Single-subject grey matter network trajectories over the disease course of autosomal dominant Alzheimer’s disease
Authors: Vermunt Lisa , Dicks Ellen , Wang Guoqiao , Dincer Aylin , Flores Shaney , Keefe Sarah J , Berman Sarah B , Cash David M , Chhatwal Jasmeer P , Cruchaga Carlos , Fox Nick C , Ghetti Bernardino , Graff-Radford Neill R , Hassenstab Jason , Karch Celeste M , Laske Christoph , Levin Johannes , Masters Colin L , McDade Eric , Mori Hiroshi , Morris John C , Noble James M , Perrin Richard J , Schofield Peter R , Xiong Chengjie , Scheltens Philip , Visser Pieter Jelle , Bateman Randall J , Benzinger Tammie L S , Tijms Betty M , Gordon Brian A ,   Dominantly Inherited Alzheimer Network (DIAN), 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 
Published: July 2020

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.