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. 2018 Jul:2018:554-557.
doi: 10.1109/EMBC.2018.8512359.

Heritability of nested hierarchical structural brain network

Moo K Chung, Zhan Luo, Nagesh Adluru, Andrew L Alexander, Richard J Davidson, H Hill Goldsmith

Heritability of nested hierarchical structural brain network

Moo K Chung et al. Annu Int Conf IEEE Eng Med Biol Soc. 2018 Jul.

Abstract

When a brain network is constructed by an existing parcellation method, the topological structure of the network changes depending on the scale of the parcellation. To avoid the scale dependency, we propose to construct a nested hierarchical structural brain network by subdividing the existing parcellation hierarchically. The method is applied in diffusion tensor imaging study of 111 twins in characterizing the topology of the brain network. The genetic contribution of the whole brain structural connectivity is determined and shown to be robustly present over different network scales.

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Figures

Fig. 1
Fig. 1
Left: AAL parcellation with 116 regions. Each parcellation is displayed as a disconnected 3D volume. Red region is the left precentral gyrus. Middle: the second layer of the hierarchical parcellation with 2 × 116 regions. Each AAL parcellation is subdivided into two disjoint regions. Right: the third layer of the hierarchical parcellation with 4 × 116 regions.
Fig. 2
Fig. 2
Hierarchical parcellation of the left precentral gyrus shown in Figure 1 up to the 8-th layer. At the 8-th layer, we have 28−1 = 128 parcellations.
Fig. 3
Fig. 3
Two representative AAL parcellations R11 (right precentral gyrus) R21and (left precentral gyrus) at the first layer will be partitioned into four subregions R12, R22, R32, R42 at the second layer. The fiber tracts will be counted between the parcellations.
Fig. 4
Fig. 4
The hierarchical connectivity matrices of MZ-(top) and DZ-twins (bottom). The parts of connectivity matrices of the layers 1, 2 and 3 are shown. They form a layered convolutional network, where the convolution is defined as the sum of tracts between sub-parcellations.
Fig. 5
Fig. 5
Left: plot of sparsity over the number of pacellations. The sparsity is measures as the ratio of zero entries over all entries in the connectivity matrix. Right: plot of total degree of nodes over the number of pacellations. The vertical axis measures the ratio of the total number of connections over every possible connection. The plots all show the sparse nature of brain networks at any spatial scale.
Fig. 6
Fig. 6
Top, middle: Edge colors are Spearman’s rank correlations thresholded at 0.3 for MZ- and DZ-twins for different layers. Node colors are the maximum correlation of all the connecting edges. Bottom: Edge colors are the heritability index (HI). Node colors are the maximum HI of all the connecting edges. MZ-twins show higher correlations compared to DZ-twins. The node and edge sizes are proportionally scaled.
Fig. 7
Fig. 7
Betti-0 plots. The number of connected components (vertical) over the thresholded correlation values (horizontal) at each layer. The plots scale up over different layers resonably well. The sudden changes in the topological structure of network match up at the same correlation values.
See this image and copyright information in PMC

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