DOCSLIB.ORG

Cognitive Genomics: Linking Genes to Behavior in the Human Brain

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Cognitive Genomics: Linking Genes to Behavior in the Human Brain PERSPECTIVE Cognitive genomics: Linking genes to behavior in the human brain Genevieve Konopka Department of Neuroscience, UT Southwestern Medical Center, Dallas, TX 75390-9111, USA ABSTRACT Correlations of genetic variation in DNA with functional brain activity have already provided a starting point for delving into human cognitive mechanisms. However, these analyses do not provide the specific genes driving the associations, which are complicated by intergenic Downloaded from http://direct.mit.edu/netn/article-pdf/1/1/3/1091843/netn_a_00003.pdf by guest on 30 September 2021 localization as well as tissue-specific epigenetics and expression. The use of brain-derived an open access journal expression datasets could build upon the foundation of these initial genetic insights and yield genes and molecular pathways for testing new hypotheses regarding the molecular bases of human brain development, cognition, and disease. Thus, coupling these human brain gene expression data with measurements of brain activity may provide genes with critical roles in brain function. However, these brain gene expression datasets have their own set of caveats, most notably a reliance on postmortem tissue. In this perspective, I summarize and examine the progress that has been made in this realm to date, and discuss the various frontiers remaining, such as the inclusion of cell-type-specific information, additional physiological measurements, and genomic data from patient cohorts. Progress in understanding the inner workings of the brain has come a long way from the preneu- roscience era of phrenology, when we were limited to conjectures about human behavior Citation: Konopka G. (2017). Cognitive genomics: Linking genes to behavior based on the shape of the skull. Over the past quarter century, technological breakthroughs in the human brain. Network have given us the ability to noninvasively peer into the operations of the human brain during Neuroscience, 1(1), 3–13. doi:10.1162/netn_a_00003 behavior, by means of a host of imaging and physiological techniques. Functional imaging DOI: has provided elegant maps of human activity at rest, as well as during any number of cogni- http://doi.org/10.1162/netn_a_00003 tive tasks. By coupling these results with neuroanatomical and structural imaging, function and Supporting Information: structure can be married to identify brain regions that work in concert to execute specific func- tions. Furthermore, when such approaches are carried out in patients with neuropsychiatric Received: 16 September 2016 disorders, the regional brain activity relevant to cognitive phenotypes can be uncovered. Accepted: 22 November 2016 Competing Interests: The author has Genetic Contributions to Cognition declared that no competing interests exist. Determining the relative contribution of genes to cognition has been a longstanding interest Corresponding Author: in the field of genetic research. Recent inquiries have focused on unlocking the genetic and Genevieve Konopka molecular mechanisms underlying human brain activity (see the discussion and references [email protected] in Medland, Jahanshad, Neale, & Thompson, 2014,andThompson, Ge, Glahn, Jahanshad, Handling Editor: & Nichols, 2013). Key insights have been made, such as the heritability of functional brain Olaf Sporns networks (Fornito et al., 2011; Fu et al., 2015; Glahn et al., 2010; Yang et al., 2016)andthe correlation of genetic variation in altered functional connectivity in specific diseases Copyright: © 2017 or phenotypes (see the references in Gaiteri, Mostafavi, Honey, De Jager, & Bennett, 2016; Massachusetts Institute of Technology Hernandez, Rudie, Green, Bookheimer, & Dapretto, 2015). As such, these advances could Published under a Creative Commons Attribution 4.0 International have profound implications for how we diagnose and treat such disorders (see the discus- (CC BY 4.0) license sion and references in Matthews & Hampshire, 2016). Furthermore, genome-wide association The MIT Press Cognitive genomics: Linking genes to behavior in the human brain studies have identified specific genomic loci that are significantly associated with subcorti- cal brain structures (Hibar et al., 2015); with educational attainment as a proxy for cognition in general (Okbay, Beauchamp, et al., 2016); with personality traits such as subjective well-being, depressive