We developed a neural network model that can account for major elements common
to human focal seizures. These include the tonic-clonic transition, slow advance of clinical semiology
and corresponding seizure territory expansion, widespread EEG synchronization, and slowing of
the ictal rhythm as the seizure approaches termination. These were reproduced by incorporating
usage-dependent exhaustion of inhibition in an adaptive neural network that receives global
feedback inhibition in addition to local recurrent projections. Our model proposes mechanisms that
may underline common EEG seizure onset patterns and status epilepticus, and postulates a role for
synaptic plasticity in the emergence of epileptic foci. Complex patterns of seizure activity and bi-
stable seizure end-points arise when stochastic noise is included. With the rapid advancement of
clinical and experimental tools, we believe that this model can provide a roadmap and potentially
an in silico testbed for future explorations of seizure mechanisms and clinical therapies.
Reference:
1 .
Liou JY, Smith EH, Bateman LM, Bruce SL, McKhann GM, Goodman RR, Emerson RG, Schevon CA, Abbott LF (2020) A model for focal seizure onset, propagation, evolution, and progression. Elife [PubMed]
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