Advertisement

A Graph Based Similarity Measure for Assessing Altered Connectivity in Traumatic Brain Injury

  • Yusuf OsmanlıoğluEmail author
  • Jacob A. Alappatt
  • Drew Parker
  • Junghoon Kim
  • Ragini Verma
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11383)

Abstract

Traumatic brain injury (TBI) arises from disruptions in the structural connectivity of brain, which further manifests itself as alterations in the functional connectivity, eventually leading to cognitive and behavioral deficits. Although patient-specific measures quantifying the severity of disease is crucial due to the heterogeneous character of the disease, neuroimaging based measures that can assess the level of injury in TBI using structural and functional connectivity is very scarce. Taking a graph theoretical approach, we propose a measure to quantify how dissimilar a TBI patient is relative to healthy subjects using their structural and functional connectomes. Over a TBI dataset with 39 moderate-to-severe TBI patients that are examined 3, 6, and 12 months post injury, and 35 healthy controls, we demonstrate that the dissimilarity scores obtained by the proposed measure distinguish patients from controls using both modalities. We also show that the dissimilarity scores significantly correlate with post-traumatic amnesia, processing speed, and executive function among TBI patients. Our results indicate the applicability of the proposed measure in quantitatively assessing the extent of injury. The measure is applicable to structural and functional connectivity, paving the way for a joint analysis in the future.

Keywords

Traumatic brain injury Severity measure Graph similarity Connectome DTI fMRI 

Notes

Acknowledgements

This work was funded by NIH grants R01HD089390-01A1, 1 R01 NS096606, 5R01NS092398, and 5R01NS065980.

