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Neurite Orientation Dispersion and Density Imaging of Rat Brain Microstructural Changes due to Middle Cerebral Artery Occlusion at a 3T MRI

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Summary

The purpose of this work was to demonstrate the feasibility of neurite orientation dispersion and density imaging (NODDI) in characterizing the brain tissue microstructural changes of middle cerebral artery occlusion (MCAO) in rats at 3T MRI, and to validate NODDI metrics with histology. A multi-shell diffusion MRI protocol was performed on 11 MCAO rats and 10 control rats at different post-operation time points of 0.5, 2, 6, 12, 24 and 72 h. NODDI orientation dispersion index (ODI) and intracellular volume fraction (Vic) metrics were compared between MCAO group and control group. The evolution of NODDI metrics was characterized and validated by histology. Infarction was consistent with significantly increased ODI and Vic in comparison to control tissues at all time points (P<0.001). Lesion ODI increased gradually from 0.5 to 72 h, while its Vic showed a more complicated and fluctuated evolution. ODI and Vic were significantly different between hyperacute and acute stroke periods (P<0.001). The NODDI metrics were found to be consistent with the histological findings. In conclusion, NODDI can reflect microstructural changes of brain tissues in MCAO rats at 3T MRI and the metrics are consistent with histology. This study helps to prepare NODDI for the diagnosis and management of ischemic stroke in translational research and clinical practice.

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References

  1. Zhang H, Schneider T, Wheeler-Kingshott CA, et al. NODDI: practical in vivo neurite orientation dispersion and density imaging of the human brain. Neuroimage, 2012,61(4):1000–1016

    Article  Google Scholar 

  2. Merino JG, Warach S. Imaging of acute stroke. Nat Rev Neurol, 2010,6(10):560–571

    Article  Google Scholar 

  3. Hui ES, Fieremans E, Jensen JH, et al. Stroke assessment with diffusional kurtosis imaging. Stroke, 2012,43(11): 2968–2973

    Article  Google Scholar 

  4. Pierpaoli C, Basser PJ. Toward a quantitative assessment of diffusion anisotropy. Magn Reson Med, 1996,36(6): 893–906

    Article  CAS  Google Scholar 

  5. Timmers I, Roebroeck A, Bastiani M, et al. Assessing Microstructural Substrates of White Matter Abnormalities: A Comparative Study Using DTI and NODDI. Plos One, 2016,11(12):e167884

    Article  Google Scholar 

  6. By S, Xu J, Box BA, et al. Application and evaluation of NODDI in the cervical spinal cord of multiple sclerosis patients. Neuroimage Clin, 2017,15:333–342

    Article  Google Scholar 

  7. Ota M, Noda T, Sato N, et al. The use of diffusional kurtosis imaging and neurite orientation dispersion and density imaging of the brain in major depressive disorder. J Psychiatr Res, 2018,98:22–29

    Article  Google Scholar 

  8. Song YK, Li XB, Huang XL, et al. A study of neurite orientation dispersion and density imaging in Wilson’s disease. J Magn Reson Imaging, 2018,48(2):423–430

    Article  Google Scholar 

  9. Wang Z, Zhang S, Liu C, et al. A study of neurite orientation dispersion and density imaging in ischemic stroke. Magn Reson Imaging, 2019,57:28–33

    Article  Google Scholar 

  10. Wen Q, Kelley DA, Banerjee S, et al. Clinically feasible NODDI characterization of glioma using multiband EPI at 7 T. Neuroimage Clin, 2015,9:291–299

    Article  Google Scholar 

  11. Sepehrband F, Clark KA, Ullmann JF, et al. Brain tissue compartment density estimated using diffusion-weighted MRI yields tissue parameters consistent with histology. Hum Brain Mapp, 2015,36(9):3687–3702

    Article  Google Scholar 

  12. Crombe A, Planche V, Raffard G, et al. Deciphering the microstructure of hippocampal subfields with in vivo DTI and NODDI: Applications to experimental multiple sclerosis. Neuroimage, 2018,172:357–368

    Article  Google Scholar 

  13. Hodgson K, Adluru G, Richards LG, et al. Predicting Motor Outcomes in Stroke Patients Using Diffusion Spectrum MRI Microstructural Measures. Front Neurol, 2019,10:72

    Article  Google Scholar 

  14. Mastropietro A, Rizzo G, Fontana L, et al. Microstructural characterization of corticospinal tract in subacute and chronic stroke patients with distal lesions by means of advanced diffusion MRI. Neuroradiology, 2019,61(9):1033–1045

    Article  Google Scholar 

  15. Adluru G, Gur Y, Anderson JS, et al. Assessment of white matter microstructure in stroke patients using NODDI. Conf Proc IEEE Eng Med Biol Soc, 2014,2014:742–745

