Tonic-clonic transitions in computer simulation

Abstract

Network simulations can help identify underlying mechanisms of epileptic activity that are hard to isolate in biologic preparations. To be useful, simulations must be sufficiently realistic to make possible biologic and clinical prediction. This requirement for large networks of sufficiently detailed neurons raises challenges both with regard to computational load and the difficulty of obtaining insights with large numbers of free parameters and the large amounts of generated data. The authors have addressed these problems by simulating computationally manageable networks of moderate size consisting of 1,000 to 3,000 neurons with multiple intrinsic and synaptic properties. Experiments on these simulations demonstrated the presence of epileptiform behavior in the form of repetitive high-intensity population events (clonic behavior) or latch-up with near maximal activity (tonic behavior). Intrinsic neuronal excitability is not always a predictor of network epileptiform activity but may paradoxically produce antiepileptic effects, depending on the settings of other parameters. Several simulations revealed the importance of random coincident inputs to shift a network from a low-activation to a high-activation epileptiform state. Finally, a simulated anticonvulsant acting on excitability tended to preferentially decrease tonic activity.

Fig. 1

Raster plot showing spikes during one second of epileptiform activity in a sample network. Drivers, expressors and inhibitors are indicated by initial letter. Field potential for expressors is superimposed on the corresponding raster. NetB connectivity.

Fig. 2

Raster plots of expressor cells from 10 runs of NetA with shuffling of driver timing and input targets A. 500 ms simulation shows different response times and different firing patterns due to reshuffling of input times. Arrow shows time of driver activity onset. B. Expansion of 50 ms of activity from each run (period of bar below A).

Fig. 3

Scatter plots of differences in seizure duration (x-axis) and number of population spikes (y-axis) between hyperexcitable and low-excitability versions of NetA. Shown are points for 27,968 out of 138,240 simulations. Simulated field potentials for individual 1-s simulation pairs are shown in A-G with arrow indicating location on the scatter plot. Solid-line: hyperexcitable network; dashed-line: low-excitability network.

Fig. 4

Activity in NetB simulation with no inhibition. Field potential for expressors is superimposed on the raster plot. Voltage traces for 2 expressors is shown at bottom.

Fig. 5

Latch-up occuring late in a simulation with augmentation of expressor →expressor NMDA strength.

Fig. 6

Feedback inhibition from expressors → inhibitors → expressors more cleanly carves out episodes of activation.

Fig. 7

Reduction of bursting in ACD simulation with repeated plateau inputs of increasing duration. Top: control; bottom: ACD effect.

Fig. 8

ACD simulation reduces prolonged latch-up period without significantly affecting other dynamics. Field recordings compare control (upper trace from Fig. 6) and ACD simulation (lower trace).

Fig. 9

Individual neurons in ACD simulation (A) tend to fire at times corresponding to beginning and end (arrows) of tonic latch-up periods from control (B).