Abstract
Neurofibrillary tangle (NFT) pathology in the medial temporal lobe (MTL) is closely linked to neurodegeneration, and is the early pathological change associated with Alzheimer’s Disease (AD). In this work, we investigate the relationship between MTL morphometry features derived from high-resolution ex vivo imaging and histology-based measures of NFT pathology using a topological unfolding framework applied to a dataset of 18 human postmortem MTL specimens. The MTL has a complex 3D topography and exhibits a high degree of inter-subject variability in cortical folding patterns which poses a significant challenge for volumetric registration methods typically used during MRI template construction. By unfolding the MTL cortex, the proposed framework explicitly accounts for the sheet-like geometry of the MTL cortex and provides a two-dimensional reference coordinate space which can be used to implicitly register cortical folding patterns across specimens based on distance along the cortex despite large anatomical variability. Leveraging this framework in a subset of 15 specimens, we characterize the associations between NFTs and morphological features such as cortical thickness and surface curvature and identify regions in the MTL where patterns of atrophy are strongly correlated with NFT pathology.
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References
Braak, H., Braak, E.: Neuropathological staging of Alzheimer-related changes. Acta Neuropathol. 82, 239–259 (1991). https://doi.org/10.1007/bf00308809
Hyman, B.T., et al.: National institute on aging–Alzheimer’s association guidelines for the neuropathologic assessment of Alzheimer’s disease. Alzheimer’s Dement. 8, 1–13 (2012). https://doi.org/10.1016/j.jalz.2011.10.007
Olsen, R.K., Palombo, D.J., Rabin, J.S., Levine, B., Ryan, J.D., Rosenbaum, R.S.: Volumetric analysis of medial temporal lobe subregions in developmental amnesia using high-resolution magnetic resonance imaging. Hippocampus 23, 855–860 (2013). https://doi.org/10.1002/hipo.22153
Small, S.A., Schobel, S.A., Buxton, R.B., Witter, M.P., Barnes, C.A.: A pathophysiological framework of hippocampal dysfunction in ageing and disease (2011). https://doi.org/10.1038/nrn3085
Joshi, S., Davis, B., Jomier, M., Gerig, G.: Unbiased diffeomorphic atlas construction for computational anatomy. NeuroImage Neuroimage (2004). https://doi.org/10.1016/j.neuroimage.2004.07.068
**e, L., et al.: Automatic clustering and thickness measurement of anatomical variants of the human perirhinal cortex. In: Golland, P., Hata, N., Barillot, C., Hornegger, J., Howe, R. (eds.) MICCAI 2014. LNCS, vol. 8675, pp. 81–88. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10443-0_11
Ravikumar, S., et al.: Building an ex vivo atlas of the earliest brain regions affected by Alzheimer’s disease pathology. In: Proceedings - International Symposium on Biomedical Imaging (2020). https://doi.org/10.1109/ISBI45749.2020.9098427
Ding, S.L., Van Hoesen, G.W.: Borders, extent, and topography of human perirhinal cortex as revealed using multiple modern neuroanatomical and pathological markers. Hum. Brain Mapp. 31, 1359–1379 (2010). https://doi.org/10.1002/hbm.20940
Fischl, B., Sereno, M.I., Tootell, R.B.H., Dale, A.M.: High-resolution intersubject averaging and a coordinate system for the cortical surface. Hum. Brain Mapp. 8, 272–284 (1999). https://doi.org/10.1002/(SICI)1097-0193(1999)8:4%3c272::AID-HBM10%3e3.0.CO;2-4
DeKraker, J., Ferko, K.M., Lau, J.C., Köhler, S., Khan, A.R.: Unfolding the hippocampus: an intrinsic coordinate system for subfield segmentations and quantitative map**. Neuroimage 167, 408–418 (2018). https://doi.org/10.1016/j.neuroimage.2017.11.054
Adler, D.H., et al.: Characterizing the human hippocampus in aging and Alzheimer’s disease using a computational atlas derived from ex vivo MRI and histology. Proc. Natl. Acad. Sci. U.S.A. 115, 4252–4257 (2018). https://doi.org/10.1073/pnas.1801093115
Yushkevich, P.A., et al.: Three-dimensional map** of neurofibrillary tangle burden in the human medial temporal lobe. Brain 139, 16–17 (2021). https://doi.org/10.1093/BRAIN/AWAB262
DeKraker, J., Lau, J.C., Ferko, K.M., Khan, A.R., Köhler, S.: Hippocampal subfields revealed through unfolding and unsupervised clustering of laminar and morphological features in 3D BigBrain. Neuroimage 206 (2020). https://doi.org/10.1016/j.neuroimage.2019.116328
Ravikumar, S., Wisse, L., Gao, Y., Gerig, G., Yushkevich, P.: Facilitating manual segmentation of 3D datasets using contour and intensity guided interpolation. In: 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019), pp. 714–718 (2019)
Ogniewicz, R.L., Kübler, O.: Hierarchic Voronoi skeletons. Pattern Recogn. 28, 343–359 (1995). https://doi.org/10.1016/0031-3203(94)00105-U
Amidror, I.: Scattered data interpolation methods for electronic imaging systems: a survey. J. Electron. Imaging 11, 157 (2002). https://doi.org/10.1117/1.1455013
Crum, W.R., Camara, O., Hill, D.L.G.: Generalized overlap measures for evaluation and validation in medical image analysis. IEEE Trans. Med. Imaging. 25, 1451–1461 (2006). https://doi.org/10.1109/TMI.2006.880587
Vercauteren, T., Pennec, X., Perchant, A., Ayache, N.: Symmetric log-domain diffeomorphic registration: a demons-based approach. In: Metaxas, D., Axel, L., Fichtinger, G., Székely, G. (eds.) MICCAI 2008. LNCS, vol. 5241, pp. 754–761. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-85988-8_90
Arena, J.D., et al.: Astroglial tau pathology alone preferentially concentrates at sulcal depths in chronic traumatic encephalopathy neuropathologic change. Brain Commun. 2 (2020). https://doi.org/10.1093/BRAINCOMMS/FCAA210
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Ravikumar, S. et al. (2021). Unfolding the Medial Temporal Lobe Cortex to Characterize Neurodegeneration Due to Alzheimer’s Disease Pathology Using Ex vivo Imaging. In: Abdulkadir, A., et al. Machine Learning in Clinical Neuroimaging. MLCN 2021. Lecture Notes in Computer Science(), vol 13001. Springer, Cham. https://doi.org/10.1007/978-3-030-87586-2_1
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