Epilepsy affects around 50 million people worldwide (1).
30% of epilepsy cases are drug-resistant and surgical removal of the the neural tissue generating seizures (epileptogenic) may be the only way to prevent seizures. When removing the epileptogenic tissue it is crucial to minimize the lesioned area, because removing too much of the brain may lead to serious impairment of its function.

To identify the epileptogenic zone, neurosurgeon typically implants electrode on the cortex (ECoG) or deep in the brain (SEEG). The measured potentials are used as indicators localizing the epileptic source. We argue that reconstruced source of this brain activity are better predictors of areas for resection. Here we present a method - kernel Electrical Source Imaging (kESI) - and its Python implementation which allow reconstruction of current sources taking into account the actual geometry of the patient's brain and the conductivity distribution. This method extends the kernel Current Source Density (kCSD) method (3, 4) to realistic geometries and complex conductivity models.

In the poster we present our most recent results in development of Python tools for reconstruction of brain activity and the progress report of kESI development.

Authors

• Marta Kowalska,
• Jakub M. Dzik,
• Chaitanya Chintaluri,
• Daniel K. Wójcik

Bibliography

1. World Health Organization, Epilepsy, available at: https://www.who.int/news-room/fact-sheets/detail/epilepsy
2. Pitts, W. H. (1952), Investigations on synaptic transmission, in 'Cybernetics, Trans. 9th Conf. Josiah Macy Foundation H. von Foerster', pp. 159-166.
3. Potworowski, J., Jakuczun, W., Łęski, S. & Wójcik, D. (2012) Kernel current source density method. Neural Comput 24(2), 541-575.
4. Kernel Current Source Density https://github.com/Neuroinflab/kCSD-python

Acknowledgement

Project funded from the Polish National Science Centre's OPUS grant (2015/17/B/ST7/04123).