Advertisement

Antiepileptic Therapy Reduces Coupling Strength Among Brain Cortical Regions in Patients with Unverricht–Lundborg Disease: A Pilot Study

  • Chang-Chia Liu
  • Petros Xanthopoulos
  • Vera Tomaino
  • Kazutaka Kobayashi
  • Basim M. Uthman
  • Panos M. Pardalos
Chapter
Part of the Springer Optimization and Its Applications book series (SOIA, volume 38)

Abstract

The unified myoclonus rating scale (UMRS) has been utilized to assess the severity of myoclonus and the efficacy of antiepileptic drug (AED) treatment in patients with Unverricht–Lundborg disease (ULD). Electroencephalographic (EEG) recordings are normally used as a supplemental tool for the diagnosis of epilepsy disorders. In this study, mutual information and nonlinear interdependence measures were applied to the EEG recordings in an attempt to identify the effect of treatment on the coupling strength and directionality of mutual information and nonlinear interdependences between different brain cortical regions. Two 1-h EEG recordings were acquired from four ULD subjects; one prior and one after a minimum of 2 months treatment with an add-on AED. Subjects in this study were siblings of same parents and suffered from ULD for approximately 37 years. Our results indicated that the coupling strength was low between different brain cortical regions in the patients with disease of less severity. Adjunctive AED treatment was associated with significant decrease of the coupling strength in all subjects. The mutual information between different brain cortical regions was also reduced after treatment. These findings could provide a new insight for developing a novel surrogate outcome measure for patients with epilepsy when clinical tools or observations could potentially fail to detect a significant difference.

keywords

Nonlinear interdependence Mutual information Electroencephalogram epilepsy Unverricht–Lundborg disease Progressive myoclonic epilepsy 

Notes

Acknowledgements

This work was partially supported by North Florida Foundation for Research and Education, Inc. North Florida/South Georgia Veterans Health System 1601 SW Archer Rd. (151), Gainesville, FL 32608.

