Resting State Networks Analysis Using Simultaneous EEG-fMRI for Epilepsy Patient

  • Rajanikant Panda
  • Rose Dawn Bharath
  • Sandhya Mangalore
  • Neeraj Upadhyay
  • A. Thamodharan
  • Silpa Kanungo
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 222)

Abstract

The resting state EEG-fMRI has opened a new avenue in not only neuro cognitive studies but it has also found practical utility in clinical applications. We studied the Resting State Networks on Epilepsy Patient to understand the neuronal substrates involved in epilepsy. Five epilepsy patients were undertaken for simultaneous EEG-fMRI study. EEG microstates was computed and was considered as explanatory variables in the GLM design for the analysis of fMRI data in an event related design. z-stats and independent component was examined for simultaneous EEG-fMRI. We hypothesized that it’s possible to analyze the affected brain areas for epileptiform discharges in epileptic patients at resting state. Microstates convolved functional image and its independent components using hybrid technique including both the neuronal and hemodynamic information was demonstrated on patients structural image. From this result we conclude that using EEG microstate and Independent Component Analysis (ICA) of resting fMRI we may examine the brain areas involved in resting state brain discharge. Also it will be useful for the analysis of EEG-fMRI data in which electrical epileptic discharge are not apparent on scalp EEG at the time of data acquisition.

