Optimizing the intrinsic parallel diffusivity in NODDI: An extensive empirical evaluation
Autoři:
Jose M. Guerrero aff001; Nagesh Adluru aff002; Barbara B. Bendlin aff003; H. Hill Goldsmith aff002; Stacey M. Schaefer aff005; Richard J. Davidson aff005; Steven R. Kecskemeti aff002; Hui Zhang aff006; Andrew L. Alexander aff001
Působiště autorů:
Department of Medical Physics, University of Wisconsin - Madison, Madison, WI, United States of America
aff001; Waisman Center, University of Wisconsin - Madison, Madison, WI, United States of America
aff002; Department of Medicine, University of Wisconsin - Madison, Madison, WI, United States of America
aff003; Department of Psychology, University of Wisconsin - Madison, Madison, WI, United States of America
aff004; Center for Healthy Minds, University of Wisconsin - Madison, Madison, WI, United States of America
aff005; Department of Computer Science, University College London, London, United Kingdom
aff006
Vyšlo v časopise:
PLoS ONE 14(9)
Kategorie:
Research Article
doi:
https://doi.org/10.1371/journal.pone.0217118
Souhrn
Purpose
NODDI is widely used in parameterizing microstructural brain properties. The model includes three signal compartments: intracellular, extracellular, and free water. The neurite compartment intrinsic parallel diffusivity (d∥) is set to 1.7 μm2⋅ms−1, though the effects of this assumption have not been extensively explored. This work investigates the optimality of d∥ = 1.7 μm2⋅ms−1 under varying imaging protocol, age groups, sex, and tissue type in comparison to other biologically plausible values of d∥.
Methods
Model residuals were used as the optimality criterion. The model residuals were evaluated in function of d∥ over the range from 0.5 to 3.0 μm2⋅ms−1. This was done with respect to tissue type (i.e., white matter versus gray matter), sex, age (infancy to late adulthood), and diffusion-weighting protocol (maximum b-value). Variation in the estimated parameters with respect to d∥ was also explored.
Results
Results show d∥ = 1.7 μm2⋅ms−1 is appropriate for adult brain white matter but it is suboptimal for gray matter with optimal values being significantly lower. d∥ = 1.7 μm2⋅ms−1 was also suboptimal in the infant brain for both white and gray matter with optimal values being significantly lower. Minor optimum d∥ differences were observed versus diffusion protocol. No significant sex effects were observed. Additionally, changes in d∥ resulted in significant changes to the estimated NODDI parameters.
Conclusion
The default (d∥) of 1.7 μm2⋅ms−1 is suboptimal in gray matter and infant brains.
Klíčová slova:
Age groups – Central nervous system – Data acquisition – Diffusion tensor imaging – Neonates – Neuroimaging – Neurites – Diffusion magnetic resonance imaging
Zdroje
1. Alexander DC, Hubbard PL, Hall MG, Moore EA, Ptito M, Parker GJ, et al. Orientationally invariant indices of axon diameter and density from diffusion MRI. Neuroimage. 2010;52(4):1374–1389. doi: 10.1016/j.neuroimage.2010.05.043 20580932
2. Assaf Y, Blumenfeld-Katzir T, Yovel Y, Basser PJ. AxCaliber: a method for measuring axon diameter distribution from diffusion MRI. Magnetic resonance in medicine. 2008;59(6):1347–1354. doi: 10.1002/mrm.21577 18506799
3. Assaf Y, Basser PJ. Composite hindered and restricted model of diffusion (CHARMED) MR imaging of the human brain. Neuroimage. 2005;27(1):48–58. doi: 10.1016/j.neuroimage.2005.03.042 15979342
4. Fieremans E, Jensen JH, Helpern JA. White matter characterization with diffusional kurtosis imaging. Neuroimage. 2011;58(1):177–188. doi: 10.1016/j.neuroimage.2011.06.006 21699989
5. Jespersen SN, Kroenke CD, Østergaard L, Ackerman JJ, Yablonskiy DA. Modeling dendrite density from magnetic resonance diffusion measurements. Neuroimage. 2007;34(4):1473–1486. doi: 10.1016/j.neuroimage.2006.10.037 17188901
6. Stanisz GJ, Wright GA, Henkelman RM, Szafer A. An analytical model of restricted diffusion in bovine optic nerve. Magnetic Resonance in Medicine. 1997;37(1):103–111. doi: 10.1002/mrm.1910370115 8978638
7. Zhang H, Schneider T, Wheeler-Kingshott CA, Alexander DC. NODDI: practical in vivo neurite orientation dispersion and density imaging of the human brain. Neuroimage. 2012;61(4):1000–1016. doi: 10.1016/j.neuroimage.2012.03.072 22484410
