The assessment of cerebral white matter microstructure in cerebral small vessel disease based on the diffusion-weighted magnetic resonance imaging

Cover Page


Cite item

Full Text

Abstract

Introduction. Multidimensional or biophysical modelling approaches are actively used to examine the complex microstructure of brain matter in diffusion-weighted MRI, where tissue structures are schematically simplified and divided into separate regions to calculate the diffusion values. This approach demonstrates greater specificity when compared with the widely used diffusion tensor MRI (DT-MRI) and its metrics.

The aim of the study was to compare DT-MRI and the biophysical diffusion models, and to evaluate their possible use in a more precise studying of the affected white matter in cerebral small vessel disease (CSVD).

Materials and methods. We examined 96 patients (including 65 women; mean age 61.0±6.6 years) with CSVD and 23 healthy volunteers, comparable in age and gender (including 15 women; mean age 58±6 years). The patients were divided into 3 groups according to the severity of white matter disease as measured using the Fazekas scale. All study subjects underwent a brain MRI (3 T) with diffusion-weighted MRI (b = 0, 1000 and 2500 sec/mm2, 64 gradient directions) followed by the data processing; we obtained DT-MRI metric maps, as well as white matter tract integrity model and model using the spherical mean technique.

Results. Significant differences were found between the study groups (except groups F0 and F1) in all metrics when the overall value of the white matter skeleton was examined (p £ 0.05): there was a decrease in tissue anisotropy and axonal density in the white matter, as well as increased intra- and extra-axonal coefficients with more severe white matter disease. Analysis of individual white matter regions showed that the radial diffusion values had greater intergroup differences than the axial diffusion values in the corpus callosum (particularly, in the body and splenium).

Conclusion. Biophysical models allow us to evaluate white matter disease in patients with CSVD using structural tissue features and indirect measures of intra- and extracellular diffusion. To clarify and increase the statistical significance of the obtained results, it is necessary to analyse the diffusion metrics using data from a larger patient sample.

