Single-subject grey matter network trajectories over the disease course of autosomal dominant Alzheimer's disease. Issue 2 (15th July 2020)

• Record Type: Journal Article
• 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;
• Is Part Of: Brain communications. Volume 2:Issue 2(2020)
• Publication Date: 2020-07-15
• Subjects: autosomal dominant -- Alzheimer's disease -- disease progression -- network -- subject-level networks; 616
• DOI: 10.1093/braincomms/fcaa102
• Languages: English
• ISSNs: 2632-1297

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. Abstract : Grey matter covariance networks are extracted from structural MRI. In the DIAN autosomal dominant Alzheimer's disease family cohort, we demonstrate for the first time the trajectory of network properties in Alzheimer's disease. These network properties related to other eurodegeneration markers and can form a non-invasive tool for studying Alzheimer's disease progression. Graphical Abstract: