An Automated Toolbox to Predict Single Subject Atrophy in Presymptomatic Granulin Mutation Carriers-Reference-Cited by-同舟云学术

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

Published:2022-03-08 Issue:1 Volume:86 Page:205-218
ISSN:1387-2877
Container-title:Journal of Alzheimer's Disease
language:
Short-container-title:JAD

Author:

Premi Enrico¹²,Costa Tommaso³⁴⁵,Gazzina Stefano¹,Benussi Alberto¹,Cauda Franco³⁴⁵,Gasparotti Roberto⁶,Archetti Silvana⁷,Alberici Antonella¹,van Swieten John C.⁸,Sanchez-Valle Raquel⁹,Moreno Fermin¹⁰¹¹,Santana Isabel¹²¹³¹⁴,Laforce Robert¹⁵,Ducharme Simon¹⁶¹⁷,Graff Caroline¹⁸¹⁹,Galimberti Daniela²⁰²¹,Masellis Mario²²,Tartaglia Carmela²³,Rowe James B.²⁴,Finger Elizabeth²⁵,Tagliavini Fabrizio²⁶,de Mendonça Alexandre²⁷,Vandenberghe Rik²⁸²⁹³⁰,Gerhard Alexander³¹³²,Butler Chris R.³³,Danek Adrian³⁴,Synofzik Matthis³⁵³⁶,Levin Johannes³⁴³⁷,Otto Markus³⁸,Ghidoni Roberta³⁹,Frisoni Giovanni⁴⁰⁴¹,Sorbi Sandro⁴²⁴³,Peakman Georgia⁴⁴,Todd Emily⁴⁴,Bocchetta Martina⁴⁴,Rohrer Johnathan D.⁴⁴,Borroni Barbara¹,

Affiliation:

1. Centre for Neurodegenerative Disorders, Department of Clinical and Experimental Sciences, University of Brescia, Brescia, Italy

2. Stroke Unit, Azienda Socio Sanitaria Territoriale Spedali Civili, Spedali Civili Hospital, Brescia, Italy

3. Focus Lab, Department of Psychology, University of Turin, Turin, Italy

4. GCS-FMRI, Koelliker Hospital and Department of Psychology, University of Turin, Turin, Italy

5. Neuroscience Institute of Turin, University of Turin, Turin, Italy

6. Neuroradiology Unit, University of Brescia, Brescia, Italy

7. Biotechnology Laboratory, Department of Diagnostic, Spedali Civili Hospital, Brescia, Italy

8. Department of Neurology, Erasmus Medical Center, Rotterdam, Netherlands

9. Neurology Department, Hospital Clinic, Institut d’Investigacions Biomèdiques, Barcelona, Spain

10. Department of Neurology, Hospital Universitario Donostia, San Sebastian, Gipuzkoa, Spain

11. Neuroscience Area, Biodonostia Health Research Institute, San Sebastian, Gipuzkoa, Spain

12. Neurology Department, Centro Hospitalar e Universitário de Coimbra, Portugal

13. Faculty of Medicine, University of Coimbra, Coimbra, Portugal

14. Centre of Neurosciences and Cell biology, Universidade de Coimbra, Coimbra, Portugal

15. Clinique Interdisciplinaire de Mémoire, Département des Sciences Neurologiques, Faculté de Médecine, CHU de Québec-Université Laval, QC, Canada

16. Department of Psychiatry, McGill University Health Centre, McGill University, Montreal, QC, Canada

17. McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, QC, Canada

18. Center for Alzheimer Research, Division of Neurogeriatrics, Department of Neurobiology, Care Sciences and Society, Bioclinicum, Karolinska Institutet, Solna, Sweden

19. Unit for Hereditary Dementias, Theme Aging, Karolinska University Hospital, Solna, Sweden

20. Department of Pathophysiology and Transplantation, “Dino Ferrari” Center, University of Milan, Milan, Italy

21. Fondazione Cà Granda, IRCCS Ospedale Maggiore Policlinico, Milan, Italy

22. Sunnybrook Health Sciences Centre, Sunnybrook Research Institute, Toronto, ON, Canada

23. Tanz Centre for Research in Neurodegenerative Diseases, University of Toronto, Toronto, ON, Canada

24. Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK

25. Department of Clinical Neurological Sciences, University of Western Ontario, London, ON, Canada

26. Fondazione Istituto di Ricovero e Cura a Carattere Scientifico Istituto Neurologico Carlo Besta, Milan, Italy

27. Laboratory of Neurosciences, Institute of Molecular Medicine, Faculty of Medicine, University of Lisbon, Lisbon, Portugal

28. Laboratory for Cognitive Neurology, Department of Neurosciences, KU Leuven, Leuven, Belgium

29. Neurology Service, University Hospitals Leuven, Belgium

30. Leuven Brain Institute, KU Leuven, Leuven, Belgium

31. Division of Neuroscience and Experimental Psychology, Wolfson Molecular Imaging Centre, University of Manchester, Manchester, UK

32. Departments of Geriatric Medicine and Nuclear Medicine, University of Duisburg-Essen, Germany

33. Nuffield Department of Clinical Neurosciences, Medical Sciences Division, University of Oxford, Oxford, UK

34. Neurologische Klinik und Poliklinik, Ludwig-Maximilians-Universität, Munich, Germany

35. Department of Neurodegenerative Diseases, Hertie-Institute for Clinical Brain Research and Center of Neurology, University of Tübingen, Tübingen, Germany

36. German Center for Neurodegenerative Diseases (DZNE), Tübingen, Germany

37. German Center for Neurodegenerative Diseases (DZNE), Munich, Germany

38. Department of Neurology, University Hospital Ulm, Ulm, Germany

39. Molecular Markers Laboratory, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy

40. Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS) Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy

41. Memory Clinic and LANVIE-Laboratory of Neuroimaging of Aging, University Hospitals and University of Geneva, Geneva, Switzerland

42. Department of Neuroscience, Psychology, Drug Research and Child Health, University of Florence, Florence, Italy

43. Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS) “Don Gnocchi”, Florence, Italy

44. Department of Neurodegenerative Disease, Dementia Research Centre, UCL Institute of Neurology, Queen Square, London, UK

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.

Publisher

IOS Press

Subject

Psychiatry and Mental health,Geriatrics and Gerontology,Clinical Psychology,General Medicine,General Neuroscience

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