Temporal order of clinical and biomarker changes in familial frontotemporal dementia
Unlike familial Alzheimer’s disease, we have been unable to accurately predict symptom onset in presymptomatic familial frontotemporal dementia (f-FTD) mutation carriers, which is a major hurdle to designing disease prevention trials. We developed multimodal models for f-FTD disease progression and...
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| Published in | Nature Medicine Vol. 28; no. 10; pp. 2194 - 2206 |
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| Main Authors | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
| Format | Journal Article Magazine Article |
| Language | English |
| Published |
New York
Nature Publishing Group US
01.10.2022
Nature Publishing Group |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1078-8956 1546-170X 1546-170X 1744-7933 |
| DOI | 10.1038/s41591-022-01942-9 |
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| Abstract | Unlike familial Alzheimer’s disease, we have been unable to accurately predict symptom onset in presymptomatic familial frontotemporal dementia (f-FTD) mutation carriers, which is a major hurdle to designing disease prevention trials. We developed multimodal models for f-FTD disease progression and estimated clinical trial sample sizes in
C9orf72
,
GRN
and
MAPT
mutation carriers. Models included longitudinal clinical and neuropsychological scores, regional brain volumes and plasma neurofilament light chain (NfL) in 796 carriers and 412 noncarrier controls. We found that the temporal ordering of clinical and biomarker progression differed by genotype. In prevention-trial simulations using model-based patient selection, atrophy and NfL were the best endpoints, whereas clinical measures were potential endpoints in early symptomatic trials. f-FTD prevention trials are feasible but will likely require global recruitment efforts. These disease progression models will facilitate the planning of f-FTD clinical trials, including the selection of optimal endpoints and enrollment criteria to maximize power to detect treatment effects.
Empirically based models of disease progression in familial frontotemporal dementia reveal the relative ordering of clinical, neuroimaging, and fluid biomarker changes and facilitate novel clinical trial designs |
|---|---|
| AbstractList | Unlike familial Alzheimer’s disease, we have been unable to accurately predict symptom onset in presymptomatic familial frontotemporal dementia (f-FTD) mutation carriers, which is a major hurdle to designing disease prevention trials. We developed multimodal models for f-FTD disease progression and estimated clinical trial sample sizes in C9orf72, GRN and MAPT mutation carriers. Models included longitudinal clinical and neuropsychological scores, regional brain volumes and plasma neurofilament light chain (NfL) in 796 carriers and 412 noncarrier controls. We found that the temporal ordering of clinical and biomarker progression differed by genotype. In prevention-trial simulations using model-based patient selection, atrophy and NfL were the best endpoints, whereas clinical measures were potential endpoints in early symptomatic trials. f-FTD prevention trials are feasible but will likely require global recruitment efforts. These disease progression models will facilitate the planning of f-FTD clinical trials, including the selection of optimal endpoints and enrollment criteria to maximize power to detect treatment effects.Empirically based models of disease progression in familial frontotemporal dementia reveal the relative ordering of clinical, neuroimaging, and fluid biomarker changes and facilitate novel clinical trial designs Unlike familial Alzheimer's disease, we have been unable to accurately predict symptom onset in presymptomatic familial frontotemporal dementia (f-FTD) mutation carriers, which is a major hurdle to designing disease prevention trials. We developed multimodal models for f-FTD disease progression and estimated clinical trial sample sizes in C9orf72, GRN and MAPT mutation carriers. Models included longitudinal clinical and neuropsychological scores, regional brain volumes and plasma neurofilament light chain (NfL) in 796 