Biology subjects

Thompson, P. M.

Publications and source records attributed to Thompson, P. M..

10 recordsLinked to original sources

bioRxiv2018

Concordance Of Genetic Variation That Increases Risk For Tourette Syndrome And That Influences Its Underlying Neurocircuitry

BACKGROUNDThere have been considerable recent advances in understanding the genetic architecture of Tourette Syndrome (TS) as well as its underlying neurocircuitry. However, the mechanisms by which genetic variations that increase risk for TS - and its main symptom dimensions - influence relevant brain regions are poorly understood. Here we undertook a genome-wide investigation of the overlap between TS genetic risk and genetic influences on the volume of specific subcortical brain structures that have been implicated in TS.\n\nMETHODSWe obtained summary statistics for the most recent TS genome-wide association study (GWAS) from the TS Psychiatric Genomics Consortium Working Group (4,644 cases and 8,695 controls) and GWAS of subcortical volumes from the ENIGMA consortium (30,717 individuals). We also undertook analyses using GWAS summary statistics of key symptom factors in TS, namely social disinhibition and symmetry behaviour. SNP Effect Concordance Analysis (SECA) was used to examine genetic pleiotropy - the same SNP affecting two traits - and concordance - the agreement in SNP effect directions across these two traits. In addition, a conditional false discovery rate (FDR) analysis was performed, conditioning the TS risk variants on each of the seven subcortical and the intracranial brain volume GWAS. Linkage Disequilibrium Score Regression (LDSR) was used as validation of SECA.\n\nRESULTSSECA revealed significant pleiotropy between TS and putaminal (p=2x10-4) and caudal (p=4x10-4) volumes, independent of direction of effect, and significant concordance between TS and lower thalamic volume (p=1x10-3). LDSR lent additional support for the association between TS and thalamic volume (p=5.85x10-2). Furthermore, SECA revealed significant evidence of concordance between the social disinhibition symptom dimension and lower thalamic volume (p=1x10-3), as well as concordance between symmetry behaviour and greater putaminal volume (p=7x10-4). Conditional FDR analysis further revealed novel variants significantly associated with TS (p<8x10-7) when conditioning on intracranial (rs2708146, q=0.046; and rs72853320, q=0.035 and hippocampal (rs1922786, q=0.001 volumes respectively.\n\nCONCLUSIONThese data indicate concordance for genetic variations involved in disorder risk and subcortical brain volumes in TS. Further work with larger samples is needed to fully delineate the genetic architecture of these disorders and their underlying neurocircuitry.

genomics↗

bioRxiv2017

Genome-wide association analysis links multiple psychiatric liability genes to oscillatory brain activity

Oscillations in neuronal activity are widely thought to play a crucial role in information processing and cortical communication 1-8. Brain oscillations have been widely investigated as biomarkers of psychiatric disorders and variation in normal human behavior 9, 10, including intelligence 11, 12, schizophrenia 13, 14, attentional deficits 15, 16, and substance use 17, 18. Beyond the biomarker, oscillatory activity may indeed cause variation in behavior, as it has been shown that disrupting oscillatory activity by blocking GABAergic fast-spiking interneurons in the frontal cortex of mice impairs behavioral flexibility8, consistent with ...

genetics↗

bioRxiv2017

White matter differences in Parkinson’s disease mapped using tractometry

Neurodegenerative disorders are characterized by a progressive loss of brain function. Improved precision in mapping the altered brain pathways can provide a deep understanding of the trajectory of decline. We propose a tractometry workflow for conducting group statistical analyses of point-wise microstructural measures along white matter fasciculi to identify patterns of abnormalities associated with disease. We combined state-of-the-art tools including fiber registration, tract simplification and fiber matching for accurate point-wise statistical analyses across populations. We test the utility of this method by identifying group differences between Parkinsons disease (PD) patients and healthy controls. We find statistically significant group differences in diffusion MRI derived measures along the anterior thalamic radiations (ATR), corticospinal tract (CST) and regions of the corpus callosum (CC). These pathways are essential for motor control systems within cortico-cortical and cortico-subcortical brain networks. Moreover, the reported pathological changes were not widespread but rather localized along several tracts. Point-wise tract analyses may therefore offer an advantage in anatomical specificity over traditional methods that assess mean microstructural measures across large regions of interest.

neuroscience↗

bioRxiv2017

FiberNet 2.0: An Automatic Neural Network Based Tool for Clustering White Matter Fibers in the Brain

