Responsive Neuromodulators Based on Artificial Neural Networks Used to Control Seizure-like Events in a Computational Model of Epilepsy

Deep brain stimulation (DBS) has been noted for its potential to suppress epileptic seizures. To date, DBS has achieved mixed results as a therapeutic approach to seizure control. Using a computational model, we demonstrate that high-complexity, biologically-inspired responsive neuromodulation is superior to periodic forms of neuromodulation (responsive and non-responsive) such as those implemented in DBS, as well as neuromodulation using random and random repetitive-interval stimulation. We configured radial basis function (RBF) networks to generate outputs modeling interictal time series recorded from rodent hippocampal slices that were perfused with low Mg²⁺/high K⁺ solution. We then compared the performance of RBF-based interictal modulation, periodic biphasic-pulse modulation, random modulation and random repetitive modulation on a cognitive rhythm generator (CRG) model of spontaneous seizure-like events (SLEs), testing efficacy of SLE control. A statistically significant improvement in SLE mitigation for the RBF interictal modulation case versus the periodic and random cases was observed, suggesting that the use of biologically-inspired neuromodulators may achieve better results for the purpose of electrical control of seizures in a clinical setting.

[1]  Berj L Bardakjian,et al.  Theta phase precession and phase selectivity: a cognitive device description of neural coding , 2009, Journal of neural engineering.

[2]  Kostas Tsakalis,et al.  Homeostasis of Brain Dynamics in Epilepsy: A Feedback Control Systems Perspective of Seizures , 2009, Annals of Biomedical Engineering.

[3]  M. Zweig,et al.  Receiver-operating characteristic (ROC) plots: a fundamental evaluation tool in clinical medicine. , 1993, Clinical chemistry.

[4]  B. Steinhoff,et al.  Chronic high‐frequency deep‐brain stimulation in progressive myoclonic epilepsy in adulthood—Report of five cases , 2011, Epilepsia.

[5]  John Hallam,et al.  Combining Regression Trees and Radial Basis Function Networks , 2000, Int. J. Neural Syst..

[6]  Leonidas D. Iasemidis,et al.  Control of Synchronization of Brain Dynamics leads to Control of Epileptic Seizures in Rodents , 2009, Int. J. Neural Syst..

[7]  Marija Cotic,et al.  Transformation of neuronal modes associated with low-Mg2 + /high-K +  conditions in an in vitro model of epilepsy , 2010, Journal of biological physics.

[8]  Jianbo Gao,et al.  Multiscale Analysis of Complex Time Series , 2007 .

[9]  Carmen Barba,et al.  Correlation between Provoked Ictal SPECT and Depth Recordings in Adult Drug‐Resistant Epilepsy Patients , 2007, Epilepsia.

[10]  W. Ditto,et al.  Controlling chaos in the brain , 1994, Nature.

[11]  Berj L. Bardakjian,et al.  Prediction of Seizure Onset in an In-Vitro Hippocampal Slice Model of Epilepsy Using Gaussian-Based and Wavelet-Based Artificial Neural Networks , 2005, Annals of Biomedical Engineering.

[12]  K. Tsakalis,et al.  Control of Epileptic Seizures: Models of Chaotic Oscillator Networks , 2005, Proceedings of the 44th IEEE Conference on Decision and Control.

[13]  A Karim,et al.  COMPARISON OF THE FUZZY–WAVELET RBFNN FREEWAY INCIDENT DETECTION MODEL WITH THE CALIFORNIA ALGORITHM , 2002 .

[14]  Leonidas D. Iasemidis,et al.  Localizing preictal temporal lobe spike foci using phase space analysis , 1990 .

[15]  S. Wiebe,et al.  Hippocampal electrical stimulation in mesial temporal lobe epilepsy , 2006, Neurology.

[16]  E A Robertson,et al.  Use of receiver operating characteristic curves to evaluate the clinical performance of analytical systems. , 1981, Clinical chemistry.

[17]  Hojjat Adeli,et al.  Principal Component Analysis-Enhanced Cosine Radial Basis Function Neural Network for Robust Epilepsy and Seizure Detection , 2008, IEEE Transactions on Biomedical Engineering.