8. Billiet T, Vandenbulcke M, Mädler B, Peeters R, Dhollander T, Zhang H, et al. Age-related microstructural differences quantified using myelin water imaging and advanced diffusion MRI. Neurobiology of aging. 2015;36(6):2107–2121. doi: 10.1016/j.neurobiolaging.2015.02.029 25840837
9. Billiet T, Mädler B, D’Arco F, Peeters R, Deprez S, Plasschaert E, et al. Characterizing the microstructural basis of “unidentified bright objects” in neurofibromatosis type 1: A combined in vivo multicomponent T2 relaxation and multi-shell diffusion MRI analysis. NeuroImage: Clinical. 2014;4:649–658. doi: 10.1016/j.nicl.2014.04.005
10. Caverzasi E, Papinutto N, Castellano A, Zhu AH, Scifo P, Riva M, et al. Neurite orientation dispersion and density imaging color maps to characterize brain diffusion in neurologic disorders. Journal of Neuroimaging. 2016;26(5):494–498. doi: 10.1111/jon.12359 27214558
11. Colgan N, Siow B, O’Callaghan JM, Harrison IF, Wells JA, Holmes HE, et al. Application of neurite orientation dispersion and density imaging (NODDI) to a tau pathology model of Alzheimer’s disease. NeuroImage. 2016;125:739–744. doi: 10.1016/j.neuroimage.2015.10.043 26505297
12. Kamagata K, Hatano T, Okuzumi A, Motoi Y, Abe O, Shimoji K, et al. Neurite orientation dispersion and density imaging in the substantia nigra in idiopathic Parkinson disease. European radiology. 2016;26(8):2567–2577. doi: 10.1007/s00330-015-4066-8 26515546
13. Owen JP, Chang YS, Pojman NJ, Bukshpun P, Wakahiro ML, Marco EJ, et al. Aberrant white matter microstructure in children with 16p11. 2 deletions. Journal of Neuroscience. 2014;34(18):6214–6223. doi: 10.1523/JNEUROSCI.4495-13.2014 24790192
14. Mardia KV, Jupp PE. Directional statistics. vol. 494. John Wiley & Sons; 2009.
15. Szafer A, Zhong J, Gore JC. Theoretical model for water diffusion in tissues. Magnetic resonance in medicine. 1995;33(5):697–712. doi: 10.1002/mrm.1910330516 7596275
16. Jelescu IO, Budde MD. Design and validation of diffusion MRI models of white matter. Frontiers in physics. 2017;5:61. doi: 10.3389/fphy.2017.00061
17. Skinner NP, Kurpad SN, Schmit BD, Tugan Muftuler L, Budde MD. Rapid in vivo detection of rat spinal cord injury with double-diffusion-encoded magnetic resonance spectroscopy. Magnetic resonance in medicine. 2017;77(4):1639–1649. doi: 10.1002/mrm.26243 27080726
18. Szczepankiewicz F, Lasič S, van Westen D, Sundgren PC, Englund E, Westin CF, et al. Quantification of microscopic diffusion anisotropy disentangles effects of orientation dispersion from microstructure: applications in healthy volunteers and in brain tumors. NeuroImage. 2015;104:241–252. doi: 10.1016/j.neuroimage.2014.09.057 25284306
19. Jespersen SN, Olesen JL, Hansen B, Shemesh N. Diffusion time dependence of microstructural parameters in fixed spinal cord. Neuroimage. 2018;182:329–342. doi: 10.1016/j.neuroimage.2017.08.039 28818694
20. Veraart J, Novikov DS, Fieremans E. TE dependent Diffusion Imaging (TEdDI) distinguishes between compartmental T2 relaxation times. Neuroimage. 2018;182:360–369. doi: 10.1016/j.neuroimage.2017.09.030 28935239
21. Novikov DS, Veraart J, Jelescu IO, Fieremans E. Mapping orientational and microstructural metrics of neuronal integrity with in vivo diffusion MRI. arXiv preprint arXiv:160909144. 2016;10(11).
22. Kaden E, Kelm ND, Carson RP, Does MD, Alexander DC. Multi-compartment microscopic diffusion imaging. NeuroImage. 2016;139:346–359. doi: 10.1016/j.neuroimage.2016.06.002 27282476
23. Jelescu IO, Veraart J, Fieremans E, Novikov DS. Degeneracy in model parameter estimation for multi-compartmental diffusion in neuronal tissue. NMR in Biomedicine. 2016;29(1):33–47. doi: 10.1002/nbm.3450 26615981
24. Jelescu IO, Veraart J, Adisetiyo V, Milla SS, Novikov DS, Fieremans E. One diffusion acquisition and different white matter models: how does microstructure change in human early development based on WMTI and NODDI? Neuroimage. 2015;107:242–256. doi: 10.1016/j.neuroimage.2014.12.009 25498427
25. Guerrero J, Adluru N, Kecskemeti S, Davidson R, Alexander A. Investigating the effects of intrinsic diffusivity on neurite orientation dispersion and density imaging (NODDI). International Society for Magnetic Resonance in Medicine (ISMRM) Singapore. 2016;.
