DOCSLIB.ORG

Design and Rationale for Examining Neuroimaging Genetics in Ischemic Stroke the MRI-GENIE Study

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Design and Rationale for Examining Neuroimaging Genetics in Ischemic Stroke the MRI-GENIE Study Design and rationale for examining neuroimaging genetics in ischemic stroke The MRI-GENIE study Anne-Katrin Giese, MD ABSTRACT Markus D. Schirmer, PhD Objective: To describe the design and rationale for the genetic analysis of acute and chronic cere- Kathleen L. Donahue, BS brovascular neuroimaging phenotypes detected on clinical MRI in patients with acute ischemic Lisa Cloonan, BA stroke (AIS) within the scope of the MRI–GENetics Interface Exploration (MRI-GENIE) study. Robert Irie, PhD Methods: MRI-GENIE capitalizes on the existing infrastructure of the Stroke Genetics Network Stefan Winzeck, MS (SiGN). In total, 12 international SiGN sites contributed MRIs of 3,301 patients with AIS. Detailed Mark J.R.J. Bouts, PhD clinical phenotyping with the web-based Causative Classification of Stroke (CCS) system and Elissa C. McIntosh, MA genome-wide genotyping data were available for all participants. Neuroimaging analyses include Steven J. Mocking, MSc the manual and automated assessments of established MRI markers. A high-throughput MRI anal- Adrian V. Dalca, PhD ysis pipeline for the automated assessment of cerebrovascular lesions on clinical scans will be Ramesh Sridharan, PhD developed in a subset of scans for both acute and chronic lesions, validated against gold stan- Huichun Xu, MD, PhD dard, and applied to all available scans. The extracted neuroimaging phenotypes will improve Petrea Frid, MD characterization of acute and chronic cerebrovascular lesions in ischemic stroke, including Eva Giralt-Steinhauer, CCS subtypes, and their effect on functional outcomes after stroke. Moreover, genetic testing MD, PhD will uncover variants associated with acute and chronic MRI manifestations of cerebrovascular Lukas Holmegaard, MD disease. Jaume Roquer, MD, PhD Johan Wasselius, MD, PhD Conclusions: The MRI-GENIE study aims to develop, validate, and distribute the MRI analysis plat- John W. Cole, MD form for scans acquired as part of clinical care for patients with AIS, which will lead to (1) novel Patrick F. McArdle, PhD genetic discoveries in ischemic stroke, (2) strategies for personalized stroke risk assessment, and Neurol Genet Joseph P. Broderick, MD (3) personalized stroke outcome assessment. 2017;3:e180; doi: 10.1212/ Jordi Jimenez-Conde, NXG.0000000000000180 MD, PhD Christina Jern, MD, PhD GLOSSARY 5 5 5 5 Brett M. Kissela, MD, MS ADC apparent diffusion coefficient; AIS acute ischemic stroke; CE cardioembolic; CCS Causative Classification of Stroke; CCSc 5 causative CCS; DICOM 5 Digital Imaging and Communications in Medicine; DWI 5 diffusion-weighted Dawn O. Kleindorfer, MD imaging; DWIv 5 DWI volume; FLAIR 5 fluid-attenuated inversion recovery; GISCOME 5 Genetics of Ischemic Stroke Robin Lemmens, MD, PhD Functional Outcome; GWAS 5 genome-wide association studies; ICC 5 intraclass correlation coefficient; LAA 5 large artery atherosclerosis; MGH 5 Massachusetts General Hospital; MRI-GENIE 5 MRI–GENetics Interface Exploration; mRS 5 Arne Lindgren, MD, PhD modified Rankin Scale; PHI 5 protected health information; QC 5 quality control; SAO 5 small artery occlusion; SiGN 5 James F. Meschia, MD Stroke Genetics Network; SNP 5 single nucleotide polymorphism; SWI 5 susceptibility-weighted imaging; TOAST 5 Trial of Org 10172 Acute Stroke Treatment; VLSM 5 voxel-based lesion–symptom mapping; WMHv 5 white matter hyperintensity Tatjana Rundek, MD, PhD volume; XNAT 5 eXtensible Neuroimaging Archive Toolkit. Ralph L. Sacco, MD, MS Reinhold Schmidt, MD Genome-wide association studies (GWAS) have been instrumental in elucidating the genetics of Pankaj Sharma, MD, PhD complex vascular traits (ischemic stroke1,2 and coronary artery disease3,4) and their risk factors Agnieszka Slowik, MD, (blood pressure,5 atrial fibrillation,6 hyperlipidemia,7 and diabetes mellitus8). Despite recent PhD advances in prevention and treatment, stroke remains a leading cause of adult neurologic Vincent Thijs, MD, PhD 9 Daniel Woo, MD, MS disability and death in the United States and