Hierarchical Genetic Organization of Human Cortical Surface Area
Surface area of the cerebral cortex is a highly heritable trait, yet little is known about genetic influences on regional cortical differentiation in humans. Using a data-driven, fuzzy clustering technique with magnetic resonance imaging data from 406 twins, we parceled cortical surface area into ge...
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| Published in | Science (American Association for the Advancement of Science) Vol. 335; no. 6076; pp. 1634 - 1636 |
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| Main Authors | , , , , , , , , , , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Washington, DC
American Association for the Advancement of Science
30.03.2012
The American Association for the Advancement of Science |
| Subjects | |
| Online Access | Get full text |
| ISSN | 0036-8075 1095-9203 1095-9203 |
| DOI | 10.1126/science.1215330 |
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| Abstract | Surface area of the cerebral cortex is a highly heritable trait, yet little is known about genetic influences on regional cortical differentiation in humans. Using a data-driven, fuzzy clustering technique with magnetic resonance imaging data from 406 twins, we parceled cortical surface area into genetic subdivisions, creating a human brain atlas based solely on genetically informative data. Boundaries of the genetic divisions corresponded largely to meaningful structural and functional regions; however, the divisions represented previously undescribed phenotypes different from conventional (non-genetically based) parcellation systems. The genetic organization of cortical area was hierarchical, modular, and predominantly bilaterally symmetric across hemispheres. We also found that the results were consistent with human-specific regions being subdivisions of previously described, genetically based lobar regionalization patterns. |
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| AbstractList | Surface area of the cerebral cortex is a highly heritable trait, yet little is known about genetic influences on regional cortical differentiation in humans. Using a data-driven, fuzzy clustering technique with magnetic resonance imaging data from 406 twins, we parceled cortical surface area into genetic subdivisions, creating a human brain atlas based solely on genetically informative data. Boundaries of the genetic divisions corresponded largely to meaningful structural and functional regions; however, the divisions represented previously undescribed phenotypes different from conventional (non-genetically based) parcellation systems. The genetic organization of cortical area was hierarchical, modular, and predominantly bilaterally symmetric across hemispheres. We also found that the results were consistent with human-specific regions being subdivisions of previously described, genetically based lobar regionalization patterns.Surface area of the cerebral cortex is a highly heritable trait, yet little is known about genetic influences on regional cortical differentiation in humans. Using a data-driven, fuzzy clustering technique with magnetic resonance imaging data from 406 twins, we parceled cortical surface area into genetic subdivisions, creating a human brain atlas based solely on genetically informative data. Boundaries of the genetic divisions corresponded largely to meaningful structural and functional regions; however, the divisions represented previously undescribed phenotypes different from conventional (non-genetically based) parcellation systems. The genetic organization of cortical area was hierarchical, modular, and predominantly bilaterally symmetric across hemispheres. We also found that the results were consistent with human-specific regions being subdivisions of previously described, genetically based lobar regionalization patterns. Surface area of the cerebral cortex is a highly heritable trait, yet little is known about genetic influences on regional cortical differentiation in humans. Using a data-driven, fuzzy clustering technique with magnetic resonance imaging data from 406 twins, we parceled cortical surface area into genetic subdivisions, creating a human brain atlas based solely on genetically informative data. Boundaries of the genetic divisions corresponded largely to meaningful structural and functional regions; however, the divisions represented previously undescribed phenotypes different from conventional (non-genetically based) parcellation systems. The genetic organization of cortical area was hierarchical, modular, and predominantly bilaterally symmetric across hemispheres. We also found that the results were consistent with human-specific regions being subdivisions of previously