Over the past three decades, a number of seizure prediction, or forecasting, methods have been developed. Although major achievements were accomplished regarding the statistical evaluation of proposed algorithms, it is recognized that further progress is still necessary for clinical application in patients. The lack of physiological motivation can partly explain this limitation. Therefore, a natural question is raised: can computational models of epilepsy be used to improve these methods? Here, we review the literature on the multiple-scale neural modeling of epilepsy and the use of such models to infer physiologic changes underlying epilepsy and epileptic seizures. The authors argue how these methods can be applied to advance the state-of-the-art in seizure forecasting.
*Neuroengineering Laboratory, Department of Electrical and Electronic Engineering, The University of Melbourne, Melbourne, Victoria, Australia;
†Brain Dynamics Unit, Brain and Psychological Sciences Research Centre, Swinburne University of Technology, Hawthorn, Victoria, Australia;
‡Centre for Neural Engineering, The University of Melbourne, Melbourne, Victoria, Australia;
§Bionics Institute, East Melbourne, Victoria, Australia;
‖St. Vincent's Hospital Melbourne, Fitzroy, Victoria, Australia;
¶INSERM, U1099, Rennes, France;
#Université de Rennes, LTSI, Rennes, France; and
**Departments of Engineering Science and Mechanics, Center for Neural Engineering, Neurosurgery, and Physics, The Pennsylvania State University, University Park, Pennsylvania, U.S.A.
Address correspondence and reprint requests to Levin Kuhlmann, PhD, Neuroengineering Laboratory, Department of Electrical and Electronic Engineering, The University of Melbourne, Victoria 3010, Australia; e-mail: email@example.com.
S. J. Schiff was supported by NIH US-German Collaborative Research in Computational Neuroscience (CRCNS) 1R01EB014641-01.