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Longitudinal Analysis Framework of DWI Data for Reconstructing Structural Brain Networks with Application to Multiple Sclerosis

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Computational Diffusion MRI

Part of the book series: Mathematics and Visualization ((MATHVISUAL))

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Abstract

We consider the problem of reconstructing brain networks in a longitudinal study, where diffusion-weighted and T1-weighted magnetic resonance images have been acquired at multiple time-points for the same subject. We introduce a method for registering diffusion-weighted and structural scans in a subject-specific half-way space and we demonstrate that half-way network metrics are strongly correlated with native network metrics. We also report sufficient agreement between the two techniques in a cohort comprising of healthy controls (n = 12) and multiple sclerosis patients (n = 12). The results remained unaffected when the analyses were evaluated in controls and patients separately. These study findings might be of particular interest in longitudinal structural network studies assessing network changes over time in normal and disease conditions.

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References

  1. Rubinov, M., Sporns, O.: Complex network measures of brain connectivity: uses and interpretations. NeuroImage 52(3), 1059–1069 (2010)

    Google Scholar 

  2. Hagmann, P., Cammoun, L., Gigandet, X., Meuli, R., Honey, C.J., Wedeen, V.J., Sporns, O.: Mapping the structural core of human cerebral cortex. PLoS Biol. 6(7), e159 (2008)

    Google Scholar 

  3. Stam, C.J.: Modern network science of neurological disorders. Nat. Rev. Neurosci. 15(10), 683–695 (2014)

    Google Scholar 

  4. Reuter, M., Fischl, B.: Avoiding asymmetry-induced bias in longitudinal image processing. NeuroImage 57(1), 19–21 (2011)

    Google Scholar 

  5. 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)

    Google Scholar 

  6. Keihaninejad, S., Zhang, H., Ryan, N.S., Malone, I.B., Modat, M., Cardoso, M.J., Cash, D.M., Fox, N.C., Ourselin, S.: An unbiased longitudinal analysis framework for tracking white matter changes using diffusion tensor imaging with application to Alzheimer’s disease. NeuroImage 72, 153–163 (2013)

    Google Scholar 

  7. Yendiki, A., Reuter, M., Wilkens, P., Rosas, H.D., Fischl, B.: Joint reconstruction of white-matter pathways from longitudinal diffusion MRI data with anatomical priors. NeuroImage 127, 277–286 (2016)

    Google Scholar 

  8. Tuladhar, A.M., van Uden, I.W.M., Rutten-Jacobs, L.C., Lawrence, A., van der Holst, H., van Norden, A., de Laat, K., van Dijk, E., Claassen, J., Kessels, R.P.C., Markus, S.H., Norris, D.G., de Leeuw, F.: Structural network efficiency predicts conversion to dementia. Neurology 86(12), 1112–1119 (2016)

    Google Scholar 

  9. Nir, T.M., Jahanshad, N., Toga, A.W., Bernstein, M.A., Jack, C.R. Jr., Weiner, M.W., Thompson, P.M., Alzheimer’s Disease Neuroimaging, I.: Connectivity network measures predict volumetric atrophy in mild cognitive impairment. Neurobiol. Aging 36(Suppl. 1), S113–S120 (2015)

    Google Scholar 

  10. Leemans, A., Jones, D.K.: The B-matrix must be rotated when correcting for subject motion in DTI data. Magn. Reson. Med. 61(6), 1336–1349 (2009)

    Google Scholar 

  11. Leung, K.K., Ridgway, G.R., Ourselin, S., Fox, N.C.: Consistent multi-time-point brain atrophy estimation from the boundary shift integral. NeuroImage 59(4), 3995–4005 (2012)

    Google Scholar 

  12. Tustison, N.J., Avants, B.B., Cook, P.A., Zheng, Y., Egan, A., Yushkevich, P.A., Gee, J.C.: N4ITK: improved N3 bias correction. IEEE Trans. Med. Imaging 29(6), 1310–1320 (2010)

