Abstract
Whether gray matter (GM) regions are differentially vulnerable in Relapsing-Remitting and Primary Progressive Multiple Sclerosis (RRMS and PPMS) is still unknown. The objective of this study was to evaluate morphometric and microstructural properties based on structural and diffusion magnetic resonance imaging (dMRI) data in these MS phenotypes, and verify if selective intra-pathological alterations characterise GM structures. Diffusion Tensor Imaging (DTI) and 3D Simple Harmonics Oscillator based Reconstruction and Estimation (3D-SHORE) models were used to fit the dMRI signals, and several features were subsequently extracted from the regional values distributions (e.g., mean, median, skewness). Statistical analyses were conducted to test for group differences and possible correlations with physical disability scores. Results highlighted 3D-SHORE sensitivity to microstructural differences in hippocampus, which was also significantly correlated to physical disability. Conversely, morphometric measurements did not reach any statistical significance. Our study emphasized the potential of dMRI, and in particular the importance of advanced models such as 3D-SHORE with respect to DTI in characterizing the two MS types. In addition, hippocampus has been revealed as particularly relevant in the distinction of RRMS from PPMS and calls for further investigation.
L. Brusini and I. B. Galazzo—These authors equally contributed as first author to this work.
M. Calabrese and G. Menegaz—These authors equally contributed as last author to this work.
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References
Lucchinetti, C., Brück, W., Parisi, J., Scheithauer, B., Rodriguez, M., Lassmann, H.: Heterogeneity of multiple sclerosis lesions: implications for the pathogenesis of demyelination. Ann. Neurol. Official J. Am. Neurol. Assoc. Child Neurol. Soc. 47(6), 707–717 (2000)
Huang, W.J., Chen, W.W., Zhang, X.: Multiple sclerosis: pathology, diagnosis and treatments. Exp. Ther. Med. 13(6), 3163–3166 (2017)
Geurts, J.J., Calabrese, M., Fisher, E., Rudick, R.A.: Measurement and clinical effect of grey matter pathology in multiple sclerosis. Lancet Neurol. 11(12), 1082–1092 (2012)
Calabrese, M., et al.: Regional distribution and evolution of gray matter damage in different populations of multiple sclerosis patients. PLoS ONE 10(8), e0135428 (2015)
Alexander, D.C., Dyrby, T.B., Nilsson, M., Zhang, H.: Imaging brain microstructure with diffusion MRI: practicality and applications. NMR Biomed. 32(4), e3841 (2019)
Novikov, D.S., Fieremans, E., Jespersen, S.N., Kiselev, V.G.: Quantifying brain microstructure with diffusion MRI: theory and parameter estimation. NMR Biomed. 32(4), e3998 (2019)
Assaf, Y., Basser, P.J.: Composite hindered and restricted model of diffusion (charmed) MR imaging of the human brain. Neuroimage 27(1), 48–58 (2005)
Zhang, H., Schneider, T., Wheeler-Kingshott, C.A., Alexander, D.C.: NODDI: practical in vivo neurite orientation dispersion and density imaging of the human brain. Neuroimage 61(4), 1000–1016 (2012)
Lampinen, B., Szczepankiewicz, F., Mårtensson, J., van Westen, D., Sundgren, P.C., Nilsson, M.: Neurite density imaging versus imaging of microscopic anisotropy in diffusion MRI: a model comparison using spherical tensor encoding. Neuroimage 147, 517–531 (2017)
Basser, P.J., Mattiello, J., LeBihan, D.: Estimation of the effective self-diffusion tensor from the NMR spin echo. J. Magn. Reson. Ser. B 103(3), 247–254 (1994)
Özarslan, E., et al.: Mean apparent propagator (map) MRI: a novel diffusion imaging method for mapping tissue microstructure. Neuroimage 78, 16–32 (2013)