worldwide. Recent GWAS have uncovered several 10,11 Bradford B. Worrall, MD, risk loci for ischemic stroke and its subtypes, specifically PITX2 and ZFHX3 for cardioem- 11,12 11,12 11 11 MSc bolic (CE) stroke, HDAC9 and TSPAN2 for large artery stroke, and ALDH2 for Steven J. Kittner, MD, small artery stroke. These results highlight the necessity for large-scale collaborations such as MPH Braxton D. Mitchell, *These authors contributed equally to the manuscript. PhD, MPH Author affiliations are provided at the end of the article. Funding information and disclosures are provided at the end of the article. Go to Neurology.org/ng for full disclosure forms. The Article Processing Charge was funded by the NIH. This is an open access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License 4.0 (CC Author list continued on next page BY-NC-ND), which permits downloading and sharing the work provided it is properly cited. The work cannot be changed in any way or used commercially without permission from the journal. Neurology.org/ng Copyright © 2017 The Author(s). Published by Wolters Kluwer Health, Inc. on behalf of the American Academy of Neurology 1 Jonathan Rosand, MD, METASTROKE and Stroke Genetics Net- study participants to data sharing was mandatory for all sites. MSc work (SiGN) to identify risk loci for these Shared data include basic demographics, vascular risk factors and Polina Golland, PhD detailed Causative Classification of Stroke (CCS) phenotyping, complex diseases. genotypic data, and clinical MRIs. Ona Wu, PhD* Improving our understanding of the mech- Study oversight. The primary aims and progress of the MRI- Natalia S. Rost, MD, anisms underlying stroke is crucial, as treat- GENIE study are overseen by a Scientific Steering Committee. In MPH* ment options remain limited for this conjunction with the SiGN Publication Committee, the MRI- GENIE Steering Committee critically reviews project proposals debilitating disease. Unlike clinical diagnosis by collaborators to avoid potential overlap with existing SiGN Correspondence to of stroke, cerebrovascular phenotypes detected projects and to assess feasibility of the proposed projects. This Dr. Rost: on brain MRI can be characterized with high effort is supported by the Phenotyping Committee, which is in [email protected] validity and quantified with good precision charge of the data access to phenotypes previously obtained Supplemental data through SiGN, current data acquisition for neuroimaging at Neurology.org/ng using advanced neuroimaging analysis techni- markers, and quality control (QC) of new neuroimaging phe- ques.13 Targeting these specific endopheno- notypes. In addition, it is responsible for the oversight of statis- types will lead to substantial pathophysiologic tical analysis of MRI-derived phenotypes and functional outcomes related to specific stroke subtypes. The Neuroimaging insight into stroke mechanisms and foster Analysis Committee is in charge of designing, validating, and future advances in individualized therapy and implementing the MRI pipeline to automatically assess acute prevention. and chronic neuroimaging markers. Moreover, it facilitates and The MRI–Genetics Interface Exploration monitors the assessment of manually obtained neuroimaging markers. The Genetic Analysis Committee conducts the primary (MRI-GENIE) study aims to bridge current genetic analyses for the MRI-GENIE study to identify genetic knowledge gaps by facilitating genetic discov- variants associated with acute and chronic MRI-based manifes- ery and developing novel therapeutic and tations of cerebrovascular disease. It is also essential in conducting preventive strategies in stroke through auto- secondary analyses and additional projects as proposed by col- laborators (detailed listings of committee memberships are mated, multimodal MRI analysis. Leveraging available in coinvestigator appendix e-1 at Neurology.org/ng). the existing infrastructure of SiGN and har- Imaging platform. An integral part of the MRI-GENIE study nessing its expertise, MRI-GENIE focuses on is the centrally maintained imaging platform hosting deidentified the subset of richly phenotyped and genotyped acute or subacute brain MRIs obtained within 48 hours of participants for