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Subtype and stage inference identifies distinct atrophy patterns in genetic frontotemporal dementia that MAP onto specific MAPT mutations
Author(s) -
Young Alexandra L.,
Bocchetta Martina,
Cash David M,
Convery Rhian S,
Moore Katrina M,
Neason Mollie R,
Thomas David L,
van Swieten John C.,
Borroni Barbara,
SanchezValle Raquel,
Moreno Fermin,
Laforce Robert,
Graff Caroline,
Synofzik Matthis,
Galimberti Daniela,
Rowe James B,
Masellis Mario,
Tartaglia Carmela,
Finger Elizabeth,
Vandenberghe Rik,
Mendonca Alexandre,
Tagliavini Fabrizio,
Santana Isabel,
Ducharme Simon,
Butler Christopher,
Gerhard Alexander,
Levin Johannes,
Danek Adrian,
Otto Markus,
Frisoni Giovanni B,
Ghidoni Roberta,
Sorbi Sandro,
Williams Steven CR,
Alexander Daniel C.,
Rohrer Jonathan D
Publication year - 2020
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.042996
Subject(s) - frontotemporal dementia , tau protein , phenotype , atrophy , genetic heterogeneity , dementia , biology , genetics , oncology , gene , disease , psychology , alzheimer's disease , medicine
Background Mutations in the MAPT gene are known to cause frontotemporal dementia (FTD), but there is heterogeneity in FTD phenotype across individuals. Here we used an unsupervised learning algorithm – Subtype and Stage Inference (SuStaIn) – to relate phenotypic heterogeneity to specific mutations in the MAPT gene. Method SuStaIn evaluates the optimal grouping of individuals into disease subtypes, where each subtype consists of a sequence (set of stages) in which biomarkers transition between different z‐scores. We applied SuStaIn to cross‐sectional regional brain volumes extracted from T1‐weighted MRI data from MAPT carriers in the GENFI study to find the best stratification of the data into subtypes, and the temporal progression of each subtype. We used data from 82 MAPT carriers (57 presymptomatic and 25 symptomatic) to identify subtypes and data from a control group of 300 non‐carriers to derive z‐scores. We subtyped and staged individuals at up to five annual follow‐up visits to assess the consistency of the subtypes longitudinally. We compared the specific mutations and clinical and neuropsychological test scores of individuals assigned to each subtype. Result SuStaIn identified two groups of MAPT carriers with distinct atrophy patterns (Figure 1), which we termed a ‘temporal’ subtype and a ‘frontotemporal’ subtype. The subtype assignments were consistent at follow‐up visits (Table 1): there were no individuals that changed from the temporal to the frontotemporal subtype or vice‐versa. Subtype assignment was strongly associated with IVS10+16, R406W and P301L mutations (Table 2): there was a one‐to‐one mapping between IVS10+16 and R406W mutations and the temporal subtype, and a near one‐to‐one mapping between P301L mutations and the frontotemporal subtype. The temporal subtype was associated with memory problems, whereas the frontotemporal subtype was associated with worse performance on tests of attention and visuospatial skills (Table 3). Conclusion Our results demonstrate the utility of SuStaIn for identifying disease subgroups and associating imaging patterns with genetics and cognition. We show that different MAPT mutations give rise to distinct atrophy patterns and clinical syndromes, providing insights into the underlying disease biology, and potential utility for patient stratification.