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Multimodal Patho-Connectomics of Brain Injury

  • Ragini VermaEmail author
  • Yusuf Osmanlioglu
  • Abdol Aziz Ould Ismail
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11383)

Abstract

The paper introduces the concept of patho-connectomics, an injury-specific connectome creation and analysis paradigm, that treats injuries as a diffuse disease pervading the whole brain network. The foundation of the “patho-connectomic” ideology of analysis is that no part of the brain can function in isolation, and abnormality in the brain network is a combination of structural and functional anomalies. Brain injuries introduce anomalies in this brain network that could affect the quality of brain tissue, break a pathway, and lead to disrupted connectivity in neural circuits. This in turn affects functionality. Thus, patho-connectomes go beyond the traditional connectome and include information of tissue quality and structural and functional connectivity, forming a comprehensive map of the brain network. Information from diffusion and functional MRI are combined to create these patho-connectomes. The creation and analysis of patho-connectomes are discussed in the case of brain tumors, that suffers from the challenges of mass effect and infiltration of the peritumoral region, which in turn affect the surgical and radiation plan, and in traumatic brain injury, where the exact injury may be difficult to determine, but the effect is diffuse manifesting in heterogenous symptoms. A network-based approach to analysis of both these forms of injury will help determine the effect of pathology on the whole brain, while incorporating recovery and plasticity. Thus, patho-connectomics with a broad network perspective on brain injuries, has the potential to cause a major paradigm shift in their research of brain injuries, facilitating subject specific analysis and paving the way for precision medicine.

Keywords

Diffusion MRI fMRI Connectomes Free water Tractography Brain tumors Neoplasms Traumatic brain injury 