26. Genç E, Fraenz C, Schlüter C, Friedrich P, Hossiep R, Voelkle MC, et al. Diffusion markers of dendritic density and arborization in gray matter predict differences in intelligence. Nature communications. 2018;9(1):1905. doi: 10.1038/s41467-018-04268-8 29765024
27. Fukutomi H, Glasser MF, Zhang H, Autio JA, Coalson TS, Okada T, 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 29448073
28. Tariq M, Schneider T, Alexander DC, Wheeler-Kingshott CAG, Zhang H. Bingham–NODDI: mapping anisotropic orientation dispersion of neurites using diffusion MRI. NeuroImage. 2016;133:207–223. doi: 10.1016/j.neuroimage.2016.01.046 26826512
29. Farooq H, Xu J, Nam JW, Keefe DF, Yacoub E, Georgiou T, et al. Microstructure imaging of crossing (MIX) white matter fibers from diffusion MRI. Scientific reports. 2016;6:38927. doi: 10.1038/srep38927 27982056
30. Jenkinson M, Beckmann CF, Behrens TE, Woolrich MW, Smith SM. Fsl. Neuroimage. 2012;62(2):782–790. doi: 10.1016/j.neuroimage.2011.09.015 21979382
31. Zhang Y, Brady M, Smith S. Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm. IEEE transactions on medical imaging. 2001;20(1):45–57. doi: 10.1109/42.906424 11293691
32. Novikov DS, Veraart J, Jelescu IO, Fieremans E. Rotationally-invariant mapping of scalar and orientational metrics of neuronal microstructure with diffusion MRI. NeuroImage. 2018;174:518–538. doi: 10.1016/j.neuroimage.2018.03.006 29544816
33. Veraart J, Fieremans E, Novikov DS. Universal power-law scaling of water diffusion in human brain defines what we see with MRI. arXiv preprint arXiv:160909145. 2016;.
34. Kunz N, Zhang H, Vasung L, O’brien KR, Assaf Y, Lazeyras F, 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 24680870
35. Veraart J, Fieremans E, Rudrapatna U, Jones D, Novikov DS. Biophysical modeling of the gray matter: does the “stick” model hold? Proceedings of the 27th Annual Meeting of ISMRM, Paris, France 2018;.
36. Mukherjee P, Miller JH, Shimony JS, Philip JV, Nehra D, Snyder AZ, et al. Diffusion-tensor MR imaging of gray and white matter development during normal human brain maturation. American Journal of Neuroradiology. 2002;23(9):1445–1456. 12372731
37. Faria AV, Zhang J, Oishi K, Li X, Jiang H, Akhter K, et al. Atlas-based analysis of neurodevelopment from infancy to adulthood using diffusion tensor imaging and applications for automated abnormality detection. Neuroimage. 2010;52(2):415–428. doi: 10.1016/j.neuroimage.2010.04.238 20420929
38. Yoshida S, Oishi K, Faria AV, Mori S. Diffusion tensor imaging of normal brain development. Pediatric radiology. 2013;43(1):15–27. doi: 10.1007/s00247-012-2496-x 23288475
39. Dean D, Planalp E, Wooten W, Adluru N, Kecskemeti S, Frye C, et al. Mapping white matter microstructure in the one month human brain. Scientific reports. 2017;7(1):9759. doi: 10.1038/s41598-017-09915-6 28852074
40. Daducci A, Canales-Rodríguez EJ, Zhang H, Dyrby TB, Alexander DC, Thiran JP. Accelerated microstructure imaging via convex optimization (AMICO) from diffusion MRI data. NeuroImage. 2015;105:32–44. doi: 10.1016/j.neuroimage.2014.10.026 25462697
41. Grussu F, Schneider T, Tur C, Yates RL, Tachrount M, Ianuş A, et al. Neurite dispersion: a new marker of multiple sclerosis spinal cord pathology? Annals of clinical and translational neurology. 2017;4(9):663–679. doi: 10.1002/acn3.445 28904988
Článek vyšel v časopise
PLOS One
2019 Číslo 9
- S diagnostikou Parkinsonovy nemoci může nově pomoci AI nástroj pro hodnocení mrkacího reflexu
- Je libo čepici místo mozkového implantátu?
- Pomůže v budoucnu s triáží na pohotovostech umělá inteligence?
- AI může chirurgům poskytnout cenná data i zpětnou vazbu v reálném čase
- Nová metoda odlišení nádorové tkáně může zpřesnit resekci glioblastomů
Nejčtenější v tomto čísle
- Graviola (Annona muricata) attenuates behavioural alterations and testicular oxidative stress induced by streptozotocin in diabetic rats
- CH(II), a cerebroprotein hydrolysate, exhibits potential neuro-protective effect on Alzheimer’s disease
- Comparison between Aptima Assays (Hologic) and the Allplex STI Essential Assay (Seegene) for the diagnosis of Sexually transmitted infections
- Assessment of glucose-6-phosphate dehydrogenase activity using CareStart G6PD rapid diagnostic test and associated genetic variants in Plasmodium vivax malaria endemic setting in Mauritania
Zvyšte si kvalifikaci online z pohodlí domova
Všechny kurzy