Journal of Computational Neuroscience

, Volume 34, Issue 2, pp 303–318 | Cite as

Inferring evoked brain connectivity through adaptive perturbation

Article

Abstract

Inference of functional networks—representing the statistical associations between time series recorded from multiple sensors—has found important applications in neuroscience. However, networksexhibiting time-locked activity between physically independent elements can bias functional connectivity estimates employing passive measurements. Here, a perturbative and adaptive method of inferring network connectivity based on measurement and stimulation—so called “evoked network connectivity” is introduced. This procedure, employing a recursive Bayesian update scheme, allows principled network stimulation given a current network estimate inferred from all previous stimulations and recordings. The method decouples stimulus and detector design from network inference and can be suitably applied to a wide range of clinical and basic neuroscience related problems. The proposed method demonstrates improved accuracy compared to network inference based on passive observation of node dynamics and an increased rate of convergence relative to network estimation employing a naïve stimulation strategy.

Keywords

Perturbative Network Estimation Functional connectivity Adaptive 

Notes

Acknowledgements

K.Q.L. acknowledges support for this research from the Cognitive Rhythms Collaborative, NSF grant DMS-1042134 S.C. acknowledges support from NIH DP1-OD003646. S.C. holds a Career Award at the Scientific Interface from the Burroughs Wellcome Fund. M.A.K. holds a Career Award at the Scientific Interface from the Burroughs Wellcome Fund.

