Please use this identifier to cite or link to this item:
http://hdl.handle.net/2445/207680
Title: | Hierarchical spectral clustering reveals brain size and shape changes in asymptomatic carriers of C9orf72 |
Author: | Bruffaerts, Rose Gors, Dorothy Bárcenas Gallardo, Alicia Vandenbulcke, Mathieu Damme, Philip van Suetens, Paul Swieten, John C. van Borroni, Barbara Sanchez Valle, Raquel Moreno, Fermín Laforce, Robert Graff, Caroline Synofzik, Matthis Galimberti, Daniela Rowe, James B. Masellis, Mario Tartaglia, Maria Carmela Finger, Elizabeth Mendonça, Alezandre de Tagliavini, Fabrizio Butler, Chris R. Santana, Isabel Gerhard, Alezander Ducharme, Simon Levin, Johannes Danek, Adrian Otto, Markus Rohrer, Jonathan D. Dupont, Patrick Claes, Peter Vandenberghe, Rik Genetic Frontotemporal Dementia Initiative (GENFI) |
Keywords: | Malalties neurodegeneratives Imatges per ressonància magnètica Neurodegenerative Diseases Magnetic resonance imaging |
Issue Date: | 6-Jul-2023 |
Publisher: | Oxford University Press |
Abstract: | Traditional methods for detecting asymptomatic brain changes in neurodegenerative diseases such as Alzheimer's disease or frontotemporal degeneration typically evaluate changes in volume at a predefined level of granularity, e.g. voxel-wise or in a priori defined cortical volumes of interest. Here, we apply a method based on hierarchical spectral clustering, a graph-based partitioning technique. Our method uses multiple levels of segmentation for detecting changes in a data-driven, unbiased, comprehensive manner within a standard statistical framework. Furthermore, spectral clustering allows for detection of changes in shape along with changes in size. We performed tensor-based morphometry to detect changes in the Genetic Frontotemporal dementia Initiative asymptomatic and symptomatic frontotemporal degeneration mutation carriers using hierarchical spectral clustering and compared the outcome to that obtained with a more conventional voxel-wise tensor- and voxel-based morphometric analysis. In the symptomatic groups, the hierarchical spectral clustering-based method yielded results that were largely in line with those obtained with the voxel-wise approach. In asymptomatic C9orf72 expansion carriers, spectral clustering detected changes in size in medial temporal cortex that voxel-wise methods could only detect in the symptomatic phase. Furthermore, in the asymptomatic and the symptomatic phases, the spectral clustering approach detected changes in shape in the premotor cortex in C9orf72. In summary, the present study shows the merit of hierarchical spectral clustering for data-driven segmentation and detection of structural changes in the symptomatic and asymptomatic stages of monogenic frontotemporal degeneration.© The Author(s) 2022. Published by Oxford University Press on behalf of the Guarantors of Brain. |
Note: | Reproducció del document publicat a: https://doi.org/10.1093/braincomms/fcac182 |
It is part of: | Brain Commun, 2022, vol. 4, p. 4 |
URI: | http://hdl.handle.net/2445/207680 |
Related resource: | https://doi.org/10.1093/braincomms/fcac182 |
ISSN: | 2632-1297 |
Appears in Collections: | Articles publicats en revistes (IDIBAPS: Institut d'investigacions Biomèdiques August Pi i Sunyer) |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
Hierarchical spectral clustering reveals brain size and shape changes in asymptomatic_BrainCommunications.pdf | 14.37 MB | Adobe PDF | View/Open |
This item is licensed under a
Creative Commons License