Spatially clustered neuronal assemblies comprise the microstructure of synchrony in chronically epileptic networks

Abstract

Epilepsy is characterized by recurrent synchronizations of neuronal activity, which are both a cardinal clinical symptom and a debilitating phenomenon. Although the temporal dynamics of epileptiform synchronizations are well described at the macroscopic level using electrophysiological approaches, less is known about how spatially distributed microcircuits contribute to these events. It is important to understand the relationship between micro and macro network activity because the various mechanisms proposed to underlie the generation of such pathological dynamics are united by the assumption that epileptic activity is recurrent and hypersynchronous across multiple scales. However, quantitative analyses of epileptiform spatial dynamics with cellular resolution have been hampered by the difficulty of simultaneously recording from multiple neurons in lesioned, adult brain tissue. We have overcome this experimental limitation and used two-photon calcium imaging in combination with a functional clustering algorithm to uncover the functional network structure of the chronically epileptic dentate gyrus in the mouse pilocarpine model of temporal lobe epilepsy. We show that, under hyperexcitable conditions, slices from the epileptic dentate gyrus display recurrent interictal-like network events with a high diversity in the activity patterns of individual neurons. Analysis reveals that multiple functional clusters of spatially localized neurons comprise epileptic networks, and that network events are composed of the coactivation of variable subsets of these clusters, which show little repetition between events. Thus, these interictal-like recurrent macroscopic events are not necessarily recurrent when viewed at the microcircuit scale and instead display a patterned but variable structure.

Fig. 1.

Slices display network events. (A) Raster plot of the population dynamics after onset detection in an epileptic slice. (B) The frequency of network events is higher in epileptic slices (n = 19 slices, control; n = 24 slices, epileptic; two-sample t test, P = 0.04; error bars represent SEM). (C) Classification of slices that displayed network events. A slice was deemed to display network events if the frequency of detected events was greater than one event per minute.

Fig. 2.

Functional clustering of neuronal dynamics. (A) Dendrogram representing the application of the FCA to neuronal dynamics observed in an epileptic slice. The vertical axis represents the step in the algorithm, and the dashed line indicates the cutoff for statistical significance. The resulting clusters of neurons are indicated by a common color, which is held constant in all resulting plots. (B) Raster plot of population activity plotted with the neuron order given from the above dendrogram. Note that neurons within the same cluster do indeed display similar synchronous dynamics. The vertical black lines indicate detected network events. (C) Spatial mapping of the clusters of neurons depicted in A and B. A corresponding polygon enclosing the cluster has been additionally overlaid to aide in the visualization of the identified clusters. Clusters of neurons are spatially localized and arranged radially throughout the granule cell layer.

Fig. 3.

Characterization of spatial properties of neuronal clusters. (A) Distributions of the spatial extent of control (n = 30) and epileptic (n = 72) clusters. The red lines indicate median values, and the box encloses the 25th to 75th percentiles. The distribution of clusters in epileptic tissue is highly skewed toward small values, indicating that these clusters are more spatially localized (Kolmogorov–Smirnov test, P = 0.03). (B) Distributions of the average pairwise distance between neurons within a cluster. Again, the distribution for epileptic tissue is skewed toward low values, confirming that these clusters tend to be spatially localized. (C) Probability distributions of the maximum pairwise distance within a cluster for control (Upper) and epileptic (Lower) tissue. Note the bimodal nature of the distribution in the case of clusters from control tissue. Clusters with a maximum distance of >150 μm are distributed throughout the GCL. (D) Average frequency of calcium activations for neurons within a cluster, separated by clusters with a maximum pairwise distance of >150 or <150 μm. Clusters with a tangential orientation (>150 μm) are composed of neurons with higher firing rates (two-sample t test; n = 14 clusters <150, n = 16 clusters >150, P = 0.00018, control; n = 25 clusters <150, n = 47 clusters >150, P = 0.004, epileptic). (E and F) Examples of the spatial mapping of clusters from a control slice (E) and an epileptic slice (F). The shade of the cluster indicates the average frequency of calcium activation for neurons within the cluster. The darker shades indicate clusters with more activity. (G and H) Raster plots depicting the activity of neurons in the clusters from E and F.

Fig. 4.

Cluster activation and composition of network events. (A) Cluster activation raster plot (Upper) and corresponding fraction of active clusters as a function of time (Lower). The colored vertical lines depict cluster activation, and the color corresponds to the clusters identified in Fig. 2. The vertical height indicates the number of neurons comprising the cluster, and the horizontal length indicates the time window during which the cluster was active. The vertical gray lines mark detected network events, and the fraction of active clusters as a function of time is shown below. The time period shown is the same as in the raster plot from Fig. 2B. (B) Schematic depicting transformation of network events into a “pattern” of 0 and 1 in which a value of 1 indicates that the cluster was active during the network event. The vertical position of the 0/1 value in the column corresponds to the vertical position of the cluster in the raster plot. Transformation is shown for the first eight network events from A. (C) Probability distributions showing the percentage of network events that are composed of each observed pattern. Here, data are shown for patterns from slices that displayed network events and had three or more clusters (n = 23 patterns, 3 slices, control; n = 123 patterns, 10 slices, epileptic). This indicates that patterns rarely repeat, and each network event is composed of the coactivation of a different subset of clusters.