Mild Traumatic Brain Injury Outcome Prediction Based on Both Graph and K-nn Methods

  • R. Bellotti
  • A. Lombardi
  • C. Guaragnella
  • N. Amoroso
  • A. Tateo
  • S. TangaroEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10154)


Cognitive impairment has mainly two, non mutually exclusive, etiologies: structural or connectivity lesions. Analogously, we present here a methodology aimed at investigating magnetic resonance imaging (MRI) scans of subject after a traumatic brain injury (TBI) to detect the presence of these heterogeneous lesions and access the information content within. In particular, we use (i) complex network topological features to capture the effect of disease on connectivity and (ii) morphological brain measurements to describe anomalous patterns from a structural perspective. This integrated base of knowledge is then used to emphasize differences arising within a cohort including normal controls and patients labeled as category-I and category-II according to their outcome after TBI. Results suggest that topological measurements provide a suitable measurement to detect category-I subjects, while structural features are effective to distinguish controls from category-II subjects.


TBI MRI Complex networks Graph theory K-nn 



Cortical reconstruction and volumetric segmentation were performed with the FreeSurfer image analysis suite, which is documented and freely available for download online. The technical details of these procedures are described in prior publications [6, 7, 9, 10, 11, 12, 13, 14, 15, 17, 19, 30]. Briefly, this processing includes motion correction and averaging [26] of multiple volumetric T1 weighted images (when more than one is available), removal of non-brain tissue using a hybrid watershed/surface deformation procedure [30], automated Talairach transformation, segmentation of the subcortical white matter and deep gray matter volumetric structures (including hippocampus, amygdala, caudate, putamen, ventricles) [12, 13] intensity normalization [32], tessellation of the gray matter white matter boundary, automated topology correction [11, 31], and surface deformation following intensity gradients to optimally place the gray/white and gray/cerebrospinal fluid borders at the location where the greatest shift in intensity defines the transition to the other tissue class [6, 7, 9]. Once the cortical models are complete, a number of deformable procedures can be performed for in further data processing and analysis including surface inflation [6], registration to a spherical atlas which utilized individual cortical folding patterns to match cortical geometry across subjects [15], parcellation of the cerebral cortex into units based on gyral and sulcal structure [8, 10], and creation of a variety of surface-based data including maps of curvature and sulcal depth. This method uses both intensity and continuity information from the entire three-dimensional MR volume in segmentation and deformation procedures to produce representations of cortical thickness, calculated as the closest distance from the gray/white boundary to the gray/CSF boundary at each vertex on the tessellated surface [9]. The maps are created using spatial intensity gradients across tissue classes and are therefore not simply reliant on absolute signal intensity. The maps produced are not restricted to the voxel resolution of the original data thus are capable of detecting submillimeter differences between groups. Procedures for the measurement of cortical thickness have been validated against histological analysis [28] and manual measurements [21, 29]. Freesurfer morphometric procedures have been demonstrated to show good test-retest reliability across scanner manufacturers and across field strengths [17, 27].


