Heritability analysis of surface-based cortical thickness estimation on a large twin cohort

Kaikai Shen, Vincent Doré, Stephen Rose, Jurgen Fripp, Katie L. McMahon, Greig I. De Zubicaray, Nicholas G. Martin, Paul M. Thompson, Margaret J. Wright, Olivier Salvado

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionpeer-review

1 Citation (Scopus)

Abstract

The aim of this paper is to assess the heritability of cerebral cortex, based on measurements of grey matter (GM) thickness derived from structural MR images (sMRI). With data acquired from a large twin cohort (328 subjects), an automated method was used to estimate the cortical thickness, and EM-ICP surface registration algorithm was used to establish the correspondence of cortex across the population. An ACE model was then employed to compute the heritability of cortical thickness. Heritable cortical thickness measures various cortical regions, especially in frontal and parietal lobes, such as bilateral postcentral gyri, superior occipital gyri, superior parietal gyri, precuneus, the orbital part of the right frontal gyrus, right medial superior frontal gyrus, right middle occipital gyrus, right paracentral lobule, left precentral gyrus, and left dorsolateral superior frontal gyrus.

Original languageEnglish
Title of host publicationMedical Imaging 2015
Subtitle of host publicationImage Processing
EditorsSebastien Ourselin, Martin A. Styner
Place of PublicationWashington
PublisherSPIE
Pages1-8
Number of pages8
Volume9413
ISBN (Electronic)9781628415032
DOIs
Publication statusPublished - 1 Jan 2015
Externally publishedYes
EventMedical Imaging 2015: Image Processing - Orlando, United States
Duration: 24 Feb 201526 Feb 2015

Publication series

NameProceedings of SPIE
PublisherSPIE
Volume9413
ISSN (Electronic)1605-7422

Conference

ConferenceMedical Imaging 2015: Image Processing
Country/TerritoryUnited States
CityOrlando
Period24/02/1526/02/15

Keywords

  • cortical thickness analysis
  • gray matter
  • heritability
  • Imaging genetics
  • structural MRI

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