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Unfolding the Medial Temporal Lobe Cortex to Characterize Neurodegeneration Due to Alzheimer’s Disease Pathology Using Ex vivo Imaging

Ravikumar, Sadhana ; Wisse, Laura LU orcid ; Lim, Sydney ; Irwin, David ; Ittyerah, Ranjit ; Xie, Long ; Das, Sandhitsu R. ; Lee, Edward ; Tisdall, M. Dylan and Prabhakaran, Karthik , et al. (2021) 4th International Workshop on Machine Learning in Clinical Neuroimaging, MLCN 2021 held in Conjunction with 24th International Conference on Medical Imaging Computing and Computer-Assisted Intervention, MICCAI 2021 In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 13001 LNCS. p.3-12
Abstract

Neurofibrillary tangle (NFT) pathology in the medial temporal lobe (MTL) is closely linked to neurodegeneration, and is the early pathological change associated with Alzheimer’s Disease (AD). In this work, we investigate the relationship between MTL morphometry features derived from high-resolution ex vivo imaging and histology-based measures of NFT pathology using a topological unfolding framework applied to a dataset of 18 human postmortem MTL specimens. The MTL has a complex 3D topography and exhibits a high degree of inter-subject variability in cortical folding patterns which poses a significant challenge for volumetric registration methods typically used during MRI template construction. By unfolding the MTL cortex, the proposed... (More)

Neurofibrillary tangle (NFT) pathology in the medial temporal lobe (MTL) is closely linked to neurodegeneration, and is the early pathological change associated with Alzheimer’s Disease (AD). In this work, we investigate the relationship between MTL morphometry features derived from high-resolution ex vivo imaging and histology-based measures of NFT pathology using a topological unfolding framework applied to a dataset of 18 human postmortem MTL specimens. The MTL has a complex 3D topography and exhibits a high degree of inter-subject variability in cortical folding patterns which poses a significant challenge for volumetric registration methods typically used during MRI template construction. By unfolding the MTL cortex, the proposed framework explicitly accounts for the sheet-like geometry of the MTL cortex and provides a two-dimensional reference coordinate space which can be used to implicitly register cortical folding patterns across specimens based on distance along the cortex despite large anatomical variability. Leveraging this framework in a subset of 15 specimens, we characterize the associations between NFTs and morphological features such as cortical thickness and surface curvature and identify regions in the MTL where patterns of atrophy are strongly correlated with NFT pathology.

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organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
keywords
Cortical unfolding, Ex vivo MRI, Medial temporal lobe
host publication
Machine Learning in Clinical Neuroimaging - 4th International Workshop, MLCN 2021, Held in Conjunction with MICCAI 2021, Proceedings
series title
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
editor
Abdulkadir, Ahmed ; Kia, Seyed Mostafa ; Habes, Mohamad ; Kumar, Vinod ; Rondina, Jane Maryam ; Tax, Chantal and Wolfers, Thomas
volume
13001 LNCS
pages
10 pages
publisher
Springer Science and Business Media B.V.
conference name
4th International Workshop on Machine Learning in Clinical Neuroimaging, MLCN 2021 held in Conjunction with 24th International Conference on Medical Imaging Computing and Computer-Assisted Intervention, MICCAI 2021
conference location
Strasbourg, France
conference dates
2021-09-27 - 2021-09-27
external identifiers
  • scopus:85116324966
ISSN
1611-3349
0302-9743
ISBN
9783030875855
DOI
10.1007/978-3-030-87586-2_1
language
English
LU publication?
yes
additional info
Publisher Copyright: © 2021, Springer Nature Switzerland AG.
id
2daeb4f1-bd4a-4912-a8ee-622d9316740d
date added to LUP
2021-10-28 15:44:23
date last changed
2025-03-25 04:43:17
@inproceedings{2daeb4f1-bd4a-4912-a8ee-622d9316740d,
  abstract     = {{<p>Neurofibrillary tangle (NFT) pathology in the medial temporal lobe (MTL) is closely linked to neurodegeneration, and is the early pathological change associated with Alzheimer’s Disease (AD). In this work, we investigate the relationship between MTL morphometry features derived from high-resolution ex vivo imaging and histology-based measures of NFT pathology using a topological unfolding framework applied to a dataset of 18 human postmortem MTL specimens. The MTL has a complex 3D topography and exhibits a high degree of inter-subject variability in cortical folding patterns which poses a significant challenge for volumetric registration methods typically used during MRI template construction. By unfolding the MTL cortex, the proposed framework explicitly accounts for the sheet-like geometry of the MTL cortex and provides a two-dimensional reference coordinate space which can be used to implicitly register cortical folding patterns across specimens based on distance along the cortex despite large anatomical variability. Leveraging this framework in a subset of 15 specimens, we characterize the associations between NFTs and morphological features such as cortical thickness and surface curvature and identify regions in the MTL where patterns of atrophy are strongly correlated with NFT pathology.</p>}},
  author       = {{Ravikumar, Sadhana and Wisse, Laura and Lim, Sydney and Irwin, David and Ittyerah, Ranjit and Xie, Long and Das, Sandhitsu R. and Lee, Edward and Tisdall, M. Dylan and Prabhakaran, Karthik and Detre, John and Mizsei, Gabor and Trojanowski, John Q. and Robinson, John and Schuck, Theresa and Grossman, Murray and Artacho-Pérula, Emilio and de Onzoño Martin, Maria Mercedes Iñiguez and del Mar Arroyo Jiménez, María and Muñoz, Monica and Romero, Francisco Javier Molina and del Pilar Marcos Rabal, Maria and Sánchez, Sandra Cebada and González, José Carlos Delgado and de la Rosa Prieto, Carlos and Parada, Marta Córcoles and Wolk, David and Insausti, Ricardo and Yushkevich, Paul}},
  booktitle    = {{Machine Learning in Clinical Neuroimaging - 4th International Workshop, MLCN 2021, Held in Conjunction with MICCAI 2021, Proceedings}},
  editor       = {{Abdulkadir, Ahmed and Kia, Seyed Mostafa and Habes, Mohamad and Kumar, Vinod and Rondina, Jane Maryam and Tax, Chantal and Wolfers, Thomas}},
  isbn         = {{9783030875855}},
  issn         = {{1611-3349}},
  keywords     = {{Cortical unfolding; Ex vivo MRI; Medial temporal lobe}},
  language     = {{eng}},
  pages        = {{3--12}},
  publisher    = {{Springer Science and Business Media B.V.}},
  series       = {{Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)}},
  title        = {{Unfolding the Medial Temporal Lobe Cortex to Characterize Neurodegeneration Due to Alzheimer’s Disease Pathology Using Ex vivo Imaging}},
  url          = {{http://dx.doi.org/10.1007/978-3-030-87586-2_1}},
  doi          = {{10.1007/978-3-030-87586-2_1}},
  volume       = {{13001 LNCS}},
  year         = {{2021}},
}