Submitted Successfully!
To reward your contribution, here is a gift for you: A free trial for our video production service.
Thank you for your contribution! You can also upload a video entry or images related to this topic.
Version Summary Created by Modification Content Size Created at Operation
1
Abdulmajeed Alotaibi
 + 1147 word(s) 1147 2021-09-03 08:21:54 |
2 The format is correct
Lindsay Dong
Meta information modification 1147 2021-09-28 04:11:27 |

Video Upload Options

Do you have a full video?
Send video materials Upload full video

Confirm

Are you sure to Delete?
Yes No
Cite
If you have any further questions, please contact Encyclopedia Editorial Office.
Alotaibi, A. NODDI for Multiple Sclerosis Imaging. Encyclopedia. Available online: https://encyclopedia.pub/entry/14456 (accessed on 23 April 2024).
Alotaibi A. NODDI for Multiple Sclerosis Imaging. Encyclopedia. Available at: https://encyclopedia.pub/entry/14456. Accessed April 23, 2024.
Alotaibi, Abdulmajeed. "NODDI for Multiple Sclerosis Imaging" Encyclopedia, https://encyclopedia.pub/entry/14456 (accessed April 23, 2024).
Alotaibi, A. (2021, September 23). NODDI for Multiple Sclerosis Imaging. In Encyclopedia. https://encyclopedia.pub/entry/14456
Alotaibi, Abdulmajeed. "NODDI for Multiple Sclerosis Imaging." Encyclopedia. Web. 23 September, 2021.
NODDI for Multiple Sclerosis Imaging
Edit
This entry is adapted from the peer-reviewed paper 10.3390/brainsci11091151

Multiple sclerosis (MS) is characterised by widespread damage of the central nervous system that includes alterations in normal-appearing white matter (NAWM) and demyelinating white matter (WM) lesions. Neurite orientation dispersion and density imaging (NODDI) has been proposed to provide a precise characterisation of WM microstructures. NODDI maps can be calculated for the Neurite Density Index (NDI) and Orientation Dispersion Index (ODI), which estimate orientation dispersion and neurite density. Although NODDI has not been widely applied in MS, this technique is promising in investigating the complexity of MS pathology, as it is more specific than diffusion tensor imaging (DTI) in capturing microstructural alterations. 

neurite orientation dispersion and density imaging NODDI multiple sclerosis MS

1. Introduction

Multiple sclerosis (MS) is an autoimmune disease that affects almost 2.5 million individuals worldwide, often in young adulthood [1] MS is characterised by widespread damage of the central nervous system that includes alterations in normal-appearing white matter (NAWM) and demyelinating white matter (WM) lesions [2].
Magnetic resonance imaging (MRI) plays an essential role in diagnosing and monitoring the disease course and the treatment effectiveness in MS. However, conventional MRI techniques have limited sensitivity to quantify microstructural alterations accompanying neuroaxonal degeneration in brain WM and MS lesions [3][4][5]. Quantitative MRI biomarkers of the brain provide a more sensitive detection of axonal degeneration and may become a more reliable outcome measure [6][7]. Diffusion tensor imaging (DTI) is a quantitative technique that has been widely used to characterise the microstructural abnormalities within both NAWM and WM lesions in MS [8]. Many studies have reported reduced fractional anisotropy (FA) and increased mean diffusivity (MD) in NAWM in MS patients compared with healthy participants [9][10][11][12] and one meta-analysis has confirmed a significant reduction of FA that suggested a widespread WM damage in MS [13]. Although these abnormalities may occur early in MS [14], DTI changes in the NAWM in patients with MS are linked with significant disability [15]. MRI-histopathological studies have confirmed a high correlation between the DTI changes and axonal count in WM lesions and NAWM, indicating that these abnormalities may reflect pathological alterations related to disability [16][17][18]. Despite the DTI sensitivity in detecting microstructural changes in WM, the lack of specificity is one of the caveats of DTI. In particular, DTI indices are affected by the orientation dispersion of fibres [19], which may lead to misinterpretation [20]. Furthermore, the interpretation of DTI parameters becomes more complex when two or more different tissues with diffusion properties are present in a single voxel [20].
A biophysical diffusion model known as neurite orientation dispersion and density imaging (NODDI) has been proposed to overcome some DTI limitations, providing a more precise characterization of WM microstructures [21]. NODDI describes brain tissue as a simplified combination of three compartments. A Watson distribution assumption of sticks models the first compartment, known as the intracellular compartment (axons and dendrites), and the signal of each stick is assumed to be a degenerated diffusion tensor with a perpendicular diffusivity equal to zero. The extracellular compartment is a Gaussian anisotropic diffusion, as seen in DTI, and the free water compartment is an isotropic Gaussian diffusion such as Cerebral Spinal Fluid (CSF). After fitting the NODDI model, maps can be calculated for the Neurite Density Index (NDI), the Orientation Dispersion Index (ODI), and isotropic signal fraction (isoVF). These maps explicitly estimate the orientation dispersion and neurite density, all of which contribute to conventional DTI parameters such as FA [20]. Although NODDI has not been widely applied in MS, this technique is promising in investigating the complexity of MS pathology, as it is more specific than DTI in detecting microstructural alterations [20][22][23][24][25]. Recent studies found that DTI and myelin-sensitive imaging are less sensitive than NODDI to neurite density and changes in NAWM and WM lesions in patients with MS [23][24][26].

