Variability and Heritability of Mouse Brain Structure:
Microscopic MRI Atlases and Connectomes for Diverse Strains
Nian Wanga, Robert J Andersona, David G Ashbrookb, Vivek Gopalakrishnanc, Youngser Parkd, Carey E Priebe d,e, Yi Qia, Rick Laopraserta, Joshua T Vogelsteinc,d,f,g, Robert W Williamsb, G Allan Johnsona,h,*
aDuke Center for In Vivo Microscopy, Department of Radiology, Duke University, Durham, NC 27710, USA
bDepartment of Genetics, Genomics and Informatics, University of Tennessee Health Science Center, Memphis, TN 38163, USA
cDepartment of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21287, USA
dCenter for Imaging Science, Johns Hopkins University, Baltimore, MD 21287, USA
eDepartment of Applied Mathematics and Statistics, Johns Hopkins University, Baltimore, MD 21287, USA
fInstitute for Computational Medicine, Johns Hopkins University, Baltimore, MD 21287, USA
gKavli Neuroscience Discovery Institute, Johns Hopkins University, Baltimore, MD 21287, USA
hDepartment of Biomedical Engineering, Duke University, Durham, NC 27710, USA
*Corresponding Author and Contact Details:
G. Allan Johnson, PhD
Duke University Medical Center Box 3302
Durham, NC 27710, USA
Neuroimage Publication: NeuroImage Volume 222, 15 November 2020, 117274
Genome-wide association studies have demonstrated significant links between human brain structure and com- mon DNA variants. Similar studies with rodents have been challenging because of smaller brain volumes. Using high field MRI (9.4 T) and compressed sensing, we have achieved microscopic resolution and sufficiently high throughput for rodent population studies. We generated whole brain structural MRI and diffusion connectomes for four diverse isogenic lines of mice (C57BL/6J, DBA/2J, CAST/EiJ, and BTBR) at spatial resolution 20,000 times higher than human connectomes. We measured narrow sense heritability ( h 2 ) I.e. the fraction of variance explained by strains in a simple ANOVA model for volumes and scalar diffusion metrics, and estimates of residual technical error for 166 regions in each hemisphere and connectivity between the regions. Volumes of discrete brain regions had the highest mean heritability (0.71 ± 0.23 SD, n = 332), followed by fractional anisotropy (0.54 ± 0.26), radial diffusivity (0.34 ± 0.022), and axial diffusivity (0.28 ± 0.19). Connection profiles were statisti- cally different in 280 of 322 nodes across all four strains. Nearly 150 of the connection profiles were statistically different between the C57BL/6J, DBA/2J, and CAST/EiJ lines. Microscopic whole brain MRI/DTI has allowed us to identify significant heritable phenotypes in brain volume, scalar DTI metrics, and quantitative connectomes.
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NW acquired the MR data, generated the DTI images and connectomes and produced many of the figures. He assisted in preparation of the manuscript. RJA assisted in creating and mapping the labels. DGA performed the heritability and statistical analysis of the volume and scalar data. VG, YP, CEP and JTV developed the Omnibus Embedding-MANOVA method and performed the connectome analysis. YQ performed the active staining and specimen preparation. RL participated in atlas refinements and label mapping. RWW and GAJ defined the project and wrote the manuscript.
We are grateful to Gary Cofer, James Cook, and Lucy Upchurch for invaluable technical assistance. We thank Tatiana Johnson for special care in manuscript preparation and submission.