Graph characteristics of diffusion tractography networks.

<p>Properties of the first principal network are given for each of the diffusion data sets analysed. The most connected vertex in each case is based on the sum of absolute weights of edges connected to the vertices, ignoring self-connections. Efficiency measures are calculated following Latora & Marchiori <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0060997#pone.0060997-Latora1" target="_blank">[41]</a>. Unweighted versions of all measured are used, where the option exists.</p

Eigenvalues of the first 20 principal networks derived from each subject's first diffusion MRI data set.

<p>The pattern of fall-off is very similar from subject to subject, indicating consistency in subnetwork weights across our cohort.</p

Correspondence between region names and indices used in this paper.

<p>Note that each region has one index value for each of the left and right hemispheres.</p

Result of partitioning the thresholded cortical thickness association matrix, using a well-established modularity maximisation algorithm.

<p>Two groups of vertices, of equal size, emerge. These are delimited by horizontal and vertical black lines. Since this algorithm does not identify an ordering for vertices, they are ordered numerically within each group.</p

Visualisation of the full association matrix derived from all cortical thickness data.

<p>The matrix is shown twice, with the gyral regions ordered either numerically by index (left), or by their loading in the first principal network (right). The scale is based on Pearson's correlation coefficient between regions, across all participants.</p

Graph characteristics of cortical thickness networks.

<p>Properties of the full network, plus each principal network containing more than one vertex, are given. The most connected vertex in each case is based on the sum of absolute weights of edges connected to the vertices, ignoring self-connections. Efficiency measures are calculated following Latora & Marchiori <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0060997#pone.0060997-Latora1" target="_blank">[41]</a>. Unweighted versions of all measured are used, where the option exists.</p

Illustration of the principal networks approach using a simple graph with five vertices (A).

<p>The first and second principal networks (B,C) capture the two canonical subnetworks in the graph.</p

First principal network derived from diffusion data in each of two repeat scans of two subjects.

<p>The location of each vertex is based on the original segmentation, using the spatial median of the corresponding region. All four graphs emphasise the strong interconnections between subregions of cingulate cortex.</p

Dendrogram showing the results of hierarchical clustering applied to the cortical thickness data, using (1-correlation) as the distance measure.

<p>“Height”, on the <i>y</i>-axis, refers to the maximum distance between vertices in each pair of clusters. The coloured lines at the bottom of the figure indicate which vertices appear in each of the three main PNs.</p

Voxel-wise analysis of white matter tracts.

<p>Regions showing altered Radial Diffusivity (A), Mean Diffusivity (B) and Fractional anisotropy (C) in patients compared to controls. The regions in the middle cerebellar peduncle are shown in light blue; the regions in superior cerebellar peduncle are shown in red. Axial diffusivity was not significantly affected (data not shown).</p