Heritability Estimation of Reliable Connectomic Features

  • Linhui Xie
  • Enrico Amico
  • Paul Salama
  • Yu-chien Wu
  • Shiaofen Fang
  • Olaf Sporns
  • Andrew J. Saykin
  • Joaquín Goñi
  • Jingwen YanEmail author
  • Li ShenEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11083)


Brain imaging genetics is an emerging research field to explore the underlying genetic architecture of brain structure and function measured by different imaging modalities. However, not all the changes in the brain are a consequential result of genetic effect and it is usually unknown which imaging phenotypes are promising for genetic analyses. In this paper, we focus on identifying highly heritable measures of structural brain networks derived from diffusion weighted imaging data. Using the twin data from the Human Connectome Project (HCP), we evaluated the reliability of fractional anisotropy measure, fiber length and fiber number of each edge in the structural connectome and seven network level measures using intraclass correlation coefficients. We then estimated the heritability of those reliable network measures using SOLAR-Eclipse software. Across all 64,620 network edges between 360 brain regions in the Glasser parcellation, we observed \(\sim \)5% of them with significantly high heritability in fractional anisotropy, fiber length or fiber number. All the tested network level measures, capturing the network integrality, segregation or resilience, are highly heritable, with variance explained by the additive genetic effect ranging from 59% to 77%.


Structural connectivity Heritability Reliability HCP 


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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Linhui Xie
    • 1
  • Enrico Amico
    • 6
    • 7
  • Paul Salama
    • 1
  • Yu-chien Wu
    • 2
  • Shiaofen Fang
    • 4
  • Olaf Sporns
    • 5
  • Andrew J. Saykin
    • 2
  • Joaquín Goñi
    • 6
    • 7
  • Jingwen Yan
    • 2
    • 3
    Email author
  • Li Shen
    • 8
    Email author
  1. 1.Department of Electrical and Computer EngineeringIndiana University-Purdue University IndianapolisIndianapolisUSA
  2. 2.Department of Radiology and Imaging SciencesIndiana University School of MedicineIndianapolisUSA
  3. 3.Department of BioHealth InformaticsIndiana University-Purdue University IndianapolisIndianapolisUSA
  4. 4.Department of Computer ScienceIndiana University-Purdue University IndianapolisIndianapolisUSA
  5. 5.Department of Psychological and Brain ScienceIndiana UniversityBloomingtonUSA
  6. 6.School of Industrial EngineeringPurdue UniversityWest LafayetteUSA
  7. 7.Weldon School of Biomedical EngineeringPurdue UniversityWest LafayetteUSA
  8. 8.Biostatistics, Epidemiology and InformaticsUniversity of PennsylvaniaPhiladelphiaUSA

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