Cited by 26Advanced Neuroimaging Techniques and ApplicationsAdvanced MRI Techniques and ApplicationsFunctional Brain Connectivity Studies

A framework for optimal whole-sample histological quantification of neurite orientation dispersion in the human spinal cord

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Grussu, Francesco, Schneider, Torben, Yates, Richard L., Zhang, Hui, Wheeler-Kingshott, Claudia A.M. Gandini, DeLuca, Gabriele C., Alexander, Daniel C.·2016·Journal of Neuroscience Methods

Abstract

BACKGROUND: The complexity of fibre distributions in tissues is an important microstructural feature, now measurable in vivo by magnetic resonance imaging (MRI) through orientation dispersion (OD) indices. OD metrics have gained popularity for the characterisation of neurite morphology, but they still lack systematic validation. This paper demonstrates a framework for whole-sample histological quantification of OD in spinal cord specimens, potentially useful for validating MRI-derived OD estimates. NEW METHOD: Our methodological framework is based on (i) sagittal sectioning; (ii) Palmgren's silver staining; (iii) structure tensor (ST) analysis; (iv) directional statistics. Novel elements are the data-driven optimisation of the spatial scale of ST analysis, and a new multivariate, weighted directional statistical approach for anisotropy-informed quantification of OD. RESULTS: Palmgren's silver staining of sagittal spinal cord sections provides robust visualisation of neuronal elements, enabling OD quantification. The choice of spatial scale of ST analysis influences OD values, and weighted directional statistics provide OD maps with high contrast-to-noise. Segmentation of neurites prior to OD quantification is recommended. COMPARISON WITH EXISTING METHODS: Our framework can potentially provide OD even in demyelinating diseases, where myelin-based histology is not suitable. As compared to conventional univariate approaches, our multivariate weighted directional statistics improve the contrast-to-noise of OD maps and more accurately describe the distribution of ST metrics. CONCLUSIONS: Our framework enables practical whole-specimen characterisation of OD in the spinal cord. We recommend tuning the scale of ST analysis for optimal OD quantification, as well as neurite segmentation and weighted directional statistics, of which examples are provided herein.

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  • Areas of nonspecific staining were excluded with a combination of manual and automatic segmentation to calculate CV.

    “excluded areas of nonspecific staining with a combination of manual and automatic segmentation to calculate CV, given their potential to bias this metric.33”

    From Neurite dispersion: a new marker of multiple sclerosis spinal cord pathology? (2017) · cites this paper as [33]

    Section 2.2.3 says the authors manually masked areas with large vessels and concentrations of neuronal cell bodies. Sections 2.3 and 4.7 describe automated neurite segmentation used to assess CV while excluding non-neurite pixels, but section 4.7 presents automatic removal of nonspecifically stained areas as a future improvement—not something the study did. (LLM, conf=0.88)

    Experimental — accuracy not yet measured
  • Neurite orientation dispersion is a marker of microstructural pathology, as it detects trends of reduced geometrical complexity of neurite architecture within multiple sclerosis lesions.

    “Through the combination of state‐of‐the‐art histology33 and post mortem NODDI26, 27 MRI, we show for the first time that neurite orientation dispersion is a marker of microstructural pathology, as it detects trends of reduced geometrical complexity of neurite architecture within multiple sclerosis lesions.”

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Research works printed but not linked: 9

