Single-subject grey matter network trajectories over the disease course of autosomal dominant Alzheimer’s disease
Author:
Vermunt Lisa1ORCID, Dicks Ellen1ORCID, Wang Guoqiao2, Dincer Aylin3, Flores Shaney3, Keefe Sarah J3, Berman Sarah B45, Cash David M6, Chhatwal Jasmeer P7, Cruchaga Carlos8910, Fox Nick C1112, Ghetti Bernardino13, Graff-Radford Neill R14, Hassenstab Jason151617, Karch Celeste M8, Laske Christoph1819, Levin Johannes20, Masters Colin L2122, McDade Eric1516, Mori Hiroshi23, Morris John C1516, Noble James M24, Perrin Richard J1525, Schofield Peter R2627, Xiong Chengjie2, Scheltens Philip1, Visser Pieter Jelle128, Bateman Randall J81516, Benzinger Tammie L S315, Tijms Betty M1ORCID, Gordon Brian A31517ORCID, Allegri Ricardo, Amtashar Fatima, Benzinger Tammie, Berman Sarah, Bodge Courtney, Brandon Susan, Brooks William, Buck Jill, Buckles Virginia, Chea Sochenda, Chrem Patricio, Chui Helena, Cinco Jake, Jack Clifford, D’Mello Mirelle, Donahue Tamara, Douglas Jane, Edigo Noelia, Erekin-Taner Nilufer, Fagan Anne, Farlow Marty, Farrar Angela, Feldman Howard, Flynn Gigi, Fox Nick, Franklin Erin, Fujii Hisako, Gant Cortaiga, Gardener Samantha, Ghetti Bernardino, Goate Alison, Goldman Jill, Gordon Brian, Gray Julia, Gurney Jenny, Hassenstab Jason, Hirohara Mie, Holtzman David, Hornbeck Russ, DiBari Siri Houeland, Ikeuchi Takeshi, Ikonomovic Snezana, Jerome Gina, Jucker Mathias, Kasuga Kensaku, Kawarabayashi Takeshi, Klunk William, Koeppe Robert, Kuder-Buletta Elke, Laske Christoph, Levin Johannes, Marcus Daniel, Martins Ralph, Mason Neal Scott, Maue-Dreyfus Denise, McDade Eric, Montoya Lucy, Mori Hiroshi, Nagamatsu Akem, Neimeyer Katie, Noble James, Norton Joanne, Perrin Richard, Raichle Marc, Ringman John, Roh Jee Hoon, Schofield Peter, Shimada Hiroyuki, Shiroto Tomoyo, Shoji Mikio, Sigurdson Wendy, Sohrabi Hamid, Sparks Paige, Suzuki Kazushi, Swisher Laura, Taddei Kevin, Wang Jen, Wang Peter, Weiner Mike, Wolfsberger Mary, Xiong Chengjie, Xu Xiong,
Affiliation:
1. Department of Neurology, Amsterdam Neuroscience, Alzheimer Center Amsterdam, Amsterdam, UMC, VU University, Netherlands 2. Division of Biostatistics, Washington University in St. Louis, MO, USA 3. Mallinckrodt Institute of Radiology, Washington University in St. Louis, MO, USA 4. Department of Neurology, Alzheimer’s Disease Research Center, Pittsburgh, PA 5. Pittsburgh Institute for Neurodegenerative Diseases, University of Pittsburgh, Pittsburgh, PA 6. UCL Queen Square Institute of Neurology, London, UK 7. Department of Neurology, Massachusetts General Hospital, Boston, MA, USA 8. Department of Psychiatry, Washington University in St. Louis, MO, USA 9. Hope Center for Neurological Disorders, . Washington University in St. Louis, MO, USA 10. NeuroGenomics and Informatics, Washington University in St. Louis, St. Louis, MO, USA 11. Dementia Research Centre, Department of Neurodegenerative Disease, UK 12. Dementia Research Institute at UCL, UCL Institute of Neurology, London, UK 13. Department of Pathology and Laboratory Medicine, Indiana University, IN, USA 14. Mayo Clinic Florida, FL, USA 15. Knight Alzheimer's Disease Research Center, Washington University in St. Louis, MO, USA 16. Department of Neurology, Washington University in St. Louis, MO, USA 17. Department of Psychological & Brain Sciences, Washington University in St. Louis, MO, USA 18. German Center for Neurodegenerative Diseases (DZNE), Tübingen, Germany 19. Section for Dementia Research, Hertie Institute for Clinical Brain Research and Department of Psychiatry and Psychotherapy, University of Tübingen, Germany 20. Ludwig-Maximilians-Universität München, Germany 21. Florey Institute, Melbourne, Australia 22. The University of Melbourne, Melbourne, Australia 23. Department of Clinical Neuroscience, Osaka City University Medical School, Japan 24. Department of Neurology, Taub Institute for Research on Alzheimer's Disease and the Aging Brain, GH Sergievsky Center, Columbia University Medical Center, NY, USA 25. Department of Pathology and Immunology, Washington University School of Medicine, St. Louis MO, USA 26. Neuroscience Research Australia, Sydney, Australia 27. School of Medical Sciences, UNSW Sydney, Sydney, Australia 28. Department of Psychiatry and Neuropsychology, Maastricht University, School for Mental Health and Neuroscience, Alzheimer Center Limburg, Netherlands