carriers and 412 noncarrier controls. We found that the temporal ordering of clinical and biomarker progression differed by genotype. In prevention-trial simulations using model-based patient selection, atrophy and NfL were the best endpoints, whereas clinical measures were potential endpoints in early symptomatic trials. f-FTD prevention trials are feasible but will likely require global recruitment efforts. These disease progression models will facilitate the planning of f-FTD clinical trials, including the selection of optimal endpoints and enrollment criteria to maximize power to detect treatment effects. Unlike familial Alzheimer's disease, we have been unable to accurately predict symptom onset in presymptomatic familial frontotemporal dementia (f-FTD) mutation carriers, which is a major hurdle to designing disease prevention trials. We developed multimodal models for f-FTD disease progression and estimated clinical trial sample sizes in C9orf72, GRN and MAPT mutation carriers. Models included longitudinal clinical and neuropsychological scores, regional brain volumes and plasma neurofilament light chain (NfL) in 796 carriers and 412 noncarrier controls. We found that the temporal ordering of clinical and biomarker progression differed by genotype. In prevention-trial simulations using model-based patient selection, atrophy and NfL were the best endpoints, whereas clinical measures were potential endpoints in early symptomatic trials. f-FTD prevention trials are feasible but will likely require global recruitment efforts. These disease progression models will facilitate the planning of f-FTD clinical trials, including the selection of optimal endpoints and enrollment criteria to maximize power to detect treatment effects.Unlike familial Alzheimer's disease, we have been unable to accurately predict symptom onset in presymptomatic familial frontotemporal dementia (f-FTD) mutation carriers, which is a major hurdle to designing disease prevention trials. We developed multimodal models for f-FTD disease progression and estimated clinical trial sample sizes in C9orf72, GRN and MAPT mutation carriers. Models included longitudinal clinical and neuropsychological scores, regional brain volumes and plasma neurofilament light chain (NfL) in 796 carriers and 412 noncarrier controls. We found that the temporal ordering of clinical and biomarker progression differed by genotype. In prevention-trial simulations using model-based patient selection, atrophy and NfL were the best endpoints, whereas clinical measures were potential endpoints in early symptomatic trials. f-FTD prevention trials are feasible but will likely require global recruitment efforts. These disease progression models will facilitate the planning of f-FTD clinical trials, including the selection of optimal endpoints and enrollment criteria to maximize power to detect treatment effects. Unlike familial Alzheimer’s disease, we have been unable to accurately predict symptom onset in presymptomatic familial frontotemporal dementia (f-FTD) mutation carriers, which is a major hurdle to designing disease prevention trials. We developed multimodal models for f-FTD disease progression and estimated clinical trial sample sizes in C9orf72 , GRN and MAPT mutation carriers. Models included longitudinal clinical and neuropsychological scores, regional brain volumes and plasma neurofilament light chain (NfL) in 796 carriers and 412 noncarrier controls. We found that the temporal ordering of clinical and biomarker progression differed by genotype. In prevention-trial simulations using model-based patient selection, atrophy and NfL were the best endpoints, whereas clinical measures were potential endpoints in early symptomatic trials. f-FTD prevention trials are feasible but will likely require global recruitment efforts. These disease progression models will facilitate the planning of f-FTD clinical trials, including the selection of optimal endpoints and enrollment criteria to maximize power to detect treatment effects. Empirically based models of disease progression in familial frontotemporal dementia reveal the relative ordering of clinical, neuroimaging, and fluid biomarker changes and facilitate novel clinical trial