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Hippocampal subfields revealed through unfolding and unsupervised clustering of laminar and morphological features in 3D BigBrain

J. DeKraker, J.C. Lau, K.M. Ferko, A.R. Khan, S. Köhler
doi: https://doi.org/10.1101/599571
J. DeKraker
1Brain and Mind Institute, University of Western Ontario
2Robarts Research Institute, Schulich School of Medicine and Dentistry, University of Western Ontario
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  • For correspondence: jdekrake@uwo.ca
J.C. Lau
2Robarts Research Institute, Schulich School of Medicine and Dentistry, University of Western Ontario
3School of Biomedical Engineering, University of Western Ontario
4Dept Clinical Neurological Sciences, Division of Neurosurgery, University of Western Ontario
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K.M. Ferko
1Brain and Mind Institute, University of Western Ontario
2Robarts Research Institute, Schulich School of Medicine and Dentistry, University of Western Ontario
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A.R. Khan
2Robarts Research Institute, Schulich School of Medicine and Dentistry, University of Western Ontario
4Dept Clinical Neurological Sciences, Division of Neurosurgery, University of Western Ontario
5Dept Medical Biophysics, University of Western Ontario
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S. Köhler
1Brain and Mind Institute, University of Western Ontario
6Dept Psychology, University of Western Ontario
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Abstract

The internal structure of the human hippocampus is challenging to map using histology or neuroimaging due to its complex archicortical folding. Here, we aimed to overcome this challenge using a unique combination of three methods. First, we leveraged a histological dataset with unprecedented 3D coverage, 3D BigBrain. Second, we imposed a computational unfolding framework that respects the topological continuity of hippocampal subfields, which are traditionally defined by laminar composition. Third, we adapted neocortical parcellation techniques to map the hippocampus with respect to not only laminar but also morphological features. Unsupervised clustering of these features revealed subdivisions that closely resemble ground-truth manual subfield segmentations. Critically, we also show that morphological features alone are sufficient to derive most hippocampal subfield boundaries. Moreover, some features showed differences within subfields along the hippocampal longitudinal axis. Our findings highlight new characteristics of internal hippocampal structure, and offer new avenues for its characterization with in-vivo neuroimaging.

Footnotes

  • ↵* co-senior authorship

  • Updates include separate analyses using laminar and morphological features separately. Laminar features are predominantly used in histology whereas MRI relies most heavily on morphological features of the hippocampus. This new analysis reveals that in addition to deriving subfields using laminar features, morphological features are sufficient to derive most hippocampal subfields. General readability is also improved.

  • https://osf.io/x542s/

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC 4.0 International license.
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Posted May 28, 2019.
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Hippocampal subfields revealed through unfolding and unsupervised clustering of laminar and morphological features in 3D BigBrain
J. DeKraker, J.C. Lau, K.M. Ferko, A.R. Khan, S. Köhler
bioRxiv 599571; doi: https://doi.org/10.1101/599571
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Hippocampal subfields revealed through unfolding and unsupervised clustering of laminar and morphological features in 3D BigBrain
J. DeKraker, J.C. Lau, K.M. Ferko, A.R. Khan, S. Köhler
bioRxiv 599571; doi: https://doi.org/10.1101/599571

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