Understanding epilepsy through network modeling

Epilepsy is one of the most common neurological disorders. According to the World Health Organization, it affects ≈50 million people worldwide at any given time, and ≈2% of the current world population has had epilepsy, has epilepsy now, or will experience it at some point in the future (1). In many patients epilepsy can be treated pharmacologically or through brain surgery, but a substantial subset of cases remains intractable, in part, because of our still incomplete understanding of the underlying causes of epilepsy. Determining how brains become seizure-prone and epileptic is therefore of vital importance for our understanding of this disorder, and could potentially enable the development of new treatment strategies.

Although the term epilepsy describes a family of disorders with variable causes and manifestations, all forms of epilepsy are accompanied by recurrent seizures caused by abnormal electrical activity in the brain, often in the form of synchronized discharges of nerve cells (neurons) that are not synchronously active during normal brain activity. In an elegant series of studies (2, 3) culminating in an article in this issue of PNAS (4), Soltesz and coworkers have incorporated the results of decades of work by many researchers in a physiologically realistic, cellularly detailed computer model of the dentate gyrus, a brain structure involved in temporal lobe epilepsy, which is the most common form of epilepsy in adults. With this computer model they perform a sequence of virtual experiments that would not be possible in the brain itself, and demonstrate that the emergence of a small number of highly connected “hub” neurons after brain injury is sufficient for the development of hyperexcitability and epilepsy.

The neuronal network computer model Soltesz et al. have constructed is part of a new generation of large-scale, highly detailed, and complex network models that push the limit of …

*E-mail: astrid.prinz{at}emory.edu