Connectome-Based Pattern Learning Predicts Histology and Surgical Outcome of Epileptogenic Malformations of Cortical Development

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10433)


Focal cortical dysplasia (FCD) type II, a surgically amenable epileptogenic malformation, is characterized by intracortical dyslamination and dysmorphic neurons, either in isolation (IIA) or together with balloon cells (IIB). While evidence suggests diverging local function between these two histological grades, patterns of connectivity to the rest of the brain remain unknown. We present a novel MRI framework that subdivides a given FCD lesion into a set of smaller cortical patches using hierarchical clustering of resting-state functional connectivity profiles. We assessed the yield of this connectome-based subtyping to predict histological grade and response to surgery in individual patients. As the human functional connectome consists of multiple large-scale communities (e.g., the default mode and fronto-parietal networks), we dichotomized connectivity profiles of lesional patches into connectivity to the cortices belonging to the same functional community (intra-community) and to other communities (inter-community). Clustering these community-based patch profiles in 27 patients with histologically-proven FCD objectively identified three distinct lesional classes with (1) decreased intra- and inter-community connectivity, (2) decreased intra-community but normal inter-community connectivity, and (3) increased intra- as well as inter-community connectivity, relative to 34 healthy controls. Ensemble classifiers informed by these classes predicted histological grading (i.e., IIA vs. IIB) and post-surgical outcome (i.e., seizure-free vs. non-free) with high accuracy (≥84%, above-chance significance based on permutation tests, p < 0.01), suggesting benefits of MRI-based connectome stratification for individualized presurgical prognostics.


MRI Functional connectivity Epilepsy Disease prediction 


  1. 1.
    Bernasconi, A., Bernasconi, N., Bernhardt, B.C., Schrader, D.: Advances in MRI for ‘cryptogenic’ epilepsies. Nat. Rev. Neurol. 7, 99–108 (2011)CrossRefGoogle Scholar
  2. 2.
    Besseling, R.M., Jansen, J.F., de Louw, A.J., Vlooswijk, M.C., Hoeberigs, M.C., Aldenkamp, A.P., Backes, W.H., Hofman, P.A.: Abnormal profiles of local functional connectivity proximal to focal cortical dysplasias. PLoS ONE 11, e0166022 (2016)CrossRefGoogle Scholar
  3. 3.
    Hong, S.J., Bernhardt, B.C., Caldairou, B., Hall, J.A., Guiot, M.C., Schrader, D., Bernasconi, N., Bernasconi, A.: Multimodal MRI profiling of focal cortical dysplasia type II. Neurology 88, 734–742 (2017)CrossRefGoogle Scholar
  4. 4.
    Fonov, V., Evans, A.C., Botteron, K., Almli, C.R., McKinstry, R.C., Collins, D.L., Grp, B.D.C.: Unbiased average age-appropriate atlases for pediatric studies. NeuroImage 54, 313–327 (2011)CrossRefGoogle Scholar
  5. 5.
    Kim, J.S., Singh, V., Lerch, J., Ad-Dab’bagh, Y., MacDonald, D., Lee, J.M., Kim, S.I., Evans, A.C.: Automated 3-D extraction and evaluation of the inner and outer cortical surfaces using a Laplacian map and partial volume effect classification. NeuroImage 27, 210–221 (2005)CrossRefGoogle Scholar
  6. 6.
    Chao-Gan, Y., Yu-Feng, Z.: DPARSF: a MATLAB toolbox for “Pipeline” data analysis of resting-state fMRI. Frontiers Syst. Neurosci. 4, 13 (2010)Google Scholar
  7. 7.
    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
  8. 8.
    Greve, D.N., Fischl, B.: Accurate and robust brain image alignment using boundary-based registration. NeuroImage 48, 63–72 (2009)CrossRefGoogle Scholar
  9. 9.
    Tzourio-Mazoyer, N., Landeau, B., Papathanassiou, D., Crivello, F., Etard, O., Delcroix, N., Mazoyer, B., Joliot, M.: Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. NeuroImage 15, 273–289 (2002)CrossRefGoogle Scholar
  10. 10.
    Cammoun, L., Gigandet, X., Meskaldji, D., Thiran, J.P., Sporns, O., Do, K.Q., Maeder, P., Meuli, R., Hagmann, P.: Mapping the human connectome at multiple scales with diffusion spectrum MRI. J. Neurosci. Methods 203, 386–397 (2012)CrossRefGoogle Scholar
  11. 11.
    Yeo, B.T., Krienen, F.M., Sepulcre, J., Sabuncu, M.R., Lashkari, D., Hollinshead, M., Roffman, J.L., Smoller, J.W., Zollei, L., Polimeni, J.R., Fischl, B., Liu, H., Buckner, R.L.: The organization of the human cerebral cortex estimated by intrinsic functional connectivity. J. Neurophysiol. 106, 1125–1165 (2011)CrossRefGoogle Scholar
  12. 12.
    Berndt, D.J., Clifford, J.: Using dynamic time warping to find patterns in time series. In: Proceedings of the 3rd International Conference on Knowledge Discovery and Data Mining, pp. 359–370. AAAI Press, Seattle, WA (1994)Google Scholar
  13. 13.
    Ward, J.H.: Hierarchical grouping to optimize an objective function. J. Am. Stat. Assoc. 58, 236–244 (1963)MathSciNetCrossRefGoogle Scholar
  14. 14.
    Dietterich, T.G.: Ensemble methods in machine learning. In: Kittler, J., Roli, F. (eds.) MCS 2000. LNCS, vol. 1857, pp. 1–15. Springer, Heidelberg (2000). doi: 10.1007/3-540-45014-9_1CrossRefGoogle Scholar
  15. 15.
    Bernhardt, B.C., Hong, S.J., Bernasconi, A., Bernasconi, N.: Magnetic resonance imaging pattern learning in temporal lobe epilepsy: classification and prognostics. Ann. Neurol. 77, 436–446 (2015)CrossRefGoogle Scholar
  16. 16.
    Gross, R.E., Mahmoudi, B., Riley, J.P.: Less is more: novel less-invasive surgical techniques for mesial temporal lobe epilepsy that minimize cognitive impairment. Curr. Opin. Neurol. 28, 182–191 (2015)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Neuroimaging of Epilepsy LaboratoryMcGill UniversityMontrealCanada
  2. 2.Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and HospitalMcGill UniversityMontrealCanada

Personalised recommendations