symptoms, and neuroticism (Okbay, Baselmans, et al., 2016); and with cognitive disorders such as schizophrenia (Schizophrenia Working Group of the Psychiatric Genomics Consortium, 2014). These molecular and genetic insights provide a baseline for ultimately pinpointing drug targets in a number of cognitive disorders, as well as deepening our understanding of both the developmental and evolutionary origins of human cognition. Thus, further investigations into the molecular mechanisms underlying human brain activity are needed to bridge the gap between genes and behavior. Quantifying Gene Expression in the Human Brain Downloaded from http://direct.mit.edu/netn/article-pdf/1/1/3/1091843/netn_a_00003.pdf by guest on 30 September 2021 The genome revolution, followed rapidly by implementation of the high-throughput techno- Microarrays: logies of microarrays and next-generation sequencing, has permitted investigations of human Technology that uses preselected brain gene expression in a spatiotemporal manner, by quantifying RNA amounts at a genome- oligonucleotides to quantify RNA wide level (e.g., Kang et al., 2011). The analysis of gene transcription across the entire human amounts on a genome-wide basis. brain allows for distinguishing the genes expressed in specific brain regions during a given (See Box 1.) developmental time period, and thus results in a quantitative measurement of gene expres- sion levels. These datasets are different from the genetic associations mentioned above, in which changes at the DNA level are identified. Such genetic variation might be within re- gions of DNA of unknown functional significance (e.g., do the variants affect gene expres- sion?) and might also interact with unknown epigenetic markers in a tissue-specific manner, leading to further ambiguity about the resultant gene expression. Surveying the vast transcrip- tional landscape of the developing and adult human brain has been facilitated by the work of the Allen Institute for Brain Science in collaboration with a number of academic groups, to develop several reference gene expression atlases of the human brain, by using a combi- In situ hybridization: nation of in situ hybridization, microarrays, and RNA sequencing (RNA-seq) throughout the Probe-based technology used to human lifespan (Hawrylycz et al., 2015; Hawrylycz et al., 2012; Miller et al., 2014; Zeng et al., assess the spatial expression of 2012;seeBox 1). One of the caveats to these assessments of human brain gene ex- specific RNA molecules (See Box 1.) pression is that they are naturally limited to postmortem tissue. Although careful statistical RNA sequencing (RNA-seq): analyses take into consideration experimental covariates such as postmortem interval and RNA Unbiased quantification of RNA quality, there is always the possibility that patterns of gene expression in behaving molecules on a genome-wide basis. individuals cannot be fully recapitulated in postmortem tissue. Nevertheless, these assess- (See Box 1.) ments provide critical insights into human brain gene expression patterns, based on develop- mental stage (Kang et al., 2011; Miller et al., 2014), gender (Kang et al., 2011), hemispheric lateralization (or lack thereof; Hawrylycz et al., 2012; Johnson et al., 2009; Pletikos et al., 2014), and human-specific evolution (Bakken et al., 2016; Bernard et al., 2012). These obser- vations can then be compared with disease-relevant datasets. For example, genetic data from autism spectrum disorder (ASD) patients were integrated with the BrainSpan gene expression dataset (www.brainspan.org) to identify ASD-relevant coexpression networks (Parikshak et al., 2013; Willsey et al., 2013). Furthermore, genomic profiling of disease tissue itself can be insightful, as has been the case for ASD, where such profiling has identified differentially ex- pressed networks of mRNAs and microRNAs in ASD brains as compared to matched con- trols (Voineagu et al., 2011; Y. E. Wu, Parikshak, Belgard, & Geschwind, 2016). Together, these studies of human brain gene expression have facilitated the prioritization of specific genes and molecular pathways for further in-depth analyses. However, such follow-up studies are largely limited to animal models, and there appears to be a large divide between what can be observed at the gene expression level in brain tissue and at the behavioral level in humans. Network Neuroscience 4 Cognitive genomics: Linking genes to behavior in the human brain Box 1. Descriptions of Gene Expression Detection Methods In situ hybridization is carried out by hybridizing gene-specific RNA probes to tissue samples. This method provides spatial resolution of the mRNA expression of individual genes. High- quality tissue specimens and highly specific probes to each gene are required for accurate detection. A major advantage of in situ hybridization is the ability to couple it with immuno- histochemistry to make mRNA and protein correlations. However, the quantification of in situ hybridization is challenging, depending on the method used. The greatest advantages of in situ hybridization over the other technologies discussed below are its spatial resolution and ability to detect expression
