Stiliyan Kalitzin, Epilepsy Institute of The Netherlands (SEIN)

1. State transitions in the epileptic brain Stiliyan Kalitzin, Epilepsy Institute of The Netherlands (SEIN)

2. Central Didactic Question 1: Why do epileptic seizures occur ? This question is not about : What are epileptic seizures ? What causes epilepsy ? (neural loss, channel protein mutations, etc) This question is about: Autonomous transitions to epileptic type of activity and back

3. Why is this question relevant for ISPWxx ? • Trial-and error “neuro-phenomenology” approach has its limits. To understand prediction, predictability and to be able to actually predict seizures we need to know how do they set on. • Even more so, to avoid, stop or contain the seizure onset we need to know what are we dealing with. • On a more “mundane” level, even the classical diagnostic challenge of finding the seizure onset site (SOS) can benefit from proper knowledge of the onset mechanism. General Warning : all concepts that follow are (computer) model inspired. Living brain is a system with nearly infinite number of degrees of freedom and of humongous complexity.

4. Candidates: • Attractor deformation scenario, precipitated by • - plastic changes from sensory or other input • - fluctuating chemical (metabolic) compounds • B. Transitions in multi-stable (multi-attractor) systems • - sensory input • - stochastic fluctuations (thermal noise) • C. Intrinsic instability – intermittency • - no precipitating factor Attractor: invariant, asymptotically stable manifold in the phase space of the system. Irreducible (no sub-attractors)  connected

5. seizure Autonomous normal Fluctuations (noise) B. Multi-attractor scenario Attractor1 Attractor2 Fluctuations seizure normal Control Parameter Generic models for autonomous epileptic state transitions A. Attractor deformation scenario Attractor Non-autonomous Control Parameter

6. Complex non-linear models: Autonomous seizure generation

7. Example: complex Z^4 model with two deformation dimensions Phase-space plot HC slice, low Mg ictal data, Hilbert-transform complex reconstruction (courtesy: P. Carlen, H. Khosravaniand M. Derchansky)

8. repulsor attractor Topological attractors in phase-spaces. Analytic models and reconstructions Fixed point Method: relative critical point sets detecting vector-field ridges and convergence manifolds Kalitzin, S. N., Staal J., ter Haar Romeny B., Viergever MA. (2001). "A computational method for segmenting topological point-sets and application to image analysis." Pattern Analysis and Machine Intelligence, IEEE Transactions on 23(5): 447-459.

9. Modelling bistability in cortico-thalamic circuits

10. Bifurcation diagram Input distribution Input Normal activity - steady state Paroxysmal activity - limit cycle normal and paroxysmal only normal only paroxysmal © SEIN, 2004 Medical Physics Department

11. Extra bonus from modeling: Closed-loop seizure abortion as prescribed from model simulation in bi-stable system Closed - loop seizure control Counter - stimulus parameters _ + Suffczynski, P., Kalitzin S. & Lopes da Silva, F.H., Dynamics of non - convulsive epileptic phenomena modeled by a bistable neuronal network. Neuroscience 126(2) p. 467-484, 2004 Medical Physics Department

12. Statistical validation of multi-attracor scenario for ictal transitions in rats and humans

13. Modeling distributions of ictal and interictal durations Suffczynski S., Lopes da Silva FH., Parra J., Velis D., Bouwman B., Clementina M., van Rijn P., van Hese P., Boon P., Houman K., Derchansky M., Carlen P., Kalitzin S., (2006), ”Dynamics of epileptic phenomena determined from statistics of ictal transitions,” IEEE Trans Biomed Eng 53(3): 524-32.

14. A+B combined scenario

15. Multi-stability (multi-homeostatic system) involving synaptic plasticity Kalitzin, S., van Dijk BW, Spekreijse H. (2000). "Self-organized dynamics in plastic neural networks: bistability and coherence." Biol Cybern 83(2): 139-50.

16. Ergodic C. Intermittency in continuous deterministic systems (dynamical billiards)

18. Pre-ictal? Alternative: Discrete intermittent systems Elan L. Ohayon, Hon C. Kwan, W. McIntyre Burnham, Piotr Suffczynski, Stiliyan Kalitzin, Emergent Complex Patterns in Autonomous Distributed Systems: Mechanisms for Attention Recovery and Relation to Models of Clinical Epilepsy, IEEE SMC’2004 Conference Proceedings, October 10-13 2004 The Hague, p. 2066-2072

19. The “EPI-TABLE”

20. Central Didactic Question 2: When do we know that we know more than nothing ? This question is about: Defining (weak) predictability “Existence of a set of retrospective measurements such that a statistically significant association between the outcomes of these measurements and the time to the first seizure following these measurements can be demonstrated”.