References

  1. 1.
    Basser, P.J., Jones, D.K.: Diffusion-tensor MRI: theory, experimental design and data analysis - a technical review. NMR Biomed. 15(7–8), 456–467 (2002)CrossRefGoogle Scholar
  2. 2.
    Pierpaoli, C., et al.: Diffusion tensor MR imaging of the human brain. Radiology 201(3), 637–648 (1996)CrossRefGoogle Scholar
  3. 3.
    Tuch, D.S., et al.: Diffusion MRI of complex neural architecture. Neuron 40(5), 885–895 (2003)CrossRefGoogle Scholar
  4. 4.
    Pierpaoli, C., Basser, P.J.: Toward a quantitative assessment of diffusion anisotropy. Magn. Reson. Med. 36(6), 893–906 (1996)CrossRefGoogle Scholar
  5. 5.
    Caruyer, E., Verma, R.: On facilitating the useof HARDI in population studies by creating rotation-invariant markers. Med. Image Anal. 20(1), 87–96 (2015)CrossRefGoogle Scholar
  6. 6.
    Caruyer, E., et al.: A comparative study of 16 tractography algorithms for the corticospinal tract: reproducibility and subject-specificity. In: ISMRM (2014)Google Scholar
  7. 7.
    Honnorat, N., Parker, D., Tunç, B., Davatzikos, C., Verma, R.: Subject-specific structural parcellations based on randomized AB-divergences. In: Descoteaux, M., Maier-Hein, L., Franz, A., Jannin, P., Collins, D.L., Duchesne, S. (eds.) MICCAI 2017. LNCS, vol. 10433, pp. 407–415. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-66182-7_47CrossRefGoogle Scholar
  8. 8.
    Descoteaux, M., et al.: Deterministic and probabilistic tractography based on complex fibre orientation distributions. IEEE Trans. Med. Imaging 28(2), 269–286 (2009)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Hagmann, P., et al.: MR connectomics: principles and challenges. J. Neurosci. Methods 194(1), 34–45 (2010)CrossRefGoogle Scholar
  10. 10.
    Molenaar, R.J.: Ion channels in glioblastoma. In: ISRN Neurology (2011)Google Scholar
  11. 11.
    Wang, N., Jain, R.K., Batchelor, T.T.: New directions in anti-angiogenic therapy for glioblastoma. Neurotherapeutics 14(2), 321–332 (2017)CrossRefGoogle Scholar
  12. 12.
    Pasternak, O., et al.: Free water elimination and mapping from diffusion MRI. Magn. Reson. Med.: Official J. Soc. Magn. Reson. Med./Soc. Magn. Reson. Med. 62, 717–730 (2009)CrossRefGoogle Scholar
  13. 13.
    Ismail, A.A.O., et al.: Characterizing peritumoral tissue using DTI-Based free water elimination. In: Crimi, A., et al. (eds.) BrainLes 2018. LNCS, vol. 11383, pp. 123–131. Springer, Cham (2018)Google Scholar
  14. 14.
    Garyfallidis, E., et al.: Recognition of white matter bundles using local and global streamline-based registration and clustering. Neuroimage 170, 283–293 (2017)CrossRefGoogle Scholar
  15. 15.
    Tunc, B., et al.: Automated tract extraction via atlas based adaptive clustering. Neuroimage 102(P2), 596–607 (2014)CrossRefGoogle Scholar
  16. 16.
    Tunc, B., et al.: Individualized map of white matter pathways: connectivity-based paradigm for neurosurgical planning. Neurosurgery 79(4), 568–577 (2016)CrossRefGoogle Scholar
  17. 17.
    Hulkower, M.B., et al.: A decade of DTI in traumatic brain injury: 10 years and 100 articles later. AJNR Am. J. Neuroradiol. 34, 2064–2074 (2013)CrossRefGoogle Scholar
  18. 18.
    Solmaz, B., et al.: Assessing connectivity related injury burden in diffuse traumatic brain injury. Hum. Brain Mapp. 38(6), 2913–2922 (2017)CrossRefGoogle Scholar
  19. 19.
    Kim, J., et al.: Disrupted structural connectome is associated with both psychometric and real-world neuropsychological impairment in diffuse traumatic brain injury. J. Int. Neuropsychol. Soc. 20(9), 887–896 (2014)CrossRefGoogle Scholar
  20. 20.
    Osmanlıoğlu, Y., Alappatt, J.A., Parker, D., Kim, J., Verma, R.: A graph based similarity measure for assessing altered connectivity in traumatic brain injury. In: Crimi, A., et al. (eds.) BrainLes 2018. LNCS, vol. 11383, pp. 189–198. Springer, Cham (2018)Google Scholar
  21. 21.
    Osmanlıoğlu, Y., et al.: A graph representation and similarity measure for brain networks with nodal features. In: Stoyanov, D., et al. (eds.) GRAIL/Beyond MIC -2018. LNCS, vol. 11044, pp. 14–23. Springer, Cham (2018).  https://doi.org/10.1007/978-3-030-00689-1_2CrossRefGoogle Scholar
  22. 22.
    Hillary, F.G., et al.: The rich get richer: brain injury elicits hyperconnectivity in core subnetworks. PLoS ONE 9(8), e104021 (2014)CrossRefGoogle Scholar
  23. 23.
    Sporns, O.: The human connectome: origins and challenges. Neuroimage 80, 53–61 (2013)CrossRefGoogle Scholar
  24. 24.
    Ingalhalikar, M., et al.: Sex differences in the structural connectome of the human brain. Proc. Natl. Acad. Sci. U.S.A. 111(2), 823–828 (2014)CrossRefGoogle Scholar
  25. 25.
    Tunc, B., et al.: Establishing a link between sex-related differences in the structural connectome and behaviour. Phil. Trans. R. Soc. Lond. B Biol. Sci. 371(1688), 20150111 (2016)CrossRefGoogle Scholar
  26. 26.
    Fair, D.A., et al.: Functional brain networks develop from a “local to distributed” organization. PLoS Comput. Biol. 5(5), e1000381 (2009)MathSciNetCrossRefGoogle Scholar
  27. 27.
    van den Heuvel, M.P., et al.: Abnormal rich club organization and functional brain dynamics in schizophrenia. JAMA Psychiatry 70(8), 783–792 (2013)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Ragini Verma
    • 1
    Email author
  • Yusuf Osmanlioglu
    • 1
  • Abdol Aziz Ould Ismail
    • 1
  1. 1.Penn Patho-Connectomics Lab, Department of RadiologyUniversity of PennsylvaniaPhiladelphiaUSA

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