Epilepsy comes in many varieties, and is characterised as a seizure-inducing condition of the brain. Events such as strokes, brain injuries or toxic exposures are some of the causes of epilepsy also known as epileptogenic events.
The development of anti-epileptogenic treatments awaits identification of a so-called epileptogenic marker: a measurable event which occurs during the development of epilepsy.
The time required to accomplish calculations of datasets would have taken more than two months by an ordinary PC, instead of a little more than two days using high-throughput compute
A collaboration led by Massimo Rizzi of the Mario Negri Institute for Pharmacological Research appears to have pinpointed just such a marker with the help of EGI. All it took was some heavy-duty high-throughput compute and a handful of mice.
Scientists Rizzi and his colleagues thought an alteration in brain electrical activity following the exposure to a risk factor might be a smart place to look for an epileptogenic marker. Rizzi’s team focused their attention on an animal model of epilepsy. They found that the epidural electrocorticograms (ECoGs) of mice developing epilepsy from the induced trauma would rapidly alter between nearly periodic and then irregular behaviour of brain electrical activity.
Noting this signal, the scientists applied an experimental anti-epileptogenic treatment that successfully reduced the rate of occurrence of this oscillation pattern. The results led Rizzi and his team to confidently assert that high rates of dynamic intermittency can be considered as a marker of epileptogenesis.
Rizzi’s team made good use of the computational and storage resources at the Italian National Institute of Nuclear Physics (INFN).
Their research was published in Scientific Reports and it holds out promise for use in the development of anti-epileptogenic therapies.
Scientists credit a recent breakthrough in epilepsy research to the computational power provided by the Italian National Institute of Nuclear Physics (INFN). Courtesy INFN.
M. Rizzi et al. 2016. Scientific Report Journal. doi:10.1038/srep31129 (abstract)