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From brain volumes to subgroup classification in genetic mutation carriers for frontotemporal dementia: A cluster analysis in the GENFI study
Author(s) -
Bocchetta Martina,
Todd Emily G.,
Nicholas Jennifer M.,
Heller Carolin,
Swift Imogen J.,
Peakman Georgia,
Cash David M.,
Convery Rhian S.,
Russell Lucy L.,
Thomas David L.,
Iglesias Juan Eugenio,
van Swieten John C.,
Jiskoot Lize C.,
Seelaar Harro,
Borroni Barbara,
Galimberti Daniela,
SanchezValle Raquel,
Laforce Robert,
Moreno Fermin,
Synofzik Matthis,
Graff Caroline,
Masellis Mario,
Tartaglia Maria Carmela,
Rowe James B.,
Vandenberghe Rik,
Finger Elizabeth,
Tagliavini Fabrizio,
Mendonca Alexandre,
Santana Isabel,
Butler Christopher,
Ducharme Simon,
Gerhard Alexander,
Danek Adrian,
Levin Johannes,
Otto Markus,
Sorbi Sandro,
Ber Isabelle Le,
Pasquier Florence,
Rohrer Jonathan D.
Publication year - 2021
Publication title -
alzheimer's and dementia
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 6.713
H-Index - 118
eISSN - 1552-5279
pISSN - 1552-5260
DOI - 10.1002/alz.052189
Subject(s) - frontotemporal dementia , c9orf72 , atrophy , dementia , psychology , genetic heterogeneity , mutation , oncology , brain size , medicine , disease , genetics , biology , magnetic resonance imaging , phenotype , gene , radiology
Background Genetic frontotemporal dementia (FTD) is highly heterogeneous, with carriers of mutations in the same gene manifesting different phenotypes. Using in vivo MR images from the Genetic FTD Initiative (GENFI), we aimed to identify subgroups within the same genetic group whose brains were affected differently. Method Cortical and subcortical volumes of interest were generated using automated parcellation methods on volumetric 3T T1‐weighted MRI scans for 479 carriers (198 GRN , 202 C9orf72 , and 79 MAPT mutation carriers). W‐scores for 85 volumes of interest were computed from a linear regression model carried out on 298 non‐carrier cognitively normal controls adjusting for the effect of age, sex, total intracranial volume and scanner type. Cluster analyses with the Ward agglomerating method were performed on all w‐scores while considering the three genetic groups independently. The identified clusters were then compared for age, estimated years from onset, global and sum of boxes scores of the CDR® plus NACC FTLD (at baseline and after one year), neurofilament light chain (NfL) levels in the plasma and w‐scores in brain regions typically showing early atrophy (Kruskal‐Wallis test). Result We identified three clusters among the GRN mutation carriers and four in the MAPT and C9orf72 groups, which were all significantly different for the variables reported in the Table (p‐value<0.003). For all three genetic groups, one cluster was formed by patients with a clinical diagnosis of FTD, with more extensive atrophy and increased disease severity. For the remaining clusters, there seemed to be an association with disease severity for MAPT and GRN mutation carriers but not so for C9orf72 expansion carriers where clinical scores were not clearly associated with a specific cluster. Conclusion By only looking at regional brain volumes, we were able to detect different clusters within carriers of mutations in the same gene, with C9orf72 expansion carriers being the most heterogenous group. Further investigations with specific cognitive, clinical and biomarkers correlates, including further follow‐up visits, are needed.