Abstract

The twin study design in brain imaging offers a very effective way of determining heritability of the human brain. The difference in variability between monozygotic (MZ) and same-sex dizygotic (DZ) twins can be used in determining heritability. We propose to determine the extent of heritability of both structural and functional brain networks at the voxel-level using 200 pairs of twin (400 individuals) of fMRI/DTI and MRI. To obtain high- resolution heritability map of the brain networks, the project requires taking more than 25 thousands voxels for fMRI and 1.2million voxels for MRI/DTI as network nodes, which is a serious computational challenge. The project proposes many new algorithms for constructing large-scale brain networks and subsequently mapping the heritability of the networks. This study will provide the brain imaging community with the baseline brain network heritability maps as well as a versatile open-source toolbox of algorithms for modeling and visualizing large-scale brain networks of three different imaging modalities.

Public Health Relevance

The goal of this project is to develop algorithms and open-source software for determining the heritability of large-scale brain networks using DTI, fMRI and MRI of 200 pairs of twins. The proposed algorithms will be used to establish the baseline map for the genetic influences on brain networks at the voxel-level. Considering recent surge of interests in the fusion of genetics and imaging phenotypes, such detailed heritability maps will be highly useful.

  Funding Agency

Agency
National Institute of Health (NIH)
Institute
National Institute of Biomedical Imaging and Bioengineering (NIBIB)
Type
Research Project (R01)
Project #
5R01EB022856-02
Application #
9360100
Study Section
Special Emphasis Panel (ZRG1)
Program Officer
Peng, Grace
Project Start
2016-09-27
Project End
2019-06-30
Budget Start
2017-07-01
Budget End
2018-06-30
Support Year
2
Fiscal Year
2017
Total Cost
Indirect Cost

  Institution

Name
University of Wisconsin Madison
Department
Biostatistics & Other Math Sci
Type
Schools of Medicine
DUNS #
161202122
City
Madison
State
WI
Country
United States
Zip Code
53715

  Publications

Chung, Moo K; Wang, Yanli; Wu, Guorong (2018) Discrete Heat Kernel Smoothing in Irregular Image Domains. Conf Proc IEEE Eng Med Biol Soc 2018:5101-5104
Lee, Hyekyoung; Chung, Moo K; Kang, Hyejin et al. (2018) ABNORMAL HOLE DETECTION IN BRAIN CONNECTIVITY BY KERNEL DENSITY OF PERSISTENCE DIAGRAM AND HODGE LAPLACIAN. Proc IEEE Int Symp Biomed Imaging 2018:20-23
Solo, Victor; Poline, Jean-Baptiste; Lindquist, Martin A et al. (2018) Connectivity in fMRI: Blind Spots and Breakthroughs. IEEE Trans Med Imaging 37:1537-1550
Chung, Moo K (2018) Statistical Challenges of Big Brain Network Data. Stat Probab Lett 136:78-82
Wang, Yuan; Ombao, Hernando; Chung, Moo K (2018) Topological Data Analysis of Single-Trial Electroencephalographic Signals. Ann Appl Stat 12:1506-1534
Chung, Moo K; Luo, Zhan; Adluru, Nagesh et al. (2018) Heritability of nested hierarchical structural brain network. Conf Proc IEEE Eng Med Biol Soc 2018:554-557
Chung, Moo K; Chuang, Ying Ji; Vorperian, Houri K (2017) Online Statistical Inference for Large-Scale Binary Images. Med Image Comput Comput Assist Interv 10434:729-736
Chung, Moo K; Vilalta-Gil, Victoria; Lee, Hyekyoung et al. (2017) Exact Topological Inference for Paired Brain Networks via Persistent Homology. Inf Process Med Imaging 2017:299-310
Chung, Moo K; Lee, Hyekyoung; Solo, Victor et al. (2017) Topological Distances Between Brain Networks. Connectomics Neuroimaging (2017) 10511:161-170
Chung, Moo K; Hanson, Jamie L; Adluru, Nagesh et al. (2017) Integrative Structural Brain Network Analysis in Diffusion Tensor Imaging. Brain Connect 7:331-346