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HippUnfold: Automated hippocampal unfolding, morphometry, and subfield segmentation

View ORCID ProfileJordan DeKraker, View ORCID ProfileRoy AM Haast, Mohamed D Yousif, Bradley Karat, View ORCID ProfileStefan Köhler, View ORCID ProfileAli R Khan
doi: https://doi.org/10.1101/2021.12.03.471134
Jordan DeKraker
1Robarts Research Institute, Schulich School of Medicine and Dentistry, University of Western Ontario, Canada
2Brain and Mind Institute, University of Western Ontario, Canada
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  • For correspondence: jordan.dekraker@mail.mcgill.ca alik@robarts.ca
Roy AM Haast
1Robarts Research Institute, Schulich School of Medicine and Dentistry, University of Western Ontario, Canada
3Aix-Marseille University, CNRS, CRMBM, UMR 7339, Marseille, France
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Mohamed D Yousif
1Robarts Research Institute, Schulich School of Medicine and Dentistry, University of Western Ontario, Canada
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Bradley Karat
1Robarts Research Institute, Schulich School of Medicine and Dentistry, University of Western Ontario, Canada
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Stefan Köhler
2Brain and Mind Institute, University of Western Ontario, Canada
4Dept Psychology, University of Western Ontario, Canada
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  • ORCID record for Stefan Köhler
Ali R Khan
1Robarts Research Institute, Schulich School of Medicine and Dentistry, University of Western Ontario, Canada
2Brain and Mind Institute, University of Western Ontario, Canada
5School of Biomedical Engineering, University of Western Ontario, Canada
6Dept Medical Biophysics, University of Western Ontario, Canada
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  • ORCID record for Ali R Khan
  • For correspondence: jordan.dekraker@mail.mcgill.ca alik@robarts.ca
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Abstract

The archicortical hippocampus differs, like the neocortex, in its folding patterns between individuals. Here, we present an automated and robust BIDS-App, HippUnfold, for defining and indexing subject-specific hippocampal folding in MRI, analogous to popular tools used in neocortical reconstruction. This is critical for inter-individual alignment, with topology as the basis for homology. This topological framework enables qualitatively new analyses of morphological and laminar structure in the hippocampus or hippocampal subfields, and is critical for the advancement of neuroimaging analyses at a meso- or micro-scale. HippUnfold uses state-of-the-art deep learning combined with previously developed topological constraints on hippocampal tissue. It is designed to work with commonly employed sub-millimetric MRI acquisitions, with extensibility to microscopic resolutions as well. In this paper we illustrate the power of HippUnfold in feature extraction, and its construct validity compared to several extant hippocampal subfield analysis methods.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • https://hippunfold.readthedocs.io/en/latest/?badge=latest

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 4.0 International license.
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Posted December 05, 2021.
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HippUnfold: Automated hippocampal unfolding, morphometry, and subfield segmentation
Jordan DeKraker, Roy AM Haast, Mohamed D Yousif, Bradley Karat, Stefan Köhler, Ali R Khan
bioRxiv 2021.12.03.471134; doi: https://doi.org/10.1101/2021.12.03.471134
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HippUnfold: Automated hippocampal unfolding, morphometry, and subfield segmentation
Jordan DeKraker, Roy AM Haast, Mohamed D Yousif, Bradley Karat, Stefan Köhler, Ali R Khan
bioRxiv 2021.12.03.471134; doi: https://doi.org/10.1101/2021.12.03.471134

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