About the authors

Elena I. Kremneva

Research Center of Neurology

Author for correspondence.
Email: kremneva@neurology.ru
Россия, Moscow

Ivan I. Maximov

University of Oslo

Email: kremneva@neurology.ru
Норвегия, Oslo

Larisa A. Dobrynina

Research Center of Neurology

Email: kremneva@neurology.ru
ORCID iD: 0000-0001-9929-2725

D. Sci. (Med.), Head, 3rd Neurology department

Россия, Moscow

Marina V. Krotenkova

Research Center of Neurology

Email: kremneva@neurology.ru
ORCID iD: 0000-0003-3820-4554

D. Sci. (Med.), Head, Neuroradiology department

Россия, 125367 Moscow, Volokolamskoye shosse, 80

References

  1. Brown R. XXVII. A brief account of microscopical observations made in the months of June, July and August 1827, on the particles contained in the pollen of plants; and on the general existence of active molecules in organic and inorganic bodies. Philosophical Magazine Series 2 1828; 4(21): 61–173.
  2. Koh D.M., Collins D.J. Diffusion-weighted MRI in the body: applications and challenges in oncology. AJR Am J Roentgenol 2007; 188: 1622–1635. doi: 10.2214/AJR.06.1403. PMID: 17515386.
  3. Moseley M.E., Cohen Y., Kucharczyk J. et al. Diffusion-weighted MR imaging of anisotropic water diffusion in cat central nervous system. Radiology 1990; 176: 439–445. doi: 10.1148/radiology.176.2.2367658. PMID: 2367658.
  4. Drake-Pérez M., Boto J., Fitsiori A. et al. Clinical applications of diffusion weighted imaging in neuroradiology. Insights Imaging 2018; 9: 535–547. doi: 10.1007/s13244-018-0624-3. PMID: 29846907.
  5. Basser P.J., Pajevic S., Pierpaoli C. et al. In vivo tractography using DT-MRI data. Magn Res Med 2000; 44: 625–632. doi: 10.1002/1522-2594(200010)44:4<625::aid-mrm17>3.0.co;2-o. PMID: 11025519.
  6. Van Hecke W., Emsell L., Sunaert S. Diffusion Tensor Imaging - A Practical Handbook. New York, 2016.
  7. Beaulieu C. The basis of anisotropic water diffusion in the nervous system — a technical review. NMR Biomed 2002; 15: 435–455. doi: 10.1002/nbm.782. PMID: 12489094.
  8. Jensen J.H., Helpern J.A. MRI quantification of non-Gaussian water diffusion by kurtosis analysis. NMR Biomed 2010; 23: 698–710. doi: 10.1002/nbm.1518. PMID: 20632416.
  9. Metzler-Baddeley C., O’Sullivan M.J., Bells S. et al. How and how not to correct for CSF-contamination in diffusion MRI. Neuroimage 2012; 59: 1394–1403. doi: 10.1016/j.neuroimage.2011.08.043. PMID: 21924365.
  10. Wheeler-Kingshott C.A., Ciccarelli O., Schneider T. et al. A new approach to structural integrity assessment based on axial and radial diffusivities. Funct Neurol 2012; 27: 85–90. PMID: 23158579.
  11. de Santis S., Gabrielli A., Palombo M. et al. Non-Gaussian diffusion imaging: a brief practical review. Magn Reson Imaging 2011; 29: 1410–1416. doi: 10.1016/j.mri.2011.04.006. PMID: 21601404.
  12. Kamagata K., Motoi Y., Tomiyama H. et al. Relationship between cognitive impairment and white-matter alteration in Parkinson’s disease with dementia: Tract-based spatial statistics and tract-specific analysis. Eur Radiol 2013; 23: 1946–1955. doi: 10.1007/s00330-013-2775-4. PMID: 23404139.
  13. Jensen J.H., Helpern J.A., Ramani A. et al. Diffusional kurtosis imaging: the quantification of non-Gaussian water diffusion by means of magnetic resonance imaging. Magn Reson Med 2005; 53: 1432–1440. doi: 10.1002/mrm.20508. PMID: 15906300.
  14. Kamagata K., Hatano T., Aoki S. What is NODDI and what is its role in Parkinson’s assessment? Expert Rev Neurother 2016; 16: 241–243. doi: 10.1586/14737175.2016.1142876. PMID: 26777076.
