Enrico Premi, Università degli Studi di Brescia
Tommaso Costa, Università degli Studi di Torino
Stefano Gazzina, Università degli Studi di Brescia
Alberto Benussi, Università degli Studi di Brescia
Franco Cauda, Università degli Studi di Torino
Roberto Gasparotti, Università degli Studi di Brescia
Silvana Archetti, Spedali Civili Di Brescia
Antonella Alberici, Università degli Studi di Brescia
John C. Van Swieten, Erasmus MC
Raquel Sanchez-Valle, Hospital Clinic Barcelona
Fermin Moreno, Osakidetza, Donostia University Hospital
Isabel Santana, Centro Hospitalar e Universitário de Coimbra
Robert Laforce, CHU de Québec - Université Laval
Simon Ducharme, School of Medicine
Caroline Graff, Karolinska Institutet
Daniela Galimberti, Università degli Studi di Milano
Mario Masellis, Sunnybrook Health Sciences Centre
Carmela Tartaglia, Tanz Centre for Research in Neurodegenerative Diseases
James B. Rowe, Department of Clinical Neurosciences
Elizabeth Finger, Western UniversityFollow
Fabrizio Tagliavini, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan
Alexandre De Mendonça, Faculdade de Medicina, Universidade de Lisboa
Rik Vandenberghe, Departement Neurowetenschappen
Alexander Gerhard, The University of Manchester
Chris R. Butler, University of Oxford Medical Sciences Division
Adrian Danek, Ludwig-Maximilians-Universität München
Matthis Synofzik, Hertie-Institut für klinische Hirnforschung
Johannes Levin, Deutsches Zentrum für Neurodegenerative Erkrankungen e.V.
Markus Otto, IRCCS Centro San Giovanni di Dio Fatebenefratelli
Roberta Ghidoni, IRCCS Centro San Giovanni di Dio Fatebenefratelli

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Journal of Alzheimer's Disease





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Background: Magnetic resonance imaging (MRI) measures may be used as outcome markers in frontotemporal dementia (FTD). Objectives: To predict MRI cortical thickness (CT) at follow-up at the single subject level, using brain MRI acquired at baseline in preclinical FTD. Methods: 84 presymptomatic subjects carrying Granulin mutations underwent MRI scans at baseline and at follow-up (31.2±16.5 months). Multivariate nonlinear mixed-effects model was used for estimating individualized CT at follow-up based on baseline MRI data. The automated user-friendly preGRN-MRI script was coded. Results: Prediction accuracy was high for each considered brain region (i.e., prefrontal region, real CT at follow-up versus predicted CT at follow-up, mean error ≤1.87%). The sample size required to detect a reduction in decline in a 1-year clinical trial was equal to 52 subjects (power=0.80, alpha=0.05). Conclusion: The preGRN-MRI tool, using baseline MRI measures, was able to predict the expected MRI atrophy at follow-up in presymptomatic subjects carrying GRN mutations with good performances. This tool could be useful in clinical trials, where deviation of CT from the predicted model may be considered an effect of the intervention itself.