Alterations in Sensorimotor and Mesiotemporal Cortices and Diffuse White Matter Changes in Primary Progressive Multiple Sclerosis Detected by Adiabatic Relaxometry
Status PubMed-not-MEDLINE Jazyk angličtina Země Švýcarsko Médium electronic-ecollection
Typ dokumentu časopisecké články
PubMed
34594184
PubMed Central
PMC8476998
DOI
10.3389/fnins.2021.711067
Knihovny.cz E-zdroje
- Klíčová slova
- DWI, T1 mapping, T2 mapping, adiabatic T1ρ mapping, adiabatic T2ρ mapping, diffusion weighted imaging, primary progressive multiple sclerosis,
- Publikační typ
- časopisecké články MeSH
Background: The research of primary progressive multiple sclerosis (PPMS) has not been able to capitalize on recent progresses in advanced magnetic resonance imaging (MRI) protocols. Objective: The presented cross-sectional study evaluated the utility of four different MRI relaxation metrics and diffusion-weighted imaging in PPMS. Methods: Conventional free precession T1 and T2, and rotating frame adiabatic T1ρ and T2ρ in combination with diffusion-weighted parameters were acquired in 13 PPMS patients and 13 age- and sex-matched controls. Results: T1ρ, a marker of crucial relevance for PPMS due to its sensitivity to neuronal loss, revealed large-scale changes in mesiotemporal structures, the sensorimotor cortex, and the cingulate, in combination with diffuse alterations in the white matter and cerebellum. T2ρ, particularly sensitive to local tissue background gradients and thus an indicator of iron accumulation, concurred with similar topography of damage, but of lower extent. Moreover, these adiabatic protocols outperformed both conventional T1 and T2 maps and diffusion tensor/kurtosis approaches, methods previously used in the MRI research of PPMS. Conclusion: This study introduces adiabatic T1ρ and T2ρ as elegant markers confirming large-scale cortical gray matter, cerebellar, and white matter alterations in PPMS invisible to other in vivo biomarkers.
Center for Magnetic Resonance Research University of Minnesota Minneapolis MN United States
Central European Institute of Technology Masaryk University Neuroscience Centre Brno Czechia
Department of Neurology School of Medicine University of Minnesota Minneapolis MN United States
Faculty of Medicine Institute of Biostatistics and Analyses Masaryk University Brno Czechia
Institute of Biostatistics and Analyses Ltd Masaryk University Spin Off Brno Czechia
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Anderson V. M., Fisniku L. K., Khaleeli Z., Summers M. M., Penny S. A., Altmann D. R., et al. (2010). Hippocampal atrophy in relapsing-remitting and primary progressive MS: a comparative study. Multiple Sclerosis J. 16 1083–1090. 10.1177/1352458510374893 PubMed DOI
Antel J., Antel S., Caramanos Z., Arnold D. L., Kuhlmann T. (2012). Primary progressive multiple sclerosis: part of the MS disease spectrum or separate disease entity? Acta Neuropathol. 123 627–638. 10.1007/s00401-012-0953-0 PubMed DOI
Bodini B., Chard D., Altmann D. R., Tozer D., Miller D. H., Thompson A. J., et al. (2016). White and gray matter damage in primary progressive MS: the chicken or the egg? Neurology 86 170–176. 10.1212/wnl.0000000000002237 PubMed DOI PMC
Bonnier G., Fischi-Gomez E., Roche A., Hilbert T., Kober T., Krueger G., et al. (2018). Personalized pathology maps to quantify diffuse and focal brain damage. Neuroimage Clin. 21:101607. 10.1016/j.nicl.2018.11.017 PubMed DOI PMC
Bonnier G., Maréchal B., Fartaria M. J., Falkowskiy P., Marques J. P., Simioni S., et al. (2017). The combined quantification and interpretation of multiple quantitative magnetic resonance imaging metrics enlightens longitudinal changes compatible with brain repair in relapsing-remitting multiple sclerosis patients. Front. Neurol. 8:506. 10.3389/fneur.2017.00506 PubMed DOI PMC
Ceccarelli A., Rocca M. A., Valsasina P., Rodegher M., Pagani E., Falini A., et al. (2009). A multiparametric evaluation of regional brain damage in patients with primary progressive multiple sclerosis. Hum. Brain Mapp. 30 3009–3019. 10.1002/hbm.20725 PubMed DOI PMC
Cortese R., Collorone S., Ciccarelli O., Toosy A. T. (2019). Advances in brain imaging in multiple sclerosis. Ther. Adv. Neurol. Disord. 12:1756286419859722. PubMed PMC