Load more

Recommended publications
  • Imaging Genetic Strategies for Predicting the Quality of Sleep Using Depression-Specific Biomarkers
    Imaging genetic strategies for predicting the quality of sleep using depression-specific biomarkers Mansu Kim 1, Xiaohui Yao 1, Bo-yong Park 2, Jingwen Yan 3, and Li Shen1,* 1Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, USA 2McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Canada 3Department of BioHealth Informatics, Indiana University School of Informatics and Computing, Indiana University, USA * Correspondence to [email protected] Overview Joint-connectivity-based sparse CCA Background: Sleep is an essential phenomenon for SNP connectivity Imaging genetics model: The joint-connectivity-based sparse canonical ··· X* samples × maintaining good health and wellbeing [1]. correlation analysis (JCB-SCCA) was applied on preprocessed features n Some studies reported that genetic and p SNPs (Figure 1). JCB-SCCA has an advantage for incorporating connectivity Loading vector u Maximum imaging biomarkers that depression is information and can handle multi-modal neuroimaging datasets. correlation Brain associated with sleep disorder [2]–[4]. connectivity ··· Prior biological knowledge: The average connectivity matrix computed ··· K samples Y* modalitiesn × Imaging genetics: Many studies adopted imaging from the HCP dataset and linkage disequilibrium obtained from 1,000 q voxels genetics methodology to find associations genome project were used as the prior connectivity information of the K = 2 Loading vector V between imaging and genetic biomarkers. In algorithm. The parameters of the algorithm were tuned jointly by nested K = 1 this study, we examine the imaging genetics five-fold cross-validation. Figure 1. JCB-SCCA association in depression and extract biomarkers for predicting the quality of sleep. Experiments and Results Prediction task: We built a prediction model for the Pittsburgh sleep quality index (PSQI) using the identified biomarkers.
    [Show full text]
  • Independent Evidence for an Association Between General Cognitive Ability and a Genetic Locus for Educational Attainment J
    View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by Hofstra Northwell Academic Works (Hofstra Northwell School of Medicine) Donald and Barbara Zucker School of Medicine Journal Articles Academic Works 2015 Independent evidence for an association between general cognitive ability and a genetic locus for educational attainment J. W. Trampush Hofstra Northwell School of Medicine T. Lencz Hofstra Northwell School of Medicine S. Guha Northwell Health S. Mukherjee Northwell Health P. DeRosse Northwell Health See next page for additional authors Follow this and additional works at: https://academicworks.medicine.hofstra.edu/articles Part of the Psychiatry Commons Recommended Citation Trampush JW, Lencz T, Guha S, Mukherjee S, DeRosse P, John M, Andreassen O, Deary I, Glahn D, Malhotra AK, . Independent evidence for an association between general cognitive ability and a genetic locus for educational attainment. 2015 Jan 01; 168(5):Article 927 [ p.]. Available from: https://academicworks.medicine.hofstra.edu/articles/927. Free full text article. This Article is brought to you for free and open access by Donald and Barbara Zucker School of Medicine Academic Works. It has been accepted for inclusion in Journal Articles by an authorized administrator of Donald and Barbara Zucker School of Medicine Academic Works. Authors J. W. Trampush, T. Lencz, S. Guha, S. Mukherjee, P. DeRosse, M. John, O. A. Andreassen, I. J. Deary, D. C. Glahn, A. K. Malhotra, and +41 additional authors This article is available at Donald and Barbara Zucker School of Medicine Academic Works: https://academicworks.medicine.hofstra.edu/articles/927 HHS Public Access Author manuscript Author Manuscript Author ManuscriptAm J Med Author Manuscript Genet B Neuropsychiatr Author Manuscript Genet.