2. Neurite Orientation Dispersion and Density Imaging in Multiple Sclerosis Imaging

The NDI was reduced in MS lesions and NAWM compared to healthy controls (Figure 1). This significant reduction of the NDI corresponds to reduced intracellular signal fraction and may reflect underlying damage or loss of neurites. Previous work has shown that this reduction is accompanied by higher MD and lower FA [24], and the higher ODI suggests demyelination, axonal loss, and less coherence in the fibre orientation (bending or fanning axons) [23]. This meta-analysis demonstrates a significant reduction of intracellular signal fraction in the NAWM of MS subjects compared with the WM of controls, suggesting early axonal pathology outside demyelination in WM lesions [27][28][29][30]. This consistent reduction among all studies proves that the NDI (through its sensitivity to the intra-axonal compartment) may quantify and differentiate microstructural changes in WM MS lesions, in the NAWM of MS, and in healthy WM. This suggests a loss of axonal density in WM lesions and NAWM in MS compared with healthy controls, which is consistent with previous DTI studies [10][31] reporting altered water diffusion in NAWM and WM MS lesions compared with controlled subjects. Interestingly, the reduction of the NDI in WM lesions and NAWM is in line with previous pathological studies showing axonal loss within WM lesions and a lesser degree in NAWM [17][30][31][32]. This consistent reduction of the NDI reported may further assist researchers in relying on NODDI metrics in biophysically understanding the axonal loss/damage in WM lesions and NAWM.

Brainsci 11 01151 g008 550
Figure 1. Illustrates NODDI metrics in a single slice of one MS subject. The MS lesion in the major white matter tracts (blue arrow) and periventricular lesion (green arrow) are marked in a structural MRI image (A), NDI map (B), and ODI map (C). Both the NDI and ODI are decreased in the MS lesion [20].
Few studies reported a reduced ODI within the WM lesions than in healthy WM, suggesting that neurites have less orientation variability or loss of neuronal fibres, which can be more severe in lesions [20][24][25]. In contrast, the higher ODI within the WM lesions and NAWM, more than in healthy WM, might suggest a loss of fibre coherence and relatively maintained neuronal fibre density [20][24][25][26].
However, our results contradict these reported results, showing no differences in the orientation dispersion of neurites in patients with MS. In addition, the ODI may vary across regions of the brain and can be very diverse across MS subjects, depending on the stage of MS. Therefore, the ODI may be extracted from heterogeneous areas of interest. Most importantly, the variability of b-values/scanning protocols may lead to heterogeneity and influence the ODI findings of this meta-analysis. For this reason, prospective researchers in the field may consider that NODDI metrics require careful interpretation and a study/scanning protocol design. 

3. Conclusions

The NDI is significantly reduced in MS lesions and NAWM than WM from healthy participants, which corresponds to the reduced intracellular signal fraction and may reflect underlying damage or loss of neurites. We were unable to demonstrate differences in the ODI in MS lesions and NAWM compared to WM from healthy participants but have identified that heterogeneity in studies may limit the meta-analysis. Further analysis of the NODDI approach in MS for the characterisation of disease-related ultrastructural changes is justified.

