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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 Yan
  • Li Shen
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11083)

Abstract

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%.

Keywords

Structural connectivity Heritability Reliability HCP 

References

  1. 1.
    Bohlken, M.M., et al.: Heritability of structural brain network topology: a dti study of 156 twins. Hum. Brain Mapp. 35(10), 5295–305 (2014).  https://doi.org/10.1002/hbm.22550CrossRefGoogle Scholar
  2. 2.
    Burzynska, A.Z., et al.: Age-related differences in white matter microstructure: region-specific patterns of diffusivity. Neuroimage 49(3), 2104–2112 (2010)CrossRefGoogle Scholar
  3. 3.
    Christiaens, D., Reisert, M., Dhollander, T., Sunaert, S., Suetens, P., Maes, F.: Global tractography of multi-shell diffusion-weighted imaging data using a multi-tissue model. Neuroimage 123, 89–101 (2015)CrossRefGoogle Scholar
  4. 4.
    Ganjgahi, H., Winkler, A.M., Glahn, D.C., Blangero, J., Kochunov, P., Nichols, T.E.: Fast and powerful heritability inference for family-based neuroimaging studies. NeuroImage 115, 256–268 (2015)CrossRefGoogle Scholar
  5. 5.
    Glasser, M.F., et al.: A multi-modal parcellation of human cerebral cortex. Nature 536(7615), 171–178 (2016)CrossRefGoogle Scholar
  6. 6.
    Gong, G., He, Y., Evans, A.C.: Brain connectivity: gender makes a difference. Neuroscientist 17(5), 575–591 (2011)CrossRefGoogle Scholar
  7. 7.
    Jeurissen, B., Tournier, J.D., Dhollander, T., Connelly, A., Sijbers, J.: Multi-tissue constrained spherical deconvolution for improved analysis of multi-shell diffusion MRI data. NeuroImage 103, 411–426 (2014)CrossRefGoogle Scholar
  8. 8.
    Jin, Y., et al.: Heritability of white matter fiber tract shapes: a HARDI study of 198 twins. In: Liu, T., Shen, D., Ibanez, L., Tao, X. (eds.) MBIA 2011. LNCS, vol. 7012, pp. 35–43. Springer, Heidelberg (2011).  https://doi.org/10.1007/978-3-642-24446-9_5CrossRefGoogle Scholar
  9. 9.
    Kochunov, P., et al.: Heritability of fractional anisotropy in human white matter: a comparison of Human Connectome Project and ENIGMA-DTI data. Neuroimage 111, 300–11 (2015).  https://doi.org/10.1016/j.neuroimage.2015.02.050CrossRefGoogle Scholar
  10. 10.
    Koo, T.K., Li, M.Y.: A guideline of selecting and reporting intraclass correlation coefficients for reliability research. J. Chiropr. Med. 15(2), 155–163 (2016)CrossRefGoogle Scholar
  11. 11.
    Lopez-Larson, M.P., Anderson, J.S., Ferguson, M.A., Yurgelun-Todd, D.: Local brain connectivity and associations with gender and age. Dev. Cogn. Neurosci. 1(2), 187–197 (2011)CrossRefGoogle Scholar
  12. 12.
    McGraw, K.O., Wong, S.P.: Forming inferences about some intraclass correlation coefficients. Psychol. Methods 1(1), 30 (1996)CrossRefGoogle Scholar
  13. 13.
    Nir, T.M., et al.: Effectiveness of regional DTI measures in distinguishing Alzheimer’s disease, MCI, and normal aging. NeuroImage Clin. 3, 180–195 (2013)CrossRefGoogle Scholar
  14. 14.
    Rubinov, M., Sporns, O.: Complex network measures of brain connectivity: uses and interpretations. Neuroimage 52(3), 1059–1069 (2010)CrossRefGoogle Scholar
  15. 15.
    Shen, K.K., et al.: Investigating brain connectivity heritability in a twin study using diffusion imaging data. Neuroimage 100, 628–41 (2014).  https://doi.org/10.1016/j.neuroimage.2014.06.041CrossRefGoogle Scholar
  16. 16.
    Smith, R.E., Tournier, J.D., Calamante, F., Connelly, A.: Anatomically-constrained tractography: improved diffusion MRI streamlines tractography through effective use of anatomical information. Neuroimage 62(3), 1924–1938 (2012)CrossRefGoogle Scholar
  17. 17.
    Smith, R.E., Tournier, J.D., Calamante, F., Connelly, A.: SIFT2: enabling dense quantitative assessment of brain white matter connectivity using streamlines tractography. Neuroimage 119, 338–351 (2015)CrossRefGoogle Scholar
  18. 18.
    Tournier, J., Calamante, F., Connelly, A., et al.: MRtrix: diffusion tractography in crossing fiber regions. Int. J. Imaging Syst. Technol. 22(1), 53–66 (2012)CrossRefGoogle Scholar
  19. 19.
    Van Essen, D.C., et al.: The WU-Minn human connectome project: an overview. Neuroimage 80, 62–79 (2013)CrossRefGoogle Scholar
  20. 20.
    Wang, G.Z., et al.: Correspondence between resting-state activity and brain gene expression. Neuron 88(4), 659–666 (2015)CrossRefGoogle Scholar
  21. 21.
    Yeo, B.T., et al.: The organization of the human cerebral cortex estimated by intrinsic functional connectivity. J. Neurophysiol. 106(3), 1125–1165 (2011)CrossRefGoogle Scholar
  22. 22.
    Zhao, T., et al.: Age-related changes in the topological organization of the white matter structural connectome across the human lifespan. Hum. Brain Mapp. 36(10), 3777–3792 (2015)CrossRefGoogle Scholar

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
  • Li Shen
    • 8
  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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