Dehmeshki J., Chard D. T., Leary S. M., Watt H. C., Silver N. C., Tofts P. S., et al. (2003). The normal appearing grey matter in primary progressive multiple sclerosis. J. Neurol. 250 67–74. PubMed
Dusek P., Dezortova M., Wuerfel J. (2013). Imaging of iron. Int. Rev. Neurobiol. 110 195–239. PubMed
Enzinger C., Barkhof F., Ciccarelli O., Filippi M., Kappos L., Rocca M. A., et al. (2015). Nonconventional MRI and microstructural cerebral changes in multiple sclerosis. Nat. Rev. Neurol. 11 676–686. 10.1038/nrneurol.2015.194 PubMed DOI
Filip P., Svatkova A., Carpenter A. F., Eberly L. E., Nestrasil I., Nissi M. J., et al. (2020). Rotating frame MRI relaxations as markers of diffuse white matter abnormalities in multiple sclerosis. NeuroImage Clin. 26:102234. 10.1016/j.nicl.2020.102234 PubMed DOI PMC
Filippi M., Rocca M. A., Ciccarelli O., De Stefano N., Evangelou N., Kappos L., et al. (2016). MRI criteria for the diagnosis of multiple sclerosis: MAGNIMS consensus guidelines. Lancet Neurol. 15 292–303. 10.1016/s1474-4422(15)00393-2 PubMed DOI PMC
Fisher E., Lee J.-C., Nakamura K., Rudick R. A. (2008). Gray matter atrophy in multiple sclerosis: a longitudinal study. Ann. Neurol. 64 255–265. 10.1002/ana.21436 PubMed DOI
Green A. J., Gelfand J. M., Cree B. A., Bevan C., Boscardin W. J., Mei F., et al. (2017). Clemastine fumarate as a remyelinating therapy for multiple sclerosis (ReBUILD): a randomised, controlled, double-blind, crossover trial. Lancet 390 2481–2489. 10.1016/s0140-6736(17)32346-2 PubMed DOI
Hannoun S., Bagory M., Durand-Dubief F., Ibarrola D., Comte J.-C., Confavreux C., et al. (2012). Correlation of diffusion and metabolic alterations in different clinical forms of multiple sclerosis. PLoS One 7:e32525. 10.1371/journal.pone.0032525 PubMed DOI PMC
Jensen J. H., Helpern J. A., Ramani A., Lu H., Kaczynski K. (2005). Diffusional kurtosis imaging: the quantification of non-gaussian water diffusion by means of magnetic resonance imaging. Magn. Reson. Med. 53 1432–1440. 10.1002/mrm.20508 PubMed DOI
Khaleeli Z., Altmann D. R., Cercignani M., Ciccarelli O., Miller D. H., Thompson A. J. (2008). Magnetization transfer ratio in gray matter: a potential surrogate marker for progression in early primary progressive multiple sclerosis. Arch. Neurol. 65 1454–1459. 10.1001/archneur.65.11.1454 PubMed DOI
Lassmann H. (2018). Multiple sclerosis pathology. Cold Spring Harb. Perspect. Med. 8:a028936. PubMed PMC
Leary S. M., Silver N. C., Stevenson V. L., Barker G. J., Miller D. H., Thompson A. J. (1999). Magnetisation transfer of normal appearing white matter in primary progressive multiple sclerosis. Multiple Sclerosis J. 5 313–316. 10.1191/135245899678846384 PubMed DOI
Lommers E., Simon J., Reuter G., Delrue G., Dive D., Degueldre C., et al. (2019). Multiparameter MRI quantification of microstructural tissue alterations in multiple sclerosis. Neuroimage Clin. 23:101879. 10.1016/j.nicl.2019.101879 PubMed DOI PMC
Manfredonia F., Ciccarelli O., Khaleeli Z., Tozer D. J., Sastre-Garriga J., Miller D. H., et al. (2007). Normal-appearing brain T1 relaxation time predicts disability in early primary progressive multiple sclerosis. Arch. Neurol. 64 411–415. 10.1001/archneur.64.3.411 PubMed DOI
Mangia S., Carpenter A. F., Tyan A. E., Eberly L. E., Garwood M., Michaeli S. (2014). Magnetization transfer and adiabatic T1ρ MRI reveal abnormalities in normal-appearing white matter of subjects with multiple sclerosis. Multiple Sclerosis J. 20 1066–1073. 10.1177/1352458513515084 PubMed DOI PMC
Mehta V., Pei W., Yang G., Li S., Swamy E., Boster A., et al. (2013). Iron is a sensitive biomarker for inflammation in multiple sclerosis lesions. PLoS One 8:e57573. 10.1371/journal.pone.0057573 PubMed DOI PMC
Mesaros S., Rocca M. A., Pagani E., Sormani M. P., Petrolini M., Comi G., et al. (2011). Thalamic damage predicts the evolution of primary-progressive multiple sclerosis at 5 years. Am. J. Neuroradiol. 32 1016–1020. 10.3174/ajnr.a2430 PubMed DOI PMC