References

  1. 1.
    Aarabi, A., Wallois, F., Grebe, R. Does spatiotemporal synchronization of EEG change prior to absence seizures? Brain Res 1188,207–221 (2008)CrossRefGoogle Scholar
  2. 2.
    Arnhold, J., Grassberger, P., Lehnertz, K., Elger, C.E. A robust method for detecting interdependences: Application to intracranially recorded EEG. Physica D 134, 419–430 (1999)zbMATHCrossRefGoogle Scholar
  3. 3.
    Cao, L. Practical method for determining the minimum embedding dimension of a scalar time series. Physica D 110(1–2), 43–50 (1997)zbMATHCrossRefGoogle Scholar
  4. 4.
    Chew, N.K., Mir, P., Edwards, M.J., Cordivari, C., Martino, D., Schneider, S.A., Kim, H.-T., Quinn, N.P., Bhatia, K.P. The natural history of Unverricht-Lundborg disease: A report of eight genetically proven cases. Mov Dis 23(1), 107–113 (2007)Google Scholar
  5. 5.
    Cover, T.M., Thomas, J.A. Elements of Information Theory. Wiley, New York (1991)CrossRefGoogle Scholar
  6. 6.
    Dominguez, L.G., Wennberg, R.A., Gaetz, W., Cheyne, D., Snead, O.C., Perez Velazquez, J.L. Enhanced synchrony in epileptiform activity? Local versus distant phase synchronization in generalized seizures. J Neurosci 25(35), 8077–8084 (2005)CrossRefGoogle Scholar
  7. 7.
    Duckrow, R.B., Albano, A.M. Comment on performance of different synchronization measures in real data: A case study on electroencephalographic signals. Phys Rev E 67(6), 063901 (Jun 2003)Google Scholar
  8. 8.
    Efron, B., Gong, G. A leisurely look at the bootstrap, the jackknife, and cross-validation. Am. Stat. 37(1), 36–48 (1983)MathSciNetGoogle Scholar
  9. 9.
    Ferlazzoa, E., Magauddaa, A., Strianob, P., Vi-Hongc, N., Serraa, S., Gentonc, P. Long-term evolution of EEG in Unverricht-Lundborg disease. Epilepsy Res 73, 219–227 (2007)CrossRefGoogle Scholar
  10. 10.
    Iasemidis, L.D., Pappas, K.E., Gilmore, R.L., Roper, S.N., Sackellares, J.C. Preictal entrainment of a critical cortical mass is a necessary condition for seizure occurrence. Epilepsia 37S(5), 90 (1996)Google Scholar
  11. 11.
    Iasemidis, L., Shiau, D.-S., Sackellares, J.C., Pardalos, P.M., Prasad, A. Dynamical resetting of the human brain at epileptic seizures: Application of nonlinear dynamics and global optimization tecniques. IEEE Trans Biomed Eng 51(3), 493–506 (2004)CrossRefGoogle Scholar
  12. 12.
    Klimesch, W. Memory processes, brain oscillations and EEG synchronization. Int J Psychophysiol, 24(1–2), 61–100 (1996)CrossRefGoogle Scholar
  13. 13.
    Klimesch, W. EEG alpha and theta oscillations reflect cognitive and memory performance: A review and analysis. Brain Res Rev 29(2–3), 169–195 (1999)CrossRefGoogle Scholar
  14. 14.
    Kozachenko, L.F., Leonenko, N.N. Sample estimate of entropy of a random vector. Problems Inform Transmission 23, 95–101 (1987)MathSciNetzbMATHGoogle Scholar
  15. 15.
    Kraskov, A., Stögbauer, H., Grassberger, P. Estimating mutual information. Phys Rev E 69, 066138 (2004)Google Scholar
  16. 16.
    Lalioti, M.D., Antonarakis, S.E., Scott, H.S. The epilepsy, the protease inhibitor and the dodecamer: Progressive myoclonus epilepsy, cystatin b and a 12-mer repeat expansion. Cytogenet Genome Res 100, 213–223 (2003)CrossRefGoogle Scholar
  17. 17.
    Lundborg, H.B. Die progressive Myoclonus-Epilepsie (Unverrichts Myoclonie) Almqvist and Wiksell, Uppsala (1903)Google Scholar
  18. 18.
    Moretti, D.V., Miniussi, C., Frisoni, G.B., Geroldi, C., Zanetti, O., Binetti, G., Rossini, P.M. Hippocampal atrophy and EEG markers in subjects with mild cognitive impairment. Clin Neurophysiol, 118(12), 716–2729 (2007)CrossRefGoogle Scholar
  19. 19.
    Mormann, F., Andrzejak, R.G., Kreuz, T., Rieke, C., David, P., Elger, C.E., Lehnertz, K. Automated detection of a preseizure state based on a decrease in synchronization in intracranial electroencephalogram recordings from epilepsy patients. Phys Rev E 67(2), 021912 (2003)Google Scholar
  20. 20.
    Mormann, F., Lehnertz, K., David, P., Elger, C.E. Mean phase coherence as a measure for phase synchronization and its application to the EEG of epilepsy patients. Physica D 144, 358–369 (2000)zbMATHCrossRefGoogle Scholar
  21. 21.
    Nicolaou, N., Nasuto, S.J. Comment on “performance of different synchronization measures in real data: A case study on electroencephalographic signals”. Phys Rev E 72, 063901 (2005)Google Scholar
  22. 22.
