An Automated Toolbox to Predict Single Subject Atrophy in Presymptomatic Granulin Mutation Carriers

Premi, Enrico; Costa, Tommaso; Gazzina, Stefano; Benussi, Alberto; Cauda, Franco; Gasparotti, Roberto; Archetti, Silvana; Alberici, Antonella; Swieten, John C. van; Sanchez-Valle, Raquel; Moreno, Fermin; Santana, Isabel; Laforce, Robert; Ducharme, Simon; Graff, Caroline; Galimberti, Daniela; Masellis, Mario; Tartaglia, Carmela; Rowe, James B.; Finger, Elizabeth; Tagliavini, Fabrizio; de Mendonca, Alexandre; Vandenberghe, Rik; Gerhard, Alexander; Butler, Chris R.; Danek, Adrian; Synofzik, Matthis; Levin, Johannes; Otto, Markus; Ghidoni, Roberta; Frisoni, Giovanni; Sorbi, Sandro; Peakman, Georgia; Todd, Emily; Bocchetta, Martina; Rohrer, Johnathan D. und Borroni, Barbara (2022): An Automated Toolbox to Predict Single Subject Atrophy in Presymptomatic Granulin Mutation Carriers. In: Journal of Alzheimers Disease, Bd. 86, Nr. 1: S. 205-218

Abstract

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 followup 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.

• Dokumententyp: Zeitschriftenartikel
• Fakultät: Medizin
• Themengebiete: 600 Technik, Medizin, angewandte Wissenschaften > 610 Medizin und Gesundheit
• ISSN: 1387-2877
• Sprache: Englisch