Three-dimensional cardiovascular imaging-genetics: a mass univariate framework

Figures

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  Fig. 1.. Computational image analysis. (A) Short axis cardiac magnetic resonance image demonstrating automated segmentation of the endocardial and epicardial boundaries of the left ventricle. (B) The segmentation is used to construct a three dimensional mesh of the cardiac surfaces (left ventricle shown as a mesh, right ventricle shown as a solid) that is co-registered to a standard coordinate space. Phenotypic parameters, such as wall thickness, are then derived for each vertex in the model

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  Fig. 2.. Outline of three-dimensional mass univariate framework. A statistical atlas provides point-wise measures of ventricular geometry and function which can be linked to a given predictor through a general linear model. Using mass univariate regression, three-dimensional maps of a test statistic and the degree of association (β) can be derived. Threshold free cluster enhancement (TFCE) coupled with permutation testing produces vertex-wise P-values weighted to the degree of coherent spatial support. Finally, P-values are corrected for multiple testing. Regression coefficients enclosed by significance contours are represented on a model of the left ventricle

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  Fig. 3.. Applying three-dimensional analysis to single nucleotide polymorphism (SNP) replication. β coefficients are plotted on the surface of the left ventricle for the effect of 4 distinct SNPs on wall thickness (WT) adjusted for age, gender, body surface area and systolic blood pressure. Yellow contours enclose standardized regression coefficients reaching significance after multiple testing

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  Fig. 4.. Assessment of power using synthetic data. Plots of our framework’s sensitivity at different sample sizes N and signal intensities I to detect a synthetic signal on (A) 10% and (B) 60% of the LV surface. A black line on the plots indicates a threshold of 80% sensitivity