Single-subject grey matter network trajectories over the disease course of autosomal dominant Alzheimer's disease

Lisa Vermunt, Ellen Dicks, Guoqiao Wang, Aylin Dincer, Shaney Flores, Sarah J. Keefe, Sarah B. Berman, David M. Cash, Jasmeer P. Chhatwal, Carlos Cruchaga, Nick C. Fox, Bernardino Ghetti, Neill R. Graff-Radford, Jason Hassenstab, Celeste M. Karch, Christoph Laske, Johannes Levin, Colin L. Masters, Eric McDade, Hiroshi MoriJohn C. Morris, James M. Noble, Richard J. Perrin, Peter R. Schofield, Chengjie Xiong, Philip Scheltens, Pieter Jelle Visser, Randall J. Bateman, Tammie L.S. Benzinger, Betty M. Tijms, Brian A. Gordon, Ricardo Allegri, Fatima Amtashar, Courtney Bodge, Susan Brandon, William Brooks, Jill Buck, Virginia Buckles, Sochenda Chea, Patricio Chrem, Helena Chui, Jake Cinco, Clifford Jack, Mirelle D'Mello, Tamara Donahue, Jane Douglas, Noelia Edigo, Nilufer Erekin-Taner, Anne Fagan, Marty Farlow, Angela Farrar, Howard Feldman, Gigi Flynn, Nick Fox, Erin Franklin, Hisako Fujii, Cortaiga Gant, Samantha Gardener, Bernardino Ghetti, Alison Goate, Jill Goldman, Julia Gray, Jenny Gurney, Jason Hassenstab, Mie Hirohara, David Holtzman, Russ Hornbeck, Siri Houeland Dibari, Takeshi Ikeuchi, Snezana Ikonomovic, Gina Jerome, Mathias Jucker, Kensaku Kasuga, Takeshi Kawarabayashi, William Klunk, Robert Koeppe, Elke Kuder-Buletta, Christoph Laske, Johannes Levin, Daniel Marcus, Ralph Martins, Neal Scott Mason, Denise Maue-Dreyfus, Lucy Montoya, Hiroshi Mori, Akem Nagamatsu, Katie Neimeyer, James Noble, Joanne Norton, Richard Perrin, Marc Raichle, John Ringman, Jee Hoon Roh, Peter Schofield, Hiroyuki Shimada, Tomoyo Shiroto, Mikio Shoji, Wendy Sigurdson, Hamid Sohrabi, Paige Sparks, Kazushi Suzuki, Laura Swisher, Kevin Taddei, Jen Wang, Peter Wang, Mike Weiner, Mary Wolfsberger, Chengjie Xiong, Xiong Xu

Research output: Contribution to journalArticlepeer-review

7 Scopus citations


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.

Original languageEnglish
Article numberfcaa102
JournalBrain Communications
Issue number2
StatePublished - 1 Dec 2020


  • Alzheimer's disease
  • Autosomal dominant
  • Disease progression
  • Network
  • Subject-level networks


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