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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

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 ~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%.

Original languageEnglish (US)
Title of host publicationConnectomics in NeuroImaging - 2nd International Workshop, CNI 2018, Held in Conjunction with MICCAI 2018, Proceedings
EditorsGuorong Wu, Markus D. Schirmer, Ai Wern Chung, Islem Rekik, Brent Munsell
PublisherSpringer Verlag
Pages58-66
Number of pages9
ISBN (Print)9783030007546
DOIs
StatePublished - Jan 1 2018
Event2nd International Workshop on Connectomics in NeuroImaging, CNI 2018 held in conjunction with the 21st International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2018 - Granada, Spain
Duration: Sep 20 2018Sep 20 2018

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11083 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other2nd International Workshop on Connectomics in NeuroImaging, CNI 2018 held in conjunction with the 21st International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2018
CountrySpain
CityGranada
Period9/20/189/20/18

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Keywords

  • HCP
  • Heritability
  • Reliability
  • Structural connectivity

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Xie, L., Amico, E., Salama, P., Wu, Y. C., Fang, S., Sporns, O., Saykin, A. J., Goñi, J., Yan, J., & Shen, L. (2018). Heritability estimation of reliable connectomic features. In G. Wu, M. D. Schirmer, A. W. Chung, I. Rekik, & B. Munsell (Eds.), Connectomics in NeuroImaging - 2nd International Workshop, CNI 2018, Held in Conjunction with MICCAI 2018, Proceedings (pp. 58-66). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11083 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-030-00755-3_7