  15. Abdalla G., Sanverdi E., Machado P.M. et al. Role of diffusional kurtosis imaging in grading of brain gliomas: a protocol for systematic review and meta-analysis. BMJ Open 2018; 8: e025123. doi: 10.1136/bmjopen-2018-025123. PMID: 30552282.
  16. Maximov I.I., Tonoyan A.S., Pronin I.N. Differentiation of glioma malignancy grade using diffusion MRI. Physica Medica 2017; 40: 24–32, doi: 10.1016/j.ejmp.2017.07.002. PMID: 28712716.
  17. Jelescu I.O, Budde M.D. Design and validation of diffusion MRI models of white matter. Front. Phys. 2017: 61. doi: 10.3389/fphy.2017.00061. PMID: 29755979.
  18. Groeschel S., Hagberg G.E., Schultz T. et al. Assessing white matter microstructure in brain regions with different myelin architecture using MRI. PLoS One 2016; 11: e0167274. doi: 10.1371/journal.pone.0167274. PMID: 27898701.
  19. Novikov D.S., Fieremans E., Jespersen S.N., Kiselev V.G. Quantifying brain microstructure with diffusion MRI: Theory and parameter estimation. NMR Biomed 2019; 32: e3998. doi: 10.1002/nbm.3998. PMID: 30321478.
  20. Stanisz G.J., Szafer A., Wright G.A., Henkelman R.M. An analytical model of restricted diffusion in bovine optic nerve. Magn Reson Med 1997; 37: 103–111. doi: 10.1002/mrm.1910370115. PMID: 8978638.
  21. Panagiotaki E., Schneider T., Siow B. et al. Compartment models of the diffusion MR signal in brain white matter: a taxonomy and comparison. Neuroimage 2012; 59: 2241–2254. doi: 10.1016/j.neuroimage.2011.09.081. PMID: 22001791.
  22. Le Bihan D., Breton E., Lallemand D. et al. MR imaging of intravoxel incoherent motions: application to diffusion and perfusion in neurologic disorders. Radiology 1986; 161: 401–407. doi: 10.1148/radiology.161.2.3763909. PMID: 3763909.
  23. Maximov I.I., Vellmer S. Isotropically weighted intravoxel incoherent motion brain imaging at 7T. Magn Reson Imaging 2019; 57: 124–132. doi: 10.1016/j.mri.2018.11.007. PMID: 30472300.
  24. Koh D.M., Collins D.J., Orton M.R. Intravoxel incoherent motion in body diffusion-weighted MRI: reality and challenges. AJR Am J Roentgenol 2011; 196: 1351–1361. doi: 10.2214/AJR.10.5515. PMID: 21606299.
  25. Wirestam R., Brockstedt S., Lindgren A. et al. The perfusion fraction in volunteers and in patients with ischaemic stroke. Acta Radiol 1997; 38: 961–964. doi: 10.1080/02841859709172110. PMID: 9394649.
  26. Le Bihan D., Douek P., Argyropoulou M. et al. Diffusion and perfusion magnetic resonance imaging in brain tumors. Top Magn Reson Imaging 1993; 5: 25–31. doi: 10.1097/00002142-199300520-00005. PMID: 8416686.
  27. Neil J.J., Bosch C.S., Ackerman J.J. An evaluation of the sensitivity of the intravoxel incoherent motion (IVIM) method of blood flow measurement to changes in cerebral blood flow. Magn Reson Med 1994: 32: 60–65. doi: 10.1002/mrm.1910320109. PMID: 8084238.
  28. Luciani A., Vignaud A., Cavet M. et al. Liver cirrhosis: intravoxel incoherent motion MR imaging pilot study. Radiology 2008; 249: 891–899. doi: 10.1148/radiol.2493080080. PMID: 19011186.
  29. Wong S.M., Zhang C.E., van Bussel F.C. et al. Simultaneous investigation of microvasculature and parenchyma in cerebral small vessel disease using intravoxel incoherent motion imaging, Neuroimage Clin 2017; 14: 216–221. doi: 10.1016/j.nicl.2017.01.017. PMID: 28180080.
  30. Iima M., Le Bihan D. Clinical intravoxel incoherent motion and diffusion MR imaging: past, present, and future. Radiology 2016; 278: 13–32. doi: 10.1148/radiol.2015150244. PMID: 26690990.
  31. 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 2012; 61:1000–1016. doi: 10.1016/j.neuroimage.2012.03.072. PMID: 22484410.
