Detecting Epileptic Regions Based on Global Brain Connectivity Patterns

  • Andrew Sweet
  • Archana Venkataraman
  • Steven M. Stufflebeam
  • Hesheng Liu
  • Naoro Tanaka
  • Joseph Madsen
  • Polina Golland
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8149)

Abstract

We present a method to detect epileptic regions based on functional connectivity differences between individual epilepsy patients and a healthy population. Our model assumes that the global functional characteristics of these differences are shared across patients, but it allows for the epileptic regions to vary between individuals. We evaluate the detection performance against intracranial EEG observations and compare our approach with two baseline methods that use standard statistics. The baseline techniques are sensitive to the choice of thresholds, whereas our algorithm automatically estimates the appropriate model parameters and compares favorably with the best baseline results. This suggests the promise of our approach for pre-surgical planning in epilepsy.

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References

  1. 1.
    Lüders, H., et al.: The Epileptogenic Zone: General Principles. Epileptic Disorders: International Epilepsy Journal with Videotape 8(suppl. 2) , S1–S9 (2006)Google Scholar
  2. 2.
    Kramer, M., et al.: Epilepsy as a Disorder of Cortical Network Organization. The Neuroscientist 18(4), 360–372 (2012)CrossRefGoogle Scholar
  3. 3.
    Fox, M., Raichle, M.: Spontaneous Fluctuations in Brain Activity Observed with Functional Magnetic Resonance Imaging. Nature 8, 700–711 (2007)Google Scholar
  4. 4.
    Luo, C., et al.: Disrupted Functional Brain Connectivity in Partial Epilepsy: a Resting-State fMRI Study. PloS one 7(1), e28196 (2011)Google Scholar
  5. 5.
    Stufflebeam, S., et al.: Localization of Focal Epileptic Discharges using Functional Connectivity Magnetic Resonance Imaging. Journal of Neurosurgery, 1–5 (2011)Google Scholar
  6. 6.
    Greicius, M.D., et al.: Resting-State Functional Connectivity in Major Depression: Abnormally Increased Contributions from Subgenual Cingulate Cortex and Thalamus. Biological Psychiatry 62, 429–437 (2007)CrossRefGoogle Scholar
  7. 7.
    Varoquaux, G., Baronnet, F., Kleinschmidt, A., Fillard, P., Thirion, B.: Detection of Brain Functional-connectivity Difference in Post-stroke Patients using Group-level Covariance Modeling. In: Jiang, T., Navab, N., Pluim, J.P.W., Viergever, M.A. (eds.) MICCAI 2010, Part I. LNCS, vol. 6361, pp. 200–208. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  8. 8.
    Venkataraman, A., Kubicki, M., Golland, P.: From Brain Connectivity Models to Identifying Foci of a Neurological Disorder. In: Ayache, N., Delingette, H., Golland, P., Mori, K. (eds.) MICCAI 2012, Part I. LNCS, vol. 7510, pp. 715–722. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  9. 9.
    Fischl, B.: FreeSurfer. NeuroImage 62, 774–781 (2012)CrossRefGoogle Scholar
  10. 10.
    Smith, S., et al.: Advances in Functional and Structural MR Image Analysis and Implementation as FSL. NeuroImage 23(51), 208–219 (2004)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Andrew Sweet
    • 1
  • Archana Venkataraman
    • 1
  • Steven M. Stufflebeam
    • 2
  • Hesheng Liu
    • 2
  • Naoro Tanaka
    • 2
  • Joseph Madsen
    • 3
  • Polina Golland
    • 1
  1. 1.MIT Computer Science and Artificial Intelligence LaboratoryCambridgeUK
  2. 2.Athinoula A. Martinos Center for Biomedical ImagingBostonUK
  3. 3.Boston Children’s HospitalBostonUK

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