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

Autor:	Vermunt L; Department of Neurology, Amsterdam Neuroscience, Alzheimer Center Amsterdam, Amsterdam, UMC, VU University, Netherlands., Dicks E; Department of Neurology, Amsterdam Neuroscience, Alzheimer Center Amsterdam, Amsterdam, UMC, VU University, Netherlands., Wang G; Division of Biostatistics, Washington University in St. Louis, MO, USA., Dincer A; Mallinckrodt Institute of Radiology, Washington University in St. Louis, MO, USA., Flores S; Mallinckrodt Institute of Radiology, Washington University in St. Louis, MO, USA., Keefe SJ; Mallinckrodt Institute of Radiology, Washington University in St. Louis, MO, USA., Berman SB; Department of Neurology, Alzheimer's Disease Research Center, Pittsburgh, PA.; Pittsburgh Institute for Neurodegenerative Diseases, University of Pittsburgh, Pittsburgh, PA., Cash DM; UCL Queen Square Institute of Neurology, London, UK., Chhatwal JP; Department of Neurology, Massachusetts General Hospital, Boston, MA, USA., Cruchaga C; Department of Psychiatry, Washington University in St. Louis, MO, USA.; Hope Center for Neurological Disorders, . Washington University in St. Louis, MO, USA.; NeuroGenomics and Informatics, Washington University in St. Louis, St. Louis, MO, USA., Fox NC; Dementia Research Centre, Department of Neurodegenerative Disease, UK.; Dementia Research Institute at UCL, UCL Institute of Neurology, London, UK., Ghetti B; Department of Pathology and Laboratory Medicine, Indiana University, IN, USA., Graff-Radford NR; Mayo Clinic Florida, FL, USA., Hassenstab J; Knight Alzheimer's Disease Research Center, Washington University in St. Louis, MO, USA.; Department of Neurology, Washington University in St. Louis, MO, USA.; Department of Psychological & Brain Sciences, Washington University in St. Louis, MO, USA., Karch CM; Department of Psychiatry, Washington University in St. Louis, MO, USA., Laske C; German Center for Neurodegenerative Diseases (DZNE), Tübingen, Germany.; Section for Dementia Research, Hertie Institute for Clinical Brain Research and Department of Psychiatry and Psychotherapy, University of Tübingen, Germany., Levin J; Ludwig-Maximilians-Universität München, Germany., Masters CL; Florey Institute, Melbourne, Australia.; The University of Melbourne, Melbourne, Australia., McDade E; Knight Alzheimer's Disease Research Center, Washington University in St. Louis, MO, USA.; Department of Neurology, Washington University in St. Louis, MO, USA., Mori H; Department of Clinical Neuroscience, Osaka City University Medical School, Japan., Morris JC; Knight Alzheimer's Disease Research Center, Washington University in St. Louis, MO, USA.; Department of Neurology, Washington University in St. Louis, MO, USA., Noble JM; Department of Neurology, Taub Institute for Research on Alzheimer's Disease and the Aging Brain, GH Sergievsky Center, Columbia University Medical Center, NY, USA., Perrin RJ; Knight Alzheimer's Disease Research Center, Washington University in St. Louis, MO, USA.; Department of Pathology and Immunology, Washington University School of Medicine, St. Louis MO, USA., Schofield PR; Neuroscience Research Australia, Sydney, Australia.; School of Medical Sciences, UNSW Sydney, Sydney, Australia., Xiong C; Division of Biostatistics, Washington University in St. Louis, MO, USA., Scheltens P; Department of Neurology, Amsterdam Neuroscience, Alzheimer Center Amsterdam, Amsterdam, UMC, VU University, Netherlands., Visser PJ; Department of Neurology, Amsterdam Neuroscience, Alzheimer Center Amsterdam, Amsterdam, UMC, VU University, Netherlands.; Department of Psychiatry and Neuropsychology, Maastricht University, School for Mental Health and Neuroscience, Alzheimer Center Limburg, Netherlands., Bateman RJ; Department of Psychiatry, Washington University in St. Louis, MO, USA.; Knight Alzheimer's Disease Research Center, Washington University in St. Louis, MO, USA.; Department of Neurology, Washington University in St. Louis, MO, USA., Benzinger TLS; Mallinckrodt Institute of Radiology, Washington University in St. Louis, MO, USA.; Knight Alzheimer's Disease Research Center, Washington University in St. Louis, MO, USA., Tijms BM; Department of Neurology, Amsterdam Neuroscience, Alzheimer Center Amsterdam, Amsterdam, UMC, VU University, Netherlands., Gordon BA; Mallinckrodt Institute of Radiology, Washington University in St. Louis, MO, USA.; Knight Alzheimer's Disease Research Center, Washington University in St. Louis, MO, USA.; Department of Psychological & Brain Sciences, Washington University in St. Louis, MO, USA.
Jazyk:	angličtina
Zdroj:	Brain communications [Brain Commun] 2020 Jul 15; Vol. 2 (2), pp. fcaa102. Date of Electronic Publication: 2020 Jul 15 (Print Publication: 2020).
DOI:	10.1093/braincomms/fcaa102
Abstrakt:	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 T 1 -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. (© The Author(s) (2020). Published by Oxford University Press on behalf of the Guarantors of Brain.)
Databáze:	MEDLINE
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