Top

Published in:

01-11-2020 | Diagnostic Neuroradiology

Higher b-values improve the correlation between diffusion MRI and the cortical microarchitecture

Authors: Sandy Mournet, G. Okubo, I. Koubiyr, B. Zhang, H. Kusahara, V. H. Prevost, N. Ichinose, B. Triaire, B. Hiba, V. Dousset, T. Tourdias

Published in: Neuroradiology | Issue 11/2020

Abstract

Purpose

In diffusion MRI (dMRI), it remains unclear to know how much increase of b-value is conveying additional biological meaning. We tested the correlations between cortical microarchitecture and diffusion metrics computed from standard (1000 s/mm²), high (3000 s/mm²), to very high (5000 s/mm²) b-value dMRI.

Methods

Healthy volunteers were scanned with a dMRI pulse sequence that was first optimized together with a T1-WI and T2-WI. Averaged cortical surface map of estimated myelin (T1-WI/T2-WI) was compared with surface maps of mean diffusivity (MD) computed from each b-value (MD₁₀₀₀, MD₃₀₀₀, and MD₅₀₀₀) and to surface map of mean kurtosis (MK computed from the 0-, 1000-, to 3000-s/mm² shells) in 360 cortical parcels using Spearman correlations, multiple linear regressions, and Akaike information criteria (AIC).

Results

Surface map from MD₁₀₀₀ showed variations not related to myelin but the MD₃₀₀₀ and MD₅₀₀₀ maps inversely mirrored estimated myelin map; lower MD values being observed in more myelinated cortical areas. MK mirrored myelinated cortical areas. Quantitatively, Spearman correlations between myelin and MD became more and more negative as long as b-values increased while the correlation was positive between myelin and MK. Multiple regression models confirmed negative associations between myelin and MD that were significantly better from MD₁₀₀₀ to MD₃₀₀₀ and MD₅₀₀₀ (R² = 0.33, p < 0.001; R² = 0.43, p < 0.001; and R² = 0.50, p < 0.001) and positive association between myelin and MK (R² = 0.53, p < 0.001). Comparisons of the 3 statistical models showed the best performances with MK and MD₅₀₀₀ (AIC_MK < AIC_MD5000 < AIC_MD3000 < AIC_MD1000).

Conclusion

Higher b-values are more closely related to subtle cellular variations of the cortical microarchitecture.

Available only for authorised users

Le Bihan D (2013) Apparent diffusion coefficient and beyond: what diffusion MR imaging can tell us about tissue structure. Radiology 268(2):318–322. https://doi.org/10.1148/radiol.13130420CrossRefPubMed

DeLano MC, Cooper TG, Siebert JE, Potchen MJ, Kuppusamy K (2000) High-b-value diffusion-weighted MR imaging of adult brain: image contrast and apparent diffusion coefficient map features. AJNR Am J Neuroradiol 21(10):1830–1836PubMed

De Santis S, Gabrielli A, Palombo M, Maraviglia B, Capuani S (2011) Non-Gaussian diffusion imaging: a brief practical review. Magn Reson Imaging 29(10):1410–1416. https://doi.org/10.1016/j.mri.2011.04.006CrossRefPubMed

Dudink J, Larkman DJ, Kapellou O, Boardman JP, Allsop JM, Cowan FM, Hajnal JV, Edwards AD, Rutherford MA, Counsell SJ (2008) High b-value diffusion tensor imaging of the neonatal brain at 3T. AJNR Am J Neuroradiol 29(10):1966–1972. https://doi.org/10.3174/ajnr.A1241

Baumann PS, Cammoun L, Conus P, Do KQ, Marquet P, Meskaldji D, Meuli R, Thiran JP, Hagmann P (2012) High b-value diffusion-weighted imaging: a sensitive method to reveal white matter differences in schizophrenia. Psychiatry Res 201(2):144–151. https://doi.org/10.1016/j.pscychresns.2011.08.003CrossRefPubMed

Glasser MF, Goyal MS, Preuss TM, Raichle ME, Van Essen DC (2014) Trends and properties of human cerebral cortex: correlations with cortical myelin content. Neuroimage 93(Pt 2):165–175. https://doi.org/10.1016/j.neuroimage.2013.03.060CrossRefPubMed

Glasser MF, Van Essen DC (2011) Mapping human cortical areas in vivo based on myelin content as revealed by T1- and T2-weighted MRI. J Neurosci 31(32):11597–11616. https://doi.org/10.1523/JNEUROSCI.2180-11.2011CrossRefPubMedPubMedCentral

Glasser MF, Coalson TS, Robinson EC, Hacker CD, Harwell J, Yacoub E, Ugurbil K, Andersson J, Beckmann CF, Jenkinson M, Smith SM, Van Essen DC (2016) A multi-modal parcellation of human cerebral cortex. Nature 536(7615):171–178. https://doi.org/10.1038/nature18933CrossRefPubMedPubMedCentral

Nakamura K, Chen JT, Ontaneda D, Fox RJ, Trapp BD (2017) T1-/T2-weighted ratio differs in demyelinated cortex in multiple sclerosis. Ann Neurol 82(4):635–639. https://doi.org/10.1002/ana.25019CrossRefPubMed

10.

