Predicting which brain region to treat using a new method of brain mapping

Scientific Title: Predicting which brain region to treat: Multimodal Ictogenic Network Mapping (MINM)

Lead investigator: Dr Diana Cash, King’s College London

Co-investigators: Dr Michel Mesquita, KCL, Dr Marc Goodfellow, University of Exeter, Dr Laura Mantoan, KCL

Collaborator: Prof Mark Richardson, KCL

Pilot grant: £29,903, 12 months

“Our vision is to establish a translational platform to develop, test and optimise non-invasive multimodal mapping of seizure-generating networks in the brain, and to develop minimally-destructive techniques to disrupt them.” Dr Diana Cash, King’s College London


Resective brain surgery for focal epilepsy can be successful, but a significant number of patients do not become seizure free. Recent innovations in dynamic computational modelling have shown promise in mapping brain networks responsible for seizure generation (ictogenicity) and in predicting the outcome of specific resection strategies. It is highly challenging to translate this technique into prospective clinical use, because the model-predicted optimal resection may be substantially different from that predicted by conventional approaches.

The Study

In this pilot study, the researchers will test the feasibility of detecting epileptogenic networks using clinically-relevant functional neuroimaging, EEG and computer modelling techniques, in an animal model of epilepsy. Simultaneous specialised MRI and EEG scanning will be conducted prior to developing epilepsy, and then at regular intervals afterwards, in order to map the dynamic evolution of epileptogenic networks. The research team will then use this data to develop predictive modelling exploiting diverse information at different spatial scales and to determine the brain areas for surgical treatment – the ictogenic foci. The later steps will involve genetically modifying neurons in these brain areas to make them sensitive to light (optogenetics). This will briefly silence these model-predicted brain areas, in an attempt to validate the methodology before similar techniques can be applied to people with epilepsy.


This work will enable the researchers to properly test the efficacy of their proposed ictogenic network mapping for brain resection, using clinically relevant methods and endpoints, in an animal model. In future work, they will test these predictions in people with epilepsy. A successful outcome may be in clinical use in 5-10 years.

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