The brains white matter fiber tracts are impaired in a range of common and devastating conditions, from Alzheimers disease to brain trauma, and in developmental disorders such as autism and neurogenetic syndromes. Many studies now examine the connectivity and microstructure of the brains neural pathways, spurring the development of algorithms to extract and measure tracts and fiber bundles. Clustering white matter (WM) fibers, from whole-brain tractography, into anatomically meaningful bundles is still a challenging problem. Existing tract segmentation methods use atlases or regions of interest (ROI) or unsupervised spectral clustering. Even so, atlas-based segmentation does not always partition the brain into a set of recognizable fiber bundles. Deep learning techniques can be applied to automatically segment and cluster white matter fibers. Here we propose a robust approach using convolutional neural networks (CNNs) to learn shape features of the fiber bundles, which we then exploit to cluster WM fibers into bundles. In a range of tests across diverse fiber bundles, we illustrate the accuracy of our method, and its ability to suppress false positive fibers.

neuroscience↗

bioRxiv2017

Heritability of regional brain volumes in large-scale neuroimaging and genetic studies

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. We observed omni-genic impact across the genome as well as 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 and brain regions. The variation in heritability across regions was not related to measurement reliability. Heritability estimates exhibit a symmetric pattern across left and right hemispheres and are consistent in females and males. Our main findings in UK Biobank are consistent with those in Alzheimers Disease Neuroimaging Initiative (n ~ 1100), Philadelphia Neurodevelopmental Cohort (n ~ 600), and Pediatric Imaging, Neurocognition, and Genetics (n ~ 500) datasets, with more stable estimates in UK Biobank.

genetics↗

bioRxiv2017

Amygdalar atrophy as the genetically mediated hub of limbic degeneration in Alzheimer’s disease

Pharmacological progress, basic science and medical practice can benefit from objective biomarkers that assist in early diagnosis and prognostic stratification of diseases. In the field of Alzheimers disease (AD), the clinical presentation of early stage dementia may not fulfill any diagnostic criteria for years, and quantifying structural brain changes by magnetic resonance imaging (MRI) has shown promise in the discovery of sensitive biomarkers. Although hippocampal atrophy is often used as an in vivo measure of AD, data-driven neuroimaging has revealed complex patterns of regional brain vulnerability that may not perfectly map to anatomical boundaries. In addition to aiding diagnosis, decoding genetic influences on neuroimaging measures of the disease can enlighten molecular mechanisms of the underlying pathology in living patients and guide the therapeutic design.\n\nHere, we aimed to extract a data-driven MRI feature of brain atrophy in AD by decomposing structural neuroimages using independent component analysis (ICA), a method for performing unbiased computational search in high dimensional data spaces. Our study of the AD Neuroimaging Initiative dataset (n=1,100 subjects) revealed a disease-vulnerable feature with a network-like topology, comprising amygdala, hippocampus, fornix and the inter-connecting white-matter tracts of the limbic system. Whole-genome sequencing identified a nonsynonymous variant (rs34173062) in SHARPIN, a gene coding for a synaptic protein, as a significant modifier of this new MRI feature (p=2.1x10-10). The risk variant was brought to replication in the UK Biobank dataset (n=8,428 subjects), where it was associated with reduced cortical thickness in areas co-localizing with those of the discovery sample (left entorhinal cortex p=0.002, right entorhinal cortex p=8.6x10-4; same direction), as well as with the history of AD in both parents (p=2.3x10-6; same direction).\n\nIn conclusion, our study shows that ICA can transform voxel-wise volumetric measures of the brain into a data-driven feature of neurodegeneration in AD. Structure of the limbic system, as the most vulnerable focus of brain atrophy in AD, is affected by genetic variability of SHARPIN. The elevated risk of dementia in carriers of the minor allele supports engagement of SHARPIN in the disease pathways, and its role in neurotransmitter receptor scaffolding and integrin signaling may inform on new molecular mechanisms of AD pathophysiology.\n\nAbbreviationsAlzheimers disease (AD), genome-wide association study (GWAS), independent component analysis (ICA), mild cognitive impairment (MCI), medial temporal circuit (MTC), single-nucleotide polymorphism (SNP), tensor-based morphometry (TBM)

