Automated hippocampal unfolding for morphometry and subfield segmentation with HippUnfold
- University of Western Ontario, Canada
- Aix-Marseille University, France
Abstract
Like neocortical structures, the archicortical hippocampus differs in its folding patterns across individuals. Here, we present an automated and robust BIDS-App, HippUnfold, for defining and indexing individual-specific hippocampal folding in MRI, analogous to popular tools used in neocortical reconstruction. Such tailoring is critical for inter-individual alignment, with topology serving as the basis for homology. This topological framework enables qualitatively new analyses of morphological and laminar structure in the hippocampus or its subfields. It is critical for refining current neuroimaging analyses at a meso- as well as micro-scale. HippUnfold uses state-of-the-art deep learning combined with previously developed topological constraints to generate uniquely folded surfaces to fit a given subject's hippocampal conformation. It is designed to work with commonly employed sub-millimetric MRI acquisitions, with possible extension to microscopic resolution. In this paper we describe the power of HippUnfold in feature extraction, and highlight its unique value compared to several extant hippocampal subfield analysis methods.
Data availability
All code for the HippUnfold application has been made available at https://github.com/khanlab/hippunfold. Data and code to generate the Figures shown in this study have been made available at https://zenodo.org/record/6360647.
-
Human Connectome Project - AgingConnectome Coordination Facility.
-
Human Connectome Project - Young AdultConnectome Coordination Facility.
-
IKND Magdeburg Atlas for New (Fast) ASHSNeuroimaging Tools & Resources Collaboratory.
Article and author information
Author details
Funding
Canadian HIV Trials Network, Canadian Institutes of Health Research (366062)
- Stefan Köhler
- Ali R Khan
Canada Research Chairs (950-231964)
- Ali R Khan
Natural Sciences and Engineering Research Council of Canada (6639)
- Ali R Khan
Canada Foundation for Innovation (37427)
- Ali R Khan
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Ethics
Human subjects: Informed consent and consent to publish were obtained by the original authors of the open source data examined here. Each of the three datasets included research ethics board approvals, as well as informed consent and, in the HCP-Aging dataset, assessment of the subjects' ability to provide consent. For the single epilepsy patient case examined here, research ethics board approval and informed consent were collected at the Western University (HSREB # 108952, Lawson: R-17-156).
Copyright
© 2022, DeKraker et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
Metrics
-
- 4,373
- views
-
- 452
- downloads
-
- 63
- citations
Views, downloads and citations are aggregated across all versions of this paper published by eLife.
Citations by DOI
-
- 63
- citations for umbrella DOI https://doi.org/10.7554/eLife.77945
Download links
A two-part list of links to download the article, or parts of the article, in various formats.
Downloads (link to download the article as PDF)
Open citations (links to open the citations from this article in various online reference manager services)
Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)
Automated hippocampal unfolding for morphometry and subfield segmentation with HippUnfold
eLife 11:e77945.
https://doi.org/10.7554/eLife.77945