whom clinical MRIs have been symptom onset from all contributing SiGN sites. The MRI- obtained. This article outlines the premises, GENIE Imaging Platform is maintained centrally at MGH and has been described previously.14 Initially developed in the scope methodology, and aims of MRI-GENIE. of an NIH-funded project to create a centralized system to share canonical human stroke data (R01 NS063925-01A1, O. Wu/ METHODS MRI-GENIE capitalizes on SiGN, an ongoing Sorensen—PI), the underlying technology for the imaging plat- multicenter, NIH-funded collaboration within the community form integrates the eXtensible Neuroimaging Archive Toolkit of stroke neurologists, geneticists, and neuroimaging analysts, (XNAT)15 as the back-end data repository with a flexible, open- which enabled the initial development of the SiGN Imaging source content management system with user-friendly features 14 Platform. We have amassed the largest-to-date collection of (conglomeration of Plone,16 Deliverance,17 and NGINX18) as the ischemic stroke cases with comprehensively ascertained cerebro- front end for the users. An example of such features is the ability vascular phenotypes and genome-wide data. The project is fun- to search the imaging repository by stroke-specific clinical
Load more

Recommended publications
  • Imaging the Genetics of Brain Structure and Function Biological Psychology
    Biological Psychology 79 (2008) 1–8 Contents lists available at ScienceDirect Biological Psychology journal homepage: www.elsevier.com/locate/biopsycho Editorial Imaging the genetics of brain structure and function ARTICLE INFO ABSTRACT Article history: Imaging genetics combines brain imaging and genetics to detect genetic variation in brain structure and Available online 11 April 2008 function related to behavioral traits, including psychiatric endpoints, cognition, and affective regulation. This special issue features extensive reviews of the current state-of-the-art of the field and adds new findings from twin and candidate gene studies on functional MRI. Here we present a brief overview and discuss a number of desirable future developments which include more specific a priori hypotheses, more standardization of MRI measurements within and across laboratories, and larger sample sizes that allows testing of multiple genes and their interactions up to a scale that allows genetic whole genome association studies. Based on the overall tenet of the contributions to this special issue we predict that imaging genetics will increasingly impact on the classification systems for psychiatric disorders and the early detection and treatment of vulnerable individuals. ß 2008 Elsevier B.V. All rights reserved. Biological Psychology, quite literally, deals with the connection 1. Imaging genetics between the body and the mind. In the past two decades two specific instances of body-mind connections have captured the What exactly is imaging genetics? In the
    [Show full text]
  • Sparse Deep Neural Networks on Imaging Genetics for Schizophrenia Case-Control Classification
    medRxiv preprint doi: https://doi.org/10.1101/2020.06.11.20128975; this version posted June 12, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission. Sparse Deep Neural Networks on Imaging Genetics for Schizophrenia Case-Control Classification Jiayu Chen1,*, Xiang Li2,*, Vince D. Calhoun1,2,3, Jessica A. Turner1,3, Theo G. M. van Erp4,5, Lei Wang6, Ole A. Andreassen7, Ingrid Agartz7,8,9, Lars T. Westlye7,10 , Erik Jönsson7,9, Judith M. Ford11,12, Daniel H. Mathalon11,12, Fabio Macciardi4, Daniel S. O’Leary13, Jingyu Liu1,2, †, Shihao Ji2, † 1Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS): (Georgia State University, Georgia Institute of Technology, and Emory University), Atlanta, GA, USA; 2Department of Computer Science, Georgia State University, Atlanta, GA, USA; 3Psychology Department and Neuroscience Institute, Georgia State University, Atlanta, GA, USA; 4Department of Psychiatry and Human Behavior, School of Medicine, University of California, Irvine, Irvine, CA, USA; 5Center for the Neurobiology of Learning and Memory, University of California, Irvine, Irvine, CA, 92697, USA; 6Department of Psychiatry and Behavioral Sciences, Northwestern University, Chicago, IL, USA; 7Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Institute of Clinical Medicine, University