neuroscience↗

bioRxiv2017

Genetic markers of ADHD-related variations in intracranial volume

Attention-Deficit/Hyperactivity Disorder (ADHD) is a common and highly heritable neurodevelopmental disorder with a complex pathophysiology, where genetic risk is hypothesized to be mediated by alterations in structure and function of diverse brain networks. We tested one aspect of this hypothesis by investigating the genetic overlap between ADHD (n=55,374) and (mainly subcortical) brain volumes (n=11,221-24,704), using the largest publicly available studies. At the level of common variant genetic architecture, we discovered a significant negative genetic correlation between ADHD and intracranial volume (ICV). Meta-analysis of individual variants found significant loci associated with both ADHD risk and ICV; additional loci were identified for ADHD and amygdala, caudate nucleus, and putamen volumes. Gene-set analysis in the ADHD-ICV meta-analytic data showed significant association with variation in neurite outgrowth-related genes. In summary, our results suggest new hypotheses about biological mechanisms involved in ADHD etiology and highlight the need to study additional brain parameters.

genetics↗

bioRxiv2017

FiberNET: An ensemble deep learning framework for clustering white matter fibers

White matter tracts are commonly analyzed in studies of micro-structural integrity and anatomical connectivity in the brain. Over the last decade, it has been an open problem as to how best to cluster white matter fibers, extracted from whole-brain tractography, into anatomically meaningful groups. Some existing techniques use region of interest (ROI) based clustering, atlas-based labeling, or unsupervised spectral clustering. ROI-based clustering is popular for analyzing anatomical connectivity among a set of ROIs, but it does not always partition the brain into recognizable fiber bundles. Here we propose an approach using convolutional neural networks (CNNs) to learn shape features of the fiber bundles, which are then exploited to cluster white matter fibers. To achieve such clustering, we first need to re-parameterize the fibers in an intrinsic space. The clustering is performed in induced parameterized coordinates. To our knowledge, this is one of the first approaches for fiber clustering using deep learning techniques. The results show strong accuracy - on a par with or better than other state-of-the-art methods.

bioinformatics↗

bioRxiv2017

Volumetric grey matter alterations in adolescents and adults born very preterm suggest accelerated brain maturation

Previous research investigating structural neurodevelopmental alterations in individuals who were born very preterm demonstrated a complex pattern of grey matter changes that defy straightforward summary. Here we addressed this problem by characterising volumetric brain alterations in individuals who were born very preterm from adolescence to adulthood at three hierarchically related levels - global, modular and regional. We demarcated structural components that were either particularly resilient or vulnerable to the impact of very preterm birth. We showed that individuals who were born very preterm had smaller global grey matter volume compared to controls, with subcortical and medial temporal regions being particularly affected. Conversely, frontal and lateral parieto-temporal cortices were relatively resilient to the effects of very preterm birth, possibly indicating compensatory mechanisms. Exploratory analyses supported this hypothesis by showing a stronger association of lateral parieto-temporal volume with IQ in the very preterm group compared to controls. We then related these alterations to brain maturation processes. Very preterm individuals exhibited a higher maturation index compared to controls, indicating accelerated brain ageing and this was specifically associated with younger gestational age. We discuss how the findings of accelerated maturation might be reconciled with evidence of delayed maturation at earlier stages of development.

neuroscience↗

bioRxiv2017

Improving data availability for brain image biobanking in healthy subjects: practice-based suggestions from an international multidisciplinary working group

Brain imaging is now ubiquitous in clinical practice and research. The case for bringing together large amounts of image data from well-characterised healthy subjects and those with a range of common brain diseases across the life course is now compelling. This report follows a meeting of international experts from multiple disciplines, all interested in brain image biobanking. The meeting included neuroimaging experts (clinical and non-clinical), computer scientists, epidemiologists, clinicians, ethicists, and lawyers involved in creating brain image banks. The meeting followed a structured format to discuss current and emerging brain image banks; applications such as atlases; conceptual and statistical problems (e.g. defining normality); legal, ethical and technological issues (e.g. consents, potential for data linkage, data security, harmonisation, data storage and enabling of research data sharing). We summarise the lessons learned from the experiences of a wide range of individual image banks, and provide practical recommendations to enhance creation, use and reuse of neuroimaging data. Our aim is to maximise the benefit of the image data, provided voluntarily by research participants and funded by many organisations, for human health. Our ultimate vision is of a federated network of brain image biobanks accessible for large studies of brain structure and function.

neuroscience↗