Michaeli S., Burns T. C., Kudishevich E., Harel N., Hanson T., Sorce D. J., et al. (2009). Detection of neuronal loss using T1ρ MRI assessment of 1H2O spin dynamics in the aphakia mouse. J. Neurosci. Methods 177 160–167. 10.1016/j.jneumeth.2008.10.025 PubMed DOI PMC
Michaeli S., Sorce D. J., Idiyatullin D., Ugurbil K., Garwood M. (2004). Transverse relaxation in the rotating frame induced by chemical exchange. J. Magn. Reson. 169 293–299. 10.1016/j.jmr.2004.05.010 PubMed DOI
Michaeli S., Sorce D. J., Springer C. S., Jr., Ugurbil K., Garwood M. (2006). T1ρ MRI contrast in the human brain: modulation of the longitudinal rotating frame relaxation shutter-speed during an adiabatic RF pulse. J. Magn. Reson. 181 135–147. 10.1016/j.jmr.2006.04.002 PubMed DOI
Mitsumori F., Watanabe H., Takaya N. (2009). Estimation of brain iron concentration in vivo using a linear relationship between regional iron and apparent transverse relaxation rate of the tissue water at 4.7 T. Magn. Reson. Med. 62 1326–1330. 10.1002/mrm.22097 PubMed DOI
Ontaneda D., Thompson A. J., Fox R. J., Cohen J. A. (2017). Progressive multiple sclerosis: prospects for disease therapy, repair, and restoration of function. Lancet 389 1357–1366. 10.1016/s0140-6736(16)31320-4 PubMed DOI
Peterson J. W., Bö L., Mörk S., Chang A., Trapp B. D. (2001). Transected neurites, apoptotic neurons, and reduced inflammation in cortical multiple sclerosis lesions. Ann. Neurol. 50 389–400. 10.1002/ana.1123 PubMed DOI
Petracca M., Margoni M., Bommarito G., Inglese M. (2018). Monitoring progressive multiple sclerosis with novel imaging techniques. Neurol. Ther. 7 265–285. 10.1007/s40120-018-0103-2 PubMed DOI PMC
Rocca M. A., Absinta M., Filippi M. (2012). The role of advanced magnetic resonance imaging techniques in primary progressive MS. J. Neurol. 259 611–621. 10.1007/s00415-011-6195-6 PubMed DOI
Rocca M. A., Riccitelli G., Rodegher M., Ceccarelli A., Falini A., Falautano M., et al. (2010). Functional MR imaging correlates of neuropsychological impairment in primary-progressive multiple sclerosis. AJNR Am. J. Neuroradiol. 31 1240–1246. 10.3174/ajnr.a2071 PubMed DOI PMC
Rovaris M., Bozzali M., Iannucci G., Ghezzi A., Caputo D., Montanari E., et al. (2002). Assessment of normal-appearing white and gray matter in patients with primary progressive multiple sclerosis: a diffusion-tensor magnetic resonance imaging Study. Arch. Neurol. 59 1406–1412. PubMed
Rovaris M., Gallo A., Valsasina P., Benedetti B., Caputo D., Ghezzi A., et al. (2005). Short-term accrual of gray matter pathology in patients with progressive multiple sclerosis: an in vivo study using diffusion tensor MRI. Neuroimage 24 1139–1146. 10.1016/j.neuroimage.2004.10.006 PubMed DOI
Simpson S., Taylor B. V., van der Mei I. (2015). The role of epidemiology in MS research: past successes, current challenges and future potential. Mult. Scler. 21 969–977. 10.1177/1352458515574896 PubMed DOI
Tallantyre E. C., Bø L., Al-Rawashdeh O., Owens T., Polman C. H., Lowe J., et al. (2009). Greater loss of axons in primary progressive multiple sclerosis plaques compared to secondary progressive disease. Brain 132 1190–1199. 10.1093/brain/awp106 PubMed DOI
Tallantyre E. C., Bø L., Al-Rawashdeh O., Owens T., Polman C. H., Lowe J. S., et al. (2010). Clinico-pathological evidence that axonal loss underlies disability in progressive multiple sclerosis. Mult. Scler. 16 406–411. 10.1177/1352458510364992 PubMed DOI
Trapp B. D., Peterson J., Ransohoff R. M., Rudick R., Mörk S., Bö L. (1998). Axonal transection in the lesions of multiple sclerosis. N. Engl. J. Med. 338 278–285. PubMed
Vrenken H., Geurts J. J. (2007). Gray and normal-appearing white matter in multiple sclerosis: an MRI perspective. Expert Rev. Neurother. 7 271–279. 10.1586/14737175.7.3.271 PubMed DOI
Winkler A. M., Ridgway G. R., Webster M. A., Smith S. M., Nichols T. E. (2014). Permutation inference for the general linear model. NeuroImage 92 381–397. 10.1016/j.neuroimage.2014.01.060 PubMed DOI PMC
Winkler A. M., Webster M. A., Brooks J. C., Tracey I., Smith S. M., Nichols T. E. (2016). Non-parametric combination and related permutation tests for neuroimaging. Hum. Brain Mapp. 37 1486–1511. 10.1002/hbm.23115 PubMed DOI PMC
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