    [Show full text]
  • Imaging Genetics Approach to Parkinson's Disease and Its
    www.nature.com/scientificreports OPEN Imaging genetics approach to Parkinson’s disease and its correlation with clinical score Received: 07 November 2016 Mansu Kim1,2, Jonghoon Kim1,2, Seung-Hak Lee1,2 & Hyunjin Park2,3 Accepted: 24 March 2017 Parkinson’s disease (PD) is a progressive neurodegenerative disorder associated with both underlying Published: 21 April 2017 genetic factors and neuroimaging findings. Existing neuroimaging studies related to the genome in PD have mostly focused on certain candidate genes. The aim of our study was to construct a linear regression model using both genetic and neuroimaging features to better predict clinical scores compared to conventional approaches. We obtained neuroimaging and DNA genotyping data from a research database. Connectivity analysis was applied to identify neuroimaging features that could differentiate between healthy control (HC) and PD groups. A joint analysis of genetic and imaging information known as imaging genetics was applied to investigate genetic variants. We then compared the utility of combining different genetic variants and neuroimaging features for predicting the Movement Disorder Society-sponsored unified Parkinson’s disease rating scale (MDS-UPDRS) in a regression framework. The associative cortex, motor cortex, thalamus, and pallidum showed significantly different connectivity between the HC and PD groups. Imaging genetics analysis identified PARK2, PARK7, HtrA2, GIGYRF2, and SNCA as genetic variants that are significantly associated with imaging phenotypes. A linear regression model combining genetic and neuroimaging features predicted the MDS-UPDRS with lower error and higher correlation with the actual MDS-UPDRS compared to other models using only genetic or neuroimaging information alone. Parkinson’s disease (PD) is a progressive neurodegenerative disorder characterized by bradykinesia, resting trem- ors, rigidity, and difficulty with voluntary movement1.
    [Show full text]
  • The Emerging Spectrum of Allelic Variation in Schizophrenia
    Molecular Psychiatry (2013) 18, 38 -- 52 & 2013 Macmillan Publishers Limited All rights reserved 1359-4184/13 www.nature.com/mp EXPERT REVIEW The emerging spectrum of allelic variation in schizophrenia: current evidence and strategies for the identification and functional characterization of common and rare variants BJ Mowry1,2 and J Gratten1 After decades of halting progress, recent large genome-wide association studies (GWAS) are finally shining light on the genetic architecture of schizophrenia. The picture emerging is one of sobering complexity, involving large numbers of risk alleles across the entire allelic spectrum. The aims of this article are to summarize the key genetic findings to date and to compare and contrast methods for identifying additional risk alleles, including GWAS, targeted genotyping and sequencing. A further aim is to consider the challenges and opportunities involved in determining the functional basis of genetic associations, for instance using functional genomics, cellular models, animal models and imaging genetics. We conclude that diverse approaches will be required to identify and functionally characterize the full spectrum of risk variants for schizophrenia. These efforts should adhere to the stringent standards of statistical association developed for GWAS and are likely to entail very large sample sizes. Nonetheless, now more than any previous time, there are reasons for optimism and the ultimate goal of personalized interventions and therapeutics, although still distant, no longer seems unattainable. Molecular Psychiatry (2013) 18, 38--52; doi:10.1038/mp.2012.34; published online 1 May 2012 Keywords: CNV; functional genomics; GWAS; schizophrenia; sequencing; SNP THE NATURE OF THE PROBLEM complications (obesity, nicotine dependence, metabolic syndrome 13 Schizophrenia is a chronic psychiatric disorder characterized by and premature mortality), low employment and substantial 14 delusional beliefs, auditory hallucinations, disorganized thought homelessness.
    [Show full text]
  • CHIMGEN: a Chinese Imaging Genetics Cohort to Enhance Cross-Ethnic and Cross-Geographic Brain Research
    Molecular Psychiatry (2020) 25:517–529 https://doi.org/10.1038/s41380-019-0627-6 PERSPECTIVE CHIMGEN: a Chinese imaging genetics cohort to enhance cross- ethnic and cross-geographic brain research 1 1 2 3,4 5 6 7 Qiang Xu ● Lining Guo ● Jingliang Cheng ● Meiyun Wang ● Zuojun Geng ● Wenzhen Zhu ● Bing Zhang ● 8,9 10 11 12 13 14 15,16 Weihua Liao ● Shijun Qiu ● Hui Zhang ● Xiaojun Xu ● Yongqiang Yu ● Bo Gao ● Tong Han ● 17 18 1 1 1 19 20 Zhenwei Yao ● Guangbin Cui ● Feng Liu ● Wen Qin ● Quan Zhang ● Mulin Jun Li ● Meng Liang ● 21 22 23 24 25,26 27 28 Feng Chen ● Junfang Xian ● Jiance Li ● Jing Zhang ● Xi-Nian Zuo ● Dawei Wang ● Wen Shen ● 29 30 31,32 33,34 35,36 37 38 Yanwei Miao ● Fei Yuan ● Su Lui ● Xiaochu Zhang ● Kai Xu ● Long Jiang Zhang ● Zhaoxiang Ye ● 1,39 Chunshui Yu ● for the CHIMGEN Consortium Received: 26 September 2018 / Revised: 21 November 2019 / Accepted: 27 November 2019 / Published online: 11 December 2019 © The Author(s) 2019. This article is published with open access Abstract The Chinese Imaging Genetics (CHIMGEN) study establishes the largest Chinese neuroimaging genetics cohort and aims to identify genetic and environmental factors and their interactions that are associated with neuroimaging and behavioral 1234567890();,: 1234567890();,: phenotypes. This study prospectively collected genomic, neuroimaging, environmental, and behavioral data from more than 7000 healthy Chinese Han participants aged 18–30 years. As a pioneer of large-sample neuroimaging genetics cohorts of non-Caucasian populations, this cohort can provide new insights into ethnic differences in genetic-neuroimaging associations by being compared with Caucasian cohorts.
    [Show full text]
  • Introduction to Imaging Genetics Half Day Morning Course / 8:00-12:00
    Introduction to Imaging Genetics Half Day Morning Course / 8:00-12:00 Organizers: Jason Stein, PhD, University of North Carolina at Chapel Hill, United States This course will introduce the fundamentals of “Imaging Genetics,” the process of modeling and understanding how genetic variation influences the structure and function of the human brain as measured through brain imaging. The course begins with a brief history of imaging genetics, including discussion on replicability and significance thresholds. Then, we will review recent findings in neuropsychiatric disease risk, what neuroimaging genetics can learn from neuropsychiatric genetics, and how neuroimaging genetics can be used to explain missing mechanisms in neuropsychiatric genetics. We will cover datasets and methods for conducting common and rare variant associations, as well as bioinformatic tools to interpret findings in the context of gene regulation. Overall this course will provide the neuroimager who is not familiar with genetics techniques an understanding of the current state genetics field when exploring neuroimaging phenotypes. Course Schedule: 8:00-8:45 A brief history of imaging genetics Jason Stein, PhD, University of North Carolina at Chapel Hill, United States 8:45-9:30 The genetic influences on neuropsychiatric disease risk Sven Cichon, Dr. rer. nat., Universitat Basel, Switzerland 9:30-10:15 The effect of common genetic variation on human brain structure Paul Thompson, Imaging Genetics Center, Keck School of Medicine of University of Southern California, United States 10:15-10:30 Break 10:30-11:15 The effect of rare variation on human brain structure Carrie Bearden, University of California, Los Angeles, United States 11:15-12:00 Connecting genetic variation to gene regulation Bernard Ng, PhD, University of British Columbia, Canada .
