Figure 1.
Network models based on clustering coefficient C and path length L.
Left: ordered model with high C and high L, middle: small-world model with high C and low L, right: random model with low C and low L. Adapted from Watts and Strogatz, Nature 1998.
Figure 2.
Schematic representation of construction of graphs from EEG time series.
EEG time series are measured from scalp electrodes. Phase Lag Index (PLI) as a measure of functional connectivity is calculated between all pairs of electrodes. From the PLI adjacency matrix, the functional brain network is reconstructed and network measures are computed.
Figure 3.
Patient disposition for EEG subsample.
Table 1.
Number of patients with correct EEG data (intent-to-treat population.
Table 2.
Baseline demographics and characteristics of the subset of subjects for whom EEG data were available (intent-to-treat population).
Figure 4.
Local clustering of AD functional networks.
The normalised clustering coefficient gamma in the beta band during 24-weeks intervention was significantly different between the groups. Blue, dotted line: control product; red, solid line: Souvenaid. X-axis: time (weeks), y-axis: gamma in beta band. Error bars represent standard errors of the mean.
Table 3.
Descriptive statistics for the normalised clustering coefficient gamma (intent-to-treat population).
Figure 5.
Global integration of AD functional networks.
The normalised path length lambda in the beta band during 24-weeks intervention was significantly different between the groups. Blue, dotted line: control product; red, solid line: Souvenaid. X-axis: time (weeks), y-axis: lambda in beta band. Error bars represent standard errors of the mean.
Table 4.
Descriptive statistics for the normalised path length lambda (intent-to-treat population).