  32. Andica C., Kamagata K., Hatano T. et al. MR biomarkers of degenerative brain disorders derived from diffusion imaging. J Magn Reson Imaging 2019; doi: 10.1002/jmri.27019. PMID: 31837086.
  33. Adluru G., Gur Y., Anderson J.S. et al. Assessment of white matter microstructure in stroke patients using NODDI. Conf Proc IEEE Eng Med Biol Soc 2014; 2014: 742–745. doi: 10.1109/EMBC.2014.6943697. PMID: 25570065.
  34. Churchill N.W., Caverzasi E., Graham S.J. et al. White matter microstructure in athletes with a history of concussion: comparing diffusion tensor imaging (DTI) and neurite orientation dispersion and density imaging (NODDI). Hum Brain Mapp 2017; 38: 4201–4211. doi: 10.1002/hbm.23658. PMID: 28556431.
  35. Kunz N., Zhang H., Vasung L. et al. Assessing white matter microstructure of the newborn with multi-shell diffusion MRI and biophysical compartment models. Neuroimage 2014; 96: 288–299. doi: 10.1016/j.neuroimage.2014.03.057. PMID: 24680870.
  36. Fukutomi H., Glasser M.F., Zhang H. et al. Neurite imaging reveals microstructural variations in human cerebral cortical gray matter. Neuroimage 2018; 182: 488–499. doi: 10.1016/j.neuroimage.2018.02.017. PMID: 29448073.
  37. Yi S.Y., Barnett B.R., Torres-Velazquez M. et al. Detecting microglial density with quantitative multi-compartment diffusion MRI. Front Neurosci 2019; 13: 81. doi: 10.3389/fnins.2019.00081. PMID: 30837826.
  38. Tariq M., Schneider T., Alexander D.C. et al. Bingham-NODDI: Mapping anisotropic orientation dispersion of neurites using diffusion MRI. Neuroimage 2016; 133: 207–223. doi: 10.1016/j.neuroimage.2016.01.046. PMID: 26826512.
  39. Fieremans E., Jensen J.H., Helpern J.A. White matter characterization with diffusional kurtosis imaging. Neuroimage 2011; 58: 177–188. doi: 10.1016/j.neuroimage.2011.06.006. PMID: 21699989.
  40. Kaden E., Kelm N.D., Carson R.P. et al. Multi-compartment microscopic diffusion imaging. Neuroimage. 2016; 139: 346–359. doi: 10.1016/j.neuroimage.2016.06.002. PMID: 27282476.
  41. Sykova E., Nicholson C. Diffusion in brain extracellular space. Physiol Rev 2008; 88: 1277–1340. doi: 10.1152/physrev.00027.2007. PMID: 18923183.
  42. Perge J.A., Koch K., Miller R. et al. How the optic nerve allocates space, energy capacity, and information. J Neurosci 2009; 29: 7917–7928. doi: 10.1523/JNEUROSCI.5200-08.2009. PMID: 19535603.
  43. Grussu F., Schneider T., Zhang H., Alexander D.C. et al. Neurite orientation dispersion and density imaging of the healthy cervical spinal cord in vivo. Neuroimage 2015; 111: 590–601. doi: 10.1016/j.neuroimage.2015.01.045. PMID: 25652391.
  44. Chklovskii D.B., Schikorski T., Stevens C.F. Wiring optimization in cortical circuits. Neuron 2002; 34: 341–347. doi: 10.1016/S0896-6273(02)00679-7. PMID: 11988166.
  45. Sepehrband F., Clark K.A., Ullmann J.F. et al. Brain tissue compartment density estimated using diffusion weighted MRI yields tissue parameters consistent with histology. Hum Brain Mapp 2015; 36: 3687–702. doi: 10.1002/hbm.22872. PMID: 26096639.
  46. Gorelick P.B., Scuteri A., Black S.E. et al. Vascular contributions to cognitive impairment and dementia: a statement for healthcare professionals from the American Heart Association/American Stroke Association. Stroke 2011; 42: 2672–2713. doi: 10.1161/STR.0b013e3182299496. PMID: 21778438.
  47. Wardlaw J.M., Smith C., Dichgans M. Mechanisms of sporadic cerebral small vessel disease: insights from neuroimaging. Lancet Neurol 2013; 12: 483–497. doi: 10.1016/S1474-4422(13)70060-7. PMID: 23602162.
  48. Raja R., Rosenberg G., Caprihan A. Review of diffusion MRI studies in chronic white matter diseases. Neurosci Lett 2019; 694: 198–207. doi: 10.1016/j.neulet.2018.12.007. PMID: 30528980.
