Long-term effects of temporal lobe epilepsy on local neural networks: a graph theoretical analysis of corticography recordings.
- Authors
- van Dellen, Edwin; Douw, Linda; Baayen, Johannes C; Heimans, Jan J; Ponten, Sophie C; Vandertop, W Peter; Velis, Demetrios N; Stam, Cornelis J; Reijneveld, Jaap C
- Year
- 2009
- Journal
- PloS one
- PMID
- 19956634
- DOI
- 10.1371/journal.pone.0008081
- PMCID
- PMC2778557
PURPOSE: Pharmaco-resistant temporal lobe epilepsy (TLE) is often treated with surgical intervention at some point. As epilepsy surgery is considered a last resort by most physicians, a long history of epileptic seizures prior to surgery is not uncommon. Little is known about the effects of ongoing TLE on neural functioning. A better understanding of these effects might influence the moment of surgical intervention. Functional connectivity (interaction between spatially distributed brain areas) and network structure (integration and segregation of information processing) are thought to be essential for optimal brain functioning. We report on the impact of TLE duration on temporal lobe functional connectivity and network characteristics. METHODS: Functional connectivity of the temporal lobe at the time of surgery was assessed by means of interictal electrocorticography (ECoG) recordings of 27 TLE patients by using the phase lag index (PLI). Graphs (abstract network representations) were reconstructed from the PLI matrix and characterized by the clustering coefficient C (local clustering), the path length L (overall network interconnectedness), and the "small world index" S (network configuration). RESULTS: Functional connectivity (average PLI), clustering coefficients, and the small world index were negatively correlated with TLE duration in the broad frequency band (0.5-48 Hz). DISCUSSION: Temporal lobe functional connectivity is lower in patients with longer TLE history, and longer TLE duration is correlated with more random network configuration. Our findings suggest that the neural networks of TLE patients become more pathological over time, possibly due to temporal lobe changes associated with long-standing lesional epilepsy.
Three network types.Three network types based on the model of Watts and Strogatz, 1998. In an ordered network the points are connected to their nearest neighbours (left) but there are no long-distance connections. In a random network (right), there is no local clustering. In a small-world network (middle), some local connections are rewired to long distance connections, resulting in high clustering combined with a short path length.
Intra-cranial corticography recording.Picture of a grid, placed directly on the cortex for intra-cranial recording.
Visualisation of the functional connectivity pattern over the temporal lobe.A grid of 4x5 electrodes is placed directly on the temporal lobe and the registered area is schematically documented. 5 Epochs of representative ECoG recordings are selected per patient. The PLI values are calculated based on these recordings, resulting in a matrix with synchronization values for all possible electrode combinations. The figure shows a mean PLI value for each grid electrode based on this synchronization matrix, representing the average synchronization with the other channels.
Correlation between epilepsy duration and functional connectivity.The Phase Lag Index (PLI) value is given as a function of epilepsy duration of each patient, showing a decreased PLI with longer epilepsy duration.
Correlation between epilepsy duration and network characteristics.The clustering coefficient C and average shortest path length L were calculated for each patient and compared to 1000 random networks, resulting in a ratio C/Cs and L/Ls. Network characteristics were plotted into a graph as a function of epilepsy duration, showing a decrease of C/Cs (5A) and no change of L/Ls (5B), resulting in a decrease of the small world index S (5C). The shown results represent the network characteristics for an average number of connections k = 3.
Network representation on the ECoG grid.Network representation of patient 13 for an average number of connections k = 3. The characteristics of this network are: C/Cs = 2.12, L/Ls = 1.07 and S = 1.98. Note that some edges are printed bold. These edges represent connections between three so-called ‘hub’ vertices (vertices with a relative high number of connections), which increase the small world properties of the network in two ways. Firstly, the edges mark a local cluster, which increases the network characteristic C/Cs. Secondly, this particular cluster also increases the global integration of the network as almost all connected vertices in the network can be reached through these hubs, resulting in a lower L/Ls.
| # | Section | Preview |
|---|---|---|
| 40 | Discussion — Network Properties | In our study, we have found a correlation between higher seizure frequency and a higher clustering… |
| 41 | Discussion — Network Properties | evidence of a robust correlation between epilepsy duration and these network characteristics. Apart… |
| 42 | Discussion — Network Properties | Recently, a correlation has been shown between small world network topology and cognitive… |
| 43 | Discussion — Conclusion | In the present study we have shown that there is both a decrease in connectivity of functional… |
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| Title | Year | PMID |
|---|---|---|
| Advances in Electrophysiological Research. | 2015 | 26259089 |
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