    Google Scholar 

  13. Hickman, S.I., Barker, G.J., Molyneux, P.D., Miller, D.H.: Technical note: the comparison of hypointense lesions from ‘pseudo-T1’ and T1-weighted images in secondary progressive multiple sclerosis. Mult. Scler. 8(5), 433–435 (2002)

    Google Scholar 

  14. Prados, F., Cardoso, M.J., Kanber, B., Ciccarelli, O., Kapoor, R., Gandini Wheeler-Kingshott, C.A., Ourselin, S.: A multi-time-point modality-agnostic patch-based method for lesion filling in multiple sclerosis. NeuroImage 139, 376–384 (2016)

    Google Scholar 

  15. Bhushan, C., Haldar, J.P., Joshi, A.A., Leahy, R.M.: Correcting susceptibility-induced distortion in diffusion-weighted MRI using constrained nonrigid registration. In: Signal Information Processing Association Annual Summit and Conference (APSIPA), pp.1–9. Asia-Pacific, Singapore (2012)

    Google Scholar 

  16. Andersson, J.L., Sotiropoulos, S.N.: An integrated approach to correction for off-resonance effects and subject movement in diffusion MR imaging. NeuroImage 125, 1063–1078 (2016)

    Google Scholar 

  17. Alexa, M.: Linear combination of transformations. In: SIGGRAPH ’02: Proceedings of the 29th Annual Conference on Computer Graphics and Interactive Techniques, pp. 380–387. ACM, New York, NY (2002)

    Google Scholar 

  18. Cardoso, M.J., Modat, M., Wolz, R., Melbourne, A., Cash, D., Rueckert, D., Ourselin, S.: Geodesic information flows: spatially-variant graphs and their application to segmentation and fusion. IEEE Trans. Med. Imaging 34(9), 1976–1988 (2015)

    Google Scholar 

  19. Prados, F., Cardoso, M., Burgos, N., Wheeler-Kingshott, C., Ourselin, S.: Niftyweb: web based platform for image processing on the cloud. In: Proceedings of the 24th Annual Meeting of ISMRM, p. 2201. ISMRM, Singapore (2016)

    Google Scholar 

  20. Smith, R.E., Tournier, J.D., Calamante, F., Connelly, A.: Anatomically-constrained tractography: improved diffusion MRI streamlines tractography through effective use of anatomical information. NeuroImage 62(3), 1924–1938 (2012)

    Google Scholar 

  21. Tax, C.M.W., Jeurissen, B., Vos, S.B., Viergever, M.A., Leemans, A.: Recursive calibration of the fiber response function for spherical deconvolution of diffusion MRI data. NeuroImage 86, 67–80 (2014)

    Google Scholar 

  22. Tournier, J.D., Calamante, F., Gadian, D.G., Connelly, A.: Direct estimation of the fiber orientation density function from diffusion-weighted MRI data using spherical deconvolution. NeuroImage 23(3), 1176–1185 (2004)

    Google Scholar 

  23. Tournier, J.D., Calamante, F., Connelly, A.: Robust determination of the fibre orientation distribution in diffusion MRI: non-negativity constrained super-resolved spherical deconvolution. NeuroImage 35(4), 1459–1472 (2007)

    Google Scholar 

  24. Tournier, J.D., Calamante, F., Connelly, A.: Improved probabilistic streamlines tractography by 2nd order integration over fibre orientation distributions. In: Joint Annual Meeting ISMRM-ESMRMB (2010)

    Google Scholar 

  25. Smith, R.E., Tournier, J.D., Calamante, F., Connelly, A.: SIFT2: enabling dense quantitative assessment of brain white matter connectivity using streamlines tractography. NeuroImage 119, 338–351 (2015)

    Article  Google Scholar 

  26. Calamante, F., Tournier, J.D., Jackson, G.D., Connelly, A.: Track-density imaging (TDI): super-resolution white matter imaging using whole-brain track-density mapping. NeuroImage 53(4), 1233–1243 (2010)

    Article  Google Scholar 

  27. Clayden, J., Maniega, S.M., Stokey, A.J., King, M.D., Bastin, M.E., Clark, C.A.: TractoR: magnetic resonance imaging and tractography with R. J. Stat. Softw. 44, 1–18 (2011)

