Abstracts

Rationale: Severe brain trauma changes neural network structure by altering neuronal connectivity. It is followed by self repair including replacment of lost connections. Despite the efficacy of this process, post traumatic epileptogenesis is a frequent consequence. It is generally assumed epileptogenesis consists of series of changes in the neural network including: axonal sprouting, an imbalance between inhibition and excitation, and homeostatic adjustments within neurons. However, the timing, extent, and strategies for execution of these adaptive changes are unknown, and we do not know how they are influenced by the nature of the initial circuit compromise / injury. By modeling this process, we hope to understand the range of changes that lead to epilepsy, as well as determine whether rebuilding circuits produce patterns of activity that could serve as early bio-markers for pro-epileptic changes. Such biomarkers would make feasible the administration of yet-to-be-discovered effective treatments that allow network repair but prevent epileptogenesis. Methods: We used a large-scale computer model of the CA3 region of the hippocampus to study the impact of network changes on the spatial and temporal patterns of synchronous activity propagation. Our experiments were designed using two network topologies, one with a Gaussian distribution of the length of synaptic connections, and a scale-free network where the number of neurons with a specific radius of connectivity was inversely proportional to the radius length. Each type of network was subjected to neuronal injury using following strategies of cell death: a) random, or preferential death of the following classes of neurons: b) highly connected cells c) most active d) interneurons. Afterwards each network was rewired using the following strategies: a) restoring damaged pathways, b) random with constant radii of connectivity c) random with expanding radii of connectivity. Results: The results of our computational simulations were compared with multiphoton imaging of neuronal activity recorded in organotypic slice cultures of rat hippocampus over a period of 3weeks in vitro, during which time the cultures became uniformly epileptic. Comparisons of model output and physiological recordings were based on measurements of cell activity correlations at different synaptic path lengths and correlation time windows. We also compared networks' synaptic structures by comparing log-log plots of the number of connections per neuron. We present the results of these simulations including the sequence of network changes that most closely reproduced the changes observed in-vitro. Conclusions: Modeling studies provide useful information regarding the types of circuit changes that lead to spontaneous epileptiform activity. Ensemble recordings of real recovering neural networks constrain the models sufficiently that the importance of broad recovery strategies can be robustly evaluated; these evaluations will be presented.