Abstract
Abstract
Structural grey matter covariance networks provide an individual quantification of morphological patterns in the brain. The network integrity is disrupted in sporadic Alzheimer’s disease, and network properties show associations with the level of amyloid pathology and cognitive decline. Therefore, these network properties might be disease progression markers. However, it remains unclear when and how grey matter network integrity changes with disease progression. We investigated these questions in autosomal dominant Alzheimer’s disease mutation carriers, whose conserved age at dementia onset allows individual staging based upon their estimated years to symptom onset. From the Dominantly Inherited Alzheimer Network observational cohort, we selected T1-weighted MRI scans from 269 mutation carriers and 170 non-carriers (mean age 38 ± 15 years, mean estimated years to symptom onset −9 ± 11), of whom 237 had longitudinal scans with a mean follow-up of 3.0 years. Single-subject grey matter networks were extracted, and we calculated for each individual the network properties which describe the network topology, including the size, clustering, path length and small worldness. We determined at which time point mutation carriers and non-carriers diverged for global and regional grey matter network metrics, both cross-sectionally and for rate of change over time. Based on cross-sectional data, the earliest difference was observed in normalized path length, which was decreased for mutation carriers in the precuneus area at 13 years and on a global level 12 years before estimated symptom onset. Based on longitudinal data, we found the earliest difference between groups on a global level 6 years before symptom onset, with a greater rate of decline of network size for mutation carriers. We further compared grey matter network small worldness with established biomarkers for Alzheimer disease (i.e. amyloid accumulation, cortical thickness, brain metabolism and cognitive function). We found that greater amyloid accumulation at baseline was associated with faster decline of small worldness over time, and decline in grey matter network measures over time was accompanied by decline in brain metabolism, cortical thinning and cognitive decline. In summary, network measures decline in autosomal dominant Alzheimer’s disease, which is alike sporadic Alzheimer’s disease, and the properties show decline over time prior to estimated symptom onset. These data suggest that single-subject networks properties obtained from structural MRI scans form an additional non-invasive tool for understanding the substrate of cognitive decline and measuring progression from preclinical to severe clinical stages of Alzheimer’s disease.
Funder
Alzheimer Nederland Fellowship 2018 ZonMW Memorabel Innovative Medicine Initiative – Joint Undertaking European Union's Seventh Framework Programme European Federation of Pharmaceutical Industries and Associations) EFPIA Barnes Jewish Hospital Foundation Dominantly Inherited Alzheimer Network National Institute on Aging German Center for Neurodegenerative Diseases National Institutes of Health-funded National Institute of Neurological Disorders and Stroke (NINDS) Center Core for Brain Imaging National Science Foundation National Institutes of Health the Swiss National Science Foundation National Institute for Health Research University College London Hospitals Biomedical Research Centre Medical Research Council (MRC) Dementias Platform UK
Publisher
Oxford University Press (OUP)
Subject
General Earth and Planetary Sciences,General Environmental Science
Cited by
11 articles.
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