    Article  Google Scholar 

  28. Bland, J.M., Altman, D.G.: Statistical methods for assessing agreement between two methods of clinical measurement. Lancet 8, 307–310 (1986)

    Article  Google Scholar 

  29. Werring, D.J., Clark, C.A., Barker, G.J., Thompson, A.J., Miller, D.H.: Diffusion tensor imaging of lesions and normal-appearing white matter in multiple sclerosis. Neurology 52(8), 1626–1632 (1999)

    Article  Google Scholar 

  30. Alexander, D.C., Pierpaoli, C., Basser, P.J., Gee, J.C.: Spatial transformations of diffusion tensor magnetic resonance images. IEEE Trans. Med. Imaging 20(11), 1131–1139 (2001)

    Article  Google Scholar 

  31. Zhang, H., Yushkevich, P.A., Alexander, D.C., Gee, J.C.: Deformable registration of diffusion tensor MR images with explicit orientation optimization. Med. Image Anal. 10(5), 764–785 (2006)

    Article  Google Scholar 

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Acknowledgements

TC is funded by the Leonard Wolfson Experimental Neurology Centre. FP is supported by the Guarantors of Brain. BK and SO are funded by the National Institute for Health Research University College London Hospitals Biomedical Research Centre (NIHR BRC UCLH/UCL High Impact Initiative). SO receives funding from the EPSRC (EP/H046410/1, EP/J020990/1, EP/K005278), the MRC (MR/J01107X/1) and the NIHR Biomedical Research Unit (Dementia) at UCL. This work was also supported by the Medical Research Council, the UK Multiple Sclerosis Society (grant 892/08) and the Brain Research Trust.

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Authors and Affiliations

  1. NMR Research Unit, Queen Square MS Centre, Department of Neuroinflammation, UCL Institute of Neurology, London, UK

    Thalis Charalambous, Ferran Prados, Carmen Tur, Baris Kanber, Declan Chard, Claudia A. M. Wheeler-Kingshott, Alan Thompson & Ahmed Toosy

  2. Translational Imaging Group, Centre for Medical Image Computing (CMIC), Department of Medical Physics and Bioengineering, University College London, London, UK

    Ferran Prados, Baris Kanber & Sebastien Ourselin

  3. Dementia Research Centre, Department of Neurodegenerative Disease, UCL Institute of Neurology, London, UK

    Sebastien Ourselin

  4. UCL GOS Institute of Child Health, University College London, London, UK

    Jonathan D. Clayden

  5. Brain Connectivity Center, C. Mondino National Neurological Institute, Pavia, Italy

    Claudia A. M. Wheeler-Kingshott

Authors
  1. Thalis Charalambous
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  2. Ferran Prados
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  3. Carmen Tur
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  4. Baris Kanber
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  5. Sebastien Ourselin
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  6. Declan Chard
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  7. Jonathan D. Clayden
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  8. Claudia A. M. Wheeler-Kingshott
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  9. Alan Thompson
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  10. Ahmed Toosy
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Corresponding author

Correspondence to Thalis Charalambous .

Editor information

Editors and Affiliations

  1. Centre for Medical Image Computing, Department of Computer Science, University College London, London, United Kingdom

    Enrico Kaden

  2. Institute of Neurology, Faculty of Brain Sciences, University College London, London, United Kingdom

    Francesco Grussu

  3. Psychiatry Neuroimaging Laboratory, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, USA

    Lipeng Ning

  4. Brain Research Imaging Centre, School of Psychology, Cardiff University, Cardiff, United Kingdom

    Chantal M. W. Tax

  5. Center for Biomedical Imaging, New York University School of Medicine, New York, New York, USA

    Jelle Veraart

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Charalambous, T. et al. (2018). Longitudinal Analysis Framework of DWI Data for Reconstructing Structural Brain Networks with Application to Multiple Sclerosis. In: Kaden, E., Grussu, F., Ning, L., Tax, C., Veraart, J. (eds) Computational Diffusion MRI. Mathematics and Visualization. Springer, Cham. https://doi.org/10.1007/978-3-319-73839-0_16

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