Data-driven staging of genetic frontotemporal dementia using multi-modal MRI.
Authors
Sanchez-Valle, Raquel
Moreno, Fermin
Laforce, Robert
Graff, Caroline
Synofzik, Matthis
Galimberti, Daniela
Rowe, James B
Masellis, Mario
Tartaglia, Maria Carmela
Finger, Elizabeth
Vandenberghe, Rik
de Mendonça, Alexandre
Tagliavini, Fabrizio
Santana, Isabel
Butler, Chris
Gerhard, Alex
Danek, Adrian
Levin, Johannes
Otto, Markus
Frisoni, Giovanni
Ghidoni, Roberta
Sorbi, Sandro
Jiskoot, Lize C
Seelaar, Harro
van Swieten, John C
Rohrer, Jonathan D
Iturria-Medina, Yasser
Ducharme, Simon
GENetic Frontotemporal Dementia Initiative (GENFI)
Publication Date
2022-04-15Journal Title
Hum Brain Mapp
ISSN
1065-9471
Publisher
Wiley
Language
en
Type
Article
This Version
AO
VoR
Metadata
Show full item recordCitation
McCarthy, J., Borroni, B., Sanchez-Valle, R., Moreno, F., Laforce, R., Graff, C., Synofzik, M., et al. (2022). Data-driven staging of genetic frontotemporal dementia using multi-modal MRI.. Hum Brain Mapp https://doi.org/10.1002/hbm.25727
Description
Funder: Fondation Brain Canada; Id: http://dx.doi.org/10.13039/100009408
Funder: Fonds de Recherche du Québec ‐ Santé; Id: http://dx.doi.org/10.13039/501100000156
Funder: Health Canada; Id: http://dx.doi.org/10.13039/501100000008
Funder: Brain Canada Foundation; Id: http://dx.doi.org/10.13039/100009408
Abstract
Frontotemporal dementia in genetic forms is highly heterogeneous and begins many years to prior symptom onset, complicating disease understanding and treatment development. Unifying methods to stage the disease during both the presymptomatic and symptomatic phases are needed for the development of clinical trials outcomes. Here we used the contrastive trajectory inference (cTI), an unsupervised machine learning algorithm that analyzes temporal patterns in high-dimensional large-scale population datasets to obtain individual scores of disease stage. We used cross-sectional MRI data (gray matter density, T1/T2 ratio as a proxy for myelin content, resting-state functional amplitude, gray matter fractional anisotropy, and mean diffusivity) from 383 gene carriers (269 presymptomatic and 115 symptomatic) and a control group of 253 noncarriers in the Genetic Frontotemporal Dementia Initiative. We compared the cTI-obtained disease scores to the estimated years to onset (age-mean age of onset in relatives), clinical, and neuropsychological test scores. The cTI based disease scores were correlated with all clinical and neuropsychological tests (measuring behavioral symptoms, attention, memory, language, and executive functions), with the highest contribution coming from mean diffusivity. Mean cTI scores were higher in the presymptomatic carriers than controls, indicating that the method may capture subtle pre-dementia cerebral changes, although this change was not replicated in a subset of subjects with complete data. This study provides a proof of concept that cTI can identify data-driven disease stages in a heterogeneous sample combining different mutations and disease stages of genetic FTD using only MRI metrics.
Keywords
RESEARCH ARTICLE, RESEARCH ARTICLES, disease progression, frontotemporal dementia, magnetic resonance imaging, unsupervised machine learning
Sponsorship
Medical Research Council (MR/J009482/1)
Medical Research Council (MR/M008983/1)
Medical Research Council (MC_U105597119)
Medical Research Council (MC_UU_00005/12)
Identifiers
hbm25727
External DOI: https://doi.org/10.1002/hbm.25727
This record's URL: https://www.repository.cam.ac.uk/handle/1810/333639
Rights
Licence:
http://creativecommons.org/licenses/by-nc/4.0/
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