    [Show full text]
  • [For Consideration As Part of the ENIGMA Special Issue] Ten Years
    [For consideration as part of the ENIGMA Special Issue] Ten years of Enhancing Neuro-Imaging Genetics through Meta-Analysis: An overview from the ENIGMA Genetics Working Group Sarah E. Medland1,2,3, Katrina L. Grasby1, Neda Jahanshad4, Jodie N. Painter1, Lucía Colodro- Conde1,2,5,6, Janita Bralten7,8, Derrek P. Hibar4,9, Penelope A. Lind1,5,3, Fabrizio Pizzagalli4, Sophia I. Thomopoulos4, Jason L. Stein10, Barbara Franke7,8, Nicholas G. Martin11, Paul M. Thompson4 on behalf of the ENIGMA Genetics Working Group 1 Psychiatric Genetics, QIMR Berghofer Medical Research Institute, Brisbane, Australia. 2 School of Psychology, University of Queensland, Brisbane, Australia. 3 Faculty of Medicine, University of Queensland, Brisbane, Australia. 4 Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Marina del Rey, CA, USA. 5 School of Biomedical Sciences, Queensland University of Technology, Brisbane, Australia. 6 Faculty of Psychology, University of Murcia, Murcia, Spain. 7 Department of Human Genetics, Radboud university medical center, Nijmegen, The Netherlands. 8 Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands. 9 Personalized Healthcare, Genentech, Inc., South San Francisco, USA. 10 Department of Genetics & UNC Neuroscience Center, University of North Carolina at Chapel Hill, Chapel Hill, USA. 11 Genetic Epidemiology, QIMR Berghofer Medical Research Institute, Brisbane, Australia. Abstract Here we review the motivation for creating the ENIGMA (Enhancing NeuroImaging Genetics through Meta Analysis) Consortium and the genetic analyses undertaken by the consortium so far. We discuss the methodological challenges, findings and future directions of the Genetics Working Group.
    [Show full text]
  • Imaging Genetics in Neurodevelopmental Psychopathology
    Received: 26 August 2016 | Revised: 2 February 2017 | Accepted: 10 March 2017 DOI: 10.1002/ajmg.b.32542 REVIEW ARTICLE Imaging genetics in neurodevelopmental psychopathology Marieke Klein1 | Marjolein van Donkelaar1 | Ellen Verhoef2 | Barbara Franke1,3 1 Radboud University Medical Center, Donders Institute for Brain, Cognition and Behaviour, Neurodevelopmental disorders are defined by highly heritable problems during development Department of Human Genetics, Nijmegen, and brain growth. Attention-deficit/hyperactivity disorder (ADHD), autism spectrum disorders The Netherlands (ASDs), and intellectual disability (ID) are frequent neurodevelopmental disorders, with common 2 Max Planck Institute for Psycholinguistics, comorbidity among them. Imaging genetics studies on the role of disease-linked genetic variants Department of Language and Genetics, Nijmegen, The Netherlands on brain structure and function have been performed to unravel the etiology of these disorders. 3 Radboud University Medical Center, Donders Here, we reviewed imaging genetics literature on these disorders attempting to understand the Institute for Brain, Cognition and Behaviour, mechanisms of individual disorders and their clinical overlap. For ADHD and ASD, we selected Department of Psychiatry, Nijmegen, replicated candidate genes implicated through common genetic variants. For ID, which is mainly The Netherlands caused by rare variants, we included genes for relatively frequent forms of ID occurring Correspondence comorbid with ADHD or ASD. We reviewed case-control studies and studies of risk variants in Barbara Franke, PhD, Department of Human Genetics (855), Radboud University Medical healthy individuals. Imaging genetics studies for ADHD were retrieved for SLC6A3/DAT1, Center, PO Box 9101, 6500 HB Nijmegen, The DRD2, DRD4, NOS1, and SLC6A4/5HTT. For ASD, studies on CNTNAP2, MET, OXTR, and Netherlands.