designs Unlike familial Alzheimer’s disease, we have been unable to accurately predict symptom onset in presymptomatic familial frontotemporal dementia (f-FTD) mutation carriers, which is a major hurdle to designing disease prevention trials. We developed multimodal models for f-FTD disease progression and estimated clinical trial sample sizes in C9orf72 , GRN, and MAPT mutation carriers. Models included longitudinal clinical and neuropsychological scores, regional brain volumes, and plasma neurofilament light chain (NfL) in 796 carriers and 412 non-carrier controls. We found that the temporal ordering of clinical and biomarker progression differed by genotype. In prevention-trial simulations employing model-based patient selection, atrophy and NfL were the best endpoints, whereas clinical measures were potential endpoints in early symptomatic trials. F-FTD prevention trials are feasible but will likely require global recruitment efforts. These disease progression models will facilitate the planning of f-FTD clinical trials, including the selection of optimal endpoints and enrollment criteria to maximize power to detect treatment effects. Empirically-based models of disease progression in familial frontotemporal dementia reveal the relative ordering of clinical, neuroimaging, and fluid biomarker changes and facilitate novel clinical trial designs |
| Author | Masellis, Mario Knopman, David Wolf, Amy Jiskoot, Lize C. Goldman, Jill Lapid, Maria I. Gendron, Tania F. Kramer, Joel Peakman, Georgia Huey, Edward D. Weintraub, Sandra Graff-Radford, Jonathon Galimberti, Daniela Mendez, Mario F. Heuer, Hilary W. Onyike, Chiadi U. Finger, Elizabeth Tartaglia, M. Carmela Cash, David M. Hsiung, Ging-Yuek Levin, Johannes Wendelberger, Barbara Otto, Markus Jones, David T. Sanchez-Valle, Raquel Petrucelli, Leonard Rohrer, Jonathan D. Boxer, Adam. L. Roberson, Erik D. Quintana, Melanie Kantarci, Kejal Vandenberghe, Rik Mackenzie, Ian R. Santana, Isabel Butler, Chris R. Ljubenkov, Peter Russell, Lucy L. Cobigo, Yann Boeve, Bradley F. VandeVrede, Lawren Fields, Julie A. Synofzik, Matthis Forsberg, Leah Casaletto, Kaitlin B. Goh, Sheng-Yang Matt Ducharme, Simon Rao, Meghana Geschwind, Daniel Domoto-Reilly, Kimiko Litvan, Irene Clark, Annie L. Heller, Carolin Borroni, Barbara Grossman, Murray Staffaroni, Adam M. Hall, Matthew G. H. Rademakers, Rosa Irwin, David Taylor, Jack Carson Gerhard, Alexander Rascovsky, Katya Bouz |
| AuthorAffiliation | 65 Inserm 1172, Lille, France 56 McConnell Brain Imaging Centre, Montreal Neurological Institute, Department of Neurology & Neurosurgery, McGill University, Montreal Canada 41 Unit for Hereditary Dementias, Theme Aging, Karolinska University Hospital, Solna, Sweden 43 Departments of Geriatric Medicine and Nuclear Medicine, Center for Translational Neuro- and Behavioral Sciences, University Medicine Essen, Essen, Germany 42 Division of Neuroscience and Experimental Psychology, Wolfson Molecular Imaging Centre, University of Manchester, Manchester, UK 14 Departments of Neurology and Psychiatry, Washington University School of Medicine, Washington University, St. Louis, MO, USA 67 University Hospital of Coimbra (HUC), Neurology Service, Faculty of Medicine, University of Coimbra, Portugal 19 University of North Carolina, Department of Neurology, Chapel Hill, NC, USA 54 Leuven Brain Institute, KU Leuven, Leuven, Belgium 37 IRCCS Fondazione Don Carlo Gnocchi, Florence, Italy 45 Clinique Interdiscipli |
| AuthorAffiliation_xml | – name: 43 Departments of Geriatric Medicine and Nuclear Medicine, Center for Translational Neuro- and Behavioral Sciences, University Medicine Essen, Essen, Germany – name: 66 CHU, CNR-MAJ, Labex Distalz, LiCEND Lille, France – name: 55 Douglas Mental Health University Institute, Department of Psychiatry, McGill University, Montreal Canada – name: 34 Department of Clinical Neurological Sciences, University of Western Ontario, London, Ontario Canada – name: 2 Berry Consultants, Austin, TX – name: 5 Mayo Clinic, Department of Neurology, Rochester, MN, USA – name: 6 Mayo Clinic, Department of Quantitative Health Sciences, Rochester, MN, USA – name: 7 Case Western Reserve University, Department of Neurology, Cleveland, OH, USA – name: 32 Fondazione IRCCS Istituto Neurologico Carlo Besta, Milano, Italy – name: 12 Indiana University School of Medicine, National Centralized Repository for Alzheimer’s, IN, USA – name: 13 Institute for Precision Health, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA – name: 24 Department of Biomedical Sciences, University of Antwerp, Antwerp, Belgium – name: 26 Tanz Centre for Research in Neurodegenerative Diseases, Division of Neurology, University of Toronto, Toronto, Ontario, Canada – name: 56 McConnell Brain Imaging Centre, Montreal Neurological Institute, Department of Neurology & Neurosurgery, McGill University, Montreal Canada – name: 50 Department of Neurodegenerative Diseases, Hertie-Institute for Clinical Brain Research and Center of Neurology, University of Tübingen, Tübingen, Germany – name: 62 Munich Cluster of Systems Neurology, Munich, Germany – name: 67 University Hospital of Coimbra (HUC), Neurology Service, Faculty of Medicine, University of Coimbra, Portugal – name: 35 Department of Neurology, Erasmus Medical Centre, Rotterdam, Netherlands – name: 59 Département de Neurologie, AP-HP - Hôpital Pitié-Salpêtrière, Paris, France – name: 57 Sorbonne Université, Paris Brain Institute – Institut du Cerveau – ICM, Inserm U1127, CNRS UMR 7225, AP-HP - Hôpital Pitié-Salpêtrière, Paris, France – name: 28 Department of Clinical Neurosciences and Cambridge University Hospitals NHS Trust and Medical Research Council Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, UK – name: 3 Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen Square London – name: 9 Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston MA – name: 21 Department of Pathology, University of British Columbia. Vancouver, BC, CA – name: 40 Center for Alzheimer Research, Division of Neurogeriatrics, Department of Neurobiology, Care Sciences and Society, Bioclinicum, Karolinska Institutet, Solna, Sweden – name: 25 University of Alabama at Birmingham, Department of Neurology, Birmingham, AL – name: 38 Nuffield Department of Clinical Neurosciences, Medical Sciences Division, University of Oxford, Oxford, UK – name: 19 University of North Carolina, Department of Neurology, Chapel Hill, NC, USA – name: 33 Division of Neurology, Department of Medicine, Sunnybrook Health Sciences Centre; Hurvitz Brain Sciences Program, Sunnybrook Research Institute; University of Toronto, Toronto, Canada – name: 45 Clinique Interdisciplinaire de Mémoire, Département des Sciences Neurologiques, CHU de Québec, and Faculté de Médecine, Université Laval, QC, Canada – name: 14 Departments of Neurology and Psychiatry, Washington University School of Medicine, Washington University, St. Louis, MO, USA – name: 60 Neurologische Klinik und Poliklinik, Ludwig-Maximilians-Universität, Munich, Germany – name: 52 Laboratory for Cognitive Neurology, Department of Neurosciences, KU Leuven, Leuven, Belgium – name: 49 Neuroscience Area, Biodonostia Health Research Institute, San Sebastian, Gipuzkoa, Spain – name: 30 Department of Biomedical, Surgical and Dental Sciences, University of Milan, Milan, Italy – name: 41 Unit for Hereditary Dementias, Theme Aging, Karolinska University Hospital, Solna, Sweden – name: 17 University of Pennsylvania, Department of Neurology, Philadelphia, PA, USA – name: 37 IRCCS Fondazione Don Carlo Gnocchi, Florence, Italy – name: 48 Cognitive Disorders Unit, Department of Neurology, Donostia University Hospital, San Sebastian, Gipuzkoa, Spain – name: 27 Northwestern University, Department of Neurology, Chicago, IL, USA – name: 1 University of California, San Francisco, Weill Institute for Neurosciences, Department of Neurology, Memory and Aging Center, San Francisco, CA – name: 11 Mayo Clinic, Department of Psychiatry and Psychology, Rochester, MN, USA – name: 69 University of California, San Francisco, Department of Epidemiology and Biostatistics, San Francisco, CA – name: 36 Department of Neurofarba, University of Florence, Italy – name: 4 Mayo Clinic, Department of Neuroscience, Jacksonville, FL, USA – name: 8 University of California, Los Angeles, Department of Neurology, Los Angeles, CA, USA – name: 63 Department of Neurology, University of Ulm, Ulm, Germany – name: 16 Mayo Clinic, Department