    [Show full text]
  • Genetic Influence on Cognitive Development Between Childhood
    Molecular Psychiatry https://doi.org/10.1038/s41380-018-0277-0 ARTICLE Genetic influence on cognitive development between childhood and adulthood 1 1 1 2 3 Josephine Mollon ● Emma E. M. Knowles ● Samuel R. Mathias ● Ruben Gur ● Juan Manuel Peralta ● 4,5,6 4,5,6 2 3 7 1,8 Daniel J. Weiner ● Elise B. Robinson ● Raquel E. Gur ● John Blangero ● Laura Almasy ● David C. Glahn Received: 17 April 2018 / Revised: 15 August 2018 / Accepted: 11 September 2018 © Springer Nature Limited 2018 Abstract Successful cognitive development between childhood and adulthood has important consequences for future mental and physical wellbeing, as well as occupational and financial success. Therefore, delineating the genetic influences underlying changes in cognitive abilities during this developmental period will provide important insights into the biological mechanisms that govern both typical and atypical maturation. Using data from the Philadelphia Neurodevelopmental Cohort (PNC), a large population-based sample of individuals aged 8 to 21 years old (n = 6634), we used an empirical relatedness matrix to establish the heritability of general and specific cognitive functions and determine if genetic factors influence 1234567890();,: 1234567890();,: cognitive maturation (i.e., Gene × Age interactions) between childhood and early adulthood. We found that neurocognitive measures across childhood and early adulthood were significantly heritable. Moreover, genetic variance on general cognitive ability, or g, increased significantly between childhood and early adulthood. Finally, we did not find evidence for decay in genetic correlation on neurocognition throughout childhood and adulthood, suggesting that the same genetic factors underlie cognition at different ages throughout this developmental period. Establishing significant Gene × Age interactions in neurocognitive functions across childhood and early adulthood is a necessary first step in identifying genes that influence cognitive development, rather than genes that influence cognition per se.
    [Show full text]
  • Polygenic Evidence and Overlapped Brain Functional Connectivities For
    Sun et al. Translational Psychiatry (2020) 10:252 https://doi.org/10.1038/s41398-020-00941-z Translational Psychiatry ARTICLE Open Access Polygenic evidence and overlapped brain functional connectivities for the association between chronic pain and sleep disturbance Jie Sun 1,2,3,WeiYan2,Xing-NanZhang2, Xiao Lin2,HuiLi2,Yi-MiaoGong2,Xi-MeiZhu2, Yong-Bo Zheng2, Xiang-Yang Guo3,Yun-DongMa2,Zeng-YiLiu2,LinLiu2,Jia-HongGao4, Michael V. Vitiello 5, Su-Hua Chang 2,6, Xiao-Guang Liu 1,7 and Lin Lu2,6 Abstract Chronic pain and sleep disturbance are highly comorbid disorders, which leads to barriers to treatment and significant healthcare costs. Understanding the underlying genetic and neural mechanisms of the interplay between sleep disturbance and chronic pain is likely to lead to better treatment. In this study, we combined 1206 participants with phenotype data, resting-state functional magnetic resonance imaging (rfMRI) data and genotype data from the Human Connectome Project and two large sample size genome-wide association studies (GWASs) summary data from published studies to identify the genetic and neural bases for the association between pain and sleep disturbance. Pittsburgh sleep quality index (PSQI) score was used for sleep disturbance, pain intensity was measured by Pain Intensity Survey. The result showed chronic pain was significantly correlated with sleep disturbance (r = 0.171, p-value < 0.001). Their genetic correlation was rg = 0.598 using linkage disequilibrium (LD) score regression analysis. Polygenic score (PGS) association analysis showed PGS of chronic pain was significantly associated with sleep and vice versa. 1234567890():,; 1234567890():,; 1234567890():,; 1234567890():,; Nine shared functional connectivity (FCs) were identified involving prefrontal cortex, temporal cortex, precentral/ postcentral cortex, anterior cingulate cortex, fusiform gyrus and hippocampus.