References

  1. Alarcon, G., Binnie, C.D., Elwes, R.D., Polkey, C.E. (1995). Power spectrum and intracranial eeg patterns at seizure onset in partial epilepsy. Electroencephalography and Clinical Neurophysiology, 94(5), 326–337.PubMedCrossRefGoogle Scholar
  2. Annegers, J.F. (2001). The epidemiology of epilepsy. In The treatment of epilepsy: Principles and practice. Lippincott Williams and Wilkins.Google Scholar
  3. Conner, C.R., Ellmore, T.M., DiSano, M.A., Pieters, T.A., Potter, A.W., Tandon, N. (2011). Anatomic and electro-physiologic connectivity of the language system: a combined dti-ccep study. Computers in Biology and Medicine, 41(12), 1100–1109 (Special Issue on Techniques for Measuring Brain Connectivity).PubMedCrossRefGoogle Scholar
  4. Dayan, P., & Abbott, L.F. (2005). Theoretical neuroscience. MIT Press.Google Scholar
  5. Danzl, P., & Moehlis, J. (2008). Spike timing control of oscillatory neuron models using impulsive and quasi-impulsive charge-balanced inputs. In Proceedings of the American control conference (pp. 171–176).Google Scholar
  6. de Curtis, M., & Gnatkovsky, V. (2009). Reevaluating the mechanisms of focal ictogenesis: the role of low-voltage fast activity. Epilepsia, 50(12), 2514–2525.PubMedCrossRefGoogle Scholar
  7. Destexhe, A., & Sejnowski, T.J. (2009). The wilson-cowan model, 36 years later. Biological Cybernetics, 101(1), 1–2.PubMedCrossRefGoogle Scholar
  8. Eden, U., Frank, L., Barbieri, R., Solo, V., Brown, E. (2004). Dynamic analysis of neural encoding by point process adaptive filtering. Neural Computation, 16(5), 971–998.PubMedCrossRefGoogle Scholar
  9. Enatsu, R., Piao, Z., O’Connor, T., Horning, K., Mosher, J., Burgess, R., Bingaman, W., Nair, D. (2012). Cortical excitability varies upon ictal onset patterns in neocortical epilepsy: a cortico-cortical evoked potential study. Clinical Neurophysiology, 123(2), 252–260.PubMedCrossRefGoogle Scholar
  10. Engel, J., Wiebe, S., French, J., Sperling, M., Williamson, P., Spencer, D., Gumnit, R., Zahn, C., Westbrook, E., Enos, B. (2003). Practice parameter: temporal lobe and localized neocortical resections for epilepsy: report of the quality standards subcommittee of the american academy of neurology, in association with the american epilepsy society and the american association of neurological surgeons. Neurology, 60(4), 538–547.PubMedCrossRefGoogle Scholar
  11. Fenno, L., Yizhar, O., Deisseroth, K. (2011). The development and application of optogenetics. Annual Review of Neuroscience, 34, 389–412.PubMedCrossRefGoogle Scholar
  12. Fisher, R.S., Webber, W.R., Lesser, R.P., Arroyo, S., Uematsu, S. (1992). High-frequency eeg activity at the start of seizures. Journal of Clinical Neurophysiology, 9(3), 441–448.PubMedCrossRefGoogle Scholar
  13. Friston, K. (1994). Functional and effective connectivity in neuroimaging: a synthesis. Human Brain Mapping, 2(1–2), 56–78.CrossRefGoogle Scholar
  14. Friston, K.J., Harrison, L., Penny, W. (2003). Dynamic causal modelling. NeuroImage, 19(4), 1273–1302.PubMedCrossRefGoogle Scholar
  15. Gibbs, F., Gibbs, E., Lennox, W. (2002). Epilepsy: a paroxysmal cerebral dysrhythmia. Epilepsy & Behavior, 3(4), 395–401.CrossRefGoogle Scholar
  16. Hasegawa, H. (2005). Synchronizations in small-world networks of spiking neurons: diffusive versus sigmoid couplings. Physical Review E, Statistical, Nonlinear and Soft Matter Physics, 72(5 Pt 2), 56139.CrossRefGoogle Scholar
  17. Juang, J.N. (1994). Applied system identification. PTR Prentice Hall, Inc.Google Scholar
  18. Keller, C.J., Bickel, S., Entz, L., Ulbert, I., Milham, M.P., Kelly, C., Mehta, A.D. (2011). Intrinsic functional architecture predicts electrically evoked responses in the human brain. Proceedings of the National Academy of Sciences of the United States of America, 108(25), 10308–10313.PubMedCrossRefGoogle Scholar
  19. Keränen, T., Riekkinen, P.J., Sillanpää, M. (1989). Incidence and prevalence of epilepsy in adults in eastern Finland. Epilepsia, 30(4), 413–421.PubMedCrossRefGoogle Scholar
  20. Keränen, T., Sillanpää, M., Riekkinen, P.J. (1988). Distribution of seizure types in an epileptic population. Epilepsia, 29(1), 1–7.PubMedCrossRefGoogle Scholar
  21. Kramer, M.A., & Cash, S.S. (2012). Epilepsy as a disorder of cortical network organization. The Neuroscientist, 8(4), 360–372.CrossRefGoogle Scholar
  22. Kringelbach, M.L., Green, A.L., Owen, S.L.F., Schweder, P.M., Aziz, T.Z. (2010). Sing the mind electric—principles of deep brain stimulation. European Journal of Neuroscience, 32(7), 1070–1079.PubMedCrossRefGoogle Scholar
  23. Kringelbach, M.L., Jenkinson, N., Owen, S.L.F., Aziz, T.Z. (2007). Translational principles of deep brain stimulation. Nature Reviews Neuroscience, 8(8), 623–635.PubMedCrossRefGoogle Scholar
  24. Matsumoto, R., Nair, D.R., LaPresto, E., Bingaman, W., Shibasaki, H., Lders, H.O. (2007). Functional connectivity in human cortical motor system: a cortico-cortical evoked potential study. Brain, 130(1), 181–197.PubMedCrossRefGoogle Scholar
