Documentation Status Docker Pulls Version https://github.com/khanlab/hippunfold/assets/25106300/0c16d33e-893a-4ac3-b127-21fa843823d5

Full Documentation: here

Hippunfold

This tool aims to automatically model the topological folding structure of the human hippocampus, and computationally unfold it. https://raw.githubusercontent.com//khanlab/hippunfold/master/docs/images/subfields_foldunfold.png Hippo Fold Unfold

This is especially useful for:

  • Visualization

  • Topologically-constrained intersubject registration

  • Parcellation (ie. registration to an unfolded atlas)

  • Morphometry (eg. thickness, surface area, curvature, and gyrification measures)

  • Quantitative mapping (eg. map your qT1 MRI data to a midthickness surface; extract laminar profiles perpendicular to this surface)

NEW: Version 1.3.x release

Major changes include the addition of unfolded space registration to a reference atlas harmonized across seven ground-truth histology samples. This method allows shifting in unfolded space, providing even better intersubject alignment.

Note: this replaces the default workflow, however you can revert to the legacy workflow, disabling unfolded space registration, by setting --atlas bigbrain or --no-unfolded-reg

Read more in our preprinted manuscript

Also the ability to specify a new experimental UNet model that is contrast-agnostic using synthseg and trained using more detailed segmentations. This generally produces more detailed results but has not been extensively tested yet.

Note: Docker containers for version 1.3.x and above do not come pre-shipped with nnU-net models (and are accordingly more lightweight!) - models are downloaded automatically when running, but please see the FAQ for more information!

Workflow

The overall workflow can be summarized in the following steps:

Pipeline Overview

For more information, see Full Documentation: here

Additional tools

For plotting, mapping fMRI, DWI or other data, and manipulating surfaces, see here

For statistical testing (spin tests) in unfolded space, see here

Relevant papers:

  • DeKraker, J., Palomero-Gallagher, N., Kedo, O., Ladbon-Bernasconi, N., Muenzing, S. E., Axer, M., … & Evans, A. C. (2023). Evaluation of surface-based hippocampal registration using ground-truth subfield definitions. bioRxiv, 2023-03. link

  • DeKraker, J., Haast, R. A., Yousif, M. D., Karat, B., Lau, J. C., Köhler, S., & Khan, A. R. (2022). Automated hippocampal unfolding for morphometry and subfield segmentation with HippUnfold. Elife, 11, e77945. link

  • DeKraker J, Ferko KM, Lau JC, Köhler S, Khan AR. Unfolding the hippocampus: An intrinsic coordinate system for subfield segmentations and quantitative mapping. Neuroimage. 2018 Feb 15;167:408-418. doi: 10.1016/j.neuroimage.2017.11.054. Epub 2017 Nov 23. PMID: 29175494. link

  • DeKraker J, Lau JC, Ferko KM, Khan AR, Köhler S. Hippocampal subfields revealed through unfolding and unsupervised clustering of laminar and morphological features in 3D BigBrain. Neuroimage. 2020 Feb 1;206:116328. doi: 10.1016/j.neuroimage.2019.116328. Epub 2019 Nov 1. PMID: 31682982. link

  • DeKraker J, Köhler S, Khan AR. Surface-based hippocampal subfield segmentation. Trends Neurosci. 2021 Nov;44(11):856-863. doi: 10.1016/j.tins.2021.06.005. Epub 2021 Jul 22. PMID: 34304910. link