Changes in topological organization of functional PET brain network with normal aging

Abstract

Recent studies about brain network have suggested that normal aging is associated with alterations in coordinated patterns of the large-scale brain functional and structural systems. However, age-related changes in functional networks constructed via positron emission tomography (PET) data are still barely understood. Here, we constructed functional brain networks composed of 90 regions in younger (mean age 36.5 years) and older (mean age 56.3 years) age groups with PET data. 113 younger and 110 older healthy individuals were separately selected for two age groups, from a physical examination database. Corresponding brain functional networks of the two groups were constructed by thresholding average cerebral glucose metabolism correlation matrices of 90 regions and analysed using graph theoretical approaches. Although both groups showed normal small-world architecture in the PET networks, increased clustering and decreased efficiency were found in older subjects, implying a degeneration process that brain system shifts from a small-world network to regular one along with normal aging. Moreover, normal senescence was related to changed nodal centralities predominantly in association and paralimbic cortex regions, e.g. increasing in orbitofrontal cortex (middle) and decreasing in left hippocampus. Additionally, the older networks were about equally as robust to random failures as younger counterpart, but more vulnerable against targeted attacks. Finally, methods in the construction of the PET networks revealed reasonable robustness. Our findings enhanced the understanding about the topological principles of PET networks and changes related to normal aging.

Figure 1. The correlation matrices of two groups.

The graphs show the correlation matrices acquired by calculating partial correlations (left for the older group and right for the younger group). The color bar in the middle indicates the partial correlation coefficient between regions. The rank and row successively represent the 90 brain regions (see Table S1).

Figure 2. The binarized matrices () of two groups.

The graphs show the binarized matrices (left for the older group and right for the younger group) which are generated by setting threshold to the correlation matrices. The rank and row successively represent the 90 brain regions (see Table S1). Such a threshold () ensures that the networks of both of the groups have the same number of nodes and links, and also show changed efficiency of information transfer (, , ). In this graph, white and black indicate the and .

Figure 3. Clustering coefficient () as a function of sparsity.

The graph shows that, at a wide range of sparsity (), the older subjects (red line) have larger value than the younger subjects (black line).

Figure 4. Path length () as a function of sparsity.

The graph shows that two groups have same value when sparsity ranges from to and the older group (red line) have larger at .

Figure 5. Global efficiency () as a function of sparsity.

is numerically easier to indicate the global efficiency than (see Material and Methods). As the sparsity thresholds increase from to , of both groups increase, and younger subjects (black line) have larger values. At high sparsity threshold (), two groups show equal values.

Figure 6. Small-world parameters of networks.

The graphs show the changes in (red line), (green line) and (blue line) in the networks of older (left panel) and younger (right panel) groups as a function of sparisty thresholds. At a wide range of sparsity, both networks have , that implies prominent small-world properties (see Materials and Methods). Note that, as the values of sparsity thresholds increase, the and values decrease rapidly, but the values decrease rapidly when sparsity range from to then change slightly.

Figure 7. Betweenness centrality () of two groups.

The below graph shows the comparison (red bar for younger group and blue bar for older group) of normalized betweenness () in each node (region) between two groups. The upper graph shows the regional changes (, ) in normalized betweenness () between two groups. The regions labeled in the upper graph indicate significant changes in between two groups (see Table 4). Note that these results were obtained from the brain networks with a sparsity of . Regions in networks of two groups showing high value () have been listed in the Table 2 and Table 3.

Figure 8. Topological robustness in networks of two groups.

The graphs show the relative size of the largest connected component as a function of the fraction of removed nodes by random failures or targeted attacks. As the response to random failures (left panel), the brain network in the older group (red line) is approximately as robust as that in the younger group (black line). Right graph shows that the older network displays remarkably reduced stability against targeted attack compared with the younger. Additionally, the statistical significant differences () of two groups was found with the ranges of and in the right graph.

Figure 9. Small-world properties in smaller-sample networks.

A and B, The graphs show the small-world parameters (, and ) of smaller-sample networks in older (A) and younger (B) groups. At a wide range of sparsity, both networks have , that implies prominent small-world properties (see Materials and Methods). C, This graph shows that older subjects (red line) have larger values than the younger subjects (black line). In the original networks, similar result of was also shown in Figure 3. D, This graph shows the global efficiency () as a function of sparsity. As the sparsity thresholds increase from to , of both groups increase and younger subjects (black line) have larger values.