    [Show full text]
  • Whole‐Exome Sequencing in 20,197 Persons for Rare Variants In
    RESEARCH PAPER Whole-exome sequencing in 20,197 persons for rare variants in Alzheimer’s disease Neha S. Raghavan1,2, Adam M. Brickman1,2,3, Howard Andrews1,2,4, Jennifer J. Manly1,2,3, Nicole Schupf1,2,3,7, Rafael Lantigua1,6, Charles J. Wolock8, Sitharthan Kamalakaran8, Slave Petrovski8,9, Giuseppe Tosto1,2,3, Badri N. Vardarajan1,2,3,5, David B. Goldstein3,6,8, Richard Mayeux1,2,3,4,7 & The Alzheimer’s Disease Sequencing Projecta 1The Taub Institute for Research on Alzheimer’s Disease and the Aging Brain, College of Physicians and Surgeons, Columbia University, The New York Presbyterian Hospital, New York, New York 2The Gertrude H. Sergievsky Center, College of Physicians and Surgeons, Columbia University, The New York Presbyterian Hospital, New York, New York 3Department of Neurology, College of Physicians and Surgeons, Columbia University, The New York Presbyterian Hospital, New York, New York 4Department of Psychiatry, College of Physicians and Surgeons, Columbia University, The New York Presbyterian Hospital, New York, New York 5Department of Systems Biology, College of Physicians and Surgeons, Columbia University, The New York Presbyterian Hospital, New York, New York 6Department of Medicine, College of Physicians and Surgeons, Columbia University, The New York Presbyterian Hospital, New York, New York 7The Department of Epidemiology, Mailman School of Public Health, College of Physicians and Surgeons, Columbia University, The New York Presbyterian Hospital, New York, New York 8Institute of Genomic Medicine, Columbia University, The New York Presbyterian Hospital, New York, New York 9AstraZeneca Centre for Genomics Research, Precision Medicine and Genomics, IMED Biotech Unit, AstraZeneca, Cambridge, CB2 0AA, United Kingdom Correspondence Abstract Richard Mayeux, Department of Neurology, Objective 710 West 168th Street, Columbia University, : The genetic bases of Alzheimer’s disease remain uncertain.
    [Show full text]
  • Local Connectome Phenotypes Predict Social, Health, and Cognitive Factors
    RESEARCH Local connectome phenotypes predict social, health, and cognitive factors 1 2,3 4,5 Michael A. Powell , Javier O. Garcia , Fang-Cheng Yeh , 2,3,6 7 Jean M. Vettel , and Timothy Verstynen 1Department of Mathematical Sciences, United States Military Academy, West Point, NY, USA 2U.S. Army Research Laboratory, Aberdeen Proving Ground, MD, USA 3Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA 4Department of Neurological Surgery, University of Pittsburgh Medical Center, Pittsburgh, PA, USA 5Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, USA 6Department of Psychological and Brain Sciences, University of California, Santa Barbara, CA, USA 7 an open access journal Department of Psychology and Center for the Neural Basis of Cognition, Carnegie Mellon University, Pittsburgh, PA, USA Keywords: Local connectome, White matter, Individual differences, Behavior prediction, Structural connectivity ABSTRACT The unique architecture of the human connectome is defined initially by genetics and subsequently sculpted over time with experience. Thus, similarities in predisposition and experience that lead to similarities in social, biological, and cognitive attributes should also be reflected in the local architecture of white matter fascicles. Here we employ a method known as local connectome fingerprinting that uses diffusion MRI to measure the fiber-wise characteristics of macroscopic white matter pathways throughout the brain. This Citation: Powell, M. A., Garcia, J. O., fingerprinting approach was applied to a large sample (N = 841) of subjects from the Yeh, F.-C., Vettel, J. M., & Verstynen, T. (2017). Local connectome phenotypes Human Connectome Project, revealing a reliable degree of between-subject correlation in predict social, health, and cognitive factors.
    [Show full text]
  • Proteomic Analysis of Postsynaptic Proteins in Regions of the Human Neocortex
    Proteomic analysis of postsynaptic proteins in regions of the human neocortex Marcia Roy1*, Oksana Sorokina2*, Nathan Skene1, Clemence Simonnet1, Francesca Mazzo3, Ruud Zwart3, Emanuele Sher3, Colin Smith1, J Douglas Armstrong2 and Seth GN Grant1. * equal contribution Author Affiliations: 1. Genes to Cognition Program, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh EH16 4SB, United Kingdom 2. School of Informatics, University of Edinburgh, Edinburgh, EH8 9AB, United Kingdom 3. Lilly Research Centre, Eli Lilly & Company, Erl Wood Manor, Windlesham, GU20 6PH, United Kingdom 1 Abstract: The postsynaptic proteome of excitatory synapses comprises ~1,000 highly conserved proteins that control the behavioral repertoire and mutations disrupting their function cause >130 brain diseases. Here, we document the composition of postsynaptic proteomes in human neocortical regions and integrate it with genetic, functional and structural magnetic resonance imaging, positron emission tomography imaging, and behavioral data. Neocortical regions show signatures of expression of individual proteins, protein complexes, biochemical and metabolic pathways. The compositional signatures in brain regions involved with language, emotion and memory functions were characterized. Integrating large-scale GWAS with regional proteome data identifies the same cortical region for smoking behavior as found with fMRI data. The neocortical postsynaptic proteome data resource can be used to link genetics to brain imaging and behavior, and to study the role of postsynaptic proteins in localization of brain functions. 2 Introduction: For almost two centuries, scientists have pursued the study of localization of function in the human cerebral neocortex using diverse methods including neuroanatomy, electrophysiology, imaging and gene expression studies. The frontal, parietal, temporal and occipital lobes of the neocortex have been commonly subdivided into Brodmann areas (BA)1 based on cytoarchitectural features, and specific behavioral functions have been ascribed to these regions.