  25. Matsumoto, R., Nair, D.R., LaPresto, E., Najm, I., Bingaman, W., Shibasaki, H., Lders, H.O. (2004). Functional connectivity in the human language system: a cortico-cortical evoked potential study. Brain, 127(10), 2316–2330.PubMedCrossRefGoogle Scholar
  26. McIntyre, C., & Hahn, P. (2010). Network perspectives on the mechanisms of deep brain stimulation. Neurobiology of Disease, 38(3), 329–337.PubMedCrossRefGoogle Scholar
  27. Moro, E., & Lang, A.E. (2006). Criteria for deep-brain stimulation in Parkinson’s disease: review and analysis. Expert Review of Neurotherapeutics, 6(11), 1695–1705.PubMedCrossRefGoogle Scholar
  28. Papoulis, A. (1984). Probability, random variables and stochastic processes with errata sheet. McGraw Hill Higher Education.Google Scholar
  29. Perlmutter, J., & Mink, J. (2006). Deep brain stimulation. Annual Review of Neuroscience, 29, 229–257.PubMedCrossRefGoogle Scholar
  30. Rubinov, M., & Sporns, O. (2010). Complex network measures of brain connectivity: uses and interpretations. NeuroImage, 52(3), 1059–1069.PubMedCrossRefGoogle Scholar
  31. Schiff, N.D., Giacino, J.T., Kalmar, K., Victor, J.D., Baker, K., Gerber, M., Fritz, B., Eisenberg, B., Biondi, T., O’Connor, J., Kobylarz, E.J., Farris, S., Machado, A., McCagg, C., Plum, F., Fins, J.J., Rezai, A.R. (2007). Behavioural improvements with thalamic stimulation after severe traumatic brain injury. Nature, 448(7153), 600–603.PubMedCrossRefGoogle Scholar
  32. Schiff, S.J., Colella, D., Jacyna, G.M., Hughes, E., Creekmore, J.W., Marshall, A., Bozek-Kuzmicki, M., Benke, G., Gaillard, W.D., Conry, J., Weinstein, S.R. (2000). Brain chirps: spectrographic signatures of epileptic seizures. Clinical Neurophysiology, 111(6), 953–958.CrossRefGoogle Scholar
  33. Schiff, S.J., Sauer, T., Kumar, R., Weinstein, S.L. (2005). Neuronal spatiotemporal pattern discrimination: the dynamical evolution of seizures. NeuroImage, 28(4), 1043–1055.PubMedCrossRefGoogle Scholar
  34. Shafi, M.M., Westover, M.B., Fox, M.D., Pascual-Leone, A. (2012). Exploration and modulation of brain network interactions with noninvasive brain stimulation in combination with neuroimaging. European Journal of Neuroscience, 35(6), 805–825.PubMedCrossRefGoogle Scholar
  35. Sparta, D.R., Stamatakis, A.M., Phillips, J.L., Hovelsø, N., van Zessen, R., Stuber, G.D. (2012). Construction of implantable optical fibers for long-term optogenetic manipulation of neural circuits. National Protocol, 7(1), 12–23.CrossRefGoogle Scholar
  36. Sporns, O. (2010). Networks of the Brain. MIT Press.Google Scholar
  37. Sunderam, S., Gluckman, B., Reato, D., Bikson, M. (2010). Toward rational design of electrical stimulation strategies for epilepsy control. Epilepsy & Behavior, 17(1), 6–22.CrossRefGoogle Scholar
  38. Valentín, A., Alarcón, G., Honavar, M., Seoane, J.J.G., Selway, R.P., Polkey, C.E., Binnie, C.D. (2005a). Single pulse electrical stimulation for identification of structural abnormalities and prediction of seizure outcome after epilepsy surgery: a prospective study. Lancet Neurology, 4(11), 718–726.PubMedCrossRefGoogle Scholar
  39. Valentín, A., Alarcón, G., García-Seoane, J.J., Lacruz, M.E., Nayak, S.D., Honavar, M., Selway, R.P., Binnie, C.D., Polkey, C.E. (2005b). Single-pulse electrical stimulation identifies epileptogenic frontal cortex in the human brain. Neurology, 65(3), 426–435.PubMedCrossRefGoogle Scholar
  40. Wang, Z., Kuruog andlu, E., Yang, X., Xu, Y., Huang, T. (2011). Time varying dynamic bayesian network for nonstationary events modeling and online inference. IEEE Transactions on Signal Processing, 59(4), 1553–1568.CrossRefGoogle Scholar
  41. Wilson, H.R., & Cowan, J.D. (1972). Excitatory and inhibitory interactions in localized populations of model neurons. Biophysical Journal, 12, 1–24.PubMedCrossRefGoogle Scholar
  42. Wilson, H.R., & Cowan, J.D. (1973). A mathematical theory of the functional dynamics of cortical and thalamic nervous tissue. Kybernetik, 13, 55–80.PubMedCrossRefGoogle Scholar
  43. Zarrelli, M.M., Beghi, E., Rocca, W.A., Hauser, W.A. (1999). Incidence of epileptic syndromes in Rochester, Minnesota: 1980–1984. Epilepsia, 40(12), 1708–1714.PubMedCrossRefGoogle Scholar
  44. Zhang, H., Benz, H., Bezerianos, A., Acharya, S., Crone, N., Maybhate, A., Zheng, X., Thakor, N. (2010). Connectivity mapping of the human ecog during a motor task with a time-varying dynamic bayesian network. In 2010 annual international conference of the IEEE in engineering in medicine and biology society (EMBC) (pp. 130–133).Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2012

Authors and Affiliations

  • Kyle Q. Lepage
    • 1
  • ShiNung Ching
    • 2
  • Mark A. Kramer
    • 1
  1. 1.Department of Mathematics & StatisticsBoston UniversityBostonUSA
  2. 2.Department of Anesthesia, Critical Care & Pain MedicineMassachusetts General HospitalBostonUSA

Personalised recommendations