  1. 1.
    Alexander, A.L., Hurley, S.A., Samsonov, A.A., Adluru, N., Hosseinbor, A.P., Mossahebi, P., Tromp, D.P., Zakszewski, E., Field, A.S.: Characterization of cerebral white matter properties using quantitative magnetic resonance imaging stains. Brain Connect. 1(6), 423–446 (2011)CrossRefGoogle Scholar
  2. 2.
    Allen, G.I., Amoroso, N., Anghel, C., Balagurusamy, V., Bare, C.J., Beaton, D., Bellotti, R., Bennett, D.A., Boehme, K.L., Boutros, P.C., et al.: Crowdsourced estimation of cognitive decline and resilience in Alzheimer’s disease. Alzheimer’s Dement. 12(6), 645–653 (2016)CrossRefGoogle Scholar
  3. 3.
    Amoroso, N., Monaco, A., Tangaro, S.: Topological measurements of DWI tractography for the Alzheimers disease detection. In: Computational and Mathematical Methods in Medicine (2016, in press)Google Scholar
  4. 4.
    Bron, E.E., Smits, M., Van Der Flier, W.M., Vrenken, H., Barkhof, F., Scheltens, P., Papma, J.M., Steketee, R.M., Orellana, C.M., Meijboom, R., et al.: Standardized evaluation of algorithms for computer-aided diagnosis of dementia based on structural MRI: the CADDementia challenge. NeuroImage 111, 562–579 (2015)CrossRefGoogle Scholar
  5. 5.
    Daianu, M., Jahanshad, N., Nir, T.M., Toga, A.W., Jack Jr., C.R., Weiner, M.W., Thompson, P.M.: Breakdown of brain connectivity between normal aging and Alzheimer’s disease: a structural k-core network analysis. Brain Connect. 3(4), 407–422 (2013). For the Alzheimer’s Disease Neuroimaging InitiativeCrossRefGoogle Scholar
  6. 6.
    Dale, A.M., Fischl, B., Sereno, M.I.: Cortical surface-based analysis: I. Segmentation and surface reconstruction. Neuroimage 9(2), 179–194 (1999)CrossRefGoogle Scholar
  7. 7.
    Dale, A.M., Sereno, M.I.: Improved localizadon of cortical activity by combining EEG and MEG with MRI cortical surface reconstruction: a linear approach. J. Cogn. Neurosci. 5(2), 162–176 (1993)CrossRefGoogle Scholar
  8. 8.
    Desikan, R.S., Ségonne, F., Fischl, B., Quinn, B.T., Dickerson, B.C., Blacker, D., Buckner, R.L., Dale, A.M., Maguire, R.P., Hyman, B.T., et al.: An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. Neuroimage 31(3), 968–980 (2006)CrossRefGoogle Scholar
  9. 9.
    Fischl, B., Dale, A.M.: Measuring the thickness of the human cerebral cortex from magnetic resonance images. Proc. Natl. Acad. Sci. 97(20), 11050–11055 (2000)CrossRefGoogle Scholar
  10. 10.
    Fischl, B., van der Kouwe, A., Destrieux, C., Halgren, E., Ségonne, F., Salat, D.H., Busa, E., Seidman, L.J., Goldstein, J., Kennedy, D., et al.: Automatically parcellating the human cerebral cortex. Cereb. Cortex 14(1), 11–22 (2004)CrossRefGoogle Scholar
  11. 11.
    Fischl, B., Liu, A., Dale, A.M.: Automated manifold surgery: constructing geometrically accurate and topologically correct models of the human cerebral cortex. IEEE Trans. Med. Imaging 20(1), 70–80 (2001)CrossRefGoogle Scholar
  12. 12.
    Fischl, B., Salat, D.H., Busa, E., Albert, M., Dieterich, M., Haselgrove, C., Van Der Kouwe, A., Killiany, R., Kennedy, D., Klaveness, S., et al.: Whole brain segmentation: automated labeling of neuroanatomical structures in the human brain. Neuron 33(3), 341–355 (2002)CrossRefGoogle Scholar
  13. 13.
    Fischl, B., Salat, D.H., van der Kouwe, A.J., Makris, N., Ségonne, F., Quinn, B.T., Dale, A.M.: Sequence-independent segmentation of magnetic resonance images. Neuroimage 23, S69–S84 (2004)CrossRefGoogle Scholar
  14. 14.
    Fischl, B., Sereno, M.I., Dale, A.M.: Cortical surface-based analysis. II: inflation, flattening, and a surface-based coordinate system. Neuroimage 9(2), 195–207 (1999)CrossRefGoogle Scholar
  15. 15.
    Fischl, B., Sereno, M.I., Tootell, R.B., Dale, A.M., et al.: High-resolution intersubject averaging and a coordinate system for the cortical surface. Hum. Brain Mapp. 8(4), 272–284 (1999)CrossRefGoogle Scholar
  16. 16.
    Guimera, R., Amaral, L.A.N.: Functional cartography of complex metabolic networks. Nature 433(7028), 895–900 (2005)CrossRefGoogle Scholar
  17. 17.
    Han, X., Jovicich, J., Salat, D., van der Kouwe, A., Quinn, B., Czanner, S., Busa, E., Pacheco, J., Albert, M., Killiany, R., et al.: Reliability of MRI-derived measurements of human cerebral cortical thickness: the effects of field strength, scanner upgrade and manufacturer. Neuroimage 32(1), 180–194 (2006)CrossRefGoogle Scholar
  18. 18.
