RT Journal Article
SR Electronic
T1 HippUnfold: Automated hippocampal unfolding, morphometry, and subfield segmentation
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2021.12.03.471134
DO 10.1101/2021.12.03.471134
A1 Jordan DeKraker
A1 Roy AM Haast
A1 Mohamed D Yousif
A1 Bradley Karat
A1 Stefan Köhler
A1 Ali R Khan
YR 2021
UL http://biorxiv.org/content/early/2021/12/05/2021.12.03.471134.abstract
AB 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 StatementThe authors have declared no competing interest.