Jillian McCarthy, McConnell Brain Imaging Centre
Barbara Borroni, Università degli Studi di Brescia
Raquel Sanchez-Valle, Universitat de Barcelona
Fermin Moreno, Osakidetza, Donostia University Hospital
Robert Laforce, CHU de Québec - Université Laval
Caroline Graff, Karolinska Universitetssjukhuset
Matthis Synofzik, Hertie-Institut für klinische Hirnforschung
Daniela Galimberti, Ospedale Maggiore Policlinico Milano
James B. Rowe, Department of Clinical Neurosciences
Mario Masellis, University of Toronto
Maria Carmela Tartaglia, Toronto Western Hospital University of Toronto
Elizabeth Finger, Western UniversityFollow
Rik Vandenberghe, Departement Neurowetenschappen
Alexandre de Mendonça, Faculdade de Medicina, Universidade de Lisboa
Fabrizio Tagliavini, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan
Isabel Santana, Centro Hospitalar e Universitário de Coimbra
Chris Butler, University of Oxford Medical Sciences Division
Alex Gerhard, Faculty of Biology, Medicine and Health
Adrian Danek, Ludwig-Maximilians-Universität München
Johannes Levin, Ludwig-Maximilians-Universität München
Markus Otto, Universitätsklinikum Ulm
Giovanni Frisoni, IRCCS Centro San Giovanni di Dio Fatebenefratelli
Roberta Ghidoni, IRCCS Centro San Giovanni di Dio Fatebenefratelli
Sandro Sorbi, Università degli Studi di Firenze
Lize C. Jiskoot, Erasmus MC
Harro Seelaar, Erasmus MC
John C. van Swieten, Erasmus MC
Jonathan D. Rohrer, UCL Queen Square Institute of Neurology
Yasser Iturria-Medina, McConnell Brain Imaging Centre
Simon Ducharme, McConnell Brain Imaging Centre

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Human Brain Mapping





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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.