Diers, Kersten;
Baumeister, Hannah;
Jessen, Frank;
Düzel, Emrah;
Berron, David;
Reuter, Martin;
(2023)
An automated, geometry-based method for hippocampal shape and thickness analysis.
Neuroimage
, 276
, Article 120182. 10.1016/j.neuroimage.2023.120182.
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Abstract
The hippocampus is one of the most studied neuroanatomical structures due to its involvement in attention, learning, and memory as well as its atrophy in ageing, neurological, and psychiatric diseases. Hippocampal shape changes, however, are complex and cannot be fully characterized by a single summary metric such as hippocampal volume as determined from MR images. In this work, we propose an automated, geometry-based approach for the unfolding, point-wise correspondence, and local analysis of hippocampal shape features such as thickness and curvature. Starting from an automated segmentation of hippocampal subfields, we create a 3D tetrahedral mesh model as well as a 3D intrinsic coordinate system of the hippocampal body. From this coordinate system, we derive local curvature and thickness estimates as well as a 2D sheet for hippocampal unfolding. We evaluate the performance of our algorithm with a series of experiments to quantify neurodegenerative changes in Mild Cognitive Impairment and Alzheimer's disease dementia. We find that hippocampal thickness estimates detect known differences between clinical groups and can determine the location of these effects on the hippocampal sheet. Further, thickness estimates improve classification of clinical groups and cognitively unimpaired controls when added as an additional predictor. Comparable results are obtained with different datasets and segmentation algorithms. Taken together, we replicate canonical findings on hippocampal volume/shape changes in dementia, extend them by gaining insight into their spatial localization on the hippocampal sheet, and provide additional, complementary information beyond traditional measures. We provide a new set of sensitive processing and analysis tools for the analysis of hippocampal geometry that allows comparisons across studies without relying on image registration or requiring manual intervention.
| Type: | Article |
|---|---|
| Title: | An automated, geometry-based method for hippocampal shape and thickness analysis |
| Location: | United States |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1016/j.neuroimage.2023.120182 |
| Publisher version: | https://doi.org/10.1016/j.neuroimage.2023.120182 |
| Language: | English |
| Additional information: | © 2023 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/) |
| Keywords: | Flattening, Hippocampus, Neuroimaging, Shape analysis, Thickness, Humans, Magnetic Resonance Imaging, Hippocampus, Algorithms, Cognitive Dysfunction, Alzheimer Disease, Atrophy |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > Div of Psychology and Lang Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > Div of Psychology and Lang Sciences > Institute of Cognitive Neuroscience |
| URI: | https://discovery-pp.ucl.ac.uk/id/eprint/10172801 |
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