<i>p</i> values for pairewise Wilcoxon tests with Bonferroni correction for the three frequency band of interest.

<p><i>p</i> corrected value should be inferior to .00238. No significant SL differences were found between the two control conditions within each of the seven areas and for the three frequency bands.</p

Schema for one trial according to observation conditions.

<p>Schema for one trial according to observation conditions.</p

An overview of the method used to calculate weighted graphs.

<p>(A) EEG time-series from the 26 scalp electrodes were separately filtered for the delta, theta, alpha low, alpha high, beta, and gamma frequency bands. Higher alpha (10–13 Hz) is shown here as largest group differences were found for this frequency band. (B) Functional connectivity between all 26×26 electrode pairs was calculated based on the phase lag index, yielding connectivity values between 0 and 1 (higher values reflect more synchronization between electrodes). (C) When using graph theoretical analysis on EEG time series, electrodes represent “nodes” and the distance between these nodes represent the “edges” in the graph. PLI scores were used to calculate the path length (distance between the nodes) and the clustering coefficients (the degree in which nodes cluster together). In addition, a randomization procedure is employed to obtain measures independent of network size. From each original graph, random networks were derived by randomly shuffling the edge weights. Mean values of weighted graphs are then determined by dividing the original graph measures by these ‘surrogate’ measures.</p

Mean normalized path length over all epochs for FXS and controls participants in the delta (0.05–4 Hz), theta (4–8 Hz), lower alpha (8–10 Hz), upper alpha (10–13 Hz), beta (13–30 Hz), and gamma (30–45 Hz) frequency range.

<p>Path length in the theta band is significant longer in FXS males as compared to controls. Asterisks represent significant differences at <i>p</i><.05. Error bars represent standard error of the mean.</p

Mean normalized clustering coefficients over all epochs for FXS and controls participants in the delta (0.05–4), theta (4–8 Hz), lower alpha (8–10 Hz), upper alpha (10–13 Hz), beta (13–30 Hz), and gamma (30–45 Hz) frequency range.

<p>Error bars represent standard error of the mean.</p

Results of the small-world index “<i>S</i>” in FXS and controls in the six EEG frequency bands.

<p>Note: Values represent mean small-world index ‘<i>S</i>’. Standard error of the mean is presented between brackets.</p

Altering neuronal excitability to preserve network connectivity in a computational model of Alzheimer's disease

<div><p>Neuronal hyperactivity and hyperexcitability of the cerebral cortex and hippocampal region is an increasingly observed phenomenon in preclinical Alzheimer’s disease (AD). In later stages, oscillatory slowing and loss of functional connectivity are ubiquitous. Recent evidence suggests that neuronal dynamics have a prominent role in AD pathophysiology, making it a potentially interesting therapeutic target. However, although neuronal activity can be manipulated by various (non-)pharmacological means, intervening in a highly integrated system that depends on complex dynamics can produce counterintuitive and adverse effects. Computational dynamic network modeling may serve as a virtual test ground for developing effective interventions. To explore this approach, a previously introduced large-scale neural mass network with human brain topology was used to simulate the temporal evolution of AD-like, activity-dependent network degeneration. In addition, six defense strategies that either enhanced or diminished neuronal excitability were tested against the degeneration process, targeting excitatory and inhibitory neurons combined or separately. Outcome measures described oscillatory, connectivity and topological features of the damaged networks. Over time, the various interventions produced diverse large-scale network effects. Contrary to our hypothesis, the most successful strategy was a selective stimulation of all excitatory neurons in the network; it substantially prolonged the preservation of network integrity. The results of this study imply that functional network damage due to pathological neuronal activity can be opposed by targeted adjustment of neuronal excitability levels. The present approach may help to explore therapeutic effects aimed at preserving or restoring neuronal network integrity and contribute to better-informed intervention choices in future clinical trials in AD.</p></div

Effect of the ‘stimulation of excitatory neurons’ intervention on structural connectivity.

<p>At three different time points, structural connectivity strength is compared with the ‘no intervention’ condition (blue lines). This is done in six different categories, based on structural connectivity level (degree). Highly connected hubs fall in the sixth category, most right in the chart. Note: error bars indicate standard deviations based on all node degrees per category. For a detailed list of region names and degree distributions see [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005707#pcbi.1005707.ref018" target="_blank">18</a>, <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005707#pcbi.1005707.t001" target="_blank">Table 1</a>].</p

Intervention timing.

<p>Overall functional connectivity (Phase Lag Index, PLI) over time is compared between the ‘no intervention’ condition, a healthy control network, and the most successful intervention, ‘stimulation of excitatory neurons’, started at three different points in time. Note that the graph starts at T = 10; the ‘early’ intervention has been active ten cycles before this graph starts. Error bars indicate the standard deviation over the total number of runs of the various strategies.</p

Global overview of relevant modeling and analysis procedures.

<p>This study focuses on the parts indicated in red: the virtual trial. The general workflow of our analysis can be described as follows: the dynamic network model is run with the degeneration algorithm and, simultaneously, one of the interventions (or no intervention). Hence, the network is damaged over time according to local neuronal activity levels, but at the same time, by changing neuronal excitability levels due to varied threshold potential (Vd) settings (see below for details), a counterstrategy is employed to diminish the effect of the damage and maintain network topology close to the original state. The resulting oscillatory, connectivity and network topology changes are then described using the selected measures (see below) to evaluate the effect of the different interventions over time, and finally these are compared statistically to obtain an impression of the most successful strategy. For a more detailed stepwise description of the analysis, please refer to the Method section.</p