References

  1. Ellwardt, E.; Zipp, F. Molecular mechanisms linking neuroinflammation and neurodegeneration in MS. Exp. Neurol. 2014, 262, 8–17.
  2. Mustafi, S.M.; Harezlak, J.; Kodiweera, C.; Randolph, J.S.; Ford, J.C.; Wishart, H.A.; Wu, Y.-C. Detecting white matter alterations in multiple sclerosis using advanced diffusion magnetic resonance imaging. Neural Regen. Res. 2019, 14, 114–123.
  3. Filippi, M.; Rocca, M.A.; Ciccarelli, O.; De Stefano, N.; Evangelou, N.; Kappos, L.; Rovira, A.; Sastre-Garriga, J.; Tintorè, M.; Frederiksen, J.L.; et al. MRI criteria for the diagnosis of multiple sclerosis: MAGNIMS consensus guidelines. Lancet Neurol. 2016, 15, 292–303.
  4. Wattjes, M.P.; Rovira, À.; Miller, D.; Yousry, T.A.; Sormani, M.P.; De Stefano, N.; Tintoré, M.; Auger, C.; Tur, C.; Filippi, M.; et al. Evidence-based guidelines: MAGNIMS consensus guidelines on the use of MRI in multiple sclerosis-Establishing disease prognosis and monitoring patients. Nat. Rev. Neurol. 2015, 11, 597–606.
  5. De Groot, C.J.A.; Bergers, E.; Kamphorst, W.; Ravid, R.; Polman, C.H.; Barkhof, F.; Van Der Valk, P. Post-mortem MRI-guided sampling of multiple sclerosis brain lesions: Increased yield of active demyelinating and (p)reactive lesions. Brain 2001, 124, 1635–1645.
  6. Cortese, R.; Collorone, S.; Ciccarelli, O.; Toosy, A.T. Advances in brain imaging in multiple sclerosis. Ther. Adv. Neurol. Disord. 2019.
  7. Tur, C.; Moccia, M.; Barkhof, F.; Chataway, J.; Sastre-Garriga, J.; Thompson, A.J.; Ciccarelli, O. Assessing treatment outcomes in multiple sclerosis trials and in the clinical setting. Nat. Rev. Neurol. 2018, 14, 75–93.
  8. Sbardella, E.; Tona, F.; Petsas, N.; Pantano, P. DTI Measurements in Multiple Sclerosis: Evaluation of Brain Damage and Clinical Implications. Mult. Scler. Int. 2013, 2013, 1–11.
  9. Rovaris, M.; Iannucci, G.; Falautano, M.; Possa, F.; Martinelli, V.; Comi, G.; Filippi, M. Cognitive dysfunction in patients with mildly disabling relapsing-remitting multiple sclerosis: An exploratory study with diffusion tensor MR imaging. J. Neurol. Sci. 2002, 195, 103–109.
  10. Werring, D.J.; Clark, C.A.; Barker, G.J.; Thompson, A.J.; Miller, D.H. Diffusion tensor imaging of lesions and normal-appearing white matter in multiple sclerosis. Neurology 1999, 52, 1626–1632.
  11. Filippi, M.; Agosta, F. Diffusion tensor imaging and functional MRI. In Handbook of Clinical Neurology; Elsevier B.V: Amsterdam, The Netherlands, 2016; pp. 1065–1087.
  12. Ciccarelli, O.; Werring, D.J.; Barker, G.J.; Griffin, C.M.; Wheeler-Kingshott, C.A.M.; Miller, D.H.; Thompson, A.J. A study of the mechanisms of normal-appearing white matter damage in multiple sclerosis using diffusion tensor imaging: Evidence of Wallerian degeneration. J. Neurol. 2003, 250, 287–292.
  13. Welton, T.; Kent, D.; Constantinescu, C.S.; Auer, D.P.; Dineen, R.A. Functionally Relevant White Matter Degradation in Multiple Sclerosis: A Tract-based Spatial Meta-Analysis. Radiology 2015, 275, 89–96.
  14. Gallo, A.; Rovaris, M.; Riva, R.; Ghezzi, A.; Benedetti, B.; Martinelli, V.; Falini, A.; Comi, G.; Filippi, M. Diffusion-tensor magnetic resonance imaging detects normal-appearing white matter damage unrelated to short-term disease activity in patients at the earliest clinical stage of multiple sclerosis. Arch. Neurol. 2005, 62, 803–808.
  15. Preziosa, P.; Rocca, M.A.; Mesaros, S.; Pagani, E.; Stosic-Opincal, T.; Kacar, K.; Absinta, M.; Caputo, D.; Drulovic, J.; Comi, G.; et al. Intrinsic damage to the major white matter tracts in patients with different clinical phenotypes of multiple sclerosis: A voxelwise diffusion-tensor MR study. Radiology 2011, 260, 541–550.
  16. Budde, M.D.; Xie, M.; Cross, A.H.; Song, S.K. Axial diffusivity is the primary correlate of axonal injury in the experimental autoimmune encephalomyelitis spinal cord: A quantitative pixelwise analysis. J. Neurosci. 2009, 29, 2805–2813.
  17. Schmierer, K.; Wheeler-Kingshott, C.A.M.; Boulby, P.A.; Scaravilli, F.; Altmann, D.R.; Barker, G.J.; Tofts, P.S.; Miller, D.H. Diffusion tensor imaging of post mortem multiple sclerosis brain. Neuroimage 2007, 35, 467–477.
