Functional Connectivity Hubs and Thalamic Hemodynamics in Rolandic Epilepsy

  • Caroline Garcia ForlimEmail author
  • Roma Siugzdaite
  • Yang Yu
  • Ye-Lei Tang
  • Wei Liao
  • Daniele MarinazzoEmail author
Conference paper
Part of the Studies in Computational Intelligence book series (SCI, volume 813)


Our brain is a complex organ with different levels of interaction and therefore can be thought as a complex network. In high level interactions, the brain network is formed by interconnected areas called nodes and their connections are the links. Hubs play a key role in information processing in the brain. Brain networks can be extracted using several imaging modalities, here we focus on networks based on resting state fMRI (rsfMRI) where spontaneous brain activity is indirectly measured resulting in a blood-oxygen-level dependent (BOLD) signal for each voxel. Brain regions or voxels are said to be functionally connected and therefore, a link exists, if they present temporal correlation. The advantages of rsfMRI are the easiness of the acquisition suitable for children and clinical population and to be able to uncover networks related to spontaneous or “default mode” of the brain. Moreover, it has been shown that the resting state networks are impaired in psychiatric and neurological disorders.

Rolandic epilepsy (RE) is one of the most common epilepsy in childhood manifesting abnormal EEG activity in central-temporal areas. Despite seizure remission during adolescence, recent studies have shown a serious of comorbidities. Moreover, the risk of cognitive impairments has been linked to interictal epileptic discharges (IED). Nevertheless, the underlying mechanisms are not fully understood.

Here, we applied two novel methods to resting state fMRI, the blind deconvolution method to recover the neural activity and to extract the hemodynamic response (HRF) and functional connectivity density (FCD). FCD is a data-driven voxel-wise new tool combining graph theory and functional connectivity that unveils densely connected regions that can work as functional hubs of information in the brain. The goal was to identify hubs of information flow and possible network disruption in RE in patients with and without IEDs.

FCD maps revealed main hubs in the posterior cingulate, precuneus, cuneus and calcarine. Patients with IEDs during the scanner showed higher FCD as compared to healthy controls and larger hub in the postcentral precentral gyri, key focal areas in RE. Patients with no IEDs during the scanner showed overall lower FCD as compared to controls and IED groups. Group comparison revealed hyper local connectivity in bilateral thalamus in the patients with IEDs compared to patients without IEDs. Additional exploratory HRF analysis showed that patients with IEDs presented higher response height in the HRF in the thalamus evidencing the inhomogeneity of the HRF among groups.

We speculate that locally abnormal information flow in bilateral thalamus might suggest the involvement of this region in the generation of spikes in RE. It also provides additional evidence for an epileptic as a network disease rather than a focus dysfunction. This hypothesis could be further confirmed in meta analysis, small group size is the main limitation of this study. To the best of our knowledge, this is the first study to combine blind deconvolution and FCD to the whole brain analysis in RE.



The work has been supported by Conselho Nacional de Desenvolvimento Científico e Tecnológico CNPq.

Compliance with Ethical Standards

Conflict of Interest: The authors declare that they have no conflict of interest.

For this type of study formal consent is not required.