    [Show full text]
  • A Critical Look at Connectomics
    EDITORIAL A critical look at connectomics There is a public perception that connectomics will translate directly into insights for disease. It is essential that scientists and funding institutions avoid misrepresentation and accurately communicate the scope of their work. onnectomes are generating interest and excitement, both among ­nervous ­system, dubbed the classic connectome because it is ­currently neuroscientists and the public. This September, the first grants the only wiring diagram for an animal’s entire nervous system at the level Cunder the Human Connectome Project, totaling $40 ­million of the synapse. Although major neurobiological insights have been made over 5 years, were awarded by the US National Institutes of Health using C. elegans and its known connectivity and genome, there are still (NIH). In the public arena, striking, colorful pictures of human brains many questions remaining that can only be answered by hypothesis-driven have ­accompanied claims that imply that understanding the complete experiments. For example, we still don’t completely understand the process ­connectivity of the human brain’s billions of neurons by a trillion ­synapses of axon regeneration, relevant to spinal cord injury in humans, in this is not only ­possible, but that this will also directly translate into insights comparatively simple system. Thus, a connectome, at any resolution, is for neurological and psychiatric disorders. Even a press release from only one of several complementary tools necessary to understand nervous the NIH touted that the Human Connectome Project would map the system disease and injury. ­wiring diagram of the entire living human brain and would link these There are substantial efforts aimed at generating connectomes for circuits to the full spectrum of brain function in health and disease.
    [Show full text]
  • Cognitive Characterization of Schizophrenia Risk Variants Involved in Synaptic Transmission: Evidence of CACNA1C 'S Role in Working Memory
    Neuropsychopharmacology (2017) 42, 2612–2622 © 2017 American College of Neuropsychopharmacology. All rights reserved 0893-133X/17 www.neuropsychopharmacology.org Cognitive Characterization of Schizophrenia Risk Variants Involved in Synaptic Transmission: Evidence of CACNA1C 's Role in Working Memory 1 1 2 3 4,5 3 Donna Cosgrove , Omar Mothersill , Kimberley Kendall , Bettina Konte , Denise Harold , Ina Giegling , 3 6 6 7 Annette Hartmann , Alex Richards , Kiran Mantripragada , The Wellcome Trust Case Control Consortium , Michael J Owen6, Michael C O’Donovan6, Michael Gill4, Dan Rujescu3, James Walters2, Aiden Corvin4, Derek W Morris1 and Gary Donohoe*,1 1 The Cognitive Genetics & Cognitive Therapy Group, The School of Psychology and Discipline of Biochemistry, The Centre for Neuroimaging & Cognitive Genomics, National University of Ireland Galway, Galway, Ireland; 2Institute of Psychological Medicine and Clinical Neurosciences, Cardiff University, Cardiff, UK; 3Department of Psychiatry, Psychotherapy and Psychosomatics, Martin-Luther-University Halle-Wittenberg, Halle, Germany; 4 Neuropsychiatric Genetics Research Group, Department of Psychiatry, Institute of Molecular Medicine, Trinity College Dublin, Dublin, Ireland; 5 6 School of Biotechnology, Dublin City University, Dublin, Ireland; MRC Centre for Neuropsychiatric Genetics and Genomics, Cardiff University, Cardiff, UK With 4100 common variants associated with schizophrenia risk, establishing their biological significance is a priority. We sought to establish cognitive effects of
    [Show full text]
  • Download Download
    NEUROSCIENCE RESEARCH NOTES OPEN ACCESS | EDITORIAL ISSN: 2576-828X From online resources to collaborative global neuroscience research: where are we heading? Pike See Cheah 1,2, King-Hwa Ling 2,3 and Eric Tatt Wei Ho 4,* 1 Department of Human Anatomy, Faculty of Medicine and Health Sciences, Universiti Putra Malaysia. 2 NeuroBiology & Genetics Group, Genetics and Regenerative Medicine Research Centre, Faculty of Medicine and Health Sciences, Universiti Putra Malaysia. 3 Department of Biomedical Sciences, Faculty of Medicine and Health Sciences, Universiti Putra Malaysia. 4 Center for Intelligent Signal and Imaging Research, Universiti Teknologi PETRONAS, Perak, Malaysia. *Corresponding authors: [email protected]; Tel.: +60-5-368-7899 Published: 21 July 2020 https://doi.org/10.31117/neuroscirn.v3i3.51 Keywords: neuroinformatics; machine learning; bioinformatics; brain project; online resources; databases ©2020 by Cheah et al. for use and distribution in accord with the Creative Commons Attribution (CC BY-NC 4.0) license (https://creativecommons.org/licenses/by-nc/4.0/), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original author and source are credited. 