariate (CCA, PLS) techniques in association with re-sampling techniques

ENIGMA and global neuroscience: A decade of large-scale studies of the brain in health and disease across more than 40 countries.

This review summarizes the last decade of work by the ENIGMA (Enhancing NeuroImaging Genetics through Meta Analysis) Consortium, a global alliance of over 1400 scientists across 43 countries, studying the human brain in health and disease. Building on large-scale genetic studies that discovered the first robustly replicated genetic loci associated with brain metrics, ENIGMA has diversified into over 50 working groups (WGs), pooling worldwide data and expertise to answer fundamental questions in neuroscience, psychiatry, neurology, and genetics. Most ENIGMA WGs focus on specific psychiatric and neurological conditions, other WGs study normal variation due to sex and gender differences, or development and aging; still other WGs develop methodological pipelines and tools to facilitate harmonized analyses of "big data" (i.e., genetic and epigenetic data, multimodal MRI, and electroencephalography data). These international efforts have yielded the largest neuroimaging studies to date in schizophrenia, bipolar disorder, major depressive disorder, post-traumatic stress disorder, substance use disorders, obsessive-compulsive disorder, attention-deficit/hyperactivity disorder, autism spectrum disorders, epilepsy, and 22q11.2 deletion syndrome. More recent ENIGMA WGs have formed to study anxiety disorders, suicidal thoughts and behavior, sleep and insomnia, eating disorders, irritability, brain injury, antisocial personality and conduct disorder, and dissociative identity disorder. Here, we summarize the first decade of ENIGMA's activities and ongoing projects, and describe the successes and challenges encountered along the way. We highlight the advantages of collaborative large-scale coordinated data analyses for testing reproducibility and robustness of findings, offering the opportunity to identify brain systems involved in clinical syndromes across diverse samples and associated genetic, environmental, demographic, cognitive, and psychosocial factors