    Inglese, P., Amoroso, N., Boccardi, M., Bocchetta, M., Bruno, S., Chincarini, A., Errico, R., Frisoni, G., Maglietta, R., Redolfi, A., Sensi, F., Tangaro, S., Tateo, A., Bellotti, R.: Multiple RF classifier for the hippocampus segmentation: Method and validation on EADC-ADNI harmonized hippocampal protocol. Phys. Medica 31(8), 1085–1091 (2015)CrossRefGoogle Scholar
  19. 19.
    Jovicich, J., Czanner, S., Greve, D., Haley, E., van der Kouwe, A., Gollub, R., Kennedy, D., Schmitt, F., Brown, G., MacFall, J., et al.: Reliability in multi-site structural mri studies: effects of gradient non-linearity correction on phantom and human data. Neuroimage 30(2), 436–443 (2006)CrossRefGoogle Scholar
  20. 20.
    Kumar, R., Husain, M., Gupta, R.K., Hasan, K.M., Haris, M., Agarwal, A.K., Pandey, C., Narayana, P.A.: Serial changes in the white matter diffusion tensor imaging metrics in moderate traumatic brain injury and correlation with neuro-cognitive function. J. Neurotrauma 26(4), 481–495 (2009)CrossRefGoogle Scholar
  21. 21.
    Kuperberg, G.R., Broome, M.R., McGuire, P.K., David, A.S., Eddy, M., Ozawa, F., Goff, D., West, W.C., Williams, S.C., van der Kouwe, A.J., et al.: Regionally localized thinning of the cerebral cortex in schizophrenia. Arch. Gen. Psychiatry 60(9), 878–888 (2003)CrossRefGoogle Scholar
  22. 22.
    La Rocca, M., et al.: A multiplex network model to characterize brain atrophy in structural MRI. In: Mantica, G., Stoop, R., Stramaglia, S. (eds.) Proceedings of the XXIII International Conference on Nonlinear Dynamics of Electronic Systems Emergent Complexity from Nonlinearity, in Physics, Engineering and the Life Sciences. Springer Proceedings in Physics, vol. 191, Como, Italy, 7-11 September 2015. Springer International Publishing, Cham (2017)Google Scholar
  23. 23.
    Marimont, R., Shapiro, M.: Nearest neighbour searches and the curse of dimensionality. IMA J. Appl. Math. 24(1), 59–70 (1979)CrossRefzbMATHGoogle Scholar
  24. 24.
    Newman, M.E.: Finding community structure in networks using the eigenvectors of matrices. Phys. Rev. E 74(3), 036104 (2006)MathSciNetCrossRefGoogle Scholar
  25. 25.
    Onnela, J.P., Saramäki, J., Kertész, J., Kaski, K.: Intensity and coherence of motifs in weighted complex networks. Phys. Rev. E 71(6), 065103 (2005)CrossRefGoogle Scholar
  26. 26.
    Reuter, M., Rosas, H.D., Fischl, B.: Highly accurate inverse consistent registration: a robust approach. Neuroimage 53(4), 1181–1196 (2010)CrossRefGoogle Scholar
  27. 27.
    Reuter, M., Schmansky, N.J., Rosas, H.D., Fischl, B.: Within-subject template estimation for unbiased longitudinal image analysis. Neuroimage 61(4), 1402–1418 (2012)CrossRefGoogle Scholar
  28. 28.
    Rosas, H., Liu, A., Hersch, S., Glessner, M., Ferrante, R., Salat, D., van Der Kouwe, A., Jenkins, B., Dale, A., Fischl, B.: Regional and progressive thinning of the cortical ribbon in Huntingtons disease. Neurology 58(5), 695–701 (2002)CrossRefGoogle Scholar
  29. 29.
    Salat, D.H., Buckner, R.L., Snyder, A.Z., Greve, D.N., Desikan, R.S., Busa, E., Morris, J.C., Dale, A.M., Fischl, B.: Thinning of the cerebral cortex in aging. Cereb. Cortex 14(7), 721–730 (2004)CrossRefGoogle Scholar
  30. 30.
    Ségonne, F., Dale, A., Busa, E., Glessner, M., Salat, D., Hahn, H., Fischl, B.: A hybrid approach to the skull stripping problem in MRI. Neuroimage 22(3), 1060–1075 (2004)CrossRefGoogle Scholar
  31. 31.
    Ségonne, F., Pacheco, J., Fischl, B.: Geometrically accurate topology-correction of cortical surfaces using nonseparating loops. IEEE Trans. Med. Imaging 26(4), 518–529 (2007)CrossRefGoogle Scholar
  32. 32.
    Sled, J.G., Zijdenbos, A.P., Evans, A.C.: A nonparametric method for automatic correction of intensity nonuniformity in MRI data. IEEE Trans. Med. Imaging 17(1), 87–97 (1998)CrossRefGoogle Scholar
  33. 33.
    Tijms, B.M., Wink, A.M., de Haan, W., van der Flier, W.M., Stam, C.J., Scheltens, P., Barkhof, F.: Alzheimer’s disease: connecting findings from graph theoretical studies of brain networks. Neurobiol. Aging 34(8), 2023–2036 (2013)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • R. Bellotti
    • 1
    • 2
  • A. Lombardi
    • 3
  • C. Guaragnella
    • 3
  • N. Amoroso
    • 1
    • 2
  • A. Tateo
    • 1
    • 2
  • S. Tangaro
    • 2
    Email author
  1. 1.Dipartimento Interateno di Fisica “M. Merlin”Università degli studi di Bari “A. Moro”BariItaly
  2. 2.Sezione di BariIstituto Nazionale di Fisica NucleareBariItaly
  3. 3.Dipartimento di Ingegneria Elettrica e dell’InformazionePolitecnico di BariBariItaly

Personalised recommendations