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DC Field | Value | Language |
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dc.contributor.author | Premi, E | - |
dc.contributor.author | Costa, T | - |
dc.contributor.author | Gazzina, S | - |
dc.contributor.author | Benussi, A | - |
dc.contributor.author | Cauda, F | - |
dc.contributor.author | Gasparotti, R | - |
dc.contributor.author | Archetti, S | - |
dc.contributor.author | Alberici, A | - |
dc.contributor.author | Van Swieten, JC | - |
dc.contributor.author | Sanchez-Valle, R | - |
dc.contributor.author | Moreno, F | - |
dc.contributor.author | Santana, I | - |
dc.contributor.author | Laforce, R | - |
dc.contributor.author | Ducharme, S | - |
dc.contributor.author | Graff, C | - |
dc.contributor.author | Galimberti, D | - |
dc.contributor.author | Masellis, M | - |
dc.contributor.author | Tartaglia, C | - |
dc.contributor.author | Rowe, JB | - |
dc.contributor.author | Finger, E | - |
dc.contributor.author | Tagliavini, F | - |
dc.contributor.author | De Mendonça, A | - |
dc.contributor.author | Vandenberghe, R | - |
dc.contributor.author | Gerhard, A | - |
dc.contributor.author | Butler, CR | - |
dc.contributor.author | Danek, A | - |
dc.contributor.author | Synofzik, M | - |
dc.contributor.author | Levin, J | - |
dc.contributor.author | Otto, M | - |
dc.contributor.author | Ghidoni, R | - |
dc.contributor.author | Frisoni, G | - |
dc.contributor.author | Sorbi, S | - |
dc.contributor.author | Peakman, G | - |
dc.contributor.author | Todd, E | - |
dc.contributor.author | Bocchetta, M | - |
dc.contributor.author | Rohrer, JD | - |
dc.contributor.author | Borroni, B | - |
dc.contributor.other | GENFI Consortium Members | - |
dc.date.accessioned | 2023-01-03T15:43:04Z | - |
dc.date.available | 2022-03-08 | - |
dc.date.available | 2023-01-03T15:43:04Z | - |
dc.date.issued | 2022-03-08 | - |
dc.identifier | ORCID iD: Martina Bocchetta https://orcid.org/0000-0003-1814-5024 | - |
dc.identifier.citation | Premi, E. et al. on behalf of the GENFI Consortium Members (2022) 'An Automated Toolbox to Predict Single Subject Atrophy in Presymptomatic Granulin Mutation Carriers', Journal of Alzheimer's Disease, 86 (1), pp. 205 - 218. doi: 10.3233/JAD-215447. | en_US |
dc.identifier.issn | 1387-2877 | - |
dc.identifier.uri | https://bura.brunel.ac.uk/handle/2438/25696 | - |
dc.description.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. | en_US |
dc.description.sponsorship | Swedish Frontotemporal Dementia Initiative Schörling Foundation; Swedish Research Council: JPND Prefrontals, 2015-02926 ,2018-02754; Swedish Alzheimer foundation; Swedish Brain Foundation; Karolinska Institutet Doctoral Funding; KI StratNeuro; Swedish Dementia foundation and Stockholm County Council ALF/Region Stockholm. | en_US |
dc.format.extent | 205 - 218 | - |
dc.format.medium | Print-Electronic | - |
dc.language | English | - |
dc.language.iso | en_US | en_US |
dc.publisher | IOS Press | en_US |
dc.rights | Copyright © 2021 The Authors. Published by IOS Press. This is the author accepted manuscript. This version is free to view and download for private research and study only. Not for re-distribution or re-use. The final publication is available at IOS Press through https://doi.org/10.3233/JAD-215447. | - |
dc.rights.uri | https://www.iospress.nl/service/authors/author-copyright-agreement/ | - |
dc.subject | frontotemporal dementia | en_US |
dc.subject | granulin | en_US |
dc.subject | magnetic resonance imaging | en_US |
dc.subject | mutation | en_US |
dc.subject | preclinical | en_US |
dc.subject | presymptomatic | en_US |
dc.title | An Automated Toolbox to Predict Single Subject Atrophy in Presymptomatic Granulin Mutation Carriers | en_US |
dc.type | Article | en_US |
dc.identifier.doi | https://doi.org/10.3233/JAD-215447 | - |
dc.relation.isPartOf | Journal of Alzheimer's Disease | - |
pubs.issue | 1 | - |
pubs.publication-status | Published | - |
pubs.volume | 86 | - |
dc.identifier.eissn | 1875-8908 | - |
dc.rights.holder | The Authors | - |
Appears in Collections: | Dept of Life Sciences Research Papers |
Files in This Item:
File | Description | Size | Format | |
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FullText.pdf | Copyright © 2021 The Authors. Published by IOS Press. This is the author accepted manuscript. This version is free to view and download for private research and study only. Not for re-distribution or re-use. The final publication is available at IOS Press through https://doi.org/10.3233/JAD-215447. | 996.6 kB | Adobe PDF | View/Open |
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