References

  1. 1.
    Johnson, V.E., Stewart, W., Smith, D.H.: Axonal pathology in traumatic brain injury. Exp. Neurol. 246, 35–43 (2013)CrossRefGoogle Scholar
  2. 2.
    Gale, S.D., Johnson, S.C., Bigler, E.D., Blatter, D.D.: Nonspecific white matter degeneration following traumatic brain injury. J. Int. Neuropsychological Soc. 1(1), 17–28 (1995)CrossRefGoogle Scholar
  3. 3.
    Hayes, J.P., Bigler, E.D., Verfaellie, M.: Traumatic brain injury as a disorder of brain connectivity. J. Int. Neuropsychological Soc. 22(2), 120–137 (2016)CrossRefGoogle Scholar
  4. 4.
    Solmaz, B., et al.: Assessing connectivity related injury burden in diffuse traumatic brain injury. Hum. Brain Mapp. 38(6), 2913–2922 (2017)CrossRefGoogle Scholar
  5. 5.
    Bonnelle, V., et al.: Default mode network connectivity predicts sustained attention deficits after traumatic brain injury. J. Neurosci. 31(38), 13442–13451 (2011)CrossRefGoogle Scholar
  6. 6.
    Caeyenberghs, K., et al.: Altered structural networks and executive deficits in traumatic brain injury patients. Brain Struct. Function 219(1), 193–209 (2014)CrossRefGoogle Scholar
  7. 7.
    Bullmore, E.T., Sporns, O., Solla, S.A.: Complex brain networks: graph theoretical analysis of structural and functional systems. Nat. Rev. Neurosci. 10(3), 186–198 (2009)CrossRefGoogle Scholar
  8. 8.
    Sporns, O.: From simple graphs to the connectome: networks in neuroimaging. Neuroimage 62(2), 881–886 (2012)CrossRefGoogle Scholar
  9. 9.
    Livi, L., Rizzi, A.: The graph matching problem. Pattern Anal. Appl. 16(3), 253–283 (2013)MathSciNetCrossRefGoogle Scholar
  10. 10.
    Osmanlıoğlu, Y., Ontañón, S., Hershberg, U., Shokoufandeh, A.: Efficient approximation of labeling problems with applications to immune repertoire analysis. In: 2016 23rd International Conference on Pattern Recognition (ICPR), pp. 2410–2415. IEEE (2016)Google Scholar
  11. 11.
    Ktena, S.I., et al.: Metric learning with spectral graph convolutions on brain connectivity networks. NeuroImage 169, 431–442 (2018)CrossRefGoogle Scholar
  12. 12.
    Raj, A., Mueller, S.G., Young, K., Laxer, K.D., Weiner, M.: Network-level analysis of cortical thickness of the epileptic brain. Neuroimage 52(4), 1302–1313 (2010)CrossRefGoogle Scholar
  13. 13.
    Richiardi, J., Eryilmaz, H., Schwartz, S., Vuilleumier, P., Van De Ville, D.: Decoding brain states from fmri connectivity graphs. Neuroimage 56(2), 616–626 (2011)CrossRefGoogle Scholar
  14. 14.
    Mokhtari, F., Hossein-Zadeh, G.-A.: Decoding brain states using backward edge elimination and graph kernels in fMRI connectivity networks. J. Neurosci. Methods 212(2), 259–268 (2013)CrossRefGoogle Scholar
  15. 15.
    Desikan, R.S., 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
  16. 16.
    Satterthwaite, T.D., et al.: An improved framework for confound regression and filtering for control of motion artifact in the preprocessing of resting-state functional connectivity data. Neuroimage 64, 240–256 (2013)CrossRefGoogle Scholar
  17. 17.
    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(3), 2142–2154 (2012)CrossRefGoogle Scholar
  18. 18.
    Wechsler, D., Coalson, D.L., Raiford, S.E.: WAIS-IV: Wechsler Adult Intelligence Scale. Pearson, San Antonio (2008)Google Scholar
  19. 19.
    Benton, A.L., deS. Hamsher, K., Sivan, A.B.: Multilingual Aphasia Examination: Token Test. AJA Associates, Iowa City (1994)Google Scholar
  20. 20.
    Reitan, R.M., Wolfson, D.: The Halstead-Reitan Neuropsychological Test Battery: Theory and Clinical Interpretation, vol. 4. Reitan Neuropsychology (1985)Google Scholar
  21. 21.
    Wechsler, D.: Wechsler Memory Scale Fourth Edition (WMSIV). Pearson, San Antonio (2009)Google Scholar
  22. 22.
    Rey, A.: Memorisation d’une serie de 15 mots en 5 repetitions. L’examen clinique en psychologie (1958)Google Scholar
  23. 23.
    Fornito, A., Zalesky, A., Breakspear, M.: Graph analysis of the human connectome: promise, progress, and pitfalls. Neuroimage 80, 426–444 (2013)CrossRefGoogle Scholar
  24. 24.
    Koopmans, T.C., Beckmann, M.: Assignment problems and the location of economic activities. Econometrica: J. Econometric Soc. 25, 53–76 (1957)MathSciNetCrossRefGoogle Scholar
  25. 25.
    Kuhn, H.W.: The hungarian method for the assignment problem. Naval Res. Logistics Quart. 2(1–2), 83–97 (1955)MathSciNetCrossRefGoogle Scholar
  26. 26.
    Rabinowitz, A.R., Hart, T., Whyte, J., Kim, J.: Neuropsychological recovery trajectories in moderate to severe traumatic brain injury: influence of patient characteristics and diffuse axonal injury. J. Int. Neuropsychological Soc. 24(3), 237–246 (2018)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Yusuf Osmanlıoğlu
    • 1
    Email author
  • Jacob A. Alappatt
    • 1
  • Drew Parker
    • 1
  • Junghoon Kim
    • 2
  • Ragini Verma
    • 1
  1. 1.Center for Biomedical Image Computing and Analytics, Department of RadiologyUniversity of PennsylvaniaPhiladelphiaUSA
  2. 2.CUNY School of MedicineThe City College of New YorkNew YorkUSA

Personalised recommendations