21. Statistical significance: N surrogates obtained as random permutations of predictor’s values for the same times of measurement Kalitzin, S. N., J. Parra, et al. (2007). "Quantification of unidirectional nonlinear associations between multidimensional signals." IEEE Trans Biomed Eng54(3): 454-61. • Predictability  association measure • Non-linear h2 association index – favors single mode distributions • Mutual information – weaker measure seizure Time to seizure

22. Example of stochastic parameter random walk: Weak but statistically significant predictability based on measuring the control parameter

23. Few clouds on the prediction horizon • Brain is predominantly an open system, concepts such as “spontaneous activity” or “spontaneous seizures” are agnostic categories. • Brain is unlikely to be in a “thermal equilibrium” state. Noise fluctuations do not spread evenly over all degrees of freedom and therefore one cannot observe everything at all times. • Brain is seldom a stationary system, we need ample statistics in short times. Our approach: active, stimulation based observation

24. Stimulus sequence Photosensitive epilepsy (PSE) is the most common form of human reflex epilepsy which offers a highly reproducible model to investigate whether the transition to an epileptic response may be detected Objective: To quantify the dynamical state of the brain during stimulation prior to eventual photo paroxysmal response. J. Parra, S. Kalitzin, , J Iriarte, W. Blanes, D. Velis, F. Lopes da Silva, Gamma band phase clustering and photosensitivity. Is there an underlying mechanism common to photosensitive epilepsy and visual perception? Brain 126, p. 1164-1172, 2003 10Hz photic stimulator (20Hz, 15Hz, ..) MEG/EEG recording Triggered response Patient/Subject rPCI Question: can we predict an eventual discharge from features of the triggered response ?

25. Observation: enhanced fast activity in the VEP

26. Analysis: PCI

27. No seizure Seizure

28. MEG study rPCI statistics per subject

29. rPCI in TLE seizure prediction

30. Averaged rPCI performance for 3 patients, 5 contacts

31. Localization of the Seizure Onset Sites (SOS). Interictal (>24hrs before the leader seizure) rPCI (collective data from 6 patients, 18 contacts)

32. Active paradigms of seizure anticipation: Computer model evidence for necessity of stimulation Piotr Suffczynski, Stiliyan Kalitzin , Fernando Lopes da Silva, Jaime Parra, Demetrios Velis, Fabrice Wendling Phisical Review E 78(5), 051917(9) Computer modeling: powerful reconstructive tool ( deformation of bi-stable dynamics realistic model of TLE) Almost any parameter change that affects the seizure thresholdwill have some measurable appearance. The question is: how universally this appearance is related to the seizure threshold?

33. Seizure threshold and observables co-registration

34. Different paths to seizure: different “risk estimators” S0 (energy of the background signal) Seizure threshold Driving rPCI

35. Closed-loop stimulation: direct neuronal control (neuro-feedback) Autonomous seizure generation  Automated seizure denial

37. Minimum dose/effect principle

39. Minimum intervention policy: discrete pulse control

40. Reactive pulse control Phase-space “surgery”

41. Peter Carlen Fernando Lopes da Silva Piotr Suffczynski Fabrice Wendling Thank You ! Demetrios Velis Elan Ohayon Wouter Blanes Frank van Engelen Erik Kuitert Jaime Parra Stiliyan Kalitzin

42. seizure normal Attractor1 Attractor2 seizure normal “Aggregate” A+B+C scenario deformation Control Parameter

43. Stimulation scenarios: • Provocative (reflex epilepsies) • “Non-provocative” (no adverse clinical reactions) • Curative – to subdue adverse clinical condition

44. Stimulus sequence Provocative Photo Stimulation (Visual Sensitivity) Photosensitive epilepsy (PSE) is the most common form of human reflex epilepsy which offers a highly reproducible model to investigate whether the transition to an epileptic response may be detected Objective: To quantify the dynamical state of the brain during stimulation prior to eventual photo paroxysmal response. J. Parra, S. Kalitzin, , J Iriarte, W. Blanes, D. Velis, F. Lopes da Silva, Gamma band phase clustering and photosensitivity. Is there an underlying mechanism common to photosensitive epilepsy and visual perception? Brain 126, p. 1164-1172, 2003 10Hz photic stimulator (20Hz, 15Hz, ..) MEG/EEG recording Triggered response Patient/Subject rPCI Question: can we predict an eventual discharge from features of the triggered response ?

45. Simulated PCI with constant amplitudes

46. Analysis: PCI

47. No rPCI Control Quantification: Relative Phase Clustering Index (rPCI) PCI rPCI Frequency

48. rPCI per group of subjects MEG study

49. 86 PPR , 386 control samples AUC=91% Mean rPCI values > 0.106, were > 85% sensitive and 80% specific. MEG study

50. Is there a (rPCI) build-up phenomenon??