Hierarchical spectral clustering reveals brain size and shape changes in asymptomatic carriers ofC9orf72
Authors: Bruffaerts Rose , Gors Dorothy , Bárcenas Gallardo Alicia , Vandenbulcke Mathieu , Van Damme Philip , Suetens Paul , van Swieten John C , Borroni Barbara , Sanchez-Valle Raquel , Moreno Fermin , Laforce Robert , Graff Caroline , Synofzik Matthis , Galimberti Daniela , Rowe James B , Masellis Mario , Tartaglia Maria Carmela , Finger Elizabeth , de Mendonça 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 ,   Genetic Frontotemporal dementia Initiative (GENFI), Afonso Sónia , Almeida Maria Rosario , Anderl-Straub Sarah , Andersson Christin , Antonell Anna , Archetti Silvana , Arighi Andrea , Balasa Mircea , Barandiaran Myriam , Bargalló Nuria , Bartha Robart , Bender Benjamin , Benussi Alberto , Black Sandra , Bocchetta Martina , Borrego-Ecija Sergi , Bras Jose , Canada Marta , Cantoni Valentina , Caroppo Paola , Cash David , Castelo-Branco Miguel , Convery Rhian , Cope Thomas , Fede Giuseppe Di , Díez Alina , Duro Diana , Fenoglio Chiara , Ferreira Catarina B , Fox Nick , Freedman Morris , Fumagalli Giorgio , Gabilondo Alazne , Gasparotti Roberto , Gauthier Serge , Gazzina Stefano , Giaccone Giorgio , Gorostidi Ana , Greaves Caroline , Guerreiro Rita , Heller Carolin , Hoegen Tobias , Indakoetxea Begoña , Jelic Vesna , Jiskoot Lize , Karnath Hans-Otto , Keren Ron , Langheinrich Tobias , Leitão Maria João , Lladó Albert , Loosli Sandra , Maruta Carolina , Mead Simon , Meeter Lieke , Miltenberger Gabriel , van Minkelen Rick , Mitchell Sara , Moore Katrina , Nicholas Jennifer , Öijerstedt Linn , Olives Jaume , Ourselin Sebastien , Padovani Alessandro , Panman Jessica , Papma Janne M , Peakman Georgia , Pijnenburg Yolande , Premi Enrico , Prioni Sara , Prix Catharina , Rademakers Rosa , Redaelli Veronica , Rittman Tim , Rogaeva Ekaterina , Rosa-Neto Pedro , Rossi Giacomina , Rossor Mar tin , Santiago Beatriz , Scarpini Elio , Schönecker Sonja , Semler Elisa , Shafei Rachelle , Shoesmith Christen , Tábuas-Pereira Miguel , Tainta Mikel , Taipa Ricardo , Tang-Wai David , Thomas David L , Thompson Paul , Thonberg Hakan , Timberlake Carolyn , Tiraboschi Pietro , Todd Emily , Veldsman Michele , Verdelho Ana , Villanua Jorge , Warren Jason , Wilke Carlo , Woollacott Ione , Wlasich Elisabeth , Zetterberg Henrik , Zulaica Miren 
Published: July 2022

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.