    [Show full text]
  • Reliability Issues in Imaging Genetics
    Reliability Issues in Imaging Genetics By Annika U. Eriksson Capstone Project Submitted in partial fulfillment of the requirements for the degree of Master of Biomedical Informatics Oregon Health and Science University January 2017 School of Medicine Oregon Health & Science University CERTIFICATE OF APPROVAL This is to certify that the Master’s Capstone Project of Annika U. Eriksson “Reliability Issues in Imaging Genetics” Has been approved _________________________________________ Shannon McWeeney, PhD Capstone Advisor Reliability Issues in Imaging Genetics Table of Contents Reliability Issues in Imaging Genetics .......................................................................................................... 1 Acknowledgements ................................................................................................................................... 4 Exposition: Setting the stage ..................................................................................................................... 5 Foreshadowing: Importance of reliability ................................................................................................. 6 Rising Action: History of both fields independently ................................................................................ 8 Imaging ................................................................................................................................................. 9 Genetics..............................................................................................................................................
    [Show full text]
  • Program Book
    2 16 HBM GENEVA 22ND ANNUAL MEETING OF THE ORGANIZATION FOR HUMAN BRAIN MAPPING PROGRAM June 26-30, 2016 Palexpo Exhibition and Congress Centre | Geneva, Switzerland Simultaneous Multi-Slice Accelerate advanced neuro applications for clinical routine siemens.com/sms Simultaneous Multi-Slice is a paradigm shift in MRI Simulataneous Multi-Slice helps you to: acquisition – helping to drastically cut neuro DWI 1 scan times, and improving temporal resolution for ◾ reduce imaging time for diffusion MRI by up to 68% BOLD fMRI significantly. ◾ bring advanced DTI and BOLD into clinical routine ◾ push the limits in brain imaging research with 1 MAGNETOM Prisma, Head/Neck 64 acceleration factors up to 81 2 3654_MR_Ads_SMS_7,5x10_Zoll_DD.indd 1 24.03.16 09:45 WELCOME We would like to personally welcome each of you to the 22nd Annual Meeting TABLE OF CONTENTS of the Organization for Human Brain Mapping! The world of neuroimaging technology is more exciting today than ever before, and we’ll continue our Welcome Remarks . .3 tradition of bringing inspiration to you through the exceptional scientific sessions, General Information ..................6 poster presentations and networking forums that ensure you remain at the Registration, Exhibit Hours, Social Events, cutting edge. Speaker Ready Room, Hackathon Room, Evaluations, Mobile App, CME Credits, etc. Here is but a glimpse of what you can expect and what we hope to achieve over Daily Schedule ......................10 the next few days. Sunday, June 26: • Talairach Lecture presenter Daniel Wolpert, Univ. Cambridge UK, who Educational Courses Full Day:........10 will share his work on how humans learn to make skilled movements MR Diffusion Imaging: From the Basics covering probabilistic models of learning, the role and content in activating to Advanced Applications ............10 motor memories and the intimate interaction between decision making and Anatomy and Its Impact on Structural and Functional Imaging ..................11 sensorimotor control.