    Pardalos, P.M., Sackellares, J.C., Carney, P.R., Iasemidis, L.D. Quantitative Neuroscience. Kluwer Academic Publisher, Boston, MA (2004)CrossRefGoogle Scholar
  23. 23.
    Pereda, E., Rial, R., Gamundi, A., Gonzlez, J. Assessment of changing interdependencies between human electroencephalograms using nonlinear methods. Physica D 148(1–2), 147–158 (2001)zbMATHCrossRefGoogle Scholar
  24. 24.
    Quian Quiroga, R., Arnhold, J., Grassberger, P. Learning driver-response relationships from synchronization patterns. Phys Rev E 61, 5142–5148 (2000)CrossRefGoogle Scholar
  25. 25.
    Quian Quiroga, R., Kraskov, A., Grassberger, P. Reply to “comment on ‘performance of different synchronization measures in real data: A case study on electroencephalographic signals”’. Phys Rev E 72, 063902 (2005)Google Scholar
  26. 26.
    Quian Quiroga, R., Kraskov, A., Kreuz, T., Grassberger, P. Performance of different synchronization measures in real data: A case study on electroencephalographic signals. Phys Rev E 65, 041903 (2002)Google Scholar
  27. 27.
    Quian Quiroga, R., Kraskov, A., Kreuz, T., Grassberger, P. Reply to “comment on ‘performance of different synchronization measures in real data: A case study on electroencephalographic signals”’. Phys Rev E 67, 063902 (2003)Google Scholar
  28. 28.
    Le Van Quyen, M., Martinerie, J., Baulac, M., Varela, F.J.. Anticipating epileptic seizures in real time by non-linear analysis of similarity between EEG recordings. NeuroReport 10, 2149–2155 (1999)Google Scholar
  29. 29.
    Salinsky, M.C., Oken, B.S., Morehead, L. Intraindividual analysis of antiepileptic drug effects on EEG background rhythms. Electroencephalogr Clin Neurophysiol 90(3), 186–193 (1994)CrossRefGoogle Scholar
  30. 30.
    Singer, W. Synchronization of cortical activity and its putative role in information processing and learning. Annual Review of Physiology 55, 349–374 (1993)CrossRefGoogle Scholar
  31. 31.
    Sitnikova, E., Dikanev, T., Smirnov, D., Bezruchko, B., van Luijtelaar, G. Granger causality: Cortico-thalamic interdependencies during absence seizures in WAG/Rij rats. J Neurosci Methods 170(2), 245–254 (2008)CrossRefGoogle Scholar
  32. 32.
    Sitnikova, E., van Luijtelaar, G. Cortical and thalamic coherence during spike-wave seizures in WAG/Rij rats. Epilepsy Res 71, 159–180 (2006)CrossRefGoogle Scholar
  33. 33.
    Steriade, M., Amzica, F. Dynamic coupling among neocortical neurons during evoked and spontaneous spike-wave seizure activity. J Neurophysiol 72, 2051–2069 (1994)Google Scholar
  34. 34.
    Takens, F. Detecting strange attractors in turbulence. In: Rand, D.A., Young, L.S. (eds.) Dynamical Systems and Turbulence, Lecture Notes in Mathematics, Vol. 898, pp. 366–381. Springer-Verlag, New York (1981)CrossRefGoogle Scholar
  35. 35.
    Theiler, J. Spurious dimension from correlation algorithms applied to limited time-series data. Phys Rev A 34, 2427–2432 (1986)CrossRefGoogle Scholar
  36. 36.
    Unverricht, H. Die Myoclonie. Franz Deutick, Leipzig (1891)Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  1. 1.J. Crayton Pruitt Family Department of Biomedical EngineeringUniversity of FloridaGainesvilleUSA
  2. 2.Department of Industrial and Systems EngineeringUniversity of FloridaGainesvilleUSA
  3. 3.Bioinformatics Laboratory, Experimental and Clinical Medicine DepartmentMagna Graecia UniversityCatanzaroItaly
  4. 4.Department of Industrial and Systems EngineeringUniversity of Florida, Center for Applied OptimizationGainesvilleUSA
  5. 5.Department of Neurological SurgeryNihon University School of MedicineTokyoJapan
  6. 6.Division of Applied System Neuroscience, Department of Advanced Medical ScienceNihon University School of MedicineTokyoJapan
  7. 7.Department of NeurologyUniversity of FloridaGainesvilleUSA
  8. 8.Department of NeuroscienceUniversity of FloridaGainesvilleUSA
  9. 9.The Evelyn F. and William L. McKnight Brain InstituteUniversity of FloridaGainesvilleUSA
  10. 10.Neurology Services, North Florida/South Georgia Veterans Health SystemGainesvilleUSA
  11. 11.Department of Industrial and Systems EngineeringUniversity of FloridaGainesvilleUSA
  12. 12.J. Crayton Pruitt Family Department of Biomedical EngineeringUniversity of FloridaGainesvilleUSA
  13. 13.The Evelyn F. and William L. McKnight Brain InstituteUniversity of FloridaGainesvilleUSA

Personalised recommendations