  1. 1.
    Panayiotopoulos, C.P., Michael, M., Sanders, S., Valeta, T., Koutroumanidis, M.: Benign childhood focal epilepsies: assessment of established and newly recognized syndromes. Brain 131, 2264–2286 (2008). Scholar
  2. 2.
    Kavros, P.M., Clarke, T., Strug, L.J., Halperin, J.M., Dorta, N.J., et al.: Attention impairment in rolandic epilepsy: systematic review. Epilepsia 49, 1570–1580 (2008)CrossRefGoogle Scholar
  3. 3.
    Overvliet, G.M., Besseling, R.M., Vles, J.S., Hofman P.A., Backes, W.H., et al.: Nocturnal epileptiform EEG discharges, nocturnal epileptic seizures, and language impairments in children: review of the literature. Epilepsy Behav. 19, 550–558 (2010)Google Scholar
  4. 4.
    Doesburg, S.M., Ibrahim, G.M., Lou, Smith M., Sharma, R., Viljoen, A., et al.: Altered Rolandic gamma-band activation associated with motor impairment and ictal network desynchronization in childhood epilepsy. PLoS One 8, e54943 (2013)CrossRefGoogle Scholar
  5. 5.
    Verrotti, A., Filippini, M., Matricardi, S., Agostinelli, M.F., Gobbi, G.: Memory impairment and benign epilepsy with centrotemporal spike (BECTS): a growing suspicion. Brain Cogn 84, 123–131 (2014)CrossRefGoogle Scholar
  6. 6.
    Besseling, R.M., Overvliet, G.M., Jansen, J.F., van der Kruijs, S.J., Vles, J.S., et al.: Aberrant functional connectivity between motor and language networks in rolandic epilepsy. Epilepsy Res. 107, 253–262 (2013)Google Scholar
  7. 7.
    Massa, R., de Saint-Martin, A., Carcangiu, R., Rudolf, G., Seegmuller, C., et al.: EEG criteria predictive of complicated evolution in idiopathic rolandic epilepsy. Neurology 57, 1071–1079 (2001)CrossRefGoogle Scholar
  8. 8.
    Glover, G.H.: Deconvolution of impulse response in event-related BOLD fMRI. Neuroimage 9, 416–429 (1999)CrossRefGoogle Scholar
  9. 9.
    Ashby, F.G.: Statistical Analysis of fMRI Data. MIT Press, 332 p (2011)Google Scholar
  10. 10.
    Masterton, R.A.J., Harvey, A.S., Archer, J.S., Lillywhite, L.M., Abbott, D.F., et al.: Focal epileptiform spikes do not show a canonical BOLD response in patients with benign rolandic epilepsy (BECTS). Neuroimage 51, 252–260 (2010)CrossRefGoogle Scholar
  11. 11.
    Lu, Y., Bagshaw, A.P., Grova, C., Kobayashi, E., Dubeau, F., et al.: Using voxel-specific hemodynamic response function in EEG-fMRI data analysis. Neuroimage 32, 238–247 (2006). Scholar
  12. 12.
    Jacobs, J., Hawco, C., Kobayashi, E., Boor, R., LeVan, P., et al.: Variability of the hemodynamic response as a function of age and frequency of epileptic discharge in children with epilepsy. Neuroimage 40, 601–614 (2008)CrossRefGoogle Scholar
  13. 13.
    Lemieux, L., Laufs, H., Carmichael, D., Paul, J.S., Walker, M.C., et al.: Noncanonical spike-related BOLD responses in focal epilepsy. Hum. Brain Mapp. 29, 329–345 (2007)CrossRefGoogle Scholar
  14. 14.
    Pellegrino, G., Machado, A., von Ellenrieder, N., Watanabe, S., Hall, J.A., et al.: Hemodynamic response to interictal epileptiform discharges addressed by personalized EEG-fNIRS recordings. Front Neurosci. 10, 102 (2016)Google Scholar
  15. 15.
    Wu, G.-R.G., Liao, W., Stramaglia, S., Ding, J.-R.J., Chen, H., et al.: A blind deconvolution approach to recover effective connectivity brain networks from resting state fMRI data. Med. Image Anal. 17, 365–374 (2013)CrossRefGoogle Scholar
  16. 16.
    Wu, G.-R., Marinazzo, D.: Point-process deconvolution of fMRI BOLD signal reveals effective Connectivity alterations in chronic pain patients. Brain Topogr. 28, 541–547 (2015)CrossRefGoogle Scholar
  17. 17.
    Tomasi, D., Wang, R., Wang, G.-J., Volkow, N.D.: Functional connectivity and brain activation: a synergistic approach. Cereb. Cortex 24, 2619–2629 (2014)CrossRefGoogle Scholar
  18. 18.
    Tomasi, D., Volkow, N.D.: Functional connectivity density mapping. Proc. Natl. Acad. Sci. USA 107, 9885–9890 (2010). Scholar
  19. 19.
    Berg, A.T., Berkovic, S.F., Brodie, M.J., Buchhalter, J., Cross, J.H., et al.: Revised terminology and concepts for organization of seizures and epilepsies: report of the ILAE commission on classification and terminology, 2005–2009. Epilepsia 51, 676–685 (2010). Scholar
  20. 20.
    Yan, C., Zang, Y.: DPARSF: a MATLAB toolbox for “pipeline” data analysis of resting-state fMRI. Front. Syst. Neurosci. 4, 13 (2010)Google Scholar
  21. 21.
    Song, X.-W., Dong, Z.-Y., Long, X.-Y., Li, S.-F., Zuo, X.-N., et al.: REST: a toolkit for resting-state functional magnetic resonance imaging data processing. PLoS One 6, e25031 (2011)CrossRefGoogle Scholar
  22. 22.
    Power, J.D., Barnes, K.A., Snyder, A.Z., Schlaggar, B.L., Petersen, S.E.: Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion. Neuroimage 59, 2142–2154 (2012)CrossRefGoogle Scholar
  23. 23.
    Deco, G., Jirsa, V.K.: Ongoing cortical activity at rest: criticality, multistability, and ghost attractors. J. Neurosci. 32, 3366–3375 (2012)CrossRefGoogle Scholar
  24. 24.
    Tagliazucchi, E., Balenzuela, P., Fraiman, D., Montoya, P., Chialvo, D.R.: Spontaneous BOLD event triggered averages for estimating functional connectivity at resting state. Neurosci. Lett. 488, 158–163 (2011)CrossRefGoogle Scholar
  25. 25.
    Avanzini, G., Manganotti, P., Meletti, S., Moshé, S.L., Panzica, F., et al.: The system epilepsies: a pathophysiological hypothesis. Epilepsia 53, 771–778 (2012)CrossRefGoogle Scholar
  26. 26.
    Kellaway, P.: The electroencephalographic features of benign centrotemporal (rolandic) epilepsy of childhood. Epilepsia 41, 1053–1056 (2000)CrossRefGoogle Scholar
  27. 27.
    Huguenard, J.R.: Circuit mechanisms of spike-wave discharge: are there similar underpinnings for centrotemporal spikes? Epilepsia 41, 1076–1077 (2000)CrossRefGoogle Scholar
  28. 28.
    Boor, R., Jacobs, J., Hinzmann, A., Bauermann, T., Scherg, M., et al.: Combined spike-related functional MRI and multiple source analysis in the non-invasive spike localization of benign rolandic epilepsy. Clin. Neurophysiol. 118, 901–909 (2007)CrossRefGoogle Scholar
  29. 29.
    Zhu, Y., Yu, Y., Shinkareva, S.V., Ji, G.-J., Wang, J., et al.: Intrinsic brain activity as a diagnostic biomarker in children with benign epilepsy with centrotemporal spikes. Hum. Brain Mapp. 36, 3878–3889 (2015)CrossRefGoogle Scholar
  30. 30.
    Carney, P.W., Jackson, G.D.: Insights into the mechanisms of absence seizure generation provided by EEG with functional MRI. Front. Neurol. 5, 1–13 (2014)CrossRefGoogle Scholar
  31. 31.
    Marshall, W.J., Lackner, C.L., Marriott, P., Santesso, D.L., Segalowitz, S.J.: Using phase shift Granger causality to measure directed connectivity in EEG recordings. Brain Connect. 4, 826–841 (2014)CrossRefGoogle Scholar
  32. 32.
    Vaudano, A.E., Laufs, H., Kiebel, S.J., Carmichael, D.W., Hamandi, K., et al.: Causal hierarchy within the thalamo-cortical network in spike and wave discharges. PLoS One 4, e6475 (2009)CrossRefGoogle Scholar
  33. 33.
    Lemieux, L., Daunizeau, J., Walker, M.C.: Concepts of connectivity and human epileptic activity. Front. Syst. Neurosci. 5, 12 (2011)CrossRefGoogle Scholar
  34. 34.
    Centeno, M., Carmichael, D.W.: Network connectivity in epilepsy: resting state fMRI and EEG-fMRI contributions. Front Neurol 5 (2014).

