2. A recurring neurological disorder characterized by random firing of nerve cells in the brain which cause a temporary shutdown of normal brain function Symptoms: Small jerks, temporary loss of awareness, violent grand mal events Can occur in any part of the brain http://www.ncbi.nlm.nih.gov/disease/Epilepsy.html

3. Current treatment: Anti-seizure/Anticonvulsant medications Not effective for 30% of patients Disruptive Side effects Early electrical stimulation of the brain may abort seizures

4. Study the possibility of aborting spontaneous seizures in slices of rat hippocampus, by electrical stimulation Problems Slices are stimulated at fixed time intervals, NOT in response to seizures Solution: Automate stimulation Monitor neuron activity before onset in order to predict seizure  Stimulate the slices in response to the seizure http://www.brainconnection.com/topics/?main=gal/hippocampus-2 Location of the hippocampus in the human brain

5. A reliable epileptic seizure prediction/detection algorithm is needed. When a seizure is predicted or detected, the algorithm needs to generate an electrical stimulus, analogous to a cardiac defibrillation current.

6. Detect signs of an imminent seizure, or alternatively, detect the seizure in progress. Deliver an adequate stimulus as soon as seizure onset is detected Feedback: Monitor the effects of the stimulus, and stimulate again if needed Compatibility with existing hardware and software to be interfaced with hippocampus slices in a Petri dish.

7. Four major components of the feedback stimulation algorithm Seizure prediction Seizure detection Stimulation Feedback loop and training set Three major design alternatives

8. Four major components of the feedback stimulation algorithm Seizure prediction Seizure detection Stimulation Feedback loop and training set Three major design alternatives

9. Possibility of seizure prediction still in research phase May be possible to detect changes up to 10 minutes before seizure onset No definite changes in EEG frequency or amplitude

10. Navarro et al : Analysis of Similarity method Drop in the index of similarity just before the seizure

11. Four major components of the feedback stimulation algorithm Seizure prediction Seizure detection Stimulation Feedback loop and training set Three major design alternatives

12. Changes in EEG signal at seizure onset: Amplitude Increase Slight May give a lot of false positives Frequency Increase Line length Increase Encompasses both amplitude and frequency increase

13. Seizures induced in slices of rat hippocampus Data acquired using a glass electrode and a LabVIEW detection module Real-time frequency spectrum computed

14. EEGFrequency Spectrum Normal (Interictal) Just before a seizure (Preictal) Seizure (Ictal)

15. Four major components of the feedback stimulation algorithm Seizure prediction Seizure detection Stimulation Feedback loop and training set Three major design alternatives

16. A “reset” mechanism All neurons in a region stimulated at once  All neurons in refractory period  No further random firing possible No further firing possible during the refractory period

17. Square pulses: Frequency 100-150 Hz Pulse duration 20-100 μs Source: Responsive Cortical Neurostimulation (Axon) Stimulation has to be administered early (before the seizure, or just after onset)

18. Four major components of the feedback stimulation algorithm Seizure prediction Seizure detection Stimulation Feedback loop and training set Three major design alternatives

19. Predict/Detect Seizure Give 4-6 pulses Wait 1-2 seconds Seizure stopped? No Yes Done Export data to training set

20. Before training set After training set

21. Four major components of the feedback stimulation algorithm Seizure prediction Seizure detection Stimulation Feedback loop and training set Three major design alternatives

22. Detection electrode Digidata 1322A (Client’s existing DAQ) Acquire and analyze data with C++ Stimulation electrode Seizure detection triggers signal generator Advantages Inexpensive Fast, allows low-level control Limitations May be cumbersome

23. Detection electrode Digidata 1322A (Client’s existing DAQ) Input data into MATLAB in real- time and analyze If seizure detected, send a trigger Stimulation electrode Signal generator outputs square pulses 555 timer Advantages Inexpensive Matlab allows extensive signal analysis Limitations Digidata cannot be interfaced directly with Matlab

24. Acquire and stimulate using LabVIEW Client need not purchase LabVIEW Advantages: Simple, versatile and user-friendly Can easily build learning set when seizure not detected Limitations: Cannot use clients’s DAQ

25. Build a complete feedback loop Implement the Analysis of Similarity prediction algorithm Choose the optimal DAQ Test the completed algorithm on live hippocampus slices

26. Grill W. (2001). Extracellular excitation of central neurons: implications for the mechanisms of deep brain stimulation. Thalamus and Related Systems, (1), pp.269-77. Navarro V. (2002). Seizure anticipation in human neocortical partial epilepsy. Brain, (125), pp.640-55. Jerger K. (2001). Early seizure detection. Journal of Clinical Neurophysiology, 18(3), pp.259-68. Le Van Quyen M. (2001). Anticipation of epileptic seizures from standard EEG recordings. Lancet, (357), pp.183-88. Staley K. (2004). Mechanisms of fast ripples in the hippocampus. The Journal of Neuroscience, 24(40), pp.8896-8906. http://www.epilepsynse.org.uk/pages/info/leaflets/drug.cfm#con traception