  49. Dobrynina L.A., Gadzhieva Z. Sh., Kalashnikova L.A. et al. [Neuropsychological profile and vascular risk factors in patients with cerebral microangiopathy]. Annals of clinical and experimental neurology 2018; 12(4): 5–15. doi: 10.25692/ACEN.2018.4.1. (In Russ.)
  50. Maximov I., Alnæs D., Westlye L. Towards an optimised processing pipeline for diffusion magnetic resonance imaging data: Effects of artefact corrections on diffusion metrics and their age associations in UK Biobank. Human Brain Mapping 2019; 40: 4146–4162. doi: 10.1002/hbm.24691. PMID: 31173439.
  51. Veraart J., Fieremans E., Novikov D. Diffusion MRI noise mapping using random matrix theory. Magnetic resonance in medicine 2015; 76: 1585–1593. doi: 10.1002/mrm.26059. PMID: 26599599.
  52. Kellner E., Dhital B., Kiselev V.G., Reisert M. Gibbs-ringing artifact removal based on local subvoxel-shifts. Magn Reson Med 2016; 76: 1574–1581. doi: 10.1002/mrm.26054. PMID: 26745823.
  53. Andersson, J., Sotiropoulos S. An integrated approach to correction for off-resonance effects and subject movement in diffusion MR imaging. Neuroimage 2016; 125: 1063–1078. doi: 10.1016/j.neuroimage.2015.10.019. PMID: 26481672.
  54. Veraart J., Rajan J., Peeters R.R. et al. Comprehensive framework for accurate diffusion MRI parameter estimation. Magn Reson Med. 2013; 70: 972–984. doi: 10.1002/mrm.24529. PMID: 23132517.
  55. Smith S.M., Jenkinson M., Johansen-Berg H. et al. Tract-based spatial statistics: voxelwise analysis of multi-subject di_usion data. Neuroimage 2006; 31: 1487–1505. doi: 10.1016/j.neuroimage.2006.02.024. PMID: 16624579.
  56. Hua K., Zhang J., Wakana S. et al. Tract probability maps in stereotaxic spaces: analysis of white matter anatomy and tract-specific quantification. NeuroImage 2008; 39: 336–347. doi: 10.1016/j.neuroimage.2007.07.053. PMID: 17931890.
  57. Maximov I.I., Thönneßen H., Konrad K. et al. Statistical instability of TBSS analysis based on DTI fitting algorithm J Neuroimaging 2015; 25: 883–891. doi: 10.1111/jon.12215. PMID: 25682721.
  58. Fazekas F., Chawluk J.B., Alavi A. et al. MR signal abnormalities at 1.5 T in Alzheimer's dementia and normal aging. AJR Am J Roentgenol 1987; 149: 351–356. doi: 10.2214/ajr.149.2.351. PMID: 3496763.
  59. Dobrynina L.A., Gnedovskaya E.V., Sergeyeva A.N. et al. [Subclinical cerebral manifestations and brain damage for asymptomatic first-time diagnosed arterial hypertension]. Annals of clinical and experimental neurology 2016; 10(3): 33–39. (In Russ.)
  60. Duering M., Finsterwalder S., Baykara E. et al. Free water determines diffusion alterations and clinical status in cerebral small vessel disease. Alzheimers Dement 2018; 14: 764–774. doi: 10.1016/j.jalz.2017.12.007. PMID: 29406155.
  61. Lawrence A.J., Patel B., Morris R.G. et al. Mechanisms of cognitive impairment in cerebral small vessel disease: multimodal MRI results from the St George's cognition and neuroimaging in stroke (SCANS) study. PLoS One 2013; 8: e61014. doi: 10.1371/journal.pone.0061014. PMID: 23613774.

Supplementary files

Supplementary Files
Action
1. JATS XML
Download

Copyright (c) 2020 Kremneva E.I., Maximov I.I., Dobrynina L.A., Krotenkova M.V.

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
СМИ зарегистрировано Федеральной службой по надзору в сфере связи, информационных технологий и массовых коммуникаций (Роскомнадзор).
Регистрационный номер и дата принятия решения о регистрации СМИ: серия ПИ № ФС 77-83204 от 12.05.2022.


This website uses cookies

You consent to our cookies if you continue to use our website.

About Cookies