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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.<\/jats:p>","DOI":"10.1093\/braincomms\/fcac182","type":"journal-article","created":{"date-parts":[[2022,7,18]],"date-time":"2022-07-18T15:49:38Z","timestamp":1658159378000},"source":"Crossref","is-referenced-by-count":5,"title":["Hierarchical spectral clustering reveals brain size and shape changes in asymptomatic carriers of<i>C9orf72<\/i>"],"prefix":"10.1093","volume":"4","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2631-9234","authenticated-orcid":false,"given":"Rose","family":"Bruffaerts","sequence":"first","affiliation":[{"name":"Laboratory for Cognitive Neurology, Department of Neurosciences, Experimental Neurology, and Leuven Brain Institute (LBI), KU Leuven , Leuven 3000 , Belgium"},{"name":"Computational Neurology, Department of Biomedical 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