Corrigendum to: Heritability of regional brain volumes in large-scale neuroimaging and genetic studies.
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ABSTRACT:
SUBMITTER: Zhao B
PROVIDER: S-EPMC8562323 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Brain genetics is an active research area. The degree to which genetic variants impact variations in brain structure and function remains largely unknown. We examined the heritability of regional brain volumes (P ~ 100) captured by single-nucleotide polymorphisms (SNPs) in UK Biobank (n ~ 9000). We found that regional brain volumes are highly heritable in this study population and common genetic variants can explain up to 80% of their variabilities (median heritability 34.8%). We observed omnigenic impact across the genome and examined the enrichment of SNPs in active chromatin regions. Principal components derived from regional volume data are also highly heritable, but the amount of variance in brain volume explained by the component did not seem to be related to its heritability. Heritability estimates vary substantially across large-scale functional networks, exhibit a symmetric pattern across left and right hemispheres, and are consistent in females and males (correlation = 0.638). We repeated the main analysis in Alzheimer's Disease Neuroimaging Initiative (n ~ 1100), Philadelphia Neurodevelopmental Cohort (n ~ 600), and Pediatric Imaging, Neurocognition, and Genetics (n ~ 500) datasets, which demonstrated that more stable estimates can be obtained from the UK Biobank.
| S-EPMC6611460 | biostudies-literature
Project description:Schizophrenia is a devastating psychiatric illness with high heritability. Brain structure and function differ, on average, between people with schizophrenia and healthy individuals. As common genetic associations are emerging for both schizophrenia and brain imaging phenotypes, we can now use genome-wide data to investigate genetic overlap. Here we integrated results from common variant studies of schizophrenia (33,636 cases, 43,008 controls) and volumes of several (mainly subcortical) brain structures (11,840 subjects). We did not find evidence of genetic overlap between schizophrenia risk and subcortical volume measures either at the level of common variant genetic architecture or for single genetic markers. These results provide a proof of concept (albeit based on a limited set of structural brain measures) and define a roadmap for future studies investigating the genetic covariance between structural or functional brain phenotypes and risk for psychiatric disorders.
| S-EPMC4852730 | biostudies-literature
Project description:The relationships between structural and functional measures of the human brain remain largely unknown. A majority of our limited knowledge regarding structure-function associations has been obtained through comparisons between specific groups of patients and healthy controls. Unfortunately, a direct and complete view of the associations across multiple structural and functional metrics in normal population is missing. We filled this gap by learning cross-individual co-variance among structural and functional measures using large-scale neuroimaging datasets. A discover-confirm scheme was applied to two independent samples (N = 184 and N = 340) of multi-modal neuroimaging datasets. A data mining tool, gRAICAR, was employed in the discover stage to generate quantitative and unbiased hypotheses of the co-variance among six functional and six structural imaging metrics. These hypotheses were validated using an independent dataset in the confirm stage. Fifteen multi-metric co-variance units, representing different co-variance relationships among the 12 metrics, were reliable across the two sets of neuroimaging datasets. The reliable co-variance units were summarized into a database, where users can select any location on the cortical map of any metric to examine the co-varying maps with the other 11 metrics. This database characterized the six functional metrics based on their co-variance with structural metrics, and provided a detailed reference to connect previous findings using different metrics and to predict maps of unexamined metrics. Gender, age, and handedness were associated to the co-variance units, and a sub-study of schizophrenia demonstrated the usefulness of the co-variance database.
| S-EPMC5102954 | biostudies-literature
Project description:With the advent of high throughput genetic data, there have been attempts to estimate heritability from genome-wide SNP data on a cohort of distantly related individuals using linear mixed model (LMM). Fitting such an LMM in a large scale cohort study, however, is tremendously challenging due to its high dimensional linear algebraic operations. In this paper, we propose a new method named PredLMM approximating the aforementioned LMM motivated by the concepts of genetic coalescence and Gaussian predictive process. PredLMM has substantially better computational complexity than most of the existing LMM based methods and thus, provides a fast alternative for estimating heritability in large scale cohort studies. Theoretically, we show that under a model of genetic coalescence, the limiting form of our approximation is the celebrated predictive process approximation of large Gaussian process likelihoods that has well-established accuracy standards. We illustrate our approach with extensive simulation studies and use it to estimate the heritability of multiple quantitative traits from the UK Biobank cohort.
