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ADAPTIVE CLOSED-LOOP CONTROL OF EPILEPTIC SEIZURE SUSCEPTIBILITY


Relevant for Research Area

B - Core Technologies

C - Applications


Summary

Neurotechnological intervention in epilepsy ultimately aims to prevent seizures. This requires continuous indicators of seizure probability and intervention techniques to avert the transition of benign interictal activity to seizures. Because of intra- and interindividual variability of electrophysiological signatures of epilepsy, such systems should be adaptive and tune in on the optimal settings autonomously. Based on prior work in SCOOSIE and NetControl, we will develop a closed-loop stimulation system to reduce seizure susceptibility in focal mesial temporal lobe epilepsy, one of the major forms of epilepsy in humans, in a mouse model. Monitoring continuous indicators of seizure susceptibility extracted from local field potential recordings, we will use optogenetic and electrical stimulation controlled by a closed-loop system. Machine learning methods will be used for adaptive adjustment of stimulation to reduce seizure probability.


Research Status

Previous work showed that interictal epileptic events and non-epileptic signatures can be used to define measures of seizure severity and seizure susceptibility, which vary over time. Based on networks in cell culture, we could show that new machine learning paradigms can adapt and follow such unpredictable non-stationary and non-linear activity dynamics and adjust stimulation settings accordingly. For simple tasks we could demonstrate that optimal actions were found autonomously.

Project related Publications

Janz P., Hauser P., Heining K., Nestel S., Kirsch M., Egert U., Haas C.A. (2018). Position- and time-dependent Arc expression links neuronal activity to synaptic plasticity during epileptogenesis. Frontiers Cell Neurosci 12:244.


Kilias A., Canales A., Froriep U.P., Park S., Egert U., Anikeeva P. (2018). Optogenetic entrainment of neural oscillations with hybrid fiber probes. J Neural Eng, 15:056006.


Canales A., Park S., Kilias A., Anikeeva P. (2018). Multifunctional fibers as tools for neuroscience and neuroengineering. Acc Chem Res, 51:829;838.


Kilias A., Häussler U., Heining K., Froriep U.P., Haas C.A., Egert U. (2018). Theta frequency decreases throughout the hippocampal formation in a focal epilepsy model. Hippocampus 28:375–391.


Janz P., Schwaderlapp N., Heining K., Häussler U., Korvink J.G., von Elverfeldt D., Hennig J., Egert U., LeVan P., Haas C.A. (2017). Early tissue damage and microstructural reorganization predict disease severity in experimental epilepsy. eLife 6:e25742.


Janz P., Savanthrapadian S., Häussler U., Kilias A., Nestel S., Kretz O., Kirsch M., Bartos M., Egert U., Haas C.A. (2017). Synaptic remodeling of entorhinal input contributes to an aberrant hippocampal network in temporal lobe epilepsy. Cereb Cortex, 27:2348–2364.


Häussler U., Rinas K., Kilias A., Egert U., Haas C.A. (2016). Mossy fiber sprouting and pyramidal cell dispersion in the hippocampal CA2 region in a mouse model of temporal lobe epilepsy. Hippocampus, 26:577–588.


Orcinha C., Münzner G., Gerlach J., Kilias A., Follo M., Egert U., Haas C.A. (2016). Seizure-Induced motility of differentiated dentate granule cells is prevented by the central Reelin fragment. Front Cell Neurosci, 10:1–13.


Kumar S.S., Wülfing J., Okujeni S., Boedecker J., Riedmiller M., Egert U. (2016). Autonomous optimization of targeted stimulation of neuronal networks. PLoS Comput Biol 12(8): e1005054.


Heizmann S., Kilias A., Ruther P., Egert U., Asplund M. (2016). Active control of dye release for neuronal tracing using PEDOT-PSS coated electrodes. IEEE Trans Neural Syst Rehabil Eng, 4320:1–9.