of Neurology, Jacksonville, FL, USA – name: 39 Department of Brain Sciences, Imperial College London, UK – name: 44 Cerebral Function Unit, Manchester Centre for Clinical Neurosciences, Salford Royal NHS Foundation Trust, Salford, UK – name: 20 University of California, San Diego, Department of Neurosciences, La Jolla, CA, USA – name: 10 University of Washington, Department of Neurology, Seattle, WA, USA – name: 29 Centre for Neurodegenerative Disorders, Department of Clinical and Experimental Sciences, University of Brescia, Brescia, Italy – name: 61 Center for Neurodegenerative Diseases (DZNE), Munich, Germany – name: 64 Univ Lille, France – name: 23 Applied and Translational Neurogenomics Group, VIB Center for Molecular Neurology, VIB, Antwerp, Belgium – name: 42 Division of Neuroscience and Experimental Psychology, Wolfson Molecular Imaging Centre, University of Manchester, Manchester, UK – name: 58 Centre de référence des démences rares ou précoces, IM2A, Département de Neurologie, AP-HP - Hôpital Pitié-Salpêtrière, Paris, France – name: 53 Neurology Service, University Hospitals Leuven, Belgium – name: 54 Leuven Brain Institute, KU Leuven, Leuven, Belgium – name: 15 Columbia University, Department of Neurology, New York, NY, USA – name: 46 Alzheimer’s disease and Other Cognitive Disorders Unit, Neurology Service, Hospital Clínic, Institut d’Investigacións Biomèdiques August Pi I Sunyer, University of Barcelona, Barcelona, Spain – name: 31 Fondazione IRCCS Ca’ Granda, Ospedale Maggiore Policlinico, Milan, Italy – name: 51 Center for Neurodegenerative Diseases (DZNE), Tübingen, Germany – name: 68 Center for Neuroscience and Cell Biology, Faculty of Medicine, University of Coimbra, Coimbra, Portugal – name: 22 Johns Hopkins University, Department of Psychiatry and Behavioral Sciences, Baltimore, MD, USA – name: 18 University of British Columbia, Division of Neurology, Vancouver, BC, CA – name: 47 Faculty of Medicine, University of Lisbon, Lisbon, Portugal – name: 65 Inserm 1172, Lille, France |
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| ContentType | Journal Article Magazine Article |
| Contributor | Thonberg, Hakan Ferreira, Catarina B Nelson, Annabel Onyike, Chiadi Archetti, Silvana Antonell, Anna Weintraub, Sandra Bozoki, Andrea Mitchell, Sara Ferrari, Camilla Gabilondo, Alazne Balasa, Mircea Black, Sandra Cañada, Marta Benussi, Alberto Samra, Kiran Ramanan, Vijay Shafei, Rachelle Maruta, Carolina Lladó, Albert Rogaeva, Ekaterina Dickson, Dennis Arighi, Andrea Coppola, Giovanni Jelic, Vesna Nicholas, Jennifer Freedman, Morris Fenoglio, Chiara Scarpini, Elio Villanua, Jorge Galasko, Douglas R Polito, Cristina Giaccone, Giorgio Lapid, Maria van Minkelen, Rick Redaelli, Veronica Taylor, Jack C Clark, David Borrego-Ecija, Sergi Di Fede, Giuseppe Giannini, Lucia Verdelho, Ana Rademakers, Rosa Prioni, Sara Timberlake, Carolyn Benotmane, Hanya Pressman, Peter Fagan, Anne Thomas, David L Cope, Thomas Kerwin, Diana Bargalló, Nuria Shoesmith, Christen Olives, Jaume Boeve, Bradley McGinnis, Scott Poos, Jackie Castelo-Branco, Miguel Borracci, Vittoria Seeley, William W Fumagalli, Giorgio Rossi, Giacomina Thompson, Paul Bartha, Robart Papma, Janne M Keren |
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| Copyright | The Author(s), under exclusive licence to Springer Nature America, Inc. 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. 2022. The Author(s), under exclusive licence to Springer Nature America, Inc. Distributed under a Creative Commons Attribution 4.0 International License |
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| DOI | 10.1038/s41591-022-01942-9 |