Jensen JH, Helpern JA (2010) MRI quantification of non-Gaussian water diffusion by kurtosis analysis. NMR Biomed 23(7):698–710. https://doi.org/10.1002/nbm.1518CrossRefPubMedPubMedCentral

11.

Dietrich O, Raya JG, Reiser MF (2008) Magnetic resonance noise measurements and signal-quantization effects at very low noise levels. Magn Reson Med 60(6):1477–1487. https://doi.org/10.1002/mrm.21784CrossRefPubMed

12.

Jones DK, Knosche TR, Turner R (2013) White matter integrity, fiber count, and other fallacies: the do’s and don’ts of diffusion MRI. Neuroimage 73:239–254. https://doi.org/10.1016/j.neuroimage.2012.06.081CrossRefPubMed

13.

Glasser MF, Sotiropoulos SN, Wilson JA, Coalson TS, Fischl B, Andersson JL, Xu J, Jbabdi S, Webster M, Polimeni JR, Van Essen DC, Jenkinson M, Consortium WU-MH (2013) The minimal preprocessing pipelines for the Human Connectome Project. Neuroimage 80:105–124. https://doi.org/10.1016/j.neuroimage.2013.04.127CrossRefPubMedPubMedCentral

14.

Tabesh A, Jensen JH, Ardekani BA, Helpern JA (2011) Estimation of tensors and tensor-derived measures in diffusional kurtosis imaging. Magn Reson Med 65(3):823–836. https://doi.org/10.1002/mrm.22655CrossRefPubMed

15.

Hui ES, Cheung MM, Chan KC, Wu EX (2010) B-value dependence of DTI quantitation and sensitivity in detecting neural tissue changes. Neuroimage 49(3):2366–2374. https://doi.org/10.1016/j.neuroimage.2009.10.022CrossRefPubMed

16.

Ganepola T, Nagy Z, Ghosh A, Papadopoulo T, Alexander DC, Sereno MI (2018) Using diffusion MRI to discriminate areas of cortical grey matter. Neuroimage 182:456–468. https://doi.org/10.1016/j.neuroimage.2017.12.046CrossRefPubMedPubMedCentral

17.

Rowley CD, Bazin PL, Tardif CL, Sehmbi M, Hashim E, Zaharieva N, Minuzzi L, Frey BN, Bock NA (2015) Assessing intracortical myelin in the living human brain using myelinated cortical thickness. Front Neurosci 9:396. https://doi.org/10.3389/fnins.2015.00396CrossRefPubMedPubMedCentral

18.

Melhem ER, Itoh R, Jones L, Barker PB (2000) Diffusion tensor MR imaging of the brain: effect of diffusion weighting on trace and anisotropy measurements. AJNR Am J Neuroradiol 21(10):1813–1820PubMed

19.

Grinberg F, Farrher E, Kaffanke J, Oros-Peusquens AM, Shah NJ (2011) Non-Gaussian diffusion in human brain tissue at high b-factors as examined by a combined diffusion kurtosis and biexponential diffusion tensor analysis. Neuroimage 57(3):1087–1102. https://doi.org/10.1016/j.neuroimage.2011.04.050CrossRefPubMed

20.

Le Bihan D (2007) The ‘wet mind’: water and functional neuroimaging. Phys Med Biol 52(7):R57–R90CrossRef

21.

Clark CA, Le Bihan D (2000) Water diffusion compartmentation and anisotropy at high b values in the human brain. Magn Reson Med 44(6):852–859CrossRef

22.

Hui ES, Cheung MM, Qi L, Wu EX (2008) Towards better MR characterization of neural tissues using directional diffusion kurtosis analysis. Neuroimage 42(1):122–134. https://doi.org/10.1016/j.neuroimage.2008.04.237CrossRefPubMed

23.

Wu EX, Cheung MM (2010) MR diffusion kurtosis imaging for neural tissue characterization. NMR Biomed 23(7):836–848. https://doi.org/10.1002/nbm.1506CrossRefPubMed

24.

Zhang H, Schneider T, Wheeler-Kingshott CA, Alexander DC (2012) NODDI: practical in vivo neurite orientation dispersion and density imaging of the human brain. Neuroimage 61(4):1000–1016. https://doi.org/10.1016/j.neuroimage.2012.03.072CrossRefPubMed

25.

MacKay A, Laule C, Vavasour I, Bjarnason T, Kolind S, Madler B (2006) Insights into brain microstructure from the T2 distribution. Magn Reson Imaging 24(4):515–525. https://doi.org/10.1016/j.mri.2005.12.037CrossRefPubMed

26.