| S-EPMC9060362 | biostudies-literature
Project description:The Enhancing NeuroImaging Genetics through Meta-Analysis (ENIGMA) Consortium is a collaborative network of researchers working together on a range of large-scale studies that integrate data from 70 institutions worldwide. Organized into Working Groups that tackle questions in neuroscience, genetics, and medicine, ENIGMA studies have analyzed neuroimaging data from over 12,826 subjects. In addition, data from 12,171 individuals were provided by the CHARGE consortium for replication of findings, in a total of 24,997 subjects. By meta-analyzing results from many sites, ENIGMA has detected factors that affect the brain that no individual site could detect on its own, and that require larger numbers of subjects than any individual neuroimaging study has currently collected. ENIGMA's first project was a genome-wide association study identifying common variants in the genome associated with hippocampal volume or intracranial volume. Continuing work is exploring genetic associations with subcortical volumes (ENIGMA2) and white matter microstructure (ENIGMA-DTI). Working groups also focus on understanding how schizophrenia, bipolar illness, major depression and attention deficit/hyperactivity disorder (ADHD) affect the brain. We review the current progress of the ENIGMA Consortium, along with challenges and unexpected discoveries made on the way.
| S-EPMC4008818 | biostudies-literature
Project description:IMPORTANCE:Telomere length has been associated with dementia and psychological stress, but its relationship with human brain size is unknown. OBJECTIVE:To determine if peripheral blood telomere length is associated with brain volume. DESIGN, SETTING, AND PARTICIPANTS:Peripheral blood leukocyte telomere length and brain volumes were measured for 1960 individuals in the Dallas Heart Study, a population-based, probability sample of Dallas County, Texas, residents, with a median (25th-75th percentile) age of 50 (42-58) years. Global and 48 regional brain volumes were assessed from the automated analysis of magnetic resonance imaging. MAIN OUTCOMES AND MEASURES:Telomere length and global and regional brain volumes. RESULTS:Leukocyte telomere length was associated with total cerebral volume (? [SE], 0.06 [0.01], P?<.001) including white and cortical gray matter volume (? [SE], 0.04 [0.01], P?=?.002; ? [SE], 0.07 [0.02], P?<.001, respectively), independent of age, sex, ethnicity, and total intracranial volume. While age was associated with the size of most subsegmental regions of the cerebral cortex, telomere length was associated with certain subsegmental regions. Compared with age, telomere length (TL) explained a sizeable proportion of the variance in volume of the hippocampus, amygdala, and inferior temporal region (hippocampus: ?TL [SE], 0.08 [0.02], R2, 0.91% vs ?age [SE], -0.16 [0.02], R2, 3.80%; amygdala: ?TL [SE], 0.08 [0.02], R2, 0.78% vs ?age [SE],-0.19 [0.02], R2,4.63%; inferior temporal: ?TL [SE], 0.07 [0.02], R2, 0.92% vs ?age [SE], -0.14 [0.02], R2, 3.98%) (P?<.001 for all). The association of telomere length and the size of the inferior and superior parietal, hippocampus, and fusiform regions was stronger in individuals older than 50 years than younger individuals (inferior parietal: ?>50 [SE], 0.13 [0.03], P?<.001 vs ??50 [SE], 0.02 [0.02], P?=?.51, P for interaction = .001; superior parietal: ?>50 [SE], 0.11 [0.03], P?<.001 vs ??50 [SE], 0.01 [0.02], P?=?.71, P for interaction = .004; hippocampus: ?>50 [SE], 0.10 [0.03], P?=?.004 vs ??50 [SE], 0.05 [0.02], P?=?.07, P for interaction = .04; fusiform: ?>50 [SE], 0.09 [0.03], P?=?.002, ??50 [SE], 0.03 [0.02], P?=?.31, P for interaction = .03). The volume of the hippocampus, amygdala, superior and inferior temporal, precuneus, lateral orbitofrontal, posterior cingulate, thalamus and ventral diencephalon were independently associated with telomere length after adjustment for all covariates (age, gender, ethnicity, total intracranial volume, body mass index, blood pressure, diabetes, smoking status, and APOE genotype). CONCLUSIONS AND RELEVANCE:To our knowledge, this is the first population-based study to date to evaluate telomere length as an independent predictor of global and regional brain size. Future studies are needed to determine how telomere length and anatomic structural changes are related to cognitive function, dementia, and psychological disease.