    PubMed Central  Google Scholar 

  16. Yi SY, Barnett BR, Torres-Velazquez M, et al. Detecting Microglial Density With Quantitative Multi-Compartment Diffusion MRI. Front Neurosci, 2019,13: 81

    Article  Google Scholar 

  17. Wang N, Zhang J, Cofer G, et al. Neurite orientation dispersion and density imaging of mouse brain microstructure. Brain Struct Funct, 2019,224(5):1797–1813

    Article  Google Scholar 

  18. McCunn P, Gilbert KM, Zeman P, et al. Reproducibility of Neurite Orientation Dispersion and Density Imaging (NODDI) in rats at 9.4 Tesla. Plos One, 2019,14(4): e215974

    Article  Google Scholar 

  19. Sasaki M, Honmou O, Kocsis JD. A rat middle cerebral artery occlusion model and intravenous cellular delivery. Methods Mol Biol, 2009,549:187–195

    Article  CAS  Google Scholar 

  20. Liu F, Schafer DP, McCullough LD. TTC, fluoro-Jade B and NeuN staining confirm evolving phases of infarction induced by middle cerebral artery occlusion. J Neurosci Methods, 2009,179(1):1–8

    Article  CAS  Google Scholar 

  21. Anderova M, Vorisek I, Pivonkova H, et al. Cell death/proliferation and alterations in glial morphology contribute to changes in diffusivity in the rat hippocampus after hypoxia-ischemia. J Cereb Blood Flow Metab, 2011,31(3):894–907

    Article  Google Scholar 

  22. Grussu F, Schneider T, Tur C, et al. Neurite dispersion: a new marker of multiple sclerosis spinal cord pathology? Ann Clin Transl Neurol, 2017,4(9):663–679

    Article  CAS  Google Scholar 

  23. Mintorovitch J, Yang GY, Shimizu H, et al. Diffusion-weighted magnetic resonance imaging of acute focal cerebral ischemia: comparison of signal intensity with changes in brain water and Na+, K+-ATPase activity. J Cereb Blood Flow Metab, 1994,14(2):332–336

    Article  CAS  Google Scholar 

  24. Sykova E, Svoboda J, Polak J, et al. Extracellular volume fraction and diffusion characteristics during progressive ischemia and terminal anoxia in the spinal cord of the rat. J Cereb Blood Flow Metab, 1994,14(2):301–311

    Article  CAS  Google Scholar 

  25. Caverzasi E, Papinutto N, Castellano A, et al. Neurite Orientation Dispersion and Density Imaging Color Maps to Characterize Brain Diffusion in Neurologic Disorders. J Neuroimaging, 2016,26(5):494–498

    Article  Google Scholar 

  26. Lampinen B, Szczepankiewicz F, Martensson J, et al. Neurite density imaging versus imaging of microscopic anisotropy in diffusion MRI: A model comparison using spherical tensor encoding. Neuroimage, 2017,147:517–531

    Article  Google Scholar 

  27. Binder DK, Papadopoulos MC, Haggie PM, et al. In vivo measurement of brain extracellular space diffusion by cortical surface photobleaching. J Neurosci, 2004, 24(37):8049–8056

    Article  CAS  Google Scholar 

  28. Hui ES, Du F, Huang S, et al. Spatiotemporal dynamics of diffusional kurtosis, mean diffusivity and perfusion changes in experimental stroke. Brain Res, 2012,1451: 100–109

    Article  CAS  Google Scholar 

  29. Weber RA, Hui ES, Jensen JH, et al. Diffusional kurtosis and diffusion tensor imaging reveal different time-sensitive stroke-induced microstructural changes. Stroke, 2015,46(2):545–550

    Article  Google Scholar 

  30. Zhang S, Yao Y, Shi J, et al. The temporal evolution of diffusional kurtosis imaging in an experimental middle cerebral artery occlusion (MCAO) model. Magn Reson Imaging, 2016,34(7):889–895

    Article  Google Scholar 

  31. Jespersen SN, Leigland LA, Cornea A, et al. Determination of axonal and dendritic orientation distributions within the developing cerebral cortex by diffusion tensor imaging. IEEE Trans Med Imaging, 2012,31(1):16–32

    Article  Google Scholar 

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Correspondence to Shun Zhang.

Additional information

This project was supported by National Natural Science Foundation of China (No. 81570462, No. 81730049, and No. 81801666).

Conflict of Interest Statement

The authors declare that there is no conflict of interest with any financial organization or corporation or individual that can inappropriately influence this work.

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Wang, Zx., Zhu, Wz., Zhang, S. et al. Neurite Orientation Dispersion and Density Imaging of Rat Brain Microstructural Changes due to Middle Cerebral Artery Occlusion at a 3T MRI. CURR MED SCI 41, 167–172 (2021). https://doi.org/10.1007/s11596-021-2332-3

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  • DOI: https://doi.org/10.1007/s11596-021-2332-3

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