described, genetically based lobar regionalization patterns. Brain connectivity is often described as a network of discrete independent cables analogous to a switchboard, but how is the physical structure of the brain constructed (see the Perspective by Zilles and Amunts )? Wedeen et al. (p. 1628) used high-resolution diffusion tensor imaging in humans and four species of nonhuman primates to identify and compare the geometric structure of large fiber tracts in the brain. Fiber tracts followed a highly constrained and regular geometry, which may provide an efficient solution for pathfinding during ontogenetic development. Much of development occurs through elaboration and assembly of semiautonomous building blocks. Chen et al. (p. 1634) applied statistical analysis to the form of the human cortex in brain-imaging studies that compared more than 400 di- and mono-zygotic twins. The findings suggest that the structure of the human cortex is defined by genetics. Surface area of the cerebral cortex is a highly heritable trait, yet little is known about genetic influences on regional cortical differentiation in humans. Using a data-driven, fuzzy clustering technique with magnetic resonance imaging data from 406 twins, we parceled cortical surface area into genetic subdivisions, creating a human brain atlas based solely on genetically informative data. Boundaries of the genetic divisions corresponded largely to meaningful structural and functional regions; however, the divisions represented previously undescribed phenotypes different from conventional (non-genetically based) parcellation systems. The genetic organization of cortical area was hierarchical, modular, and predominantly bilaterally symmetric across hemispheres. We also found that the results were consistent with human-specific regions being subdivisions of previously described, genetically based lobar regionalization patterns. [PUBLICATION ABSTRACT] Brain connectivity is often described as a network of discrete independent cables analogous to a switchboard, but how is the physical structure of the brain constructed (see the Perspective by Zilles and Amunts )? Wedeen et al. (p. 1628 ) used high-resolution diffusion tensor imaging in humans and four species of nonhuman primates to identify and compare the geometric structure of large fiber tracts in the brain. Fiber tracts followed a highly constrained and regular geometry, which may provide an efficient solution for pathfinding during ontogenetic development. Much of development occurs through elaboration and assembly of semiautonomous building blocks. Chen et al. (p. 1634 ) applied statistical analysis to the form of the human cortex in brain-imaging studies that compared more than 400 di- and mono-zygotic twins. The findings suggest that the structure of the human cortex is defined by genetics. Human brain structure is genetically controlled in a hierarchical, modular, and symmetric fashion. Surface area of the cerebral cortex is a highly heritable trait, yet little is known about genetic influences on regional cortical differentiation in humans. Using a data-driven, fuzzy clustering technique with magnetic resonance imaging data from 406 twins, we parceled cortical surface area into genetic subdivisions, creating a human brain atlas based solely on genetically informative data. Boundaries of the genetic divisions corresponded largely to meaningful structural and functional regions; however, the divisions represented previously undescribed phenotypes different from conventional (non–genetically based) parcellation systems. The genetic organization of cortical area was hierarchical, modular, and predominantly bilaterally symmetric across hemispheres. We also found that the results were consistent with human-specific regions being subdivisions of previously described, genetically based lobar regionalization patterns. |
| Author | Franz, Carol E. Neale, Michael C. Kremen, William S. Thompson, Wes Seidman, Larry J. Jernigan, Terry L. Lyons, Michael J. Gutierrez, E. D. Eyler, Lisa T. Panizzon, Matthew S. Chen, Chi-Hua Jak, Amy J. Dale, Anders M. Grant, Michael D. Tsuang, Ming T. Fennema-Notestine, Christine Fischi, Bruce |
| AuthorAffiliation | 4 Department of Radiology, University of California, San Diego, La Jolla, CA 92093, USA 10 Department of Psychiatry, Harvard Medical School, Boston, MA 02215, USA 1 Department of Psychiatry, University of California, San Diego, La Jolla, CA 92093, USA 2 Department of Cognitive Science, University of California, San Diego, La Jolla, CA 92093, USA 5 VA Center of Excellence for Stress and Mental Health, San Diego, CA 92093, USA 3 Veterans Administration (VA) San Diego Healthcare System, San Diego, CA 92161, USA 8 Department of Psychology, Boston University, Boston, MA 02215, USA 6 Departments of Psychiatry and Human and Molecular Genetics, Virginia Commonwealth University, Richmond, VA 23219, USA 11 Department of Neurosciences, University of California, San Diego, La Jolla, CA 92093, USA 7 Center for Behavioral Genomics, University of California, San Diego, La Jolla, CA 92093, USA 9 Department of Radiology, Harvard Medical School and Massachusetts General Hospital, Boston, MA 02115, USA |
