Diffusion - Histology Correlates

Caroline Magnain¹

¹MGH / HMS, Martinos Center for Biomedical Imaging, Charlestown, MA, United States

Synopsis

MRI parcellation and connectivity is widely used in neuroscience, however their validation have been challenging. In this talk, sevreral validation methods will be discussed, such as histology, polarized light imaging and optical coherence tomography.

Target audience

Scientists interested in validation of MRI segmentation and connectivity

Outcome/Objectives

The goal is to show methods to validate MRI parcellation and MRI connectivity

Several techniques for validation will be explored: histology, polarized light imaging and optical coherence tomography

Purpose

While structural brain mapping has improved with high resolution ex vivo MRI, the resolution and contrast of MRI has limits that constrain our ability to visualize cytoarchitectural features in association cortices, even ex vivo. Refined localization of brain areas is critical for application to diseases such as autism, schizophrenia and Alzheimer's disease, as well as functional MRI studies. Diffusion-weighted MRI (DW-MRI) allows us to probe the microstructure of the white matter by estimating the preferential directions of the diffusion of water molecules at every voxel. Although DW-MRI is now widely used to study WM integrity in health and disease, its validation has been challenging due to the absence of ground truth regarding the true connectivity of the brain. In this talk, I will present several methods for the validation of structural and connectivity MRI.

Methods

First, we will explore histology, which provides the ground truth for validation in structural and connectivity neuroimaging and has great specificity [1,2]. Second, 3D polarized light imaging (PLI) will be presented [3] and this technique maps the myelinated fibers and their pathway in the brain, taking advantage of the birefringence of the myelin sheath. Using these two methods, the whole brain can be processed. However, histology and PLI are labor intensive techniques, and inherently introduce several distortions due to the many steps used: sectioning, handling and mounting, as well as histologic staining for the first one. Therefore, irreducible distortions make the 3D reconstruction challenging, rendering any registration to MRI data for validation complex [4,5]. The last technique discussed in this course will be Optical Coherence Tomography (OCT) [6] and Polarization Sensitive Optical Coherence Tomography (PS-OCT). OCT is a 3D high resolution imaging technique that relies on intrinsic optical properties (scattering, backscattering and birefringence) of the tissue to generate contrast [7,8]. Contrary to the previous techniques, not the full brain is yet achieved but the imaging is performed prior to sectioning rendering the 3D volumetric reconstruction straightforward and the registration to OCT feasible by rigid transformation only.

Acknowledgements

No acknowledgement found.

References

[1] K. Amunts, C. Lepage, L. Borgeat, H. Mohlberg, T. Dickscheid, M.-E. Rousseau, S. Bludau, P.-L. Bazin, L. B. Lewis, A.-M. Oros-Peusquens, N. J. Shah, T. Lippert, K. Zilles, and A. C. Evans, “BigBrain: an ultrahigh-resolution 3D human brain model.,” Science (New York, N.Y.), vol. 340, pp. 1472–5, June 2013.

[2] S.-l. Ding, J. J. Royall, S. M. Sunkin, L. Ng, B. A. C. Facer, P. Lesnar, A. Guillozet-bongaarts, B. Mcmurray, A. Szafer, T. A. Dolbeare, A. Stevens, L. Tirrell, T. Benner, S. Caldejon, R. A. Dalley, N. Dee, C. Lau, J. Nyhus, M. Reding, Z. L. Riley, D. Sandman, E. Shen, A. V. D. Kouwe, A. Varjabedian, M. Write, L. Zollei, C. Dang, J. A. Knowles, C. Koch, J. W. Phillips, N. Sestan, P. Wohnoutka, H. R. Zielke, J. G. Hohmann, A. R. Jones, A. Bernard, M. J. Hawrylycz, P. R. Hof, B. Fischl, and E. S. Lein, “Comprehensive Cellular-Resolution Atlas of the Adult Human Brain,” Journal of Comparative Neurology, vol. 524, pp. 3127–3481, 2016.

[3] Axer, M., Amunts, K., Grässel, D., Palm, C., Dammers, J., Axer, H., ... & Zilles, K. (2011). A novel approach to the human connectome: ultra-high resolution mapping of fiber tracts in the brain. Neuroimage, 4(2), 1091-1101.

[4] V. Arsigny, X. Pennec, and N. Ayache, “Polyrigid and polyaffine transformations : A novel geometrical tool to deal with non-rigid deformations – Application to the registration of histological slices,” Medical Image Analysis, vol. 9, pp. 507–523, 2005.

[5] S. Osechinskiy and F. Kruggel, “Slice-to-Volume Nonrigid Registration of Histological Sections to MR Images of the Human Brain,” Anatomy Research International, vol. 2011, 2011.

[6] Huang, D., Swanson, E. A., Lin, C. P., Schuman, J. S., Stinson, W. G., Chang, W., Hee, M. R., Flotte, T., Gregory, K., Puliafito, C. A., Fujimoto, J. G., 1991. Optical Coherence Tomography. Science 254, pp 1178–1181.

[7] C. Magnain, J. C. Augustinack, M. Reuter, C. Wachinger, M. P. Frosch, T. Ragan, T. Akkin, V. J. Wedeen, D. a. Boas, and B. Fischl, “Blockface histology with optical coherence tomography: A comparison with Nissl staining,” NeuroImage, vol. 84, pp. 524–533, Sept. 2014.

[8] Wang, H., Zhu, J., Reuter, M., Vinke, L. N., Yendiki, A., Boas, D. A., ... & Akkin, T. (2014). Cross-validation of serial optical coherence scanning and diffusion tensor imaging: a study on neural fiber maps in human medulla oblongata. Neuroimage, 100, 395-404.

Proc. Intl. Soc. Mag. Reson. Med. 25 (2017)