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

Background: Magnetic resonance imaging (MRI) measures may be used as outcome markers in frontotemporal dementia (FfD). Objectives: To predict MRI cortical thickness (CT) at fo llow-up at the single subject leve!, using brain MRI acquired at baseline in preclinical FfD. Methods: 84 presymptomatic subjects carryi?g Gmnulin mutations underwent MRI scans at baseline and at fo llow-up (3 1.2± 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-MRl script was coded. Results: Prediction accuracy was high (or eacb considered brain region (i.e., prefrontal region, real CT at follow-up versus predicted CT at fo llow-up, mean error ::; 1.87% ). The sample size required to detect a reduction in decline in a 1-year clinica) tria! was equa! 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 clinica! trials, where deviation of CT from the predicted model may be considered an effect of the intervention itself.