Fukutomi H, Glasser MF, Zhang H, Autio JA, Coalson TS, Okada T, Togashi K, Van Essen DC, Hayashi T (2018) Neurite imaging reveals microstructural variations in human cerebral cortical gray matter. Neuroimage 182:488–499. https://doi.org/10.1016/j.neuroimage.2018.02.017CrossRefPubMedPubMedCentral

27.

Fukutomi H, Glasser MF, Murata K, Akasaka T, Fujimoto K, Yamamoto T, Autio JA, Okada T, Togashi K, Zhang H, Van Essen DC, Hayashi T (2019) Diffusion tensor model links to neurite orientation dispersion and density imaging at high b-value in cerebral cortical gray matter. Sci Rep 9(1):12246. https://doi.org/10.1038/s41598-019-48671-7CrossRefPubMedPubMedCentral

28.

Jones DK, Alexander DC, Bowtell R, Cercignani M, Dell’Acqua F, McHugh DJ, Miller KL, Palombo M, Parker GJM, Rudrapatna US, Tax CMW (2018) Microstructural imaging of the human brain with a ‘super-scanner’: 10 key advantages of ultra-strong gradients for diffusion MRI. Neuroimage 182:8–38. https://doi.org/10.1016/j.neuroimage.2018.05.047CrossRefPubMed

29.

Setsompop K, Kimmlingen R, Eberlein E, Witzel T, Cohen-Adad J, McNab JA, Keil B, Tisdall MD, Hoecht P, Dietz P, Cauley SF, Tountcheva V, Matschl V, Lenz VH, Heberlein K, Potthast A, Thein H, Van Horn J, Toga A, Schmitt F, Lehne D, Rosen BR, Wedeen V, Wald LL (2013) Pushing the limits of in vivo diffusion MRI for the Human Connectome Project. Neuroimage 80:220–233. https://doi.org/10.1016/j.neuroimage.2013.05.078CrossRefPubMedPubMedCentral

30.

Coupe P, Manjon JV, Chamberland M, Descoteaux M, Hiba B (2013) Collaborative patch-based super-resolution for diffusion-weighted images. Neuroimage 83:245–261. https://doi.org/10.1016/j.neuroimage.2013.06.030CrossRefPubMed

31.

Kidoh M, Shinoda K, Kitajima M, Isogawa K, Nambu M, Uetani H, Morita K, Nakaura T, Tateishi M, Yamashita Y, Yamashita Y (2019) Deep learning based noise reduction for brain MR imaging: tests on phantoms and healthy volunteers. Magn Reson Med Sci. https://doi.org/10.2463/mrms.mp.2019-0018

32.

Alexander DC, Zikic D, Ghosh A, Tanno R, Wottschel V, Zhang J, Kaden E, Dyrby TB, Sotiropoulos SN, Zhang H, Criminisi A (2017) Image quality transfer and applications in diffusion MRI. Neuroimage 152:283–298. https://doi.org/10.1016/j.neuroimage.2017.02.089CrossRefPubMed

33.

Heath F, Hurley SA, Johansen-Berg H, Sampaio-Baptista C (2018) Advances in noninvasive myelin imaging. Dev Neurobiol 78(2):136–151. https://doi.org/10.1002/dneu.22552CrossRefPubMed

34.

Shafee R, Buckner RL, Fischl B (2015) Gray matter myelination of 1555 human brains using partial volume corrected MRI images. Neuroimage 105:473–485. https://doi.org/10.1016/j.neuroimage.2014.10.054CrossRefPubMed

Title: Higher b-values improve the correlation between diffusion MRI and the cortical microarchitecture
Authors: Sandy Mournet
G. Okubo
I. Koubiyr
B. Zhang
H. Kusahara
V. H. Prevost
N. Ichinose
B. Triaire
B. Hiba
V. Dousset
T. Tourdias
Publication date: 01-11-2020
Publisher: Springer Berlin Heidelberg
Published in: Neuroradiology / Issue 11/2020
Print ISSN: 0028-3940
Electronic ISSN: 1432-1920
DOI: https://doi.org/10.1007/s00234-020-02462-4

At a glance: The STEP trials

Springer Medicine

Higher b-values improve the correlation between diffusion MRI and the cortical microarchitecture

Abstract

Purpose

Methods

Results

Conclusion

At a glance: The STEP trials

Springer Medicine

Abstract

Purpose

Methods

Results

Conclusion

Please log in to get access to this content

Other articles of this Issue 11/2020

Use of intracranial stent as rescue therapy after mechanical thrombectomy failure—9-year experience in a comprehensive stroke centre

Multiparametric flow analysis using four-dimensional flow magnetic resonance imaging can detect cerebral hemodynamic impairment in patients with internal carotid artery stenosis

Clivus length distinguishes between asymptomatic healthy controls and symptomatic adult women with Chiari malformation type I

Discrimination of intracranial aneurysm rupture status: patient-specific inflow boundary may not be a must-have condition in hemodynamic simulations

Treatment with flow diverter stent during pregnancy

Microstructural damage of the cortico-striatal and thalamo-cortical fibers in Fabry disease: a diffusion MRI tractometry study