Keywords

EEG fMRI GLM Resting State Network EEG-microstate ICA 

References

  1. 1.
    Laufsa L, Hamandi K, Walkera MC, Scottb C, Smitha S, Duncana JS, Lemieux L (2006) EEG–fMRI mapping of asymmetrical delta activity in a patient with refractory epilepsy is concordant with the epileptogenic region determined by intracranial EEG. Magn Reson Imaging 24:367–371CrossRefGoogle Scholar
  2. 2.
    Niedermeyer E, Lopes da Silva F (2004) Electroencephalography: basic principles, clinical applications, and related fields, 1st edn. Williams & Wilkins, BaltimoreGoogle Scholar
  3. 3.
    LeVan P, Tyvaert L, Moeller F, Gotman J (2010) Independent component analysis reveals dynamic ictal BOLD responses in EEG-fMRI data from focal epilepsy patients. NeuroImage 49:366–378CrossRefGoogle Scholar
  4. 4.
    Wang Z, Ives JR, Norton L, Hutchison RM, Mirsattari SM (2012) Spontaneous EEG-functional MRI in mesial temporal lobe epilepsy: implications for the neural correlates of consciousness. Epilepsy Res Treat 2012(385626):1–10Google Scholar
  5. 5.
    Biswal B, Yetkin FZ, Haughton VM, Hyde JS (1995) Functional connectivity in the motor cortex of resting human brain using echo-planar MRI. Magn Reson Med 34(4):537–541CrossRefGoogle Scholar
  6. 6.
    Britz J, Ville DVD, Michel CM (2010) BOLD correlates of EEG topography reveal rapid resting-state network dynamics. NeuroImage 52:1162–1170CrossRefGoogle Scholar
  7. 7.
    Grouiller F, Thornton RC, Groening K, Spinelli L, Duncan JS, Schaller K, Siniatchkin M, Vulliemoz S, Lemieux L, Seeck M, Michel CM, Vulliemoz S (2011) With or without spikes: localization of focal epileptic activity by simultaneous electroencephalography and functional magnetic resonance imaging. Brain 156:1–20Google Scholar
  8. 8.
    Lei W, Eichele T, Calhoun VD (2010) Reactivity of hemodynamic responses and functional connectivity to different states of alpha synchrony: a concurrent EEG-fMRI study. NeuroImage 52:1252–1260CrossRefGoogle Scholar
  9. 9.
    Musso F, Brinkmeyer J, Mobascher A, Warbrick T, Winterer G (2010) Spontaneous brain activity and EEG microstates. A novel EEG/fMRI analysis approach to explore resting-state networks. NeuroImage 52:1149–1161CrossRefGoogle Scholar
  10. 10.
    Wang Z, Norton L, Hutchison RM, Ives JR, Mirsattari SM (2011) Spontaneous EEG-functional MRI in mesial temporal lobe epilepsy: implications for the neural correlates of consciousness. Epilepsy research and treatment, Article ID 385626Google Scholar
  11. 11.
    Yuan H, Zotev V, Phillips R, Drevets WC, Bodurka J (2012) Spatiotemporal dynamics of the brain at rest-Exploring EEG microstates as electrophysiological signatures of BOLD resting state networks. NeuroImage 60:2062–2072CrossRefGoogle Scholar
  12. 12.
    Formaggio E, Storti SF, Bertoldo A, Manganotti P, Fiaschi A, Toffolo GM (2011) Integrating EEG and fMRI in epilepsy. NeuroImage 54:2719–2731CrossRefGoogle Scholar
  13. 13.
    Ives JR, Warach S, Schmitt F, Edelman RR, Schomer DL (1993) Monitoring the patient’s EEG during echo-planar MRI. Electroenceph Clin Neurophysiol 87:417–420Google Scholar
  14. 14.
    Krakow K, Wieshmann UC, Woermann FG (1999) Multimodal MR Imaging: Functional, diffusion tensor, and chemical shift imaging in a patient with localization related epilepsy 40:1459–1462Google Scholar
  15. 15.
    Salek-Haddadi A, Diehl B, Hamandi K, Merschhemke M, Liston A, Friston K, Duncan JS, Fish DR, Lemieux L (2006) Hemodynamic correlates of epileptiform discharges: An EEG-fMRI study of 63 patients with focal epilepsy. Brain Res. 88:148–166Google Scholar
  16. 16.
    Oostenveld R, Praamstra P (2001) The five percent electrode system for high-resolution EEG and ERP measurement Clin Neurophysiol 112:713–719Google Scholar
  17. 17.
    Brandeis D, Lehmann D (1989) Segments of ERP map series reveal landscape changes with visual attention and subjective contours. Electroencephalogr Clin Neurophysiol 73:507–519Google Scholar
  18. 18.
    Fingelkurts AA (2004) Making complexity simpler: multivariability and metastability in the brain Int J Neurosci 114:843–862Google Scholar
  19. 19.
    Michel CM, Murray MM, Lantz G, Gonzalez S, Spinelli L, Grave de Peralta R (2004) EEG source imaging, Clin Neurophysiol 115:2195–2222Google Scholar
  20. 20.
    Pascual-Marqui RD, Michel CM, Lehmann D (1955) Segmentation of brain electrical activity into microstates: model estimation and validation, IEEE Trans. Biomed. Eng 42:658–665Google Scholar
  21. 21.
    Jenkinson M, Bannister P, Brady J, Smith S (2002) Improved optimization for the robust and accurate linear registration and motion correction of brain images, NeuroImage 17:825–841Google Scholar
  22. 22.
    Smith S (2002) Fast robust automated brain extraction, Hum Brain Mapp 17:143–155Google Scholar
  23. 23.
    Friston KJ, Williams S, Howard R, Frackowiak RS, Turner R (1996) Movement related effects in fMRI time-series, Magn Reson Med 35:346–355Google Scholar
  24. 24.
    Beckmann CF, DeLuca M, Devlin JT, Smith SM (2005) Investigations into resting-state connectivity using independent component analysis, Philos Trans R Soc Lond B Biol Sci 360:1001–1013Google Scholar
  25. 25.
    Beckmann CF, Smith SM (2004) Probabilistic independent component analysis for functional magnetic resonance imaging, IEEE Trans Med Imag 23:137–152Google Scholar
  26. 26.
    Beckmann CF, Smith SM (2005) Tensorial extensions of independent component analysis for multisubject FMRI analysis, NeuroImage 25:294–311Google Scholar

Copyright information

© Springer India 2013

Authors and Affiliations

  • Rajanikant Panda
    • 1
  • Rose Dawn Bharath
    • 1
  • Sandhya Mangalore
    • 1
  • Neeraj Upadhyay
    • 1
  • A. Thamodharan
    • 1
  • Silpa Kanungo
    • 1
  1. 1.Cognitive Neuroscience Center, National Institute of Mental health and NeuroscienceBangaloreIndia

Personalised recommendations