Department of Physiology and Cellular Biophysics, Columbia University, New York, United States; Department of Anesthesiology, NewYork-Presbyterian Hospital/Weill Cornell Medicine, New York, United States; Department of Neurology, Columbia University Medical Center, New York, United States
Department of Neurology, Columbia University Medical Center, New York, United States
LF Abbott
Department of Physiology and Cellular Biophysics, Columbia University, New York, United States; Mortimer B. Zuckerman Mind Brain Behavior Institute, Department of Neuroscience, Columbia University, New York, United States
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.