| AuthorAffiliation_xml | – name: 7 Center for Behavioral Genomics, University of California, San Diego, La Jolla, CA 92093, USA – name: 1 Department of Psychiatry, University of California, San Diego, La Jolla, CA 92093, USA – name: 2 Department of Cognitive Science, University of California, San Diego, La Jolla, CA 92093, USA – name: 5 VA Center of Excellence for Stress and Mental Health, San Diego, CA 92093, USA – name: 10 Department of Psychiatry, Harvard Medical School, Boston, MA 02215, USA – name: 3 Veterans Administration (VA) San Diego Healthcare System, San Diego, CA 92161, USA – name: 4 Department of Radiology, University of California, San Diego, La Jolla, CA 92093, USA – name: 6 Departments of Psychiatry and Human and Molecular Genetics, Virginia Commonwealth University, Richmond, VA 23219, USA – name: 9 Department of Radiology, Harvard Medical School and Massachusetts General Hospital, Boston, MA 02115, USA – name: 11 Department of Neurosciences, University of California, San Diego, La Jolla, CA 92093, USA – name: 8 Department of Psychology, Boston University, Boston, MA 02215, USA |
| Author_xml | – sequence: 1 givenname: Chi-Hua surname: Chen fullname: Chen, Chi-Hua – sequence: 2 givenname: E. D. surname: Gutierrez fullname: Gutierrez, E. D. – sequence: 3 givenname: Wes surname: Thompson fullname: Thompson, Wes – sequence: 4 givenname: Matthew S. surname: Panizzon fullname: Panizzon, Matthew S. – sequence: 5 givenname: Terry L. surname: Jernigan fullname: Jernigan, Terry L. – sequence: 6 givenname: Lisa T. surname: Eyler fullname: Eyler, Lisa T. – sequence: 7 givenname: Christine surname: Fennema-Notestine fullname: Fennema-Notestine, Christine – sequence: 8 givenname: Amy J. surname: Jak fullname: Jak, Amy J. – sequence: 9 givenname: Michael C. surname: Neale fullname: Neale, Michael C. – sequence: 10 givenname: Carol E. surname: Franz fullname: Franz, Carol E. – sequence: 11 givenname: Michael J. surname: Lyons fullname: Lyons, Michael J. – sequence: 12 givenname: Michael D. surname: Grant fullname: Grant, Michael D. – sequence: 13 givenname: Bruce surname: Fischi fullname: Fischi, Bruce – sequence: 14 givenname: Larry J. surname: Seidman fullname: Seidman, Larry J. – sequence: 15 givenname: Ming T. surname: Tsuang fullname: Tsuang, Ming T. – sequence: 16 givenname: William S. surname: Kremen fullname: Kremen, William S. – sequence: 17 givenname: Anders M. surname: Dale fullname: Dale, Anders M. |
| BackLink | http://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=25722087$$DView record in Pascal Francis https://www.ncbi.nlm.nih.gov/pubmed/22461613$$D View this record in MEDLINE/PubMed |
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| ContentType | Journal Article |
| Copyright | Copyright © 2012 American Association for the Advancement of Science 2015 INIST-CNRS Copyright © 2012, American Association for the Advancement of Science |
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| Keywords | Human Cerebral cortex Central nervous system Differentiation Nuclear magnetic resonance imaging Encephalon Twin |
| Language | English |
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| Notes | ObjectType-Recipe-1 SourceType-Scholarly Journals-1 content type line 14 ObjectType-Article-2 ObjectType-Feature-1 content type line 23 ObjectType-Article-1 ObjectType-Feature-2 These authors contributed equally to this work. |
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| SubjectTerms | Animal genetics Biological and medical sciences Brain Brain Mapping cerebral cortex Cerebral Cortex - anatomy & histology Cerebral Cortex - metabolism Cerebral hemispheres Fundamental and applied biological sciences. Psychology Gene Expression Genes Genetic correlation Genetic Variation Genetics Human genetics Human subjects Humans Image Processing, Computer-Assisted Individualized Instruction Inferior temporal cortex Magnetic Resonance Imaging Male Medical genetics Medical imaging Middle Aged Monkeys & apes Motor cortex Neurobiology Phenotype Prefrontal cortex Scientific Concepts Statistical analysis Surface area Surface areas twins Twins, Dizygotic - genetics Twins, Monozygotic - genetics Vertebrates: nervous system and sense organs |
| Title | Hierarchical Genetic Organization of Human Cortical Surface Area |
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