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| GrantInformation_xml | – fundername: ANR-PRTS PREV-DemAls, PHRC PREDICT-PGRN – fundername: RCUK | Medical Research Council (MRC) grantid: SUAG/092 G116768; MR/M023664/1 funderid: https://doi.org/10.13039/501100000265 – fundername: 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 Strat-Neuro, Swedish Dementia Foundation, and Stockholm County Council ALF/Region Stockholm and the Frontotemporal Dementia Research Studentships in Memory of David Blechner funded through The National Brain Appeal (RCN 290173). – fundername: U.S. Department of Health & Human Services | National Institutes of Health (NIH) grantid: K23AG061253; P30 AG062422; NS092089; AG063911; AG062677; AG045390; NS092089; AG038791; 12 HD001459; AG063911; AG032306; AG019724; K24AG045333; AG045390; NS092089 funderid: https://doi.org/10.13039/100000002 – fundername: Association for Frontotemporal Degeneration, The Bluefield Project to Cure FTD, and The Rainwater Charitable Foundation. – fundername: Canadian Institutes of Health Research and the Chaire de Recherche sur les Aphasies Primaires Progressives Fondation Famille Lemaire. – fundername: NIHR Cambridge Biomedical Research Centre (BRC-1215-20014);The Wellcome Trust (220258); The Cambridge Centre for Parkinson-plus; – fundername: the Bluefield Project, the National Institute for Health Research including awards to Cambridge and UCL Biomedical Research Centres, and the JPND GENFI-PROX grant (2019–02248). UK Medical Research Council, Alzheimer’s Society and Alzheimer’s Research UK. JDR is also supported by the Miriam Marks Brain Research UK Senior Fellowship and has received funding from an MRC Clinician Scientist Fellowship (MR/M008525/1) and the NIHR Rare Disease Translational Research Collaboration (BRC149/NS/MH) – fundername: Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany’s Excellence Strategy within the framework of the Munich Cluster for Systems Neurology (EXC 2145 SyNergy – ID 390857198). – fundername: Frontotemporal Dementia Research Studentships in Memory of David Blechner funded through The National Brain Appeal (RCN 290173). – fundername: Department of Health grantid: BRC-1215-20014 – fundername: NIA NIH HHS grantid: P30 AG072977 – fundername: NIA NIH HHS grantid: U24 AG072122 – fundername: NIA NIH HHS grantid: K23 AG061253 – fundername: Wellcome Trust grantid: 220258 – fundername: Medical Research Council grantid: MR/M023664/1 – fundername: Medical Research Council grantid: MR/M008525/1 – fundername: NIA NIH HHS grantid: L30 AG069301 – fundername: NIA NIH HHS grantid: R01 AG032306 – fundername: NICHD NIH HHS grantid: K12 HD001459 |
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| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 PMCID: PMC9951811 Author Contributions Statement A.M.S., M.Q., and B.W. had full access to the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis. A.M.S., M.Q., B.W., H.W.H., L.R., H.J.R., J.D.R., and A.L.B. were responsible for concept development and design. A.M.S., M.Q., and B.W. conducted statistical analyses. M.Q. and B.W. developed the custom code for the disease progression models. L.P., T.F.G., and C.H. processed the neurofilament light chain data. Y.C., A.W., and S.Y.M.G. processed the neuroimaging data. A.M.S., M.Q. and B.W. drafted the manuscript. A.M.S., M.Q., B.W., H.W.H, L.R., H.J.R., J.D.R, and A.L.B critically revised the manuscript. A.L.B. supervised the research. B.F.B, H.J.R, J.D.R, & A.L.B obtained funding. All authors contributed to acquisition, analysis, or interpretation of data or revision of the manuscript. |
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| Snippet | Unlike familial Alzheimer’s disease, we have been unable to accurately predict symptom onset in presymptomatic familial frontotemporal dementia (f-FTD)... Unlike familial Alzheimer's disease, we have been unable to accurately predict symptom onset in presymptomatic familial frontotemporal dementia (f-FTD)... |
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| SubjectTerms | 692/53 692/617/375/132 Alzheimer's disease Atrophy Biomarkers Biomedical and Life Sciences Biomedicine C9orf72 Protein C9orf72 Protein - genetics Cancer Research Clinical trials Clinical Trials as Topic Dementia Dementia disorders Disease prevention Disease Progression Frontotemporal Dementia Frontotemporal Dementia - genetics Genotypes Humans Infectious Diseases Life Sciences Medical imaging Metabolic Diseases Molecular Medicine Mutation Mutation - genetics Neurodegenerative diseases Neuroimaging Neurons and Cognition Neurosciences tau Proteins tau Proteins - genetics Temporal variations |
| Title | Temporal order of clinical and biomarker changes in familial frontotemporal dementia |
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