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Title: | Hierarchical spectral clustering reveals brain size and shape changes in asymptomatic carriers of C9orf72 | Authors: | BRUFFAERTS, Rose Gors, Dorothy Gallardo, Alicia Barcenas Vandenbulcke, Mathieu Van Damme , Philip Suetens, Paul van Swieten, John C. Borroni, Barbara Sanchez-Valle, Raquel Moreno, Fermin Laforce, Robert, Jr. Graff, Caroline Synofzik, Matthis Galimberti, Daniela Rowe, James B. Masellis, Mario Tartaglia, Maria Carmela Finger, Elizabeth de Mendonca, Alexandre Tagliavini, Fabrizio Butler, Chris R. Santana, Isabel Gerhard, Alexander Ducharme, Simon Levin, Johannes Danek, Adrian Otto, Markus Rohrer, Jonathan D. Dupont , Patrick Claes, Peter Vandenberghe, Rik |
Issue Date: | 2022 | Publisher: | OXFORD UNIV PRESS | Source: | Brain communications, 4 (4) (Art N° fcac182) | Abstract: | Bruffaerts, Gors et al. studied structural brain changes at the asymptomatic stage of monogenic frontotemporal degeneration. They used hierarchical spectral clustering for MRI segmentation to detect changes at different levels of granularity. In asymptomatic c9orf72 expansion carriers additional structural brain changes were observed in comparison to conventional methods. 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. | Notes: | Claes, P (corresponding author), Katholieke Univ Leuven, ESAT PSI, Dept Elect Engn, Herestr 49,Box 7003, B-3000 Leuven, Belgium.; Bruffaerts, R (corresponding author), Univ Antwerp, Dept Biomed Sci, Computat Neurol, Expt Neurobiol Unit, Campus Drie Eiken Univ Pl 1, B-2610 Antwerp, Belgium. rose.bruffaerts@uantwerpen.be; peter.claes@kuleuven.be |
Keywords: | genetic frontotemporal dementia;structural MRI;tensor-based morphometry;brain segmentation;size;shape | Document URI: | http://hdl.handle.net/1942/37987 | ISSN: | 2632-1297 | e-ISSN: | 2632-1297 | DOI: | 10.1093/braincomms/fcac182 | ISI #: | 000830123800004 | Rights: | The Author(s) 2022. Published by Oxford University Press on behalf of the Guarantors of Brain. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. | Category: | A1 | Type: | Journal Contribution | Validations: | vabb 2024 |
Appears in Collections: | Research publications |
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