  18. Kim, J.H.; Budde, M.D.; Liang, H.F.; Klein, R.S.; Russell, J.H.; Cross, A.H.; Song, S.K. Detecting axon damage in spinal cord from a mouse model of multiple sclerosis. Neurobiol. Dis. 2006, 21, 626–632.
  19. Alexander, A.L.; Hasan, K.M.; Lazar, M.; Tsuruda, J.S.; Parker, D.L. Analysis of partial volume effects in diffusion-tensor MRI. Magn. Reson. Med. 2001, 45, 770–780.
  20. Schneider, T.; Brownlee, W.; Zhang, H.; Ciccarelli, O.; Miller, D.H.; Wheeler-Kingshott, C.G. Sensitivity of multi-shell NODDI to multiple sclerosis white matter changes: A pilot study. Funct. Neurol. 2017, 32, 97–101.
  21. 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.
  22. Spanò, B.; Giulietti, G.; Pisani, V.; Morreale, M.; Tuzzi, E.; Nocentini, U.; Francia, A.; Caltagirone, C.; Bozzali, M.; Cercignani, M. Disruption of neurite morphology parallels MS progression. Neurol. Neuroimmunol. NeuroInflamm. 2018, 5, e502.
  23. Granberg, T.; Fan, Q.; Treaba, C.A.C.A.; Ouellette, R.; Herranz, E.; Mangeat, G.; Louapre, C.; Cohen-Adad, J.; Klawiter, E.C.E.C.; Sloane, J.A.J.A.; et al. In vivo characterization of cortical and white matter neuroaxonal pathology in early multiple sclerosis. Brain 2017, 140, 2912–2926.
  24. Hagiwara, A.; Kamagata, K.; Shimoji, K.; Yokoyama, K.; Andica, C.; Hori, M.; Fujita, S.; Maekawa, T.; Irie, R.; Akashi, T.; et al. White Matter Abnormalities in Multiple Sclerosis Evaluated by Quantitative Synthetic MRI, Diffusion Tensor Imaging, and Neurite Orientation Dispersion and Density Imaging. Am. J. Neuroradiol. 2019, 40, 1642–1648.
  25. De Santis, S.; Bastiani, M.; Droby, A.; Kolber, P.; Zipp, F.; Pracht, E.; Stoecker, T.; Groppa, S.; Roebroeck, A. Characterizing Microstructural Tissue Properties in Multiple Sclerosis with Diffusion MRI at 7T and 3T: The Impact of the Experimental Design. Neuroscience 2019, 403, 17–26.
  26. Sacco, S.; Caverzasi, E.; Papinutto, N.; Cordano, C.; Bischof, A.; Gundel, T.; Cheng, S.; Asteggiano, C.; Kirkish, G.; Mallott, J.; et al. Neurite orientation dispersion and density imaging for assessing acute inflammation and lesion evolution in MS. Am. J. Neuroradiol. 2020, 41, 2219–2226.
  27. Bonnier, G.; Roche, A.; Romascano, D.; Simioni, S.; Meskaldji, D.; Rotzinger, D.; Lin, Y.C.; Menegaz, G.; Schluep, M.; Du Pasquier, R.; et al. Advanced MRI unravels the nature of tissue alterations in early multiple sclerosis. Ann. Clin. Transl. Neurol. 2014, 1, 423–432.
  28. Filippi, M. Mri measures of neurodegeneration in multiple sclerosis: Implications for disability, disease monitoring, and treatment. J. Neurol. 2015, 262, 1–6.
  29. Inglese, M.; Bester, M. Diffusion imaging in multiple sclerosis: Research and clinical implications. NMR Biomed. 2010, 23, 865–872.
  30. Schmierer, K.; Scaravilli, F.; Altmann, D.R.; Barker, G.J.; Miller, D.H. Magnetization transfer ratio and myelin in postmortem multiple sclerosis brain. Ann. Neurol. 2004, 56, 407–415.
  31. Klawiter, E.C.; Schmidt, R.E.; Trinkaus, K.; Liang, H.F.; Budde, M.D.; Naismith, R.T.; Song, S.K.; Cross, A.H.; Benzinger, T.L. Radial diffusivity predicts demyelination in ex vivo multiple sclerosis spinal cords. Neuroimage 2011, 55, 1454–1460.
  32. Bammer, R.; Augustin, M.; Strasser-Fuchs, S.; Seifert, T.; Kapeller, P.; Stollberger, R.; Ebner, F.; Hartung, H.P.; Fazekas, F. Magnetic resonance diffusion tensor imaging for characterizing diffuse and focal white matter abnormalities in multiple sclerosis. Magn. Reson. Med. 2000, 44, 583–591.
More
© Text is available under the terms and conditions of the Creative Commons Attribution (CC BY) license; additional terms may apply. By using this site, you agree to the Terms and Conditions and Privacy Policy.
Upload a video for this entry
Information
Subjects: Radiology, Nuclear Medicine & Medical Imaging
Contributor MDPI registered users' name will be linked to their SciProfiles pages. To register with us, please refer to https://encyclopedia.pub/register :
Abdulmajeed Alotaibi
View Times: 424
Revisions: 2 times (View History)
Update Date: 28 Sep 2021
Table of Contents
    1000/1000
    Hot Most Recent
    Feedback