Functional neural network analysis in frontotemporal dementia and Alzheimer's disease using EEG and graph theory

Abstract

Background: Although a large body of knowledge about both brain structure and function has been gathered over the last decades, we still have a poor understanding of their exact relationship. Graph theory provides a method to study the relation between network structure and function, and its application to neuroscientific data is an emerging research field. We investigated topological changes in large-scale functional brain networks in patients with Alzheimer's disease (AD) and frontotemporal lobar degeneration (FTLD) by means of graph theoretical analysis of resting-state EEG recordings. EEGs of 20 patients with mild to moderate AD, 15 FTLD patients, and 23 non-demented individuals were recorded in an eyes-closed resting-state. The synchronization likelihood (SL), a measure of functional connectivity, was calculated for each sensor pair in 0.5-4 Hz, 4-8 Hz, 8-10 Hz, 10-13 Hz, 13-30 Hz and 30-45 Hz frequency bands. The resulting connectivity matrices were converted to unweighted graphs, whose structure was characterized with several measures: mean clustering coefficient (local connectivity), characteristic path length (global connectivity) and degree correlation (network 'assortativity'). All results were normalized for network size and compared with random control networks.

Results: In AD, the clustering coefficient decreased in the lower alpha and beta bands (p < 0.001), and the characteristic path length decreased in the lower alpha and gamma bands (p < 0.05) compared to controls. In FTLD no significant differences with controls were found in these measures. The degree correlation decreased in both alpha bands in AD compared to controls (p < 0.05), but increased in the FTLD lower alpha band compared with controls (p < 0.01).

Conclusion: With decreasing local and global connectivity parameters, the large-scale functional brain network organization in AD deviates from the optimal 'small-world' network structure towards a more 'random' type. This is associated with less efficient information exchange between brain areas, supporting the disconnection hypothesis of AD. Surprisingly, FTLD patients show changes in the opposite direction, towards a (perhaps excessively) more 'ordered' network structure, possibly reflecting a different underlying pathophysiological process.

Figure 1

Clustering coefficient. Boxplots showing differences in normalized clustering coefficients (γ) for the separate frequency bands. Alpha1 = lower alpha band (8–10 Hz), alpha2 = upper alpha band (10–13 Hz).

Figure 2

Characteristic path length. Boxplots showing differences in normalized characteristic path lengths (λ) for the separate frequency bands. Alpha1 = lower alpha band (8–10 Hz), alpha2 = upper alpha band (10–13 Hz).

Figure 3

Degree Correlation. Boxplots showing differences in degree correlation (R) for the separate frequency bands. Alpha1 = lower alpha band (8–10 Hz), alpha2 = upper alpha band (10–13 Hz).

Figure 4

Graph theory principles. Graphs can represent any kind of network. Dots represent nodes, and lines connecting the dots are the connections. The degree (K) of a node is it's number of connections. The clustering coefficient (C), measuring local connectivity of a node, is the likelihood that its neighbors are connected. For node C, with neighbours B and D, the clustering coefficient is 1. The characteristic path length (L), a measure of global connectivity, is the minimum number of connections between two nodes. The path length between vertices A and B consists of three edges, indicted by the striped lines. The degree correlation (R), a measure of network clustering according to degree, is the ratio of the degrees of two neighboring nodes. Figure taken with permission from Stam and Reijneveld. Graph theoretical analysis of complex networks in the brain. Nonlinear Biomedical Physics. 2007c; 1: 3.

Figure 5

From EEG recording to unweighted graph. Multi-step procedure to obtain normalized network-derived variables. C = clustering coefficient, L = Characteristic path length, γ = normalized clustering coefficient, λ = normalized characteristic path length.

Figure 6

Unweighted graphs of the lower alpha band (8–10 Hz) for different patient groups and different fixed average degrees (K). For the AD, FTLD and control groups, the functional connectivity (SL) based graphs are shown as headplots for different values of K. Lower K values (higher threshold) result in a sparser network. On visual inspection, it is obvious that there are inter-group differences.