  1. Arthur, D., Vassilvitskii, S., 2007. k-means++: The advantages of careful seeding. In: Proceedings of the 18th Annual ACM-SIAM Symposium on Discrete Algorithms, Society for Industrial and Applied Mathematics, pp. 1027-1035.
  2. Bigun, J., Granlund, G.H., 1987. Optimal orientation detection of linear symmetry. Proceedings of the IEEE First Internaional Conference on Computer Vision, 433-438.
  3. Jähne, B., 2005. Digital Image Processing, 6th ed. Springer, Berlin.
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  5. Lombaert, H., Peyrat, J.-M., Croisille, P., Rapacchi, S., Fanton, L., Cheriet, F., Clarysse, P., Magnin, I., Delingette, H., Ayache, N., 2012. Human atlas of the cardiac fiber architecture: study on a healthy population Medical Imaging. IEEE Trans. 31, 1436-1447.
  6. Mardia, K.V., Jupp, P.E., 2009. Directional Statistics. John Wiley & Sons.
  7. Schneider, T., Brownlee, W., Zhang, H., Ciccarelli, O., Miller, D., Wheeler-Kingshott, C., 2014. Application of multi-shell NODDI in multiple sclerosis. Proceedings of the 22nd Annual Meeting of the International Society for Magnetic Resonance in Medicine.
  8. Wharton, S., Bowtell, R., 2012. Fiber orientation-dependent white matter contrast in gradient echo MRI. Proc. Natl. Acad. Sci. 109, 18559-18564.
  9. Zhang, W., Fehrenbach, J., Desmaison, A., Lobjois, V., Ducommun, B., Weiss, P., 2016. Structure tensor based analysis of cells and nuclei organization in tissues. IEEE Trans. Med. Imag. 35, 294-306.

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  • Neurite dispersion: a new marker of multiple sclerosis spinal cord pathology?

    Grussu, F., Schneider, T., Tur, C., Yates, R. L., Tachrount, M., Ianuş, A., Yiannakas, M. C., Newcombe, J., Zhang, H., Alexander, D. C., et al. (2017) · Annals of Clinical and Translational Neurology · doi:10.1002/acn3.445 · Cited by 291 · see citing works

    “We verified that histological section thickness did not vary between lesional and nonlesional tissue (data not shown) and excluded areas of nonspecific staining with a combination of manual and automatic segmentation to calculate CV, given their potential to bias this metric.(33)”
    Self-citation

Authors
Grussu, F., Schneider, T., Yates, R. L., Zhang, H., Wheeler-Kingshott, C. A. M. G., DeLuca, G. C., & Alexander, D. C.
Affiliations
  • National Hospital for Neurology and Neurosurgery (GB)
  • University of Oxford (GB)
  • University College London (GB)
Corresponding author
Grussu, Francesco
Venue
Journal of Neuroscience Methods
Publisher
Elsevier BV
Published
2016-11
Volume
273
Pages
20-32
ISSN
0165-0270 (ISSN-L)
OpenAlex
W2478162388
Cited by
26
References
4
Citations by year
2024: 2 · 2023: 3 · 2022: 3 · 2021: 2 · 2019: 5 · 2018: 6 · 2017: 4 · 2016: 1
FWCI
4.64
Type
journal article
Language
en
Open access
Locations
  • Journal of Neuroscience Methods (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • UCL Discovery (University College London) (submittedVersion)
Funders
  • National Institute for Health and Care Research (D 03/10/RAG0449)
  • University College London Hospitals NHS Foundation Trust
  • Horizon 2020 Framework Programme (634541)
  • Medical Research Council (MR/M009106/1, MR/L022656/1)
  • Engineering and Physical Sciences Research Council (EP/L022680/1, EP/M020533/1, EP/N018702/1, EP/G007748/1, 634541, EP/M00855X/1, G007748, EP/I027084/1, EP/M029778/1, H2020-EU.3.1, I027084, 1023513, M020533)
  • UCLH Biomedical Research Centre
Topics
  • Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced Neuroimaging Techniques and Applications
  • Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced MRI Techniques and Applications
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
Keywords
neurite orientation dispersion, spinal cord, histological quantification, structure tensor analysis, demyelinating disease, MRI validation, anisotropy
MeSH terms
Aged, Female, Humans, Image Processing, Computer-Assisted, Male, Spinal Cord, Neurites, Silver Staining, Anisotropy, Diffusion Tensor Imaging, Optical Imaging

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