    [Show full text]
  • Introduction to Imaging Genetics
    Introduction to Imaging Genetics Organizers: Jason Stein, PhD University of North Carolina at Chapel Hill, Chapel Hill, NC, United States This course will introduce the fundamentals of “Imaging Genetics,” the process of modeling and understanding how genetic variation influences the structure and function of the human brain as measured through brain imaging. The course begins with a brief history of imaging genetics, including discussion on replicability and significance thresholds. Then, we will discuss endophenotypes including modern methods for assessing heritability and genetic correlation. We will cover datasets and methods for conducting common and rare variant associations, as well as bioinformatic tools to interpret significant findings. We will also cover two nascent and related fields: imaging epigenetics and relating gene expression networks to brain structure and function. Overall this course will provide the neuroimager who is not familiar with genetics techniques an understanding of the current state genetics field when exploring neuroimaging phenotypes. Course Schedule: 8:00-8:30 A brief history of imaging genetics Jean-Baptiste Poline, Ph.D., University of California, Berkeley, Berkeley, CA, United States 8:30-9:00 The modern day endophenotype Roberto Toro, PhD, Institut Pasteur, Paris, France 9:00-9:30 Utilizing big datasets in imaging-genetics Derrek Hibar, Institute for Neuroimaging & Informatics, Los Angeles, United States 9:30-10:00 Imaging Epigenetics Sylvane Desrivieres, King's College London, London, United Kingdom 10:00-10:15 Break 10:15-10:45 Networks of gene expression and brain function Vilas Menon, PhD, HHMI Janelia Research Campus, Ashburn, VA, United States 10:45: 11:15 Rare variant associations David Glahn, Yale University, Hartford, United States 11:15-11:45 After the association Jason Stein, PhD, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States 11:45-12:00 Question and Answer .
    [Show full text]
  • Pattern Discovery in Brain Imaging Genetics Via SCCA Modeling with a Generic Non-Convex Penalty Lei Du Northwestern Polytechnical University, China
    University of Kentucky UKnowledge Neurology Faculty Publications Neurology 10-25-2017 Pattern Discovery in Brain Imaging Genetics via SCCA Modeling with a Generic Non-Convex Penalty Lei Du Northwestern Polytechnical University, China Kefei Liu Indiana University Xiaohui Yao Indiana University Jingwen Yan Indiana University Shannon L. Risacher Indiana University See next page for additional authors RFoigllohtw c licthiks t aond ope addn ait feionedalba wckork fosr mat :inh att nps://uknoew tab to lewtle usdg kne.ukowy .hedu/neow thisur documology_faentc bepubnefits oy u. Part of the Bioimaging and Biomedical Optics Commons, Computational Neuroscience Commons, Disease Modeling Commons, Genetics Commons, and the Neurology Commons Repository Citation Du, Lei; Liu, Kefei; Yao, Xiaohui; Yan, Jingwen; Risacher, Shannon L.; Han, Junwei; Guo, Lei; Saykin, Andrew J.; Shen, Li; Weiner, Michael W.; Aisen, Paul; Petersen, Ronald; Jack, Clifford R.; Jagust, William; Trojanowki, John Q.; Toga, Arthur W.; Beckett, Laurel; Green, Robert C.; Morris, John; Shaw, Leslie M.; Khachaturian, Zaven; Sorensen, Greg; Carrillo, Maria; Kuller, Lew; Raichle, Marc; Paul, Steven; Davies, Peter; Fillit, Howard; Hefti, Franz; Holtzman, David; Smith, Charles D.; Jicha, Gregory; Hardy, Peter A.; Sinha, Partha; Oates, Elizabeth; and Conrad, Gary, "Pattern Discovery in Brain Imaging Genetics via SCCA Modeling with a Generic Non- Convex Penalty" (2017). Neurology Faculty Publications. 19. https://uknowledge.uky.edu/neurology_facpub/19 This Article is brought to you for free and open access by the Neurology at UKnowledge. It has been accepted for inclusion in Neurology Faculty Publications by an authorized administrator of UKnowledge. For more information, please contact [email protected]. Authors Lei Du, Kefei Liu, Xiaohui Yao, Jingwen Yan, Shannon L.
    [Show full text]