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Caroline Garcia Forlim
    • 1
    • 2
    Email author
  • Roma Siugzdaite
    • 1
  • Yang Yu
    • 3
  • Ye-Lei Tang
    • 4
  • Wei Liao
    • 5
    • 6
    • 7
  • Daniele Marinazzo
    • 1
    Email author
  1. 1.Faculty of Psychology and Pedagogical Sciences, Department of Data AnalysisUniversity of GhentGhentBelgium
  2. 2.Clinic and Policlinic for Psychiatry and PsychotherapyUniversity Medical Center Hamburg-EppendorfHamburgGermany
  3. 3.Mental Health Education and Counseling Center, Zhejiang UniversityZhejiangChina
  4. 4.Department of Neurology, the Second Affiliated Hospital of Medial CollegeZhejiang, UniversityZhejiangChina
  5. 5.Center for Information in BioMedicine, Key Laboratory for Neuroinformation of Ministry of Education, School of Life Science and TechnologyUniversity of Electronic Science and Technology of ChinaChengduChina
  6. 6.Center for Cognition and Brain Disorders and the Affiliated Hospital, Hangzhou Normal UniversityHangzhouChina
  7. 7.Zhejiang Key Laboratory for Research in Assessment of Cognitive ImpairmentsHangzhouChina

Personalised recommendations