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Automated hippocampal unfolding for morphometry and subfield segmentation with HippUnfold
Jordan DeKraker1,2, Roy A M Haast1, Mohamed D Yousif1
1Robarts Research Institute, Schulich School of Medicine and Dentistry, The University of Western Ontario, London, Canada.
Elife
|December 15, 2022
Summary
HippUnfold is a new tool that maps individual brain folding patterns in the hippocampus using MRI. This allows for more precise analysis of brain structure and function across different people.
Area of Science:
- Neuroimaging
- Computational Neuroscience
- Human Anatomy
Background:
- Individual variations in hippocampal folding exist, similar to the neocortex.
- Accurate inter-individual alignment requires subject-specific anatomical mapping.
- Current methods lack detailed topological and morphological analysis of hippocampal subfields.
Purpose of the Study:
- To introduce HippUnfold, an automated BIDS-App for individual hippocampal folding analysis.
- To enable topology-based homology for advanced hippocampal research.
- To provide a framework for meso- and micro-scale neuroimaging analyses of the hippocampus.
Main Methods:
- Utilizes deep learning combined with topological constraints.
- Generates subject-specific folded surfaces matching hippocampal conformation.
- Designed for sub-millimetric MRI acquisitions, extendable to microscopic resolutions.
Main Results:
- Demonstrates HippUnfold's capability in feature extraction.
- Highlights the tool's unique advantages over existing hippocampal subfield analysis methods.
- Provides a robust method for defining and indexing individual hippocampal folding.
Conclusions:
- HippUnfold offers a novel approach for detailed hippocampal morphology analysis.
- The tool facilitates more accurate inter-individual comparisons in brain imaging studies.
- Enables new avenues for research into hippocampal structure-function relationships.