| S-EPMC5479062 | biostudies-literature
Project description:Neuroscience studies require considerable bioinformatic support and expertise. Numerous high-dimensional and multimodal datasets must be preprocessed and integrated to create robust and reproducible analysis pipelines. We describe a common data elements and scalable data management infrastructure that allows multiple analytics workflows to facilitate preprocessing, analysis and sharing of large-scale multi-level data. The process uses the Brain Imaging Data Structure (BIDS) format and supports MRI, fMRI, EEG, clinical, and laboratory data. The infrastructure provides support for other datasets such as Fitbit and flexibility for developers to customize the integration of new types of data. Exemplar results from 200+ participants and 11 different pipelines demonstrate the utility of the infrastructure.
| S-EPMC8247461 | biostudies-literature
Project description:Mapping human behaviors to brain activity has become a key focus in modern cognitive neuroscience. As methods such as functional MRI (fMRI) advance cognitive scientists show an increasing interest in investigating neural activity in terms of functional connectivity and brain networks, rather than activation in a single brain region. Due to the noisy nature of neural activity, determining how behaviors are associated with specific neural signals is not well-established. Previous research has suggested graph theory techniques as a solution. Graph theory provides an opportunity to interpret human behaviors in terms of the topological organization of brain network architecture. Graph theory-based approaches, however, only scratch the surface of what neural connections relate to human behavior. Recently, the development of data-driven methods, e.g., machine learning and deep learning approaches, provide a new perspective to study the relationship between brain networks and human behaviors across the whole brain, expanding upon past literatures. In this review, we sought to revisit these data-driven approaches to facilitate our understanding of neural mechanisms and build models of human behaviors. We start with the popular graph theory approach and then discuss other data-driven approaches such as connectome-based predictive modeling, multivariate pattern analysis, network dynamic modeling, and deep learning techniques that quantify meaningful networks and connectivity related to cognition and behaviors. Importantly, for each topic, we discuss the pros and cons of the methods in addition to providing examples using our own data for each technique to describe how these methods can be applied to real-world neuroimaging data.
| S-EPMC9244405 | biostudies-literature
Project description:The accurate prediction of general neuropsychiatric disorders, on an individual basis, using resting-state functional magnetic resonance imaging (fMRI) is a challenging task of great clinical significance. Despite the progress to chart the differences between the healthy controls and patients at the group level, the pattern classification of functional brain networks across individuals is still less developed. In this paper we identify two novel neuroimaging measures that prove to be strongly predictive neuroimaging markers in pattern classification between healthy controls and general epileptic patients. These measures characterize two important aspects of the functional brain network in a quantitative manner: (i) coordinated operation among spatially distributed brain regions, and (ii) the asymmetry of bilaterally homologous brain regions, in terms of their global patterns of functional connectivity. This second measure offers a unique understanding of brain asymmetry at the network level, and, to the best of our knowledge, has not been previously used in pattern classification of functional brain networks. Using modern pattern-recognition approaches like sparse regression and support vector machine, we have achieved a cross-validated classification accuracy of 83.9% (specificity: 82.5%; sensitivity: 85%) across individuals from a large dataset consisting of 180 healthy controls and epileptic patients. We identified significantly changed functional pathways and subnetworks in epileptic patients that underlie the pathophysiological mechanism of the impaired cognitive functions. Specifically, we find that the asymmetry of brain operation for epileptic patients is markedly enhanced in temporal lobe and limbic system, in comparison with healthy individuals. The present study indicates that with specifically designed informative neuroimaging markers, resting-state fMRI can serve as a most promising tool for clinical diagnosis, and also shed light onto the physiology behind complex neuropsychiatric disorders. The systematic approaches we present here are expected to have wider applications in general neuropsychiatric disorders.
| S-EPMC3355144 | biostudies-literature
Project description:Quality control (QC) is a critical step in large-scale studies of genetic variation. While, on average, high-throughput single nucleotide polymorphism (SNP) genotyping assays are now very accurate, the errors that remain tend to cluster into a small percentage of "problem" SNPs, which exhibit unusually high error rates. Because most large-scale studies of genetic variation are searching for phenomena that are rare (e.g., SNPs associated with a phenotype), even this small percentage of problem SNPs can cause important practical problems. Here we describe and illustrate how patterns of linkage disequilibrium (LD) can be used to improve QC in large-scale, population-based studies. This approach has the advantage over existing filters (e.g., HWE or call rate) that it can actually reduce genotyping error rates by automatically correcting some genotyping errors. Applying this LD-based QC procedure to data from The International HapMap Project, we identify over 1,500 SNPs that likely have high error rates in the CHB and JPT samples and estimate corrected genotypes. Our method is implemented in the software package fastPHASE, available from the Stephens Lab website (http://stephenslab.uchicago.edu/software.html).
| S-EPMC2475504 | biostudies-literature