[18]  Berj L. Bardakjian,et al.  System characterization of neuronal excitability in the hippocampus and its relevance to observed dynamics of spontaneous seizure-like transitions , 2010, Journal of neural engineering.

[19]  A. Babloyantz,et al.  Low-dimensional chaos in an instance of epilepsy. , 1986, Proceedings of the National Academy of Sciences of the United States of America.

[20]  Koen Van Laere,et al.  Neurostimulation for refractory epilepsy. , 2003, Acta neurologica Belgica.

[21]  K. Tsakalis,et al.  A feedback control systems view of epileptic seizures , 2006 .

[22]  Berj L. Bardakjian,et al.  Online Prediction of Onsets of Seizure-like Events in Hippocampal Neural Networks Using Wavelet Artificial Neural Networks , 2006, Annals of Biomedical Engineering.

[23]  P. Pardalos,et al.  An investigation of EEG dynamics in an animal model of temporal lobe epilepsy using the maximum Lyapunov exponent , 2009, Experimental Neurology.

[24]  K. Zou,et al.  Receiver-Operating Characteristic Analysis for Evaluating Diagnostic Tests and Predictive Models , 2007, Circulation.

[25]  Leon D. Iasemidis,et al.  Epileptic seizure prediction and control , 2003, IEEE Transactions on Biomedical Engineering.

[26]  Hojjat Adeli,et al.  Comparison of fuzzy-wavelet radial basis function neural network freeway incident detection model with California algorithm , 2002 .

[27]  M. Derchansky,et al.  Prevention of Spontaneous Seizure-like Events in Both in-silico and in-vitro Epilepsy Models , 2005, 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference.

[28]  Mojgan Hodaie,et al.  Chronic Anterior Thalamus Stimulation for Intractable Epilepsy , 2002, Epilepsia.

[29]  Hojjat Adeli,et al.  A Wavelet-Chaos Methodology for Analysis of EEGs and EEG Subbands to Detect Seizure and Epilepsy , 2007, IEEE Transactions on Biomedical Engineering.

[30]  Tipu Z. Aziz,et al.  Prediction of Parkinson's Disease tremor Onset Using a Radial Basis Function Neural Network Based on Particle Swarm Optimization , 2010, Int. J. Neural Syst..

[31]  B.L. Bardakjian,et al.  Mapped Clock Oscillators as Ring Devices and Their Application to Neuronal Electrical Rhythms , 2008, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[32]  Brian Litt,et al.  Electrical Stimulation of the Anterior Nucleus of the Thalamus for the Treatment of Intractable Epilepsy , 2004, Epilepsia.

[33]  A. Murro,et al.  Implantation of a Closed-Loop Stimulation in the Management of Medically Refractory Focal Epilepsy , 2005, Stereotactic and Functional Neurosurgery.

[34]  Dirk Van Roost,et al.  Deep Brain Stimulation in Patients with Refractory Temporal Lobe Epilepsy , 2007, Epilepsia.

[35]  Steve S. Chung,et al.  Electrical stimulation of the anterior nucleus of thalamus for treatment of refractory epilepsy , 2010, Epilepsia.

[36]  R A Wennberg,et al.  Long-term follow-up of patients with thalamic deep brain stimulation for epilepsy , 2006, Neurology.

[37]  W. J. Williams,et al.  Phase space topography and the Lyapunov exponent of electrocorticograms in partial seizures , 2005, Brain Topography.

[38]  Bruce J. Gluckman,et al.  Adaptive Electric Field Control of Epileptic Seizures , 2001, The Journal of Neuroscience.

[39]  Clement Hamani,et al.  Deep Brain Stimulation for the Treatment of Epilepsy , 2009, Int. J. Neural Syst..

[40]  P. Grassberger,et al.  Characterization of Strange Attractors , 1983 .

[41]  D. Spencer,et al.  Effect of an External Responsive Neurostimulator on Seizures and Electrographic Discharges during Subdural Electrode Monitoring , 2004, Epilepsia.