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

Premi, Enrico
•
Costa, Tommaso
•
Gazzina, Stefano
altro
GENFI Consortium Members
2022
  • journal article

Periodico
JOURNAL OF ALZHEIMER'S DISEASE
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 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.
DOI
10.3233/JAD-215447
WOS
WOS:000768536700009
Archivio
https://hdl.handle.net/11368/3097191
info:eu-repo/semantics/altIdentifier/scopus/2-s2.0-85126389368
https://journals.sagepub.com/doi/10.3233/JAD-215447
Diritti
closed access
license:copyright editore
license uri:iris.pri02
FVG url
https://arts.units.it/request-item?handle=11368/3097191
Soggetti
  • Frontotemporal dement...

  • granulin

  • magnetic resonance im...

  • mutation

  • preclinical

  • presymptomatic

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