Avram, A.V., et al.: Clinical feasibility of using mean apparent propagator (map) MRI to characterize brain tissue microstructure. Neuroimage 127, 422–434 (2016)
Brusini, L., et al.: Assessment of mean apparent propagator-based indices as biomarkers of axonal remodeling after stroke. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9349, pp. 199–206. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24553-9_25
Brusini, L., et al.: Ensemble average propagator-based detection of microstructural alterations after stroke. Int. J. Comput. Assist. Radiol. Surg. 11(9), 1585–1597 (2016)
Ma, K., et al.: Mean apparent propagator-MRI: a new diffusion model which improves temporal lobe epilepsy lateralization. Eur. J. Radiol. 108914 (2020)
Boscolo Galazzo, I., Brusini, L., Obertino, S., Zucchelli, M., Granziera, C., Menegaz, G.: On the viability of diffusion MRI-based microstructural biomarkers in ischemic stroke. Front. Neurosci. 12, 92 (2018)
Granberg, T., et al.: In vivo characterization of cortical and white matter neuroaxonal pathology in early multiple sclerosis. Brain 140(11), 2912–2926 (2017)
De Santis, S., et al.: Characterizing microstructural tissue properties in multiple sclerosis with diffusion MRI at 7 T and 3 T: the impact of the experimental design. Neuroscience 403, 17–26 (2019)
Basser, P.J., Mattiello, J., LeBihan, D.: MR diffusion tensor spectroscopy and imaging. Biophys. J. 66(1), 259–267 (1994)
Özarslan, E., Koay, C., Shepherd, T., Blackb, S., Basser, P.: Simple harmonic oscillator based reconstruction and estimation for three-dimensional q-space MRI (2009)
Stejskal, E.O., Tanner, J.E.: Spin diffusion measurements: spin echoes in the presence of a time-dependent field gradient. J. Chem. Phys. 42(1), 288–292 (1965)
Zucchelli, M., Brusini, L., Méndez, C.A., Daducci, A., Granziera, C., Menegaz, G.: What lies beneath? Diffusion EAP-based study of brain tissue microstructure. Med. Image Anal. 32, 145–156 (2016)
Merlet, S.L., Deriche, R.: Continuous diffusion signal, EAP and ODF estimation via compressive sensing in diffusion MRI. Med. Image Anal. 17(5), 556–572 (2013)
Zucchelli, M., Fick, R.H.J., Deriche, R., Menegaz, G.: Ensemble average propagator estimation of axon diameter in diffusion MRI: implications and limitations. In: 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI), pp. 465–468 (2016)
Wu, Y.C., Alexander, A.L.: Hybrid diffusion imaging. Neuroimage 36(3), 617–629 (2007)
Schmidt, P., et al.: An automated tool for detection of flair-hyperintense white-matter lesions in multiple sclerosis. Neuroimage 59(4), 3774–3783 (2012)
Eshaghi, A., et al.: Progression of regional grey matter atrophy in multiple sclerosis. Brain 141(6), 1665–1677 (2018)
Pierpaoli, C., Basser, P.J.: Toward a quantitative assessment of diffusion anisotropy. Magn. Reson. Med. 36(6), 893–906 (1996)
Carassiti, D., Altmann, D., Petrova, N., Pakkenberg, B., Scaravilli, F., Schmierer, K.: Neuronal loss, demyelination and volume change in the multiple sclerosis neocortex. Neuropathol. Appl. Neurobiol. 44(4), 377–390 (2018)
Koubiyr, I., et al.: Differential gray matter vulnerability in the 1 year following a clinically isolated syndrome. Front. Neurol. 9, 824 (2018)
Rocca, M.A., et al.: The hippocampus in multiple sclerosis. Lancet Neurol. 17(10), 918–926 (2018)
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Brusini, L. et al. (2021). Microstructural Modulations in the Hippocampus Allow to Characterizing Relapsing-Remitting Versus Primary Progressive Multiple Sclerosis. In: Crimi, A., Bakas, S. (eds) Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. BrainLes 2020. Lecture Notes in Computer Science(), vol 12658. Springer, Cham. https://doi.org/10.1007/978-3-030-72084-1_7
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