1.0 INTRODUCTION The most visible contributions from neuroinformatics Neuroscience has emerged as a richly transdisciplinary include the myriad reference atlases of brain anatomy field, poised to leverage potential synergies with (human and other mammals such as rodents, primates information technology. To investigate the complex and pig), gene and protein sequences and the nervous system in its normal function and the disease bioinformatics software tools for alignment, matching state, researchers in the field are increasingly reliant and identification. Other neuroinformatics initiatives on generating, sharing and analyzing diverse data from include the various open-source preprocessing and multiple experimental paradigms at multiple spatial processing software and workflows for data analysis as and temporal scales (Frackowiak & Markram, 2015).
    [Show full text]
  • Genetic and Environmental Influence on the Human Functional Connectome Andrew E
    Cerebral Cortex, 2019;00: 1–15 doi: 10.1093/cercor/bhz225 Original Article Downloaded from https://academic.oup.com/cercor/advance-article-abstract/doi/10.1093/cercor/bhz225/5618752 by guest on 03 March 2020 ORIGINAL ARTICLE Genetic and Environmental Influence on the Human Functional Connectome Andrew E. Reineberg 1, Alexander S. Hatoum2, John K. Hewitt1,2, Marie T. Banich3 and Naomi P. Friedman1,2 1Institute for Behavioral Genetics, University of Colorado Boulder, Boulder, CO, 80309, USA, 2Department of Psychology and Neuroscience, University of Colorado Boulder, Boulder, CO, 80309, USA, and 3Institute of Cognitive Science, University of Colorado Boulder, Boulder, CO, 80309, USA Address correspondence to Andrew E. Reineberg, email: [email protected] http://orcid.org/0000-0002-0806-440X Abstract Detailed mapping of genetic and environmental influences on the functional connectome is a crucial step toward developing intermediate phenotypes between genes and clinical diagnoses or cognitive abilities. We analyzed resting-state functional magnetic resonance imaging data from two adult twin samples (Nos = 446 and 371) to quantify genetic and environmental influence on all pairwise functional connections between 264 brain regions (∼35 000 functional connections). Nonshared environmental influence was high across the whole connectome. Approximately 14–22% of connections had nominally significant genetic influence in each sample, 4.6% were significant in both samples, and 1–2% had heritability estimates greater than 30%. Evidence of shared environmental influence was weak. Genetic influences on connections were distinct from genetic influences on a global summary measure of the connectome, network-based estimates of connectivity, and movement during the resting-state scan, as revealed by a novel connectome-wide bivariate genetic modeling procedure.
    [Show full text]
  • National Institute of Mental Health Strategic Plan for Research
    NATIONAL INSTITUTE OF MENTAL HEALTH STRATEGIC PLAN FOR RESEARCH Table of Contents Table of Contents Message from the Director ............................................................................................................................................ 1 Overview of NIMH ......................................................................................................................................................... 3 Serving as an Efficient and Effective Steward of Public Resources ............................................................................... 5 Scientific Stewardship ............................................................................................................................................... 5 Management and Accountability .............................................................................................................................. 7 Accomplishing the Mission ............................................................................................................................................ 9 Challenges and Opportunities ..................................................................................................................................... 10 Suicide Prevention................................................................................................................................................